From 92be9f12164f18ce845a5bab60cefa5f7fec6836 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 13 Oct 2024 06:11:26 +0300 Subject: [PATCH 01/95] flake.lock: Update (#9870) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/bc947f541ae55e999ffdb4013441347d83b00feb?narHash=sha256-NOiTvBbRLIOe5F6RbHaAh6%2B%2BBNjsb149fGZd1T4%2BKBg%3D' (2024-10-04) → 'github:NixOS/nixpkgs/5633bcff0c6162b9e4b5f1264264611e950c8ec7?narHash=sha256-9UTxR8eukdg%2BXZeHgxW5hQA9fIKHsKCdOIUycTryeVw%3D' (2024-10-09) Co-authored-by: github-actions[bot] --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 3fb6ced51..702527028 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1728018373, - "narHash": "sha256-NOiTvBbRLIOe5F6RbHaAh6++BNjsb149fGZd1T4+KBg=", + "lastModified": 1728492678, + "narHash": "sha256-9UTxR8eukdg+XZeHgxW5hQA9fIKHsKCdOIUycTryeVw=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "bc947f541ae55e999ffdb4013441347d83b00feb", + "rev": "5633bcff0c6162b9e4b5f1264264611e950c8ec7", "type": "github" }, "original": { From c7181bd294757dd80a7904e3dd0fea2d0be914e7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 13 Oct 2024 18:52:48 +0300 Subject: [PATCH 02/95] server : reuse cached context chunks (#9866) ggml-ci --- common/arg.cpp | 7 ++++ common/common.h | 3 +- examples/server/README.md | 1 + examples/server/server.cpp | 69 ++++++++++++++++++++++++++++++++++++-- examples/server/utils.hpp | 4 +-- 5 files changed, 78 insertions(+), 6 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 205177d46..8969fc107 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1788,6 +1788,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.n_threads_http = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP")); + add_opt(common_arg( + {"--cache-reuse"}, "N", + string_format("min chunk size to attempt reusing from the cache via KV shifting (default: %d)", params.n_cache_reuse), + [](common_params & params, int value) { + params.n_cache_reuse = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_REUSE")); add_opt(common_arg( {"--metrics"}, string_format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"), diff --git a/common/common.h b/common/common.h index 71e686156..5507b1c59 100644 --- a/common/common.h +++ b/common/common.h @@ -277,7 +277,8 @@ struct common_params { int32_t port = 8080; // server listens on this network port int32_t timeout_read = 600; // http read timeout in seconds int32_t timeout_write = timeout_read; // http write timeout in seconds - int n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) + int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) + int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT diff --git a/examples/server/README.md b/examples/server/README.md index b5feeb77b..cd0eaf847 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -147,6 +147,7 @@ The project is under active development, and we are [looking for feedback and co | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate
(env: LLAMA_ARG_SSL_CERT_FILE) | | `-to, --timeout N` | server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT) | | `--threads-http N` | number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP) | +| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
(env: LLAMA_ARG_CACHE_REUSE) | | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | | `--slots` | enable slots monitoring endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f809c46d5..015b3b2c5 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -800,7 +800,7 @@ struct server_context { int slot_prompt_len = slot_prompt.size(); // length of the Longest Common Prefix between the current slot's prompt and the input prompt - int lcp_len = common_part(slot_prompt, prompt); + int lcp_len = longest_common_prefix(slot_prompt, prompt); // fraction of the common substring length compared to the current slot's prompt length similarity = static_cast(lcp_len) / slot_prompt_len; @@ -2012,7 +2012,7 @@ struct server_context { } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - // if input prompt is too big, truncate it (if group attention self-extend is disabled) + // if input prompt is too big, truncate it if (slot.n_prompt_tokens >= slot.n_ctx) { const int n_left = slot.n_ctx - slot.params.n_keep; @@ -2042,12 +2042,74 @@ struct server_context { if (slot.params.cache_prompt) { // reuse any previously computed tokens that are common with the new prompt - slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + slot.n_past = longest_common_prefix(slot.cache_tokens, prompt_tokens); // push the prompt into the sampling context (do not apply grammar) for (int i = 0; i < slot.n_past; ++i) { common_sampler_accept(slot.smpl, slot.cache_tokens[i], false); } + + // reuse chunks from the cached prompt by shifting their KV cache in the new position + if (params.n_cache_reuse > 0) { + size_t head_c = slot.n_past; // cache + size_t head_p = slot.n_past; // current prompt + + SLT_DBG(slot, "trying to reuse chunks with size > %d, slot.n_past = %d\n", params.n_cache_reuse, slot.n_past); + + while (head_c < slot.cache_tokens.size() && + head_p < prompt_tokens.size()) { + if (llama_token_is_control(model, slot.cache_tokens[head_c])) { + break; + } + + if (llama_token_is_control(model, prompt_tokens[head_p])) { + break; + } + + size_t n_match = 0; + + while (head_c + n_match < slot.cache_tokens.size() && + head_p + n_match < prompt_tokens.size() && + slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) { + if (llama_token_is_control(model, slot.cache_tokens[head_c + n_match])) { + break; + } + + if (llama_token_is_control(model, prompt_tokens[head_p + n_match])) { + break; + } + + n_match++; + } + + if (n_match >= (size_t) params.n_cache_reuse) { + SLT_DBG(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match); + //for (size_t i = head_p; i < head_p + n_match; i++) { + // SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); + //} + + const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c; + + llama_kv_cache_seq_rm (ctx, slot.id + 1, head_p, head_c); + llama_kv_cache_seq_add(ctx, slot.id + 1, head_c, -1, kv_shift); + + for (size_t i = 0; i < n_match; i++) { + slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i]; + + common_sampler_accept(slot.smpl, slot.cache_tokens[head_p + i], false); + + slot.n_past++; + } + + head_c += n_match; + head_p += n_match; + } else { + head_c += 1; + } + } + + SLT_DBG(slot, "after context reuse, new slot.n_past = %d\n", slot.n_past); + } } } @@ -3257,6 +3319,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.on_new_task(std::bind( &server_context::process_single_task, &ctx_server, std::placeholders::_1)); + ctx_server.queue_tasks.on_update_slots(std::bind( &server_context::update_slots, &ctx_server)); diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index ad99e9574..37999604d 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -195,14 +195,14 @@ static std::string gen_chatcmplid() { // other common utils // -static size_t common_part(const std::vector & a, const std::vector & b) { +static size_t longest_common_prefix(const std::vector & a, const std::vector & b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} return i; } -static size_t common_part(const std::string & a, const std::string & b) { +static size_t longest_common_prefix(const std::string & a, const std::string & b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} From d4c19c0f5cdb1e512573e8c86c79e8d0238c73c4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 13 Oct 2024 21:31:35 +0300 Subject: [PATCH 03/95] server : accept extra_context for the infill endpoint (#9874) * server : accept extra_context for the infill endpoint ggml-ci * server : update readme [no ci] * server : use repo-level FIM pattern if possible ggml-ci --- examples/server/README.md | 21 ++++++++ examples/server/server.cpp | 102 ++++++++++++++++++++++++++++++++++--- src/llama.cpp | 56 +++++++++++++------- 3 files changed, 153 insertions(+), 26 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index cd0eaf847..eb0a7b32e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -524,9 +524,30 @@ Takes a prefix and a suffix and returns the predicted completion as stream. - `input_prefix`: Set the prefix of the code to infill. - `input_suffix`: Set the suffix of the code to infill. +- `prompt`: Added after the `FIM_MID` token +- `extra_context`: Additional context inserted before the FIM prefix. See https://github.com/ggerganov/llama.cpp/pull/9874 It also accepts all the options of `/completion`. +If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used: + +```txt +myproject +{chunk 0 filename} +{chunk 0 text} +{chunk 1 filename} +{chunk 1 text} +... +filename +[input_prefix][input_suffix][prompt] +``` + +If the tokens are missing, then the extra context is simply prefixed at the start: + +```txt +[extra_context][input_prefix][input_suffix][prompt] +``` + ### **GET** `/props`: Get server global properties. This endpoint is public (no API key check). By default, it is read-only. To make POST request to change global properties, you need to start server with `--props` diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 015b3b2c5..18bcad3f0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -139,6 +139,7 @@ struct slot_params { json input_prefix; json input_suffix; + json extra_context; }; struct server_slot { @@ -170,6 +171,7 @@ struct server_slot { // when a task is submitted, we first tokenize the prompt and store it here std::vector prompt_tokens; + std::vector extra_tokens; std::string generated_text; std::vector cache_tokens; @@ -906,8 +908,26 @@ struct server_context { } // infill - slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); - slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); + slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); + slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); + slot.params.extra_context = json_value(data, "extra_context", default_params.extra_context); + + SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.params.extra_context.size()); + for (const auto & chunk : slot.params.extra_context) { + // { "text": string, "filename": string } + if (!chunk.contains("text") || !chunk["text"].is_string()) { + send_error(task, "extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST); + return false; + } + + // filename is optional + if (chunk.contains("filename") && !chunk["filename"].is_string()) { + send_error(task, "extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST); + return false; + } + + SLT_DBG(slot, "extra_context chunk in file '%s':\n%s\n", chunk.value("filename", "").c_str(), chunk.value("text", "").c_str()); + } // get prompt if (task.cmpl_type != SERVER_TASK_CMPL_TYPE_INFILL) { @@ -1934,13 +1954,66 @@ struct server_context { } break; case SERVER_TASK_CMPL_TYPE_INFILL: { + // use FIM repo-level pattern: + // ref: https://arxiv.org/pdf/2409.12186 + // + // [FIM_REP]myproject + // [FIM_SEP]filename0 + // extra chunk 0 + // [FIM_SEP]filename1 + // extra chunk 1 + // ... + // [FIM_SEP]filename + // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID] + // auto prefix_tokens = tokenize(slot.params.input_prefix, false, false); auto suffix_tokens = tokenize(slot.params.input_suffix, false, false); - // for now pick context to fit in a single batch (ratio prefix:suffix = 3:1, TODO: configurable?) - const int n_suffix_take = std::min(suffix_tokens.size(), n_batch/4); + slot.extra_tokens.clear(); + if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) { + static const auto k_fim_repo = tokenize("myproject\n", false, false); + + slot.extra_tokens.push_back(llama_token_fim_rep(model)); + slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); + } + + for (const auto & chunk : slot.params.extra_context) { + // { "text": string, "filename": string } + const std::string text = chunk.value("text", ""); + const std::string filename = chunk.value("filename", "tmp"); + + if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { + const auto k_fim_file = tokenize(filename + "\n", false, false); + + slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model)); + slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } else { + // chunk separator in binary form to avoid confusing the AI + static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; + static const auto k_chunk_prefix_tokens = tokenize(k_chunk_prefix_str, false, false); + + slot.extra_tokens.insert(slot.extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); + } + + const auto chunk_tokens = tokenize(text, false, false); + slot.extra_tokens.insert(slot.extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); + } + + if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { + // TODO: current filename + static const auto k_fim_file = tokenize("filename\n", false, false); + + slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model)); + slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } + + // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) + const int n_suffix_take = std::min(suffix_tokens.size(), (n_batch)/4); const int n_prefix_take = std::min(prefix_tokens.size(), (n_batch - 3) - n_suffix_take); + // fill the rest of the context with extra chunks + const int n_extra_take = std::min(std::max(0, slot.n_ctx - (n_batch) - 2*slot.n_predict), slot.extra_tokens.size()); + prefix_tokens.erase(prefix_tokens.begin(), prefix_tokens.begin() + prefix_tokens.size() - n_prefix_take); suffix_tokens.resize(n_suffix_take); @@ -1954,6 +2027,11 @@ struct server_context { embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); } + SLT_DBG(slot, "extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", slot.n_ctx, n_extra_take, (int) slot.extra_tokens.size()); + + // put the extra context before the FIM prefix + embd_inp.insert(embd_inp.begin(), slot.extra_tokens.end() - n_extra_take, slot.extra_tokens.end()); + embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); embd_inp.push_back(llama_token_fim_mid(model)); @@ -2058,11 +2136,15 @@ struct server_context { while (head_c < slot.cache_tokens.size() && head_p < prompt_tokens.size()) { - if (llama_token_is_control(model, slot.cache_tokens[head_c])) { + if (llama_token_is_control(model, slot.cache_tokens[head_c]) && + slot.cache_tokens[head_c] != llama_token_fim_rep(model) && + slot.cache_tokens[head_c] != llama_token_fim_sep(model)) { break; } - if (llama_token_is_control(model, prompt_tokens[head_p])) { + if (llama_token_is_control(model, prompt_tokens[head_p]) && + prompt_tokens[head_p] != llama_token_fim_rep(model) && + prompt_tokens[head_p] != llama_token_fim_sep(model)) { break; } @@ -2071,11 +2153,15 @@ struct server_context { while (head_c + n_match < slot.cache_tokens.size() && head_p + n_match < prompt_tokens.size() && slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) { - if (llama_token_is_control(model, slot.cache_tokens[head_c + n_match])) { + if (llama_token_is_control(model, slot.cache_tokens[head_c + n_match]) && + slot.cache_tokens[head_c + n_match] != llama_token_fim_rep(model) && + slot.cache_tokens[head_c + n_match] != llama_token_fim_sep(model)) { break; } - if (llama_token_is_control(model, prompt_tokens[head_p + n_match])) { + if (llama_token_is_control(model, prompt_tokens[head_p + n_match]) && + prompt_tokens[head_p + n_match] != llama_token_fim_rep(model) && + prompt_tokens[head_p + n_match] != llama_token_fim_sep(model)) { break; } diff --git a/src/llama.cpp b/src/llama.cpp index f68024f5b..511f91802 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -6596,8 +6596,8 @@ static void llm_load_vocab( ) { vocab.special_eot_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6610,8 +6610,8 @@ static void llm_load_vocab( ) { vocab.special_eom_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6627,8 +6627,8 @@ static void llm_load_vocab( ) { vocab.special_fim_pre_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6644,8 +6644,8 @@ static void llm_load_vocab( ) { vocab.special_fim_suf_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6661,8 +6661,8 @@ static void llm_load_vocab( ) { vocab.special_fim_mid_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6677,8 +6677,8 @@ static void llm_load_vocab( ) { vocab.special_fim_pad_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6694,8 +6694,8 @@ static void llm_load_vocab( ) { vocab.special_fim_rep_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6708,8 +6708,8 @@ static void llm_load_vocab( ) { vocab.special_fim_sep_id = t.second; if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } @@ -6720,6 +6720,19 @@ static void llm_load_vocab( // this is currently determined based on the token text, which is obviously not ideal // ref: https://github.com/ggerganov/llama.cpp/issues/9606 vocab.special_eog_ids.clear(); + + if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_pad_id) == 0) { + vocab.special_eog_ids.insert(vocab.special_fim_pad_id); + } + + if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_rep_id) == 0) { + vocab.special_eog_ids.insert(vocab.special_fim_rep_id); + } + + if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_sep_id) == 0) { + vocab.special_eog_ids.insert(vocab.special_fim_sep_id); + } + for (const auto & t : vocab.token_to_id) { if (false || t.first == "<|eot_id|>" @@ -6732,13 +6745,20 @@ static void llm_load_vocab( ) { vocab.special_eog_ids.insert(t.second); if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { - LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", - __func__, t.first.c_str()); + LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n", + __func__, t.second, t.first.c_str()); vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } + } else { + // token is control, but not marked as EOG -> print a warning + if (vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && vocab.special_eog_ids.count(t.second) == 0) { + LLAMA_LOG_WARN("%s: control token: %6d '%s' is not marked as EOG\n", + __func__, t.second, t.first.c_str()); + } } } + // sanity checks if (vocab.special_eos_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) { vocab.special_eog_ids.insert(vocab.special_eos_id); LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__); From 13dca2a54a394757d56fdd652b9f0df08f44ea22 Mon Sep 17 00:00:00 2001 From: agray3 Date: Mon, 14 Oct 2024 01:49:08 +0100 Subject: [PATCH 04/95] Vectorize load instructions in dmmv f16 CUDA kernel (#9816) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Vectorize load instructions in dmmv f16 CUDA kernel Replaces scalar with vector load instructions, which substantially improves performance on NVIDIA HBM GPUs, e.g. gives a 1.27X overall speedup for Meta-Llama-3-8B-Instruct-F16 BS1 inference evaluation on H100 SXM 80GB HBM3. On GDDR GPUs, there is a slight (1.01X) speedup. * addressed comment * Update ggml/src/ggml-cuda/dmmv.cu Co-authored-by: Johannes Gäßler --------- Co-authored-by: Johannes Gäßler --- ggml/src/ggml-cuda/dmmv.cu | 34 +++++++++++++++++++++++++--------- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/ggml/src/ggml-cuda/dmmv.cu b/ggml/src/ggml-cuda/dmmv.cu index 96a5adef5..00e21b5d7 100644 --- a/ggml/src/ggml-cuda/dmmv.cu +++ b/ggml/src/ggml-cuda/dmmv.cu @@ -416,10 +416,11 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ const half * x = (const half *) vx; - + // load 2 halfs into register in a single instruction + const half2 x_reg = *((half2 *) &(x[ib + iqs])); // automatic half -> float type cast if dfloat == float - v.x = x[ib + iqs + 0]; - v.y = x[ib + iqs + 1]; + v.x = __low2float(x_reg); + v.y = __high2float(x_reg); } static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) { @@ -476,13 +477,28 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons // matrix multiplication // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2 #ifdef GGML_CUDA_F16 - tmp += __hmul2(v, { - y[iybs + iqs + j/qr + 0], - y[iybs + iqs + j/qr + y_offset] - }); + if ( y_offset == 1 ) { + // load 2 dfloats into register in a single instruction + const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr])); + tmp += __hmul2(v, y_reg); + } + else { + tmp += __hmul2(v, { + y[iybs + iqs + j/qr + 0], + y[iybs + iqs + j/qr + y_offset] + }); + } #else - tmp += v.x * y[iybs + iqs + j/qr + 0]; - tmp += v.y * y[iybs + iqs + j/qr + y_offset]; + if ( y_offset == 1 ) { + // load 2 dfloats into register in a single instruction + const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr])); + tmp += v.x * y_reg.x; + tmp += v.y * y_reg.y; + } + else { + tmp += v.x * y[iybs + iqs + j/qr + 0]; + tmp += v.y * y[iybs + iqs + j/qr + y_offset]; + } #endif // GGML_CUDA_F16 } } From a89f75e1b7b90cb2d4d4c52ca53ef9e9b466aa45 Mon Sep 17 00:00:00 2001 From: VoidIsVoid <343750470@qq.com> Date: Mon, 14 Oct 2024 15:04:36 +0800 Subject: [PATCH 05/95] server : handle "logprobs" field with false value (#9871) Co-authored-by: Gimling --- examples/server/utils.hpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 37999604d..69519ef95 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -360,9 +360,9 @@ static json oaicompat_completion_params_parse( // Handle "logprobs" field // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future - if (body.contains("logprobs")) { + if (json_value(body, "logprobs", false)) { llama_params["n_probs"] = json_value(body, "top_logprobs", 20); - } else if (body.contains("top_logprobs")) { + } else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) { throw std::runtime_error("top_logprobs requires logprobs to be set to true"); } From 4c42f93b22146c83b763d8cbee5fafc512746649 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micha=C5=82=20Tuszy=C5=84ski?= Date: Tue, 15 Oct 2024 10:20:34 +0200 Subject: [PATCH 06/95] readme : update bindings list (#9889) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index dd4927b04..08fe8cc92 100644 --- a/README.md +++ b/README.md @@ -130,6 +130,7 @@ Typically finetunes of the base models below are supported as well. - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart) - PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326) - Guile Scheme: 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"===n||"\t"===n||"\n"===n||"\r"===n?(u(),_=2):i+=n),3===_&&"!--"===i&&(_=4,r=r[0])}return u(),r}(t)),n),arguments,[])).length>1?n:n[0]}var on=_n.bind(R);export{q as Component,j as Fragment,f as Signal,e as batch,ct as cloneElement,v as computed,ht as createContext,R as createElement,V as createRef,k as effect,R as h,on as html,ft as hydrate,C as isValidElement,S as options,st as render,c as signal,Y as toChildArray,o as untracked,Mt as useCallback,Zt as useComputed,At as useContext,Ft as useDebugValue,Pt as useEffect,Wt as useErrorBoundary,Lt as useId,Tt as useImperativeHandle,Nt as useLayoutEffect,Dt as useMemo,Ht as useReducer,$t as useRef,Yt as useSignal,tn as useSignalEffect,Et as useState}; From fbc98b748e7b075e327bcf13237057f647678049 Mon Sep 17 00:00:00 2001 From: MaggotHATE Date: Tue, 15 Oct 2024 15:54:55 +0500 Subject: [PATCH 08/95] sampling : add XTC sampler (#9742) * Initial XTC commit Adds XTC sampler, not activated by default, but recommended settings by default. * Cleanup * Simplified chances calculation To be more inline with the original implementation, chance is calculated once at the beginning. * First fixes by comments Still need to look into sorting * Fixed trailing backspaces * Fixed RNG to be reproduceable Thanks to @slaren for directions * Fixed forgotten header * Moved `min_keep` Moved from conditions to a simple check at the end. * Fixed broken randomization Thanks to @slaren for explanation * Swapped sorting for a custom algorithm Shifts tokens to remove the penalized ones, then puts the penalized at the back. Should make `min_keep` still viable. * Algorithm rework 1. Scan token from top till the first non-penalizable 2. Remove the last captured token (the least probable above threshold) 3. Shift all tokens to override the remaining penalizable 4. Penalize and put them at the the bottom. * Added XTC to `test-sampling` * Simplified algorithm and more tests * Updated info in common and args * Merged back lost commits in common and arg * Update dump info in common * Fixed incorrect min_keep check * Added XTC to README * Renamed parameters, fixed info and defaults * probability is at 0 by default, but XTC is included in sampling queue * threshold higher than 0.5 switches XTC off * Initial server support * Added XTC to server UIs * Fixed labels in old server UI * Made algorithm safer and more readable * Removed xtc_threshold_max * Fixed arg after update * Quick fixes by comments * Simplified algorithm since threshold_max is removed * Renamed random distribution * Fixed tests and outdated README * Small fixes --- common/arg.cpp | 14 ++++ common/common.cpp | 2 + common/common.h | 6 ++ common/sampling.cpp | 13 +++- examples/main/README.md | 13 ++++ examples/server/public/index-new.html | 6 ++ examples/server/public/index.html | 4 ++ examples/server/server.cpp | 4 ++ include/llama.h | 3 + src/llama-sampling.cpp | 95 +++++++++++++++++++++++++++ tests/test-sampling.cpp | 45 +++++++++++-- 11 files changed, 195 insertions(+), 10 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 8969fc107..d6a8e1f6f 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -947,6 +947,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sparams.tfs_z = std::stof(value); } ).set_sparam()); + add_opt(common_arg( + {"--xtc-probability"}, "N", + string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability), + [](common_params & params, const std::string & value) { + params.sparams.xtc_probability = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--xtc-threshold"}, "N", + string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sparams.xtc_threshold), + [](common_params & params, const std::string & value) { + params.sparams.xtc_threshold = std::stof(value); + } + ).set_sparam()); add_opt(common_arg( {"--typical"}, "N", string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p), diff --git a/common/common.cpp b/common/common.cpp index 451307b55..c08f01b42 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2104,6 +2104,8 @@ void yaml_dump_non_result_info(FILE * stream, const common_params & params, cons fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); + fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability); + fprintf(stream, "xtc_threshold: %f # default: 0.1\n", sparams.xtc_threshold); fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p); fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); diff --git a/common/common.h b/common/common.h index 5507b1c59..df2ee6bd4 100644 --- a/common/common.h +++ b/common/common.h @@ -90,6 +90,8 @@ enum common_sampler_type { COMMON_SAMPLER_TYPE_TFS_Z = 4, COMMON_SAMPLER_TYPE_TYPICAL_P = 5, COMMON_SAMPLER_TYPE_TEMPERATURE = 6, + COMMON_SAMPLER_TYPE_XTC = 7, + }; // dimensionality reduction methods, used by cvector-generator @@ -108,6 +110,8 @@ struct common_sampler_params { int32_t top_k = 40; // <= 0 to use vocab size float top_p = 0.95f; // 1.0 = disabled float min_p = 0.05f; // 0.0 = disabled + float xtc_probability = 0.00f; // 0.0 = disabled + float xtc_threshold = 0.10f; // > 0.5 disables XTC float tfs_z = 1.00f; // 1.0 = disabled float typ_p = 1.00f; // typical_p, 1.0 = disabled float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities @@ -124,12 +128,14 @@ struct common_sampler_params { bool ignore_eos = false; bool no_perf = false; // disable performance metrics + std::vector samplers = { COMMON_SAMPLER_TYPE_TOP_K, COMMON_SAMPLER_TYPE_TFS_Z, COMMON_SAMPLER_TYPE_TYPICAL_P, COMMON_SAMPLER_TYPE_TOP_P, COMMON_SAMPLER_TYPE_MIN_P, + COMMON_SAMPLER_TYPE_XTC, COMMON_SAMPLER_TYPE_TEMPERATURE }; diff --git a/common/sampling.cpp b/common/sampling.cpp index cd49ade69..fb95bcd3b 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -130,10 +130,10 @@ std::string common_sampler_params::print() const { snprintf(result, sizeof(result), "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" - "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n" + "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", penalty_last_n, penalty_repeat, penalty_freq, penalty_present, - top_k, tfs_z, top_p, min_p, typ_p, temp, + top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, mirostat, mirostat_eta, mirostat_tau); return std::string(result); @@ -184,6 +184,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co case COMMON_SAMPLER_TYPE_MIN_P: llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); break; + case COMMON_SAMPLER_TYPE_XTC: + llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + break; case COMMON_SAMPLER_TYPE_TFS_Z: llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep)); break; @@ -372,6 +375,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_TOP_P: return 'p'; case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; + case COMMON_SAMPLER_TYPE_XTC: return 'x'; default : return '?'; } } @@ -384,6 +388,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_TOP_P: return "top_p"; case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; + case COMMON_SAMPLER_TYPE_XTC: return "xtc"; default : return ""; } } @@ -396,6 +401,7 @@ std::vector common_sampler_types_from_names(const std::vect { "min_p", COMMON_SAMPLER_TYPE_MIN_P }, { "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z }, { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, + { "xtc", COMMON_SAMPLER_TYPE_XTC }, }; // since samplers names are written multiple ways @@ -441,7 +447,8 @@ std::vector common_sampler_types_from_chars(const std::stri { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, - { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE } + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC } }; std::vector samplers; diff --git a/examples/main/README.md b/examples/main/README.md index f0c3031ab..620934dad 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -241,6 +241,19 @@ The `--mirostat-ent` option sets the Mirostat target entropy (tau), which repres Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0` +### XTC Sampling + +- `--xtc-probability N`: Sets the chance for token removal (checked once on sampler start) (default: 0.0). +- `--xtc-threshold N`: Sets a minimum probability threshold for tokens to be removed (default: 0.1). + +Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-probability` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one. + +By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models. + +Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 --xtc-probability 0.5`. + +Example usage: `--xtc-probability 0.5 --xtc-threshold 0.1` + ### Logit Bias - `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion. diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html index c87dd8f1e..ad4183cd9 100644 --- a/examples/server/public/index-new.html +++ b/examples/server/public/index-new.html @@ -43,6 +43,8 @@ top_k: 0, // <= 0 to use vocab size top_p: 1.0, // 1.0 = disabled min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4 + xtc_probability: 0.0, // 0 = disabled; + xtc_threshold: 0.1, // > 0.5 disables XTC; tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled @@ -836,6 +838,8 @@ return html` ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} ${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} + ${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })} + ${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })} ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })} @@ -1132,6 +1136,8 @@ document.addEventListener('DOMContentLoaded', (event) => { const snapSettings = { temperature: { snapValue: 1.0, snapRangeMultiplier: 6 }, min_p: { snapValue: 0.05, snapRangeMultiplier: 2 }, + xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 }, + xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 }, top_p: { snapValue: 1.0, snapRangeMultiplier: 4 }, tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 }, typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 }, diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 07fec6a38..88065705f 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -307,6 +307,8 @@ top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled + xtc_probability: 0.0, // 0 = disabled; + xtc_threshold: 0.1, // > 0.5 disables XTC; tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled @@ -1013,6 +1015,8 @@ ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} + ${FloatField({ label: "XTC probability", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })} + ${FloatField({ label: "XTC threshold", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 18bcad3f0..8d4380e12 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -863,6 +863,8 @@ struct server_context { slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); + slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability); + slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold); slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p); slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); @@ -1196,6 +1198,8 @@ struct server_context { {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, + {"xtc_probability", slot.sparams.xtc_probability}, + {"xtc_threshold", slot.sparams.xtc_threshold}, {"tfs_z", slot.sparams.tfs_z}, {"typical_p", slot.sparams.typ_p}, {"repeat_last_n", slot.sparams.penalty_last_n}, diff --git a/include/llama.h b/include/llama.h index 9110b5956..92d4c70c1 100644 --- a/include/llama.h +++ b/include/llama.h @@ -1101,6 +1101,9 @@ extern "C" { /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772. LLAMA_API struct llama_sampler * llama_sampler_init_temp_ext (float t, float delta, float exponent); + /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335 + LLAMA_API struct llama_sampler * llama_sampler_init_xtc (float p, float t, size_t min_keep, uint32_t seed); + /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index e255a8fc4..67a78c3ac 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1059,6 +1059,101 @@ struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, floa }; } +// xtc + +struct llama_sampler_xtc { + const float probability; + const float threshold; + const size_t min_keep; + + const uint32_t seed; + uint32_t seed_cur; + + std::mt19937 rng; +}; + +static const char * llama_sampler_xtc_name(const struct llama_sampler * /*smpl*/) { + return "xtc"; +} + +static void llama_sample_xtc_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_xtc *) smpl->ctx; + + if (ctx->probability <= 0.0f + || ctx->threshold > 0.5f + || cur_p->size < 2) { + return; + } + + std::uniform_real_distribution distribution(0.0f, 1.0f); + float chance = distribution(ctx->rng); + if (chance > ctx->probability) return; + + // in case it's not sorted/recalculated yet + llama_sampler_softmax_impl(cur_p); + + int pos_last = 0; + + for (size_t i = 0; i < cur_p->size; ++i) { + if (cur_p->data[i].p >= ctx->threshold) { + pos_last = i; + } else break; + } + + if (cur_p->size - pos_last >= ctx->min_keep && pos_last > 0) { + cur_p->data += pos_last; + cur_p->size -= pos_last; + } +} + +static struct llama_sampler * llama_sampler_xtc_clone(const struct llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_xtc *) smpl->ctx; + auto * result = llama_sampler_init_xtc(ctx->probability, ctx->threshold, ctx->min_keep, ctx->seed); + + // copy the state + { + auto * result_ctx = (llama_sampler_xtc *) result->ctx; + + result_ctx->rng = ctx->rng; + } + + return result; +} + +static void llama_sampler_xtc_free(struct llama_sampler * smpl) { + delete (llama_sampler_xtc *) smpl->ctx; +} + +static void llama_sampler_xtc_reset(struct llama_sampler * smpl) { + auto * ctx = (llama_sampler_xtc *) smpl->ctx; + ctx->seed_cur = get_rng_seed(ctx->seed); + ctx->rng.seed(ctx->seed_cur); +} + +static struct llama_sampler_i llama_sampler_xtc_i = { + /* .name = */ llama_sampler_xtc_name, + /* .accept = */ nullptr, + /* .apply = */ llama_sample_xtc_apply, + /* .reset = */ llama_sampler_xtc_reset, + /* .clone = */ llama_sampler_xtc_clone, + /* .free = */ llama_sampler_xtc_free, +}; + +struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) { + auto seed_cur = get_rng_seed(seed); + return new llama_sampler { + /* .iface = */ &llama_sampler_xtc_i, + /* .ctx = */ new llama_sampler_xtc { + /* .probability = */ p, + /* .threshold = */ t, + /* .min_keep = */ min_keep, + /* .seed = */ seed, + /* .seed_cur = */ seed_cur, + /* .rng = */ std::mt19937(seed_cur), + }, + }; +} + // mirostat struct llama_sampler_mirostat { diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 6e021c4c7..1372bdf13 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -111,6 +111,28 @@ static void test_min_p(const std::vector & probs, const std::vector & probs, const std::vector & expected_probs, float p, float t) { + const size_t n_vocab = probs.size(); + + std::vector cur; + cur.reserve(n_vocab); + for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { + const float logit = logf(probs[token_id]); + cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); + } + + llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; + APPLY(llama_sampler_init_softmax(), &cur_p); + DUMP(&cur_p); + APPLY(llama_sampler_init_xtc(p, t, 0, 0), &cur_p); + DUMP(&cur_p); + + GGML_ASSERT(cur_p.size == expected_probs.size()); + for (size_t i = 0; i < cur_p.size; i++) { + GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-5); + } +} + static void test_typical(const std::vector & probs, const std::vector & expected_probs, float p) { const size_t n_vocab = probs.size(); @@ -263,7 +285,7 @@ static void bench(llama_sampler * cnstr, const char * cnstr_name, const std::vec } const int64_t t_end = ggml_time_us(); llama_sampler_free(cnstr); - printf("%-42s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter); + printf("%-43s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter); } #define BENCH(__cnstr, __data, __n_iter) bench((__cnstr), #__cnstr, (__data), (__n_iter)) @@ -279,12 +301,13 @@ static void test_perf() { data.emplace_back(llama_token_data{i, logit, 0.0f}); } - BENCH(llama_sampler_init_top_k (40), data, 32); - BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32); - BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32); - BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32); - BENCH(llama_sampler_init_typical (0.5f, 1), data, 32); - BENCH(llama_sampler_init_softmax (), data, 32); + BENCH(llama_sampler_init_top_k (40), data, 32); + BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32); + BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32); + BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32); + BENCH(llama_sampler_init_typical (0.5f, 1), data, 32); + BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32); + BENCH(llama_sampler_init_softmax (), data, 32); } int main(void) { @@ -309,6 +332,14 @@ int main(void) { test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 0.76f); test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.00f); + printf("XTC should:\n"); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.1f}, 0.99f, 0.09f); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.2f, 0.1f}, 0.99f, 0.19f); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.3f, 0.2f, 0.1f}, 0.99f, 0.29f); + + printf("XTC should not:\n"); + test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.99f, 0.39f); + test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f); test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f); test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f); From 223c25a72fcc3f65cdfd7f5d57edd5b44b550e18 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 15 Oct 2024 16:28:55 +0300 Subject: [PATCH 09/95] server : improve infill context reuse (#9894) ggml-ci --- examples/server/README.md | 10 +++--- examples/server/server.cpp | 73 ++++++++++++++------------------------ 2 files changed, 33 insertions(+), 50 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index eb0a7b32e..fcdb02afd 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -524,10 +524,12 @@ Takes a prefix and a suffix and returns the predicted completion as stream. - `input_prefix`: Set the prefix of the code to infill. - `input_suffix`: Set the suffix of the code to infill. -- `prompt`: Added after the `FIM_MID` token -- `extra_context`: Additional context inserted before the FIM prefix. See https://github.com/ggerganov/llama.cpp/pull/9874 +- `input_extra`: Additional context inserted before the FIM prefix. +- `prompt`: Added after the `FIM_MID` token -It also accepts all the options of `/completion`. +`input_extra` is array of `{"filename": string, "text": string}` objects. + +The endpoint also accepts all the options of `/completion`. If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used: @@ -545,7 +547,7 @@ If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern]( If the tokens are missing, then the extra context is simply prefixed at the start: ```txt -[extra_context][input_prefix][input_suffix][prompt] +[input_extra][input_prefix][input_suffix][prompt] ``` ### **GET** `/props`: Get server global properties. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8d4380e12..d53cca84c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -136,10 +136,6 @@ struct slot_params { int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit std::vector antiprompt; - - json input_prefix; - json input_suffix; - json extra_context; }; struct server_slot { @@ -169,6 +165,10 @@ struct server_slot { json prompt; // can be either a string, array of strings or array of token ids + json input_prefix; + json input_suffix; + json input_extra; + // when a task is submitted, we first tokenize the prompt and store it here std::vector prompt_tokens; std::vector extra_tokens; @@ -910,12 +910,12 @@ struct server_context { } // infill - slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); - slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); - slot.params.extra_context = json_value(data, "extra_context", default_params.extra_context); + slot.input_prefix = json_value(data, "input_prefix", json()); + slot.input_suffix = json_value(data, "input_suffix", json()); + slot.input_extra = json_value(data, "input_extra", json()); - SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.params.extra_context.size()); - for (const auto & chunk : slot.params.extra_context) { + SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.input_extra.size()); + for (const auto & chunk : slot.input_extra) { // { "text": string, "filename": string } if (!chunk.contains("text") || !chunk["text"].is_string()) { send_error(task, "extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST); @@ -932,7 +932,7 @@ struct server_context { } // get prompt - if (task.cmpl_type != SERVER_TASK_CMPL_TYPE_INFILL) { + { const auto & prompt = data.find("prompt"); if (prompt == data.end()) { send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST); @@ -1958,6 +1958,8 @@ struct server_context { } break; case SERVER_TASK_CMPL_TYPE_INFILL: { + // TODO: optimize this block by reducing memory allocations and movement + // use FIM repo-level pattern: // ref: https://arxiv.org/pdf/2409.12186 // @@ -1968,10 +1970,11 @@ struct server_context { // extra chunk 1 // ... // [FIM_SEP]filename - // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID] + // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt // - auto prefix_tokens = tokenize(slot.params.input_prefix, false, false); - auto suffix_tokens = tokenize(slot.params.input_suffix, false, false); + auto tokens_prefix = tokenize(slot.input_prefix, false, false); + auto tokens_suffix = tokenize(slot.input_suffix, false, false); + auto tokens_prompt = tokenize(slot.prompt, false, false); slot.extra_tokens.clear(); if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) { @@ -1981,7 +1984,7 @@ struct server_context { slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); } - for (const auto & chunk : slot.params.extra_context) { + for (const auto & chunk : slot.input_extra) { // { "text": string, "filename": string } const std::string text = chunk.value("text", ""); const std::string filename = chunk.value("filename", "tmp"); @@ -2012,20 +2015,21 @@ struct server_context { } // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) - const int n_suffix_take = std::min(suffix_tokens.size(), (n_batch)/4); - const int n_prefix_take = std::min(prefix_tokens.size(), (n_batch - 3) - n_suffix_take); + const int n_suffix_take = std::min(tokens_suffix.size(), (n_batch/4)); + const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4) - 3); // fill the rest of the context with extra chunks const int n_extra_take = std::min(std::max(0, slot.n_ctx - (n_batch) - 2*slot.n_predict), slot.extra_tokens.size()); - prefix_tokens.erase(prefix_tokens.begin(), prefix_tokens.begin() + prefix_tokens.size() - n_prefix_take); - suffix_tokens.resize(n_suffix_take); + tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); + tokens_suffix.resize(n_suffix_take); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_fim_pre(model)); - suffix_tokens.insert(suffix_tokens.begin(), llama_token_fim_suf(model)); + tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model)); + tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); + tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model)); - auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens; - auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens; + auto embd_inp = params.spm_infill ? tokens_suffix : tokens_prefix; + auto embd_end = params.spm_infill ? tokens_prefix : tokens_suffix; if (llama_add_bos_token(model)) { embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); @@ -2140,40 +2144,17 @@ struct server_context { while (head_c < slot.cache_tokens.size() && head_p < prompt_tokens.size()) { - if (llama_token_is_control(model, slot.cache_tokens[head_c]) && - slot.cache_tokens[head_c] != llama_token_fim_rep(model) && - slot.cache_tokens[head_c] != llama_token_fim_sep(model)) { - break; - } - - if (llama_token_is_control(model, prompt_tokens[head_p]) && - prompt_tokens[head_p] != llama_token_fim_rep(model) && - prompt_tokens[head_p] != llama_token_fim_sep(model)) { - break; - } size_t n_match = 0; - while (head_c + n_match < slot.cache_tokens.size() && head_p + n_match < prompt_tokens.size() && slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) { - if (llama_token_is_control(model, slot.cache_tokens[head_c + n_match]) && - slot.cache_tokens[head_c + n_match] != llama_token_fim_rep(model) && - slot.cache_tokens[head_c + n_match] != llama_token_fim_sep(model)) { - break; - } - - if (llama_token_is_control(model, prompt_tokens[head_p + n_match]) && - prompt_tokens[head_p + n_match] != llama_token_fim_rep(model) && - prompt_tokens[head_p + n_match] != llama_token_fim_sep(model)) { - break; - } n_match++; } if (n_match >= (size_t) params.n_cache_reuse) { - SLT_DBG(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match); + SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match); //for (size_t i = head_p; i < head_p + n_match; i++) { // SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); //} From 755a9b2bf00fbae988e03a47e852b66eaddd113a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 15 Oct 2024 16:35:33 +0300 Subject: [PATCH 10/95] llama : add infill sampler (#9896) ggml-ci --- common/common.h | 4 +- common/sampling.cpp | 9 +- examples/main/main.cpp | 46 +++++----- include/llama.h | 28 ++++++ src/llama-sampling.cpp | 201 +++++++++++++++++++++++++++++++++++++++++ src/llama-sampling.h | 5 +- src/llama-vocab.cpp | 17 ++++ src/llama-vocab.h | 8 +- src/llama.cpp | 11 +++ 9 files changed, 300 insertions(+), 29 deletions(-) diff --git a/common/common.h b/common/common.h index df2ee6bd4..5ca8fd391 100644 --- a/common/common.h +++ b/common/common.h @@ -91,7 +91,7 @@ enum common_sampler_type { COMMON_SAMPLER_TYPE_TYPICAL_P = 5, COMMON_SAMPLER_TYPE_TEMPERATURE = 6, COMMON_SAMPLER_TYPE_XTC = 7, - + COMMON_SAMPLER_TYPE_INFILL = 8, }; // dimensionality reduction methods, used by cvector-generator @@ -136,7 +136,7 @@ struct common_sampler_params { COMMON_SAMPLER_TYPE_TOP_P, COMMON_SAMPLER_TYPE_MIN_P, COMMON_SAMPLER_TYPE_XTC, - COMMON_SAMPLER_TYPE_TEMPERATURE + COMMON_SAMPLER_TYPE_TEMPERATURE, }; std::string grammar; // optional BNF-like grammar to constrain sampling diff --git a/common/sampling.cpp b/common/sampling.cpp index fb95bcd3b..56cd0df6b 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -196,6 +196,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co case COMMON_SAMPLER_TYPE_TEMPERATURE: llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); break; + case COMMON_SAMPLER_TYPE_INFILL: + llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model)); + break; default: GGML_ASSERT(false && "unknown sampler type"); } @@ -376,6 +379,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; case COMMON_SAMPLER_TYPE_XTC: return 'x'; + case COMMON_SAMPLER_TYPE_INFILL: return 'i'; default : return '?'; } } @@ -389,6 +393,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; case COMMON_SAMPLER_TYPE_XTC: return "xtc"; + case COMMON_SAMPLER_TYPE_INFILL: return "infill"; default : return ""; } } @@ -402,6 +407,7 @@ std::vector common_sampler_types_from_names(const std::vect { "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z }, { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, { "xtc", COMMON_SAMPLER_TYPE_XTC }, + { "infill", COMMON_SAMPLER_TYPE_INFILL }, }; // since samplers names are written multiple ways @@ -448,7 +454,8 @@ std::vector common_sampler_types_from_chars(const std::stri { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, - { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC } + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL }, }; std::vector samplers; diff --git a/examples/main/main.cpp b/examples/main/main.cpp index fb10c20c5..65483c45f 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -569,30 +569,30 @@ int main(int argc, char ** argv) { if (!params.ctx_shift){ LOG_DBG("\n\n%s: context full and context shift is disabled => stopping\n", __func__); break; - } else { - if (params.n_predict == -2) { - LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); - break; - } - - const int n_left = n_past - params.n_keep; - const int n_discard = n_left/2; - - LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", - n_past, n_left, n_ctx, params.n_keep, n_discard); - - llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); - llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); - - n_past -= n_discard; - - LOG_DBG("after swap: n_past = %d\n", n_past); - - LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); - - LOG_DBG("clear session path\n"); - path_session.clear(); } + + if (params.n_predict == -2) { + LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); + break; + } + + const int n_left = n_past - params.n_keep; + const int n_discard = n_left/2; + + LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", + n_past, n_left, n_ctx, params.n_keep, n_discard); + + llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); + + n_past -= n_discard; + + LOG_DBG("after swap: n_past = %d\n", n_past); + + LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); + + LOG_DBG("clear session path\n"); + path_session.clear(); } } else { // context extension via Self-Extend diff --git a/include/llama.h b/include/llama.h index 92d4c70c1..02bc7f087 100644 --- a/include/llama.h +++ b/include/llama.h @@ -953,6 +953,12 @@ extern "C" { int32_t lstrip, bool special); + // check if token0 is contained as a prefix in token1 + LLAMA_API bool llama_token_is_prefix( + const struct llama_model * model, + llama_token token0, + llama_token token1); + /// @details Convert the provided tokens into text (inverse of llama_tokenize()). /// @param text The char pointer must be large enough to hold the resulting text. /// @return Returns the number of chars/bytes on success, no more than text_len_max. @@ -1148,6 +1154,28 @@ extern "C" { int32_t n_logit_bias, const llama_logit_bias * logit_bias); + // this sampler is meant to be used for fill-in-the-middle infilling + // it's supposed to be used after top_k + top_p sampling + // + // 1. if the sum of the EOG probs times the number of candidates is higher than the sum of the other probs -> pick EOG + // 2. combine probs of tokens that have the same prefix + // + // example: + // + // - before: + // "hel": 0.5 + // "hell": 0.2 + // "hello": 0.1 + // "dummy": 0.1 + // + // - after: + // "hel": 0.8 + // "dummy": 0.1 + // + // 3. discard non-EOG tokens with low prob + // 4. if no tokens are left -> pick EOT + // + LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model); // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl); diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 67a78c3ac..2e6550682 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1739,6 +1739,207 @@ struct llama_sampler * llama_sampler_init_logit_bias( }; } +// infill + +//#define GGML_DEBUG_SAMPLER_INFILL + +struct llama_sampler_infill { + const struct llama_vocab * vocab; +}; + +static const char * llama_sampler_infill_name(const struct llama_sampler * /*smpl*/) { + return "infill"; +} + +static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_infill *) smpl->ctx; + + llama_sampler_softmax_impl(cur_p); + +#if defined(GGML_DEBUG_SAMPLER_INFILL) +#define LOG_DBG_CUR LLAMA_LOG_DEBUG +#else +#define LOG_DBG_CUR(...) +#endif + + for (size_t i = 0; i < cur_p->size; ++i) { + LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); + } + + float p_txt_sum = 0.0f; + float p_eog_sum = 0.0f; + + for (size_t i = 0; i < cur_p->size; ++i) { + if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) { + p_eog_sum += cur_p->data[i].p; + } else { + p_txt_sum += cur_p->data[i].p; + } + } + + const float rat = p_eog_sum == 0.0 ? INFINITY : p_txt_sum / p_eog_sum; GGML_UNUSED(rat); + + LOG_DBG_CUR("%s: p_txt_sum = %.2f, p_eog_sum = %.2f, rat = %.2f, n = %zu\n", __func__, p_txt_sum, p_eog_sum, rat, cur_p->size); + + if (3*p_eog_sum*cur_p->size > p_txt_sum) { + LOG_DBG_CUR("%s: the ratio p_txt/p_eog = %.2f is too low -> sampling EOG\n", __func__, p_txt_sum/p_eog_sum); + + // keep just the EOG tokens + const auto size_org = cur_p->size; + + cur_p->size = 0; + + float p_sum = 0.0f; + + for (size_t i = 0; i < size_org; ++i) { + if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) { + p_sum += cur_p->data[i].p; + + cur_p->data[cur_p->size++] = cur_p->data[i]; + } + } + + // normalize probs + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].p /= p_sum; + } + + return; + } + + size_t n_combined = 0; GGML_UNUSED(n_combined); + + // combine tokens with common prefix + for (size_t i = 0; i < cur_p->size; ++i) { + for (size_t j = 0; j < cur_p->size; ++j) { + if (cur_p->data[i].logit == -INFINITY) { + break; + } + + if (i == j || cur_p->data[j].logit == -INFINITY) { + continue; + } + + if (llama_token_is_prefix_impl(*ctx->vocab, cur_p->data[i].id, cur_p->data[j].id)) { + if (cur_p->data[i].p > cur_p->data[j].p) { + cur_p->data[i].p += cur_p->data[j].p; + cur_p->data[j].logit = -INFINITY; + cur_p->data[j].p = 0.0f; + } else { + cur_p->data[j].p += cur_p->data[i].p; + cur_p->data[i].logit = -INFINITY; + cur_p->data[i].p = 0.0f; + } + + n_combined++; + } + } + } + + size_t n_non_eog = 0; + + size_t size_org = cur_p->size; + + float p_sum = 0.0f; + float thold = 0.2f; + + cur_p->size = 0; + + LOG_DBG_CUR("%s: n_combined = %zu, applying thold = %.3f\n", __func__, n_combined, thold); + + for (size_t i = 0; i < size_org; ++i) { + const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id); + + if (cur_p->data[i].p < thold && !is_eog) { + continue; + } + + if (!is_eog) { + ++n_non_eog; + } + + p_sum += cur_p->data[i].p; + + // keep this token + cur_p->data[cur_p->size++] = cur_p->data[i]; + } + + LOG_DBG_CUR("%s: n_non_eog = %zu\n", __func__, n_non_eog); + + // if no non-EOG tokens are left -> reduce cur_p to single EOT token + if (n_non_eog == 0) { + cur_p->size = 1; + cur_p->data[0].id = llama_token_eot_impl(*ctx->vocab); + cur_p->data[0].logit = 1.0f; + + return; + } + + // normalize probs + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].p /= p_sum; + + LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); + } + + size_org = cur_p->size; + p_sum = 0.0f; + thold = 1.0/(n_non_eog + 1); + + cur_p->size = 0; + + LOG_DBG_CUR("%s: applying thold = %.3f\n", __func__, thold); + + for (size_t i = 0; i < size_org; ++i) { + const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id); + + if (cur_p->data[i].p < thold && !is_eog) { + continue; + } + + p_sum += cur_p->data[i].p; + + cur_p->data[cur_p->size++] = cur_p->data[i]; + } + + // normalize probs + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].p /= p_sum; + + LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); + } + +#undef LOG_DBG_CUR +} + +static struct llama_sampler * llama_sampler_infill_clone(const struct llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_infill *) smpl->ctx; + return llama_sampler_init_infill_impl(*ctx->vocab); +} + +static void llama_sampler_infill_free(struct llama_sampler * smpl) { + delete (llama_sampler_infill *) smpl->ctx; +} + +static struct llama_sampler_i llama_sampler_infill_i = { + /* .name = */ llama_sampler_infill_name, + /* .accept = */ nullptr, + /* .apply = */ llama_sampler_infill_apply, + /* .reset = */ nullptr, + /* .clone = */ llama_sampler_infill_clone, + /* .free = */ llama_sampler_infill_free, +}; + +struct llama_sampler * llama_sampler_init_infill_impl( + const struct llama_vocab & vocab) { + return new llama_sampler { + /* .iface = */ &llama_sampler_infill_i, + /* .ctx = */ new llama_sampler_infill { + /* .vocab = */ &vocab, + }, + }; +} + // utils uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) { diff --git a/src/llama-sampling.h b/src/llama-sampling.h index d90b14713..2683f1b92 100644 --- a/src/llama-sampling.h +++ b/src/llama-sampling.h @@ -4,8 +4,6 @@ #include "llama-grammar.h" -#include - struct llama_vocab; struct llama_grammar; @@ -27,3 +25,6 @@ struct llama_sampler * llama_sampler_init_grammar_impl( const struct llama_vocab & vocab, const char * grammar_str, const char * grammar_root); + +struct llama_sampler * llama_sampler_init_infill_impl( + const struct llama_vocab & vocab); diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index a27394a37..070de9365 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1858,6 +1858,23 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token return 0; } +bool llama_token_is_prefix_impl( + const struct llama_vocab & vocab, + llama_token token0, + llama_token token1) { + char text_buf_0[128]; + char text_buf_1[128]; + + const int32_t len0 = llama_token_to_piece_impl(vocab, token0, text_buf_0, sizeof(text_buf_0) - 1, 0, false); + const int32_t len1 = llama_token_to_piece_impl(vocab, token1, text_buf_1, sizeof(text_buf_1) - 1, 0, false); + + if (len0 <= 0 || len1 <= 0) { + return false; + } + + return len0 <= len1 && memcmp(text_buf_0, text_buf_1, len0) == 0; +} + int32_t llama_detokenize_impl( const struct llama_vocab & vocab, const llama_token * tokens, diff --git a/src/llama-vocab.h b/src/llama-vocab.h index 17e14488a..d958d0073 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -48,7 +48,7 @@ struct llama_vocab { id special_cls_id = LLAMA_TOKEN_NULL; id special_mask_id = LLAMA_TOKEN_NULL; - id linefeed_id = 13; + id linefeed_id = 13; // fim tokens id special_fim_pre_id = LLAMA_TOKEN_NULL; @@ -149,6 +149,12 @@ int32_t llama_token_to_piece_impl( int32_t lstrip, bool special); +// check if token0 is contained as a prefix in token1 +bool llama_token_is_prefix_impl( + const struct llama_vocab & vocab, + llama_token token0, + llama_token token1); + int32_t llama_detokenize_impl( const struct llama_vocab & vocab, const llama_token * tokens, diff --git a/src/llama.cpp b/src/llama.cpp index 511f91802..8d44c73c8 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -21500,6 +21500,13 @@ int32_t llama_token_to_piece( return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special); } +bool llama_token_is_prefix( + const struct llama_model * model, + llama_token token0, + llama_token token1) { + return llama_token_is_prefix_impl(model->vocab, token0, token1); +} + int32_t llama_detokenize( const struct llama_model * model, const llama_token * tokens, @@ -21830,6 +21837,10 @@ struct llama_sampler * llama_sampler_init_grammar(const struct llama_model * mod return llama_sampler_init_grammar_impl(model->vocab, grammar_str, grammar_root); } +struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model) { + return llama_sampler_init_infill_impl(model->vocab); +} + // // model split // From becfd387f6919d99ec34b76c2522f90ac250c489 Mon Sep 17 00:00:00 2001 From: leo-pony Date: Wed, 16 Oct 2024 08:51:46 +0800 Subject: [PATCH 11/95] [CANN] Fix cann compilation error (#9891) Fix cann compilation error after merging llama.cpp supports dynamically loadable backends. --- ggml/src/ggml-cann.cpp | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann.cpp index db5f8f186..ec3c0a688 100644 --- a/ggml/src/ggml-cann.cpp +++ b/ggml/src/ggml-cann.cpp @@ -1148,6 +1148,7 @@ ggml_backend_cann_buffer_type(int32_t device) { for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) { ggml_backend_cann_buffer_types[i] = { /* .iface = */ ggml_backend_cann_buffer_type_interface, + /* .device = */ nullptr, /* .context = */ new ggml_backend_cann_buffer_type_context{ i, "CANN" + std::to_string(i)}, @@ -1868,7 +1869,7 @@ static ggml_backend_event_t ggml_backend_cann_event_new( ACL_CHECK(aclrtCreateEvent(&event)); return new ggml_backend_event{ - /* .backend = */ backend, + /* .device = */ nullptr, /* .context = */ event, }; } @@ -1895,10 +1896,9 @@ static void ggml_backend_cann_event_free(ggml_backend_event_t event) { * * @param event Pointer to the event structure to be recorded. */ -static void ggml_backend_cann_event_record(ggml_backend_event_t event) { +static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_event_t event) { ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)event->backend->context; - + (ggml_backend_cann_context*)backend->context; ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream())); } @@ -1916,8 +1916,7 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { ggml_backend_cann_context* cann_ctx = (ggml_backend_cann_context*)backend->context; - - if (ggml_backend_is_cann(event->backend)) { + if (ggml_backend_is_cann(backend)) { ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(), (aclrtEvent)event->context)); } else { From cd60b88bf7ad7785fb6ac9864e360cf10e42faad Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 9 Oct 2024 16:40:35 +0200 Subject: [PATCH 12/95] ggml-alloc : remove buffer_id from leaf_alloc (ggml/987) This commit removes the buffer_id field from the leaf_alloc struct. The motivation for is that this field is only written to and never read/used as far as I can tell. Each tensor_alloc has a buffer_id field and this is what caused me to look into this more closely, to understand what the buffer_id in leaf_alloc was used for. --- ggml/src/ggml-alloc.c | 2 -- 1 file changed, 2 deletions(-) diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c index 28548fbbb..041de9e3e 100644 --- a/ggml/src/ggml-alloc.c +++ b/ggml/src/ggml-alloc.c @@ -348,7 +348,6 @@ struct tensor_alloc { }; struct leaf_alloc { - int buffer_id; struct tensor_alloc leaf; }; @@ -740,7 +739,6 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); - galloc->leaf_allocs[i].buffer_id = hn->buffer_id; if (leaf->view_src || leaf->data) { galloc->leaf_allocs[i].leaf.buffer_id = -1; galloc->leaf_allocs[i].leaf.offset = SIZE_MAX; From 0e41b300ed28f7fe185d938b2e3d56a0bf7411ed Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 16 Oct 2024 11:28:14 +0300 Subject: [PATCH 13/95] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 3cca9cc2f..6d31b21b9 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -564f42082f858f9674b2a2e06e9e779d9ed2c754 +2327bda7a55ac6b72614ac5ebd5c5a5e02553b9b From 1f66b699c48cb5ab3265ed72c48e8549b1674291 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Wed, 16 Oct 2024 08:35:53 +0000 Subject: [PATCH 14/95] server : fix the disappearance of the end of the text (#9867) * server: fix the disappearance of the end of the text when streaming with stop strings * simplify "send text" checks --- examples/server/server.cpp | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d53cca84c..b5e63384c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1090,22 +1090,21 @@ struct server_context { size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); - bool is_stop_full = false; + bool send_text = true; size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL); if (stop_pos != std::string::npos) { - is_stop_full = true; slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.n_sent_text, slot.generated_text.size()); - } else { - is_stop_full = false; + } else if (slot.has_next_token) { stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL); + send_text = stop_pos == std::string::npos; } // check if there is any token to predict - if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) { + if (send_text) { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.n_sent_text += result.text_to_send.size(); From 10433e8b457c4cfd759cbb41fc55fc398db4a5da Mon Sep 17 00:00:00 2001 From: Molly Sophia Date: Wed, 16 Oct 2024 18:10:21 +0800 Subject: [PATCH 15/95] llama : add tensor name for "result_norm" (#9907) Signed-off-by: Molly Sophia --- src/llama.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/llama.cpp b/src/llama.cpp index 8d44c73c8..c51b49c56 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -16095,9 +16095,11 @@ struct llm_build_context { cur = ggml_get_rows(ctx0, cur, inp_out_ids); cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM, cb, -1); - cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + cb(cur, "result_norm", -1); + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); cb(cur, "result_output", -1); + ggml_build_forward_expand(gf, cur); return gf; From 66c2c93082289325199ae1f773f3b0ab2e399a47 Mon Sep 17 00:00:00 2001 From: Joe Eli McIlvain Date: Wed, 16 Oct 2024 09:03:24 -0700 Subject: [PATCH 16/95] grammar : fix JSON Schema for string regex with top-level alt. (#9903) Prior to this commit, using a JSON Schema containing a string with `pattern` regular expression that uses top-level alternation (e.g. `"pattern": "^A|B|C|D$"`) would result in invalid JSON output from the constrained sampling grammar, because it ended up creating a grammar rule like this for the string: ``` thing ::= "\"" "A" | "B" | "C" | "D" "\"" space ``` Note that this rule will only match a starting quote for the "A" case, and will only match an ending quote for the "D" case, so this rule will always produce invalid JSON when used for sampling (that is, the JSON will always be lacking the starting quote, the ending quote, or both). This was fixed in a simple way by adding parentheses to the generated rule (for all string pattern rules, to keep it simple), such that the new generated rule looks like this (correct): ``` thing ::= "\"" ("A" | "B" | "C" | "D") "\"" space ``` --- common/json-schema-to-grammar.cpp | 2 +- examples/json_schema_to_grammar.py | 2 +- .../server/public/json-schema-to-grammar.mjs | 2 +- tests/test-json-schema-to-grammar.cpp | 21 +++++++++++++++---- 4 files changed, 20 insertions(+), 7 deletions(-) diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 881eb49e3..dadc18c8b 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -611,7 +611,7 @@ private: } return join_seq(); }; - return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space"); + return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space"); } /* diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py index a8779bf3b..fc9f0097f 100755 --- a/examples/json_schema_to_grammar.py +++ b/examples/json_schema_to_grammar.py @@ -540,7 +540,7 @@ class SchemaConverter: return self._add_rule( name, to_rule(transform()) if self._raw_pattern \ - else "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space") + else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space") def _resolve_ref(self, ref): diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs index 7267f3f9c..e67bb15c1 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public/json-schema-to-grammar.mjs @@ -529,7 +529,7 @@ export class SchemaConverter { return joinSeq(); }; - return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space") + return this._addRule(name, "\"\\\"\" (" + toRule(transform()) + ") \"\\\"\" space") } _notStrings(strings) { diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index 3a89598a8..9d2db91f5 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -696,7 +696,7 @@ static void test_all(const std::string & lang, std::function Date: Wed, 16 Oct 2024 19:24:05 +0200 Subject: [PATCH 17/95] llava : fix typo in error message [no ci] (#9884) --- examples/llava/llava.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 8558c6bdc..2c96973c8 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -432,7 +432,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); if (!image_embed_result) { clip_image_u8_free(img); - LOG_ERR("%s: coulnd't embed the image\n", __func__); + LOG_ERR("%s: couldn't embed the image\n", __func__); return NULL; } From 9e041024481f6b249ab8918e18b9477f873b5a5e Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 16 Oct 2024 19:34:28 +0200 Subject: [PATCH 18/95] llama : suppress conversion from 'size_t' to 'int' (#9046) * llama : suppress conversion from 'size_t' to 'int' This commit updates llm_tokenizer_spm.tokenize to suppress/remove the following warnings that are generated on Windows when using MSVC: ```console src\llama-vocab.cpp(211,1): warning C4267: 'argument': conversion from 'size_t' to 'int', possible loss of data src\llama-vocab.cpp(517,1): warning C4267: 'argument': conversion from 'size_t' to 'int', possible loss of data ``` This is done by adding a cast for the size_t returned from symbols.size(). I believe this is safe as it seems unlikely that symbols, which stores an entry for each UTF8 character, would become larger than INT_MAX. The motivation for this change is to reduce the number of warnings that are currently generated when building on Windows. * squash! llama : suppress conversion from 'size_t' to 'int' Move cast into for loop. --- src/llama-vocab.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 070de9365..57d56a3d3 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -221,7 +221,7 @@ struct llm_tokenizer_spm_session { } // seed the work queue with all possible 2-character tokens. - for (size_t i = 1; i < symbols.size(); ++i) { + for (int i = 1; i < (int) symbols.size(); ++i) { try_add_bigram(i - 1, i); } @@ -563,7 +563,7 @@ struct llm_tokenizer_bpe_session { index++; symbols.emplace_back(sym); } - for (size_t i = 1; i < symbols.size(); ++i) { + for (int i = 1; i < (int) symbols.size(); ++i) { add_new_bigram(i - 1, i); } From 73afe681aa76e818733fc1f30de082c1d6910bcd Mon Sep 17 00:00:00 2001 From: "Gilad S." <7817232+giladgd@users.noreply.github.com> Date: Thu, 17 Oct 2024 01:36:51 +0300 Subject: [PATCH 19/95] fix: use `vm_allocate` to allocate CPU backend buffer on macOS (#9875) * fix: use `vm_allocate` to allocate CPU backend buffer on macOS * fix: switch to `posix_memalign` to keep existing `free()` usages work * feat: move `GGML_ALIGNED_MALLOC` to `ggml-backend-impl.h`, add support for `vm_allocate` on macOS * style: formatting * fix: move const outside of `#ifndef` * style: formatting * fix: unused var * fix: transform `GGML_ALIGNED_MALLOC` and `GGML_ALIGNED_FREE` into functions and add them to `ggml-impl.h` * fix: unused var * fix: page align to `GGUF_DEFAULT_ALIGNMENT` * fix: page align to `TENSOR_ALIGNMENT` * fix: convert `TENSOR_ALIGNMENT` to a macro * fix: increase page size to `32` on iOS * fix: iOS page size * fix: `hbw_posix_memalign` alignment --- ggml/src/ggml-backend.cpp | 8 ++--- ggml/src/ggml-impl.h | 8 +++++ ggml/src/ggml.c | 74 +++++++++++++++++++++++++++------------ 3 files changed, 63 insertions(+), 27 deletions(-) diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 15d650150..6d6ffeb4e 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -682,8 +682,6 @@ ggml_backend_t ggml_backend_init_best(void) { // backend CPU -static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment - static const char * ggml_backend_cpu_buffer_get_name(ggml_backend_buffer_t buffer) { return "CPU"; @@ -702,7 +700,7 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { } static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { - free(buffer->context); + ggml_aligned_free(buffer->context, buffer->size); } static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { @@ -770,8 +768,8 @@ static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_ty } static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned - void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h) + void * data = ggml_aligned_malloc(size); + if (data == NULL) { GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); return NULL; diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index d3f4bad8c..65c4f8119 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -19,6 +19,9 @@ extern "C" { #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +// required for mmap as gguf only guarantees 32-byte alignment +#define TENSOR_ALIGNMENT 32 + // static_assert should be a #define, but if it's not, // fall back to the _Static_assert C11 keyword. // if C99 - static_assert is noop @@ -196,6 +199,11 @@ struct ggml_cgraph { struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1); +// Memory allocation + +void * ggml_aligned_malloc(size_t size); +void ggml_aligned_free(void * ptr, size_t size); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 3f01092d9..779b38d12 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -35,10 +35,6 @@ #include #endif -#ifdef GGML_USE_METAL -#include -#endif - #if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) #undef GGML_USE_LLAMAFILE #endif @@ -189,6 +185,8 @@ typedef pthread_t ggml_thread_t; #endif #if defined(__APPLE__) +#include +#include #include #endif @@ -386,22 +384,40 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi //#define GGML_SOFT_MAX_ACCELERATE #endif + +void * ggml_aligned_malloc(size_t size) { #if defined(_MSC_VER) || defined(__MINGW32__) -#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) -#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) + return _aligned_malloc(size, TENSOR_ALIGNMENT); #else -inline static void * ggml_aligned_malloc(size_t size) { if (size == 0) { GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n"); return NULL; } void * aligned_memory = NULL; #ifdef GGML_USE_CPU_HBM - int result = hbw_posix_memalign(&aligned_memory, 16, size); + int result = hbw_posix_memalign(&aligned_memory, TENSOR_ALIGNMENT, size); +#elif TARGET_OS_OSX + kern_return_t alloc_status = vm_allocate((vm_map_t) mach_task_self(), (vm_address_t *) &aligned_memory, size, VM_FLAGS_ANYWHERE); + int result = EFAULT; + switch (alloc_status) { + case KERN_SUCCESS: + result = 0; + break; + case KERN_INVALID_ADDRESS: + result = EINVAL; + break; + case KERN_NO_SPACE: + result = ENOMEM; + break; + default: + result = EFAULT; + break; + } #elif GGML_USE_METAL - int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size); + const long page_size = sysconf(_SC_PAGESIZE); + int result = posix_memalign(&aligned_memory, MAX(TENSOR_ALIGNMENT, page_size), size); #else - int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size); + int result = posix_memalign(&aligned_memory, TENSOR_ALIGNMENT, size); #endif if (result != 0) { // Handle allocation failure @@ -419,14 +435,26 @@ inline static void * ggml_aligned_malloc(size_t size) { return NULL; } return aligned_memory; +#endif } -#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size) -#ifdef GGML_USE_CPU_HBM -#define GGML_ALIGNED_FREE(ptr) if(NULL != ptr) hbw_free(ptr) + +void ggml_aligned_free(void * ptr, size_t size) { + GGML_UNUSED(size); +#if defined(_MSC_VER) || defined(__MINGW32__) + _aligned_free(ptr); +#elif GGML_USE_CPU_HBM + if (ptr != NULL) { + hbw_free(ptr); + } +#elif TARGET_OS_OSX + if (ptr != NULL) { + vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ptr, size); + } #else -#define GGML_ALIGNED_FREE(ptr) free(ptr) -#endif + free(ptr); #endif +} + inline static void * ggml_malloc(size_t size) { if (size == 0) { @@ -3869,7 +3897,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { *ctx = (struct ggml_context) { /*.mem_size =*/ mem_size, - /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size), + /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : ggml_aligned_malloc(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, /*.no_alloc_save =*/ params.no_alloc, @@ -3909,7 +3937,7 @@ void ggml_free(struct ggml_context * ctx) { __func__, i, ggml_used_mem(ctx)); if (ctx->mem_buffer_owned) { - GGML_ALIGNED_FREE(ctx->mem_buffer); + ggml_aligned_free(ctx->mem_buffer, ctx->mem_size); } found = true; @@ -19608,9 +19636,10 @@ static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask void ggml_threadpool_free(struct ggml_threadpool* threadpool) { if (!threadpool) return; + const int n_threads = threadpool->n_threads_max; + #ifndef GGML_USE_OPENMP struct ggml_compute_state* workers = threadpool->workers; - const int n_threads = threadpool->n_threads_max; ggml_mutex_lock(&threadpool->mutex); @@ -19630,8 +19659,9 @@ void ggml_threadpool_free(struct ggml_threadpool* threadpool) { ggml_cond_destroy(&threadpool->cond); #endif // GGML_USE_OPENMP - GGML_ALIGNED_FREE(threadpool->workers); - GGML_ALIGNED_FREE(threadpool); + const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads; + ggml_aligned_free(threadpool->workers, workers_size); + ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool)); } #ifndef GGML_USE_OPENMP @@ -20063,7 +20093,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl( struct ggml_cplan * cplan) { struct ggml_threadpool * threadpool = - GGML_ALIGNED_MALLOC(sizeof(struct ggml_threadpool)); + ggml_aligned_malloc(sizeof(struct ggml_threadpool)); { threadpool->cgraph = cgraph; threadpool->cplan = cplan; @@ -20084,7 +20114,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl( // Allocate and init workers state const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads; - struct ggml_compute_state * workers = GGML_ALIGNED_MALLOC(workers_size); + struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size); memset(workers, 0, workers_size); for (int j = 0; j < tpp->n_threads; j++) { From 21942002780352b4a54f4bd3e5eefa3bc7f14fe6 Mon Sep 17 00:00:00 2001 From: "Gilad S." <7817232+giladgd@users.noreply.github.com> Date: Thu, 17 Oct 2024 02:34:22 +0300 Subject: [PATCH 20/95] fix: allocating CPU buffer with size `0` (#9917) --- ggml/src/ggml-backend.cpp | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 6d6ffeb4e..4b9bac21d 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -768,14 +768,19 @@ static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_ty } static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * data = ggml_aligned_malloc(size); + auto alloc_size = size; + if (alloc_size == 0) { + alloc_size = 1; + } + + void * data = ggml_aligned_malloc(alloc_size); if (data == NULL) { - GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); + GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, alloc_size); return NULL; } - return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size); + return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, alloc_size); } static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { From f010b77a372ffcfaf4338c670d6d3ecd89aa4eb6 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Thu, 17 Oct 2024 02:46:58 +0200 Subject: [PATCH 21/95] vulkan : add backend registry / device interfaces (#9721) * vulkan : add backend registry / device interfaces * llama : print devices used on model load --- ggml/include/ggml-vulkan.h | 2 + ggml/src/ggml-backend.cpp | 9 +- ggml/src/ggml-vulkan.cpp | 284 +++++++++++++++++++++++++++---------- src/llama.cpp | 54 ++----- 4 files changed, 226 insertions(+), 123 deletions(-) diff --git a/ggml/include/ggml-vulkan.h b/ggml/include/ggml-vulkan.h index e074042ef..c03bbfe5e 100644 --- a/ggml/include/ggml-vulkan.h +++ b/ggml/include/ggml-vulkan.h @@ -24,6 +24,8 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); +GGML_API ggml_backend_reg_t ggml_backend_vk_reg(void); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 4b9bac21d..a3bc79a46 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -538,6 +538,10 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na #include "ggml-metal.h" #endif +#ifdef GGML_USE_VULKAN +#include "ggml-vulkan.h" +#endif + #ifdef GGML_USE_BLAS #include "ggml-blas.h" #endif @@ -557,6 +561,9 @@ struct ggml_backend_registry { #ifdef GGML_USE_METAL register_backend(ggml_backend_metal_reg()); #endif +#ifdef GGML_USE_VULKAN + register_backend(ggml_backend_vk_reg()); +#endif #ifdef GGML_USE_BLAS register_backend(ggml_backend_blas_reg()); #endif @@ -564,7 +571,7 @@ struct ggml_backend_registry { register_backend(ggml_backend_rpc_reg()); #endif - // TODO: sycl, vulkan, kompute, cann + // TODO: sycl, kompute, cann register_backend(ggml_backend_cpu_reg()); } diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp index 374c6ecd7..e749bbe70 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan.cpp @@ -1941,7 +1941,7 @@ static vk_device ggml_vk_get_device(size_t idx) { if (device->fp16) { device_extensions.push_back("VK_KHR_shader_float16_int8"); } - device->name = device->properties.deviceName.data(); + device->name = GGML_VK_NAME + std::to_string(idx); device_create_info = { vk::DeviceCreateFlags(), @@ -1968,7 +1968,7 @@ static vk_device ggml_vk_get_device(size_t idx) { device->buffer_type = { /* .iface = */ ggml_backend_vk_buffer_type_interface, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), idx), /* .context = */ new ggml_backend_vk_buffer_type_context{ device->name, device }, }; @@ -6378,7 +6378,7 @@ ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), 0), /* .context = */ nullptr, }; @@ -6581,9 +6581,135 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg UNUSED(backend); } -static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) { - // ggml_backend_vk_context * ctx = (ggml_backend_vk_context *) backend->context; +// TODO: enable async and synchronize +static ggml_backend_i ggml_backend_vk_interface = { + /* .get_name = */ ggml_backend_vk_name, + /* .free = */ ggml_backend_vk_free, + /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type, + /* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async, + /* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async, + /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, + /* .synchronize = */ NULL, // ggml_backend_vk_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_vk_graph_compute, + /* .supports_op = */ NULL, + /* .supports_buft = */ NULL, + /* .offload_op = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, +}; +static ggml_guid_t ggml_backend_vk_guid() { + static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; + return &guid; +} + +ggml_backend_t ggml_backend_vk_init(size_t dev_num) { + VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")"); + + ggml_backend_vk_context * ctx = new ggml_backend_vk_context; + ggml_vk_init(ctx, dev_num); + + ggml_backend_t vk_backend = new ggml_backend { + /* .guid = */ ggml_backend_vk_guid(), + /* .interface = */ ggml_backend_vk_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num), + /* .context = */ ctx, + }; + + return vk_backend; +} + +bool ggml_backend_is_vk(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); +} + +int ggml_backend_vk_get_device_count() { + return ggml_vk_get_device_count(); +} + +void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); + int dev_idx = vk_instance.device_indices[device]; + ggml_vk_get_device_description(dev_idx, description, description_size); +} + +void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); + + vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; + + vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); + + for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { + if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { + *total = heap.size; + *free = heap.size; + break; + } + } +} + +////////////////////////// + +struct ggml_backend_vk_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_vk_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_vk_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_vk_device_get_memory(ggml_backend_dev_t device, size_t * free, size_t * total) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)device->context; + ggml_backend_vk_get_device_memory(ctx->device, free, total); +} + +static ggml_backend_buffer_type_t ggml_backend_vk_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ggml_backend_vk_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(ggml_backend_dev_t dev) { + UNUSED(dev); + return ggml_backend_vk_host_buffer_type(); +} + +static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) { + UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; +} + +static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_vk_device_get_name(dev); + props->description = ggml_backend_vk_device_get_description(dev); + props->type = ggml_backend_vk_device_get_type(dev); + ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* async */ false, + /* host_buffer */ true, + /* events */ false, + }; +} + +static ggml_backend_t ggml_backend_vk_device_init(ggml_backend_dev_t dev, const char * params) { + UNUSED(params); + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ggml_backend_vk_init(ctx->device); +} + +static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -6701,97 +6827,101 @@ static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tenso return false; } - UNUSED(backend); + UNUSED(dev); } -static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) { +static bool ggml_backend_vk_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) { + return false; + } + + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + + return buft_ctx->device->idx == ctx->device; +} + +static bool ggml_backend_vk_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { const int min_batch_size = 32; return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); - UNUSED(backend); + UNUSED(dev); } -static bool ggml_backend_vk_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) { - return false; - } - - ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - - return buft_ctx->device == ctx->device; -} - -// TODO: enable async and synchronize -static ggml_backend_i ggml_backend_vk_interface = { - /* .get_name = */ ggml_backend_vk_name, - /* .free = */ ggml_backend_vk_free, - /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type, - /* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async, - /* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async, - /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, - /* .synchronize = */ NULL, // ggml_backend_vk_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_vk_graph_compute, - /* .supports_op = */ ggml_backend_vk_supports_op, - /* .supports_buft = */ ggml_backend_vk_supports_buft, - /* .offload_op = */ ggml_backend_vk_offload_op, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, +static const struct ggml_backend_device_i ggml_backend_vk_device_i = { + /* .get_name = */ ggml_backend_vk_device_get_name, + /* .get_description = */ ggml_backend_vk_device_get_description, + /* .get_memory = */ ggml_backend_vk_device_get_memory, + /* .get_type = */ ggml_backend_vk_device_get_type, + /* .get_props = */ ggml_backend_vk_device_get_props, + /* .init_backend = */ ggml_backend_vk_device_init, + /* .get_buffer_type = */ ggml_backend_vk_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_vk_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_vk_device_supports_op, + /* .supports_buft = */ ggml_backend_vk_device_supports_buft, + /* .offload_op = */ ggml_backend_vk_device_offload_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, }; -static ggml_guid_t ggml_backend_vk_guid() { - static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; - return &guid; +static const char * ggml_backend_vk_reg_get_name(ggml_backend_reg_t reg) { + UNUSED(reg); + return GGML_VK_NAME; } -ggml_backend_t ggml_backend_vk_init(size_t dev_num) { - VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")"); - - ggml_backend_vk_context * ctx = new ggml_backend_vk_context; - ggml_vk_init(ctx, dev_num); - - ggml_backend_t vk_backend = new ggml_backend { - /* .guid = */ ggml_backend_vk_guid(), - /* .interface = */ ggml_backend_vk_interface, - /* .device = */ nullptr, - /* .context = */ ctx, - }; - - return vk_backend; +static size_t ggml_backend_vk_reg_get_device_count(ggml_backend_reg_t reg) { + UNUSED(reg); + return ggml_backend_vk_get_device_count(); } -bool ggml_backend_is_vk(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); -} +static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg, size_t device) { + static std::vector devices; -int ggml_backend_vk_get_device_count() { - return ggml_vk_get_device_count(); -} + static bool initialized = false; -void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { - ggml_vk_get_device_description(device, description, description_size); -} - -void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { - GGML_ASSERT(device < (int) vk_instance.device_indices.size()); - - vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; - - vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); - - for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { - if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { - *total = heap.size; - *free = heap.size; - break; + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + for (size_t i = 0; i < ggml_backend_vk_get_device_count(); i++) { + ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context; + char desc[256]; + ggml_backend_vk_get_device_description(i, desc, sizeof(desc)); + ctx->device = i; + ctx->name = GGML_VK_NAME + std::to_string(i); + ctx->description = desc; + devices.push_back(new ggml_backend_device { + /* .iface = */ ggml_backend_vk_device_i, + /* .reg = */ reg, + /* .context = */ ctx, + }); + } + initialized = true; } } + + GGML_ASSERT(device < devices.size()); + return devices[device]; +} + +static const struct ggml_backend_reg_i ggml_backend_vk_reg_i = { + /* .get_name = */ ggml_backend_vk_reg_get_name, + /* .get_device_count = */ ggml_backend_vk_reg_get_device_count, + /* .get_device = */ ggml_backend_vk_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +ggml_backend_reg_t ggml_backend_vk_reg() { + static ggml_backend_reg reg = { + /* .iface = */ ggml_backend_vk_reg_i, + /* .context = */ nullptr, + }; + + return ® } // Extension availability diff --git a/src/llama.cpp b/src/llama.cpp index c51b49c56..68479c6db 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -8,9 +8,7 @@ #include "ggml-alloc.h" #include "ggml-backend.h" -#if defined(GGML_USE_VULKAN) -# include "ggml-vulkan.h" -#elif defined(GGML_USE_SYCL) +#if defined(GGML_USE_SYCL) # include "ggml-sycl.h" #elif defined(GGML_USE_KOMPUTE) # include "ggml-kompute.h" @@ -3418,8 +3416,6 @@ static int llama_get_device_count(const llama_model & model) { #if defined(GGML_USE_SYCL) count += ggml_backend_sycl_get_device_count(); -#elif defined(GGML_USE_VULKAN) - count += ggml_backend_vk_get_device_count(); #elif defined(GGML_USE_CANN) count += ggml_backend_cann_get_device_count(); #endif @@ -3451,10 +3447,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_mode } #elif defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); -#elif defined(GGML_USE_VULKAN) - if (host_buffer) { - buft = ggml_backend_vk_host_buffer_type(); - } #endif if (buft == nullptr) { @@ -3473,9 +3465,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_ } device -= (int)model.devices.size(); -#if defined(GGML_USE_VULKAN) - buft = ggml_backend_vk_buffer_type(device); -#elif defined(GGML_USE_SYCL) +#if defined(GGML_USE_SYCL) buft = ggml_backend_sycl_buffer_type(device); #elif defined(GGML_USE_KOMPUTE) buft = ggml_backend_kompute_buffer_type(device); @@ -3535,11 +3525,6 @@ static size_t llama_get_device_memory(const llama_model & model, int device) { size_t free; ggml_backend_sycl_get_device_memory(device, &free, &total); return free; -#elif defined(GGML_USE_VULKAN) - size_t total; - size_t free; - ggml_backend_vk_get_device_memory(device, &free, &total); - return free; #elif defined(GGML_USE_CANN) size_t total; size_t free; @@ -19095,8 +19080,7 @@ bool llama_supports_mlock(void) { } bool llama_supports_gpu_offload(void) { -#if defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) +#if defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. return true; #else @@ -19227,8 +19211,13 @@ struct llama_model * llama_load_model_from_file( case GGML_BACKEND_DEVICE_TYPE_GPU: case GGML_BACKEND_DEVICE_TYPE_GPU_FULL: + { + size_t free, total; // NOLINT + ggml_backend_dev_memory(dev, &free, &total); + LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024); model->devices.push_back(dev); break; + } } } @@ -19423,32 +19412,7 @@ struct llama_context * llama_new_context_with_model( main_gpu -= (int)model->devices.size(); } -#if defined(GGML_USE_VULKAN) - if (model->split_mode == LLAMA_SPLIT_MODE_ROW) { - LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__); - llama_free(ctx); - return nullptr; - } - if (model->split_mode == LLAMA_SPLIT_MODE_NONE) { - ggml_backend_t backend = ggml_backend_vk_init(main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } else { - for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) { - ggml_backend_t backend = ggml_backend_vk_init(device); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } -#elif defined(GGML_USE_SYCL) +#if defined(GGML_USE_SYCL) // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { ggml_backend_t backend = ggml_backend_sycl_init(main_gpu); From 3752217ed5a6a11864682fbf009bcb36afffd6bc Mon Sep 17 00:00:00 2001 From: Tim Wang Date: Thu, 17 Oct 2024 17:57:14 +1100 Subject: [PATCH 22/95] readme : update bindings list (#9918) Co-authored-by: Tim Wang --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 08fe8cc92..707904ddc 100644 --- a/README.md +++ b/README.md @@ -131,6 +131,7 @@ Typically finetunes of the base models below are supported as well. - PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326) - Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp) - Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift) +- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama) **UI:** From 99bd4ac28c32cd17c0e337ff5601393b033dc5fc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 17 Oct 2024 22:32:47 +0300 Subject: [PATCH 23/95] llama : infill sampling handle very long tokens (#9924) * llama : infill sampling handle very long tokens ggml-ci * cont : better indices ggml-ci --- include/llama.h | 6 ------ src/llama-sampling.cpp | 48 ++++++++++++++++++++++++++++++------------ src/llama-vocab.cpp | 17 --------------- src/llama.cpp | 7 ------ 4 files changed, 35 insertions(+), 43 deletions(-) diff --git a/include/llama.h b/include/llama.h index 02bc7f087..1a13360c2 100644 --- a/include/llama.h +++ b/include/llama.h @@ -953,12 +953,6 @@ extern "C" { int32_t lstrip, bool special); - // check if token0 is contained as a prefix in token1 - LLAMA_API bool llama_token_is_prefix( - const struct llama_model * model, - llama_token token0, - llama_token token1); - /// @details Convert the provided tokens into text (inverse of llama_tokenize()). /// @param text The char pointer must be large enough to hold the resulting text. /// @return Returns the number of chars/bytes on success, no more than text_len_max. diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 2e6550682..bd750c40e 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1745,6 +1745,9 @@ struct llama_sampler * llama_sampler_init_logit_bias( struct llama_sampler_infill { const struct llama_vocab * vocab; + + std::vector buf0; + std::vector buf1; }; static const char * llama_sampler_infill_name(const struct llama_sampler * /*smpl*/) { @@ -1810,27 +1813,44 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_ size_t n_combined = 0; GGML_UNUSED(n_combined); // combine tokens with common prefix - for (size_t i = 0; i < cur_p->size; ++i) { - for (size_t j = 0; j < cur_p->size; ++j) { - if (cur_p->data[i].logit == -INFINITY) { + for (size_t i0 = 0; i0 < cur_p->size; ++i0) { + for (size_t i1 = 0; i1 < cur_p->size; ++i1) { + if (cur_p->data[i0].logit == -INFINITY) { break; } - if (i == j || cur_p->data[j].logit == -INFINITY) { + if (i0 == i1 || cur_p->data[i1].logit == -INFINITY) { continue; } - if (llama_token_is_prefix_impl(*ctx->vocab, cur_p->data[i].id, cur_p->data[j].id)) { - if (cur_p->data[i].p > cur_p->data[j].p) { - cur_p->data[i].p += cur_p->data[j].p; - cur_p->data[j].logit = -INFINITY; - cur_p->data[j].p = 0.0f; - } else { - cur_p->data[j].p += cur_p->data[i].p; - cur_p->data[i].logit = -INFINITY; - cur_p->data[i].p = 0.0f; + int len0 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false); + if (len0 < 0) { + ctx->buf0.resize(len0); + len0 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i0].id, ctx->buf0.data(), ctx->buf0.size(), 0, false); + assert(len0 > 0); + } + + int len1 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false); + if (len1 < 0) { + ctx->buf1.resize(len1); + len1 = llama_token_to_piece_impl(*ctx->vocab, cur_p->data[i1].id, ctx->buf1.data(), ctx->buf1.size(), 0, false); + assert(len1 > 0); + } + + // token i0 is a prefix of token i1 + if (len0 > 0 && len0 <= len1 && memcmp(ctx->buf0.data(), ctx->buf1.data(), len0) == 0) { + int dst = i0; + int src = i1; + + // merge into the token with higher probability + if (cur_p->data[i1].p > cur_p->data[i0].p) { + std::swap(dst, src); } + cur_p->data[dst].p += cur_p->data[src].p; + cur_p->data[src].logit = -INFINITY; + cur_p->data[src].p = 0.0f; + n_combined++; } } @@ -1936,6 +1956,8 @@ struct llama_sampler * llama_sampler_init_infill_impl( /* .iface = */ &llama_sampler_infill_i, /* .ctx = */ new llama_sampler_infill { /* .vocab = */ &vocab, + /* .buf0 = */ std::vector(512), + /* .buf1 = */ std::vector(512), }, }; } diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 57d56a3d3..0a49ddbe3 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1858,23 +1858,6 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token return 0; } -bool llama_token_is_prefix_impl( - const struct llama_vocab & vocab, - llama_token token0, - llama_token token1) { - char text_buf_0[128]; - char text_buf_1[128]; - - const int32_t len0 = llama_token_to_piece_impl(vocab, token0, text_buf_0, sizeof(text_buf_0) - 1, 0, false); - const int32_t len1 = llama_token_to_piece_impl(vocab, token1, text_buf_1, sizeof(text_buf_1) - 1, 0, false); - - if (len0 <= 0 || len1 <= 0) { - return false; - } - - return len0 <= len1 && memcmp(text_buf_0, text_buf_1, len0) == 0; -} - int32_t llama_detokenize_impl( const struct llama_vocab & vocab, const llama_token * tokens, diff --git a/src/llama.cpp b/src/llama.cpp index 68479c6db..d8e2b006c 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -21466,13 +21466,6 @@ int32_t llama_token_to_piece( return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special); } -bool llama_token_is_prefix( - const struct llama_model * model, - llama_token token0, - llama_token token1) { - return llama_token_is_prefix_impl(model->vocab, token0, token1); -} - int32_t llama_detokenize( const struct llama_model * model, const llama_token * tokens, From 9f45fc1e9950a496febc575cdd196cd5cad000cc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 17 Oct 2024 23:26:32 +0300 Subject: [PATCH 24/95] llama : change warning to debug log --- src/llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index d8e2b006c..ffaa6f789 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -6735,9 +6735,9 @@ static void llm_load_vocab( vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL; } } else { - // token is control, but not marked as EOG -> print a warning + // token is control, but not marked as EOG -> print a debug log if (vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && vocab.special_eog_ids.count(t.second) == 0) { - LLAMA_LOG_WARN("%s: control token: %6d '%s' is not marked as EOG\n", + LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n", __func__, t.second, t.first.c_str()); } } From 17bb9280807cfbb6611b853aa1ef05114bd9efe9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 17 Oct 2024 23:43:05 +0300 Subject: [PATCH 25/95] readme : remove --memory-f32 references (#9925) --- examples/main/README.md | 4 ---- scripts/run-with-preset.py | 6 +++--- 2 files changed, 3 insertions(+), 7 deletions(-) diff --git a/examples/main/README.md b/examples/main/README.md index 620934dad..7e192b9f2 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -297,10 +297,6 @@ These options help improve the performance and memory usage of the LLaMA models. These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root. -### Memory Float 32 - -- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended. - ### Batch Size - `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations. diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py index ee21eab37..47cacb432 100755 --- a/scripts/run-with-preset.py +++ b/scripts/run-with-preset.py @@ -15,7 +15,7 @@ CLI_ARGS_LLAMA_CLI_PERPLEXITY = [ "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag", "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base", - "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock", + "low-vram", "main-gpu", "mirostat", "mirostat-ent", "mirostat-lr", "mlock", "model", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q", "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt", "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n", @@ -25,12 +25,12 @@ CLI_ARGS_LLAMA_CLI_PERPLEXITY = [ ] CLI_ARGS_LLAMA_BENCH = [ - "batch-size", "memory-f32", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers", + "batch-size", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers", "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose" ] CLI_ARGS_LLAMA_SERVER = [ - "alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base", + "alias", "batch-size", "ctx-size", "embedding", "host", "lora", "lora-base", "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q", "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split", "threads", "verbose" From 6f55bccbb8835d42147add4ee48807450f5ff535 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 18 Oct 2024 01:41:51 +0200 Subject: [PATCH 26/95] llama : rename batch_all to batch (#8881) This commit addresses the TODO in the code to rename the `batch_all` parameter to `batch` in `llama_decode_internal`. --- src/llama.cpp | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index ffaa6f789..dcb015d12 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -17134,10 +17134,10 @@ static void llama_graph_compute( // static int llama_decode_internal( llama_context & lctx, - llama_batch batch_all) { // TODO: rename back to batch + llama_batch batch) { lctx.is_encoding = false; - const uint32_t n_tokens_all = batch_all.n_tokens; + const uint32_t n_tokens_all = batch.n_tokens; if (n_tokens_all == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); @@ -17148,12 +17148,12 @@ static int llama_decode_internal( const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; - GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT + GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT - if (batch_all.token) { + if (batch.token) { for (uint32_t i = 0; i < n_tokens_all; ++i) { - if (batch_all.token[i] < 0 || (uint32_t)batch_all.token[i] >= model.vocab.n_vocab) { - LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch_all.token[i]); + if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) { + LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]); return -1; } } @@ -17184,9 +17184,9 @@ static int llama_decode_internal( lctx.embd_seq.clear(); // count outputs - if (batch_all.logits && !embd_pooled) { + if (batch.logits && !embd_pooled) { for (uint32_t i = 0; i < n_tokens_all; ++i) { - n_outputs += batch_all.logits[i] != 0; + n_outputs += batch.logits[i] != 0; } } else if (lctx.logits_all || embd_pooled) { n_outputs = n_tokens_all; @@ -17195,7 +17195,7 @@ static int llama_decode_internal( n_outputs = 1; } - lctx.sbatch.from_batch(batch_all, n_embd, + lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ !kv_self.recurrent, /* logits_all */ n_outputs == n_tokens_all); From 8901755ba328643c9ab071c20e1939ea52951a0e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 18 Oct 2024 07:32:19 +0300 Subject: [PATCH 27/95] server : add n_indent parameter for line indentation requirement (#9929) ggml-ci --- examples/server/README.md | 2 ++ examples/server/server.cpp | 54 +++++++++++++++++++++++++++++++++----- 2 files changed, 49 insertions(+), 7 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index fcdb02afd..09f1aa249 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -333,6 +333,8 @@ node index.js `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity. + `n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0` + `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token. By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b5e63384c..8fd443878 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -131,6 +131,7 @@ struct slot_params { int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half int32_t n_predict = -1; // new tokens to predict + int32_t n_indent = 0; // mininum line indentation for the generated text in number of whitespace characters int64_t t_max_prompt_ms = -1; // TODO: implement int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit @@ -173,6 +174,8 @@ struct server_slot { std::vector prompt_tokens; std::vector extra_tokens; + size_t last_nl_pos = 0; + std::string generated_text; std::vector cache_tokens; std::vector generated_token_probs; @@ -215,6 +218,7 @@ struct server_slot { SLT_DBG(*this, "%s", "\n"); n_prompt_tokens = 0; + last_nl_pos = 0; generated_text = ""; has_new_line = false; truncated = false; @@ -860,6 +864,7 @@ struct server_context { slot.params.stream = json_value(data, "stream", false); slot.params.cache_prompt = json_value(data, "cache_prompt", false); slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict)); + slot.params.n_indent = json_value(data, "n_indent", default_params.n_indent); slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); @@ -878,7 +883,7 @@ struct server_context { slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep); + slot.params.n_keep = json_value(data, "n_keep", default_params.n_keep); slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); slot.sparams.seed = json_value(data, "seed", default_sparams.seed); slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); @@ -1129,13 +1134,48 @@ struct server_context { SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict); } - // if we have already seen a new line, we stop after a certain time limit - if (slot.has_new_line && slot.params.t_max_predict_ms > 0 && - (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) { - slot.stopped_limit = true; - slot.has_next_token = false; + if (slot.has_new_line) { + // if we have already seen a new line, we stop after a certain time limit + if (slot.params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) { + slot.stopped_limit = true; + slot.has_next_token = false; - SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms); + SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms); + } + + // require that each new line has a whitespace prefix (i.e. indentation) of at least slot.params.n_indent + if (slot.params.n_indent > 0) { + // check the current indentation + // TODO: improve by not doing it more than once for each new line + if (slot.last_nl_pos > 0) { + size_t pos = slot.last_nl_pos; + + int n_indent = 0; + while (pos < slot.generated_text.size() && (slot.generated_text[pos] == ' ' || slot.generated_text[pos] == '\t')) { + n_indent++; + pos++; + } + + if (pos < slot.generated_text.size() && n_indent < slot.params.n_indent) { + slot.stopped_limit = true; + slot.has_next_token = false; + + // cut the last line + slot.generated_text.erase(pos, std::string::npos); + + SLT_DBG(slot, "stopped by indentation limit, n_decoded = %d, n_indent = %d\n", slot.n_decoded, n_indent); + } + } + + // find the next new line + { + const size_t pos = slot.generated_text.find('\n', slot.last_nl_pos); + + if (pos != std::string::npos) { + slot.last_nl_pos = pos + 1; + } + } + } } // check if there is a new line in the generated text From 60ce97c9d809f4b040e90b597468b839df5728d0 Mon Sep 17 00:00:00 2001 From: Ma Mingfei Date: Fri, 18 Oct 2024 13:34:36 +0800 Subject: [PATCH 28/95] add amx kernel for gemm (#8998) add intel amx isa detection add vnni kernel for gemv cases add vnni and amx kernel support for block_q8_0 code cleanup fix packing B issue enable openmp fine tune amx kernel switch to aten parallel pattern add error message for nested parallelism code cleanup add f16 support in ggml-amx add amx kernels for QK_K quant formats: Q4_K, Q5_K, Q6_K and IQ4_XS update CMakeList update README fix some compilation warning fix compiler warning when amx is not enabled minor change ggml-ci move ggml_amx_init from ggml.c to ggml-amx/mmq.cpp ggml-ci update CMakeLists with -mamx-tile, -mamx-int8 and -mamx-bf16 ggml-ci add amx as an ggml-backend update header file, the old path for immintrin.h has changed to ggml-cpu-impl.h minor change update CMakeLists.txt minor change apply weight prepacking in set_tensor method in ggml-backend fix compile error ggml-ci minor change ggml-ci update CMakeLists.txt ggml-ci add march dependency minor change ggml-ci change ggml_backend_buffer_is_host to return false for amx backend ggml-ci fix supports_op use device reg for AMX backend ggml-ci minor change ggml-ci minor change fix rebase set .buffer_from_host_ptr to be false for AMX backend --- CMakeLists.txt | 4 + Makefile | 24 +- README.md | 2 +- ggml/CMakeLists.txt | 4 + ggml/include/ggml-amx.h | 25 + ggml/include/ggml.h | 1 + ggml/src/CMakeLists.txt | 42 + ggml/src/ggml-amx.cpp | 453 +++++++ ggml/src/ggml-amx/common.h | 93 ++ ggml/src/ggml-amx/mmq.cpp | 2509 ++++++++++++++++++++++++++++++++++++ ggml/src/ggml-amx/mmq.h | 17 + ggml/src/ggml-backend.cpp | 12 +- ggml/src/ggml.c | 8 + src/llama.cpp | 17 + 14 files changed, 3204 insertions(+), 7 deletions(-) create mode 100644 ggml/include/ggml-amx.h create mode 100644 ggml/src/ggml-amx.cpp create mode 100644 ggml/src/ggml-amx/common.h create mode 100644 ggml/src/ggml-amx/mmq.cpp create mode 100644 ggml/src/ggml-amx/mmq.h diff --git a/CMakeLists.txt b/CMakeLists.txt index 64a335378..ef0932a7b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -88,6 +88,10 @@ if (NOT DEFINED GGML_LLAMAFILE) set(GGML_LLAMAFILE_DEFAULT ON) endif() +if (NOT DEFINED GGML_AMX) + set(GGML_AMX ON) +endif() + if (NOT DEFINED GGML_CUDA_GRAPHS) set(GGML_CUDA_GRAPHS_DEFAULT ON) endif() diff --git a/Makefile b/Makefile index 2793978c3..719f45d16 100644 --- a/Makefile +++ b/Makefile @@ -93,11 +93,6 @@ GGML_METAL := 1 DEPRECATE_WARNING := 1 endif -ifdef LLAMA_OPENMP -GGML_OPENMP := 1 -DEPRECATE_WARNING := 1 -endif - ifdef LLAMA_RPC GGML_RPC := 1 DEPRECATE_WARNING := 1 @@ -584,6 +579,11 @@ ifndef GGML_NO_LLAMAFILE OBJ_GGML += ggml/src/llamafile/sgemm.o endif +ifndef GGML_NO_AMX + MK_CPPFLAGS += -DGGML_USE_AMX + OBJ_GGML += ggml/src/ggml-amx.o ggml/src/ggml-amx/mmq.o +endif + ifdef GGML_RPC MK_CPPFLAGS += -DGGML_USE_RPC OBJ_GGML += ggml/src/ggml-rpc.o @@ -1087,6 +1087,19 @@ ggml/src/llamafile/sgemm.o: \ $(CXX) $(CXXFLAGS) -c $< -o $@ endif # GGML_NO_LLAMAFILE +ifndef GGML_NO_AMX +ggml/src/ggml-amx.o: \ + ggml/src/ggml-amx.cpp \ + ggml/include/ggml-amx.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + +ggml/src/ggml-amx/mmq.o: \ + ggml/src/ggml-amx/mmq.cpp \ + ggml/src/ggml-amx/mmq.h \ + ggml/include/ggml.h + $(CXX) $(CXXFLAGS) -c $< -o $@ +endif + ifdef GGML_RPC ggml/src/ggml-rpc.o: \ ggml/src/ggml-rpc.cpp \ @@ -1238,6 +1251,7 @@ clean: rm -vrf ggml/src/ggml-metal-embed.metal rm -vrf ggml/src/ggml-cuda/*.o rm -vrf ggml/src/ggml-cuda/template-instances/*.o + rm -vrf ggml/src/ggml-amx/*.o rm -rvf $(BUILD_TARGETS) rm -rvf $(TEST_TARGETS) rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp diff --git a/README.md b/README.md index 707904ddc..1088b3338 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ variety of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks -- AVX, AVX2 and AVX512 support for x86 architectures +- AVX, AVX2, AVX512 and AMX support for x86 architectures - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA) - Vulkan and SYCL backend support diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 89fdf9d1c..cfa6e3f70 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -99,6 +99,9 @@ option(GGML_AVX512 "ggml: enable AVX512" OFF) option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF) option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF) option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16" OFF) +option(GGML_AMX_TILE "ggml: enable AMX-TILE" OFF) +option(GGML_AMX_INT8 "ggml: enable AMX-INT8" OFF) +option(GGML_AMX_BF16 "ggml: enable AMX-BF16" OFF) option(GGML_FMA "ggml: enable FMA" ${INS_ENB}) if (NOT MSVC) option(GGML_F16C "ggml: enable F16C" ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512 @@ -158,6 +161,7 @@ set (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING set (GGML_METAL_STD "" CACHE STRING "ggml: metal standard version (-std flag)") option(GGML_OPENMP "ggml: use OpenMP" ON) option(GGML_RPC "ggml: use RPC" OFF) +option(GGML_AMX "ggml: use AMX" OFF) option(GGML_SYCL "ggml: use SYCL" OFF) option(GGML_SYCL_F16 "ggml: use 16 bit floats for sycl calculations" OFF) set (GGML_SYCL_TARGET "INTEL" CACHE STRING diff --git a/ggml/include/ggml-amx.h b/ggml/include/ggml-amx.h new file mode 100644 index 000000000..22b3f70f4 --- /dev/null +++ b/ggml/include/ggml-amx.h @@ -0,0 +1,25 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + + +#ifdef __cplusplus +extern "C" { +#endif + +// buffer_type API +GGML_API ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void); + +GGML_API bool ggml_backend_is_amx(ggml_backend_t backend); + +// backend API +GGML_API ggml_backend_t ggml_backend_amx_init(void); + +GGML_API void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads); + +GGML_API ggml_backend_reg_t ggml_backend_amx_reg(void); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 4508da4fb..de3c706fc 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -2488,6 +2488,7 @@ extern "C" { GGML_API int ggml_cpu_has_avx512_vbmi(void); GGML_API int ggml_cpu_has_avx512_vnni(void); GGML_API int ggml_cpu_has_avx512_bf16(void); + GGML_API int ggml_cpu_has_amx_int8 (void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); GGML_API int ggml_cpu_has_sve (void); diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 676f85a36..aa405e4d0 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -267,6 +267,26 @@ if (GGML_LLAMAFILE) set(GGML_SOURCES_LLAMAFILE llamafile/sgemm.cpp) endif() +if (GGML_AMX) + if (CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 11.0) + else() + set(GGML_AMX OFF) + message(WARNING "AMX requires gcc version > 11.0. Turning off GGML_AMX.") + endif() + + if (GGML_AMX) + message(STATUS "Using AMX") + + list(APPEND GGML_CDEF_PUBLIC GGML_USE_AMX) + + file(GLOB GGML_HEADERS_AMX "ggml-amx/*.h") + list(APPEND GGML_HEADERS_AMX "../include/ggml-amx.h") + + file(GLOB GGML_SOURCES_AMX "ggml-amx/*.cpp") + list(APPEND GGML_SOURCES_AMX "ggml-amx.cpp") + endif() +endif() + if (GGML_CUDA) cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES @@ -1180,6 +1200,18 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW add_compile_definitions($<$:__AVX512BF16__>) add_compile_definitions($<$:__AVX512BF16__>) endif() + if (GGML_AMX_TILE) + add_compile_definitions($<$:__AMX_TILE__>) + add_compile_definitions($<$:__AMX_TILE__>) + endif() + if (GGML_AMX_INT8) + add_compile_definitions($<$:__AMX_INT8__>) + add_compile_definitions($<$:__AMX_INT8__>) + endif() + if (GGML_AMX_BF16) + add_compile_definitions($<$:__AMX_BF16__>) + add_compile_definitions($<$:__AMX_BF16__>) + endif() elseif (GGML_AVX2) list(APPEND ARCH_FLAGS /arch:AVX2) elseif (GGML_AVX) @@ -1215,6 +1247,15 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW if (GGML_AVX512_BF16) list(APPEND ARCH_FLAGS -mavx512bf16) endif() + if (GGML_AMX_TILE) + list(APPEND ARCH_FLAGS -mamx-tile) + endif() + if (GGML_AMX_INT8) + list(APPEND ARCH_FLAGS -mamx-int8) + endif() + if (GGML_AMX_BF16) + list(APPEND ARCH_FLAGS -mamx-bf16) + endif() endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") message(STATUS "PowerPC detected") @@ -1340,6 +1381,7 @@ add_library(ggml ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} ${GGML_SOURCES_BLAS} ${GGML_HEADERS_BLAS} ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE} + ${GGML_SOURCES_AMX} ${GGML_HEADERS_AMX} ${GGML_SOURCES_CANN} ${GGML_HEADERS_CANN} ggml-aarch64.c ggml-aarch64.h ) diff --git a/ggml/src/ggml-amx.cpp b/ggml/src/ggml-amx.cpp new file mode 100644 index 000000000..ac6ec2342 --- /dev/null +++ b/ggml/src/ggml-amx.cpp @@ -0,0 +1,453 @@ +#include "ggml-amx.h" +#include "ggml-amx/common.h" +#include "ggml-amx/mmq.h" +#include "ggml-backend-impl.h" +#include "ggml-impl.h" + +#if defined(__gnu_linux__) +#include +#include +#endif + +#include +#include +#include + +#if defined(__AMX_INT8__) + +// AMX buffer interface +static const char * ggml_backend_amx_buffer_get_name(ggml_backend_buffer_t buffer) { + return "AMX"; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { + free(buffer->context); +} + +static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) { + return (void *)(buffer->context); +} + +static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + if (qtype_has_amx_kernels(tensor->type)) { + ggml_backend_amx_convert_weight(tensor, data, offset, size); + } else { + memcpy((char *)tensor->data + offset, data, size); + } + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_ASSERT(!qtype_has_amx_kernels(tensor->type)); + memcpy(data, (const char *)tensor->data + offset, size); + + GGML_UNUSED(buffer); +} + +static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + if (qtype_has_amx_kernels(src->type)) { + ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_backend_amx_get_alloc_size(dst)); + } else { + memcpy(dst->data, src->data, ggml_nbytes(src)); + } + return true; + } + return false; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); +} + +static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { + /* .get_name = */ ggml_backend_amx_buffer_get_name, + /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, + /* .get_base = */ ggml_backend_amx_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_amx_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_amx_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor, + /* .clear = */ ggml_backend_amx_buffer_clear, + /* .reset = */ NULL, +}; + +static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "AMX"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * data = aligned_alloc(TENSOR_ALIGNMENT, size); + if (data == NULL) { + fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } + + return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size); +} + +static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return TENSOR_ALIGNMENT; + + GGML_UNUSED(buft); +} + +static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { + return ggml_backend_amx_get_alloc_size(tensor); + + GGML_UNUSED(buft); +} + +static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + +ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { + /* .iface = */ { + /* .get_name = */ ggml_backend_amx_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_amx_buffer_type_is_host, + }, + /* .device = */ NULL, + /* .context = */ NULL, + }; + + return &ggml_backend_buffer_type_amx; +} + +// backend interface + +static const char * ggml_backend_amx_name(ggml_backend_t backend) { + return "AMX"; + + GGML_UNUSED(backend); +} + +static void ggml_backend_amx_free(ggml_backend_t backend) { + ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; + delete ctx; + delete backend; +} + +static ggml_backend_buffer_type_t ggml_backend_amx_get_default_buffer_type(ggml_backend_t backend) { + return ggml_backend_amx_buffer_type(); + + GGML_UNUSED(backend); +} + +static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + switch (node->op) { + case GGML_OP_MUL_MAT: + ggml_backend_amx_mul_mat(ctx, node); + break; + + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + break; + + default: + fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node)); + GGML_ASSERT(false); + } + } + + return GGML_STATUS_SUCCESS; + + GGML_UNUSED(backend); +} + +static struct ggml_backend_i ggml_backend_amx_i = { + /* .get_name = */ ggml_backend_amx_name, + /* .free = */ ggml_backend_amx_free, + /* .get_default_buffer_type = */ ggml_backend_amx_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_amx_graph_compute, + /* .supports_op = */ NULL, + /* .supports_buft = */ NULL, + /* .offload_op = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, +}; + +static ggml_guid_t ggml_backend_amx_guid() { + static ggml_guid guid = { 0x13, 0xb8, 0xa4, 0xc4, 0xba, 0xfe, 0x51, 0x67, 0x87, 0x44, 0x55, 0x15, 0xb2, 0x35, 0x62, 0x3e }; + return &guid; +} + +#define ARCH_GET_XCOMP_PERM 0x1022 +#define ARCH_REQ_XCOMP_PERM 0x1023 +#define XFEATURE_XTILECFG 17 +#define XFEATURE_XTILEDATA 18 + +static bool ggml_amx_init() { +#if defined(__gnu_linux__) + if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) { + fprintf(stderr, "AMX is not ready to be used!\n"); + return false; + } + return true; +#elif defined(_WIN32) + return true; +#endif +} + +ggml_backend_t ggml_backend_amx_init() { + + // invoke a Linux system call to request access to AMX features + ggml_amx_init(); + + // backend context + ggml_backend_amx_context * ctx = new ggml_backend_amx_context; + + // ggml amx backend + ggml_backend_t backend = new ggml_backend { + /* .guid = */ ggml_backend_amx_guid(), + /* .interface = */ ggml_backend_amx_i, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), + /* .context = */ ctx, + }; + + return backend; +} + +bool ggml_backend_is_amx(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_amx_guid()); +} + +void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { + GGML_ASSERT(ggml_backend_is_amx(backend_amx)); + + ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend_amx->context; + ctx->n_threads = n_threads; +} + +// device interface + +static const char * ggml_backend_amx_device_get_name(ggml_backend_dev_t dev) { + return "AMX"; + + GGML_UNUSED(dev); +} + +static const char * ggml_backend_amx_device_get_description(ggml_backend_dev_t dev) { + return "Intel Advanced Matrix Extensions"; + + GGML_UNUSED(dev); +} + +static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + // TODO + *free = 0; + *total = 0; + + GGML_UNUSED(dev); +} + +static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) { + return GGML_BACKEND_DEVICE_TYPE_CPU; + + GGML_UNUSED(dev); +} + +static void ggml_backend_amx_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_amx_device_get_name(dev); + props->description = ggml_backend_amx_device_get_description(dev); + props->type = ggml_backend_amx_device_get_type(dev); + ggml_backend_amx_device_get_memory(dev, &props->memory_free, &props->memory_total); + + // `buffer_from_host_ptr` is intended to be used in mmap, when memory layout unchanged + props->caps = { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ false, + /* .events = */ false, + }; +} + +static ggml_backend_t ggml_backend_amx_device_init(ggml_backend_dev_t dev, const char * params) { + return ggml_backend_amx_init(); + + GGML_UNUSED(dev); + GGML_UNUSED(params); +} + +static ggml_backend_buffer_type_t ggml_backend_amx_device_get_buffer_type(ggml_backend_dev_t dev) { + return ggml_backend_amx_buffer_type(); + + GGML_UNUSED(dev); +} + +static bool ggml_backend_amx_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { + + // handle only 2d gemm for now + auto is_contiguous_2d = [](const struct ggml_tensor * t) { + return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; + }; + + switch (op->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + return true; + + case GGML_OP_MUL_MAT: { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + + const enum ggml_type type = src0->type; + const int64_t ne0 = op->ne[0]; + + bool is_training = src0->grad || src1->grad; + + // amx kernels enables for Q4_0, Q4_1, Q8_0, F16 + // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256 + bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16); + + bool can_use_amx = + is_contiguous_2d(src0) && // src0 must be contiguous + is_contiguous_2d(src1) && // src1 must be contiguous + !is_training && // inference only + src1->type == GGML_TYPE_F32 && // src1 must be float32 + has_amx_kernels && // with amx kernel impls + ne0 % (TILE_N * 2) == 0; // out_features is 32x + + return can_use_amx; + } + default: + return false; + } + + GGML_UNUSED(dev); +} + +static bool ggml_backend_amx_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name; + + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_amx_device_i = { + /* .get_name = */ ggml_backend_amx_device_get_name, + /* .get_description = */ ggml_backend_amx_device_get_description, + /* .get_memory = */ ggml_backend_amx_device_get_memory, + /* .get_type = */ ggml_backend_amx_device_get_type, + /* .get_props = */ ggml_backend_amx_device_get_props, + /* .init_backend = */ ggml_backend_amx_device_init, + /* .get_buffer_type = */ ggml_backend_amx_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_amx_device_supports_op, + /* .supports_buft = */ ggml_backend_amx_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +// backend reg interface + +static const char * ggml_backend_amx_reg_get_name(ggml_backend_reg_t reg) { + return "AMX"; + + GGML_UNUSED(reg); +} + +static size_t ggml_backend_amx_reg_get_device_count(ggml_backend_reg_t reg) { + return 1; + + GGML_UNUSED(reg); +} + +static ggml_backend_dev_t ggml_backend_amx_reg_get_device(ggml_backend_reg_t reg, size_t index) { + GGML_ASSERT(index == 0); + + static ggml_backend_device ggml_backend_amx_device = { + /* .iface = */ ggml_backend_amx_device_i, + /* .reg = */ reg, + /* .context = */ nullptr, + }; + + return &ggml_backend_amx_device; + + GGML_UNUSED(reg); + GGML_UNUSED(index); +} + +static void * ggml_backend_amx_get_proc_address(ggml_backend_reg_t reg, const char * name) { + if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) { + return (void *)ggml_backend_amx_set_n_threads; + } + return NULL; + + GGML_UNUSED(reg); + GGML_UNUSED(name); +} + +static const struct ggml_backend_reg_i ggml_backend_amx_reg_i = { + /* .get_name = */ ggml_backend_amx_reg_get_name, + /* .get_device_count = */ ggml_backend_amx_reg_get_device_count, + /* .get_device = */ ggml_backend_amx_reg_get_device, + /* .get_proc_address = */ ggml_backend_amx_get_proc_address, +}; + +ggml_backend_reg_t ggml_backend_amx_reg(void) { + static struct ggml_backend_reg ggml_backend_amx_reg = { + /* .iface = */ ggml_backend_amx_reg_i, + /* .context = */ NULL, + }; + + return &ggml_backend_amx_reg; +} + +#else // if defined(__AMX_INT8__) + +ggml_backend_t ggml_backend_amx_init(void) { + fprintf(stderr, "GGML is not compiled with AMX support!\n"); + return ggml_backend_t{}; +} + +void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { + fprintf(stderr, "GGML is not compiled with AMX support!\n"); + + GGML_UNUSED(backend_amx); + GGML_UNUSED(n_threads); +} + +#endif diff --git a/ggml/src/ggml-amx/common.h b/ggml/src/ggml-amx/common.h new file mode 100644 index 000000000..2b6c63527 --- /dev/null +++ b/ggml/src/ggml-amx/common.h @@ -0,0 +1,93 @@ +#pragma once + +#include "ggml.h" +#include "ggml-cpu-impl.h" // + +#include +#include +#include + +#if defined(_OPENMP) +#include +#endif + +#define TILE_M 16 +#define TILE_N 16 +#define TILE_K 32 +#define VNNI_BLK 4 + +#define AMX_BLK_SIZE 32 + +#define TMM0 0 +#define TMM1 1 +#define TMM2 2 +#define TMM3 3 +#define TMM4 4 +#define TMM5 5 +#define TMM6 6 +#define TMM7 7 + +// parallel routines +template ::value, int>::type = 0> +inline T div_up(T x, T y) { return (x + y - 1) / y; } + +template +inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) { +#if 0 + // onednn partition pattern + T& n_my = n_end; + if (nth <= 1 || n == 0) { + n_start = 0; + n_my = n; + } else { + T n1 = div_up(n, nth); + T n2 = n1 - 1; + T T1 = n - n2 * nth; + n_my = ith < T1 ? n1 : n2; + n_start = ith <= T1 ? ith*n1 : T1 * n1 + (ith - T1) * n2; + } + n_end += n_start; +#else + // pytorch aten partition pattern + T n_my = div_up(n, nth); + n_start = ith * n_my; + n_end = std::min(n_start + n_my, n); +#endif +} + +template +inline void parallel_for(int nth, int n, const func_t& f) { +#if defined(_OPENMP) +#pragma omp parallel num_threads(nth) +{ + //int nth = omp_get_num_threads(); + int ith = omp_get_thread_num(); + int tbegin, tend; + balance211(n, nth, ith, tbegin, tend); + f(tbegin, tend); +} +#else + f(0, n); + + GGML_UNUSED(nth); +#endif +} + +// quantized types that have AMX support +inline bool qtype_has_amx_kernels(const enum ggml_type type) { + // TODO: fix padding for vnni format + return (type == GGML_TYPE_Q4_0) || + (type == GGML_TYPE_Q4_1); + //(type == GGML_TYPE_Q8_0) || + //(type == GGML_TYPE_Q4_K) || + //(type == GGML_TYPE_Q5_K) || + //(type == GGML_TYPE_Q6_K) || + //(type == GGML_TYPE_IQ4_XS); +} + +// ggml backend context +struct ggml_backend_amx_context { + int n_threads = GGML_DEFAULT_N_THREADS; + std::unique_ptr work_data; + size_t work_size = 0; +}; diff --git a/ggml/src/ggml-amx/mmq.cpp b/ggml/src/ggml-amx/mmq.cpp new file mode 100644 index 000000000..239d15121 --- /dev/null +++ b/ggml/src/ggml-amx/mmq.cpp @@ -0,0 +1,2509 @@ + +#if defined(__GNUC__) +#pragma GCC diagnostic ignored "-Wpedantic" +#pragma GCC diagnostic ignored "-Wunused-local-typedefs" +#endif + +#include "mmq.h" +#include "ggml-impl.h" +#include "ggml-quants.h" +#include +#include + +#if defined(__gnu_linux__) +#include +#include +#endif + +#if defined(_OPENMP) +#include +#endif + +#if (defined(_WIN32) || defined(_WIN64)) +#define RESTRICT __restrict +#else +#define RESTRICT __restrict__ +#endif + +#if (defined(_WIN32) || defined(_WIN64)) +#define ALWAYS_INLINE __forceinline +#elif __has_attribute(always_inline) || defined(__GNUC__) +#define ALWAYS_INLINE __attribute__((__always_inline__)) inline +#else +#define ALWAYS_INLINE inline +#endif + +#if defined(__AMX_INT8__) + +namespace { + +// Forced unrolling +template +struct Unroll { + template + ALWAYS_INLINE void operator()(const Func& f, Args... args) const { + Unroll{}(f, args...); + f(std::integral_constant{}, args...); + } +}; + +template <> +struct Unroll<1> { + template + ALWAYS_INLINE void operator()(const Func& f, Args... args) const { + f(std::integral_constant{}, args...); + } +}; + +// type traits +template struct PackedTypes {}; +template <> struct PackedTypes { using type = int8_t; }; +template <> struct PackedTypes { using type = uint8_t; }; +template <> struct PackedTypes { using type = int8_t; }; +template using packed_B_type = typename PackedTypes::type; + +template +struct do_compensate : std::integral_constant::value> {}; + +template +struct do_unpack : std::integral_constant::value || + std::is_same::value> {}; + +template +struct is_type_qkk : std::integral_constant::value || + std::is_same::value || + std::is_same::value || + std::is_same::value> {}; + +#define GGML_DISPATCH_FLOATING_TYPES(TYPE, ...) \ + [&] { \ + switch (TYPE) { \ + case GGML_TYPE_F16: { \ + using type = ggml_fp16_t; \ + constexpr int blck_size = 16; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_BF16: { \ + using type = ggml_bf16_t; \ + constexpr int blck_size = 32; \ + return __VA_ARGS__(); \ + } \ + default: \ + fprintf(stderr, "Unsupported floating data type\n"); \ + } \ + }() + +#define GGML_DISPATCH_QTYPES(QT, ...) \ + [&] { \ + switch (QT) { \ + case GGML_TYPE_Q4_0: { \ + using type = block_q4_0; \ + using vec_dot_type = block_q8_0; \ + constexpr int blck_size = QK4_0; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q4_1: { \ + using type = block_q4_1; \ + using vec_dot_type = block_q8_1; \ + constexpr int blck_size = QK4_1; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q8_0: { \ + using type = block_q8_0; \ + using vec_dot_type = block_q8_0; \ + constexpr int blck_size = QK8_0; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q4_K: { \ + using type = block_q4_K; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q5_K: { \ + using type = block_q5_K; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_Q6_K: { \ + using type = block_q6_K; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + case GGML_TYPE_IQ4_XS: { \ + using type = block_iq4_xs; \ + using vec_dot_type = block_q8_K; \ + constexpr int blck_size = QK_K; \ + return __VA_ARGS__(); \ + } \ + default: \ + fprintf(stderr, "Unsupported quantized data type: %d\n", int(TYPE)); \ + } \ + }() + +#define GGML_DISPATCH_BOOL(BOOL_V, BOOL_NAME, ...) \ + [&] { \ + if (BOOL_V) { \ + constexpr bool BOOL_NAME = true; \ + return __VA_ARGS__(); \ + } else { \ + constexpr bool BOOL_NAME = false; \ + return __VA_ARGS__(); \ + } \ + }() + +// define amx tile config data structure +struct tile_config_t{ + uint8_t palette_id = 0; + uint8_t start_row = 0; + uint8_t reserved_0[14] = {0}; + uint16_t colsb[16] = {0}; + uint8_t rows[16] = {0}; +}; + +// Notes: amx tile config +// +// Typically, TMUL calculates A and B of size 16 x 64 containing INT8 values, +// and accumulate the result to a 16 x 16 matrix C containing INT32 values, +// +// As many GGUF quantized types as `block_size` of 32, so a 16-16-32 config is used +// instead of the normally used 16-16-64 config. +// +// Block A: {16, 32}, dtype = int8_t +// Block B: {16, 32}, dtype = uint8_t/int8_t +// Block C: {16, 16}, dtype = int32_t +// +// Block B needs to be prepacked to vnni format before feeding into TMUL: +// packed_B: from {n, k} to {k/vnni_blk, n, vnni_blck}, viewed in 2d, we get {8, 64} +// +// Therefore, we get tileconfig: +// A B C +// rows 16 8 16 +// colsb 32 64 16 +// +// For tile distribution, follow a 2-2-4 pattern, e.g. A used TMM2-TMM3, B used TMM0-TMM1, +// C used TMM4-TMM7: +// B TMM0 B TMM1 +// A TMM2 C TMM4 C TMM6 +// A TMM3 C TMM5 C TMM7 +// +// Each `amx` kernel handles 4 blocks at a time: 2MB * 2NB, when m < 2 * BLOCK_M, unpack A +// will be needed. +// +// Here another commonly used pattern 1-3-3 is skipped, as it is mostly used when m <=16; +// and the sinlge batch gemm (m=1) has a special fast path with `avx512-vnni`. +// +// ref: https://www.intel.com/content/www/us/en/developer/articles/code-sample/ +// advanced-matrix-extensions-intrinsics-functions.html +// + +#define TC_CONFIG_TILE(i, r, cb) tc.rows[i] = r; tc.colsb[i] = cb +void ggml_tile_config_init(void) { + static thread_local bool is_first_time = true; + + if (!is_first_time) { + return; + } + + static thread_local tile_config_t tc; + tile_config_t current_tc; + _tile_storeconfig(¤t_tc); + + // load only when config changes + if (tc.palette_id == 0 || (memcmp(¤t_tc.colsb, &tc.colsb, sizeof(uint16_t) * 8) != 0 && + memcmp(¤t_tc.rows, &tc.rows, sizeof(uint8_t) * 8) != 0)) { + tc.palette_id = 1; + tc.start_row = 0; + TC_CONFIG_TILE(TMM0, 8, 64); + TC_CONFIG_TILE(TMM1, 8, 64); + TC_CONFIG_TILE(TMM2, 16, 32); + TC_CONFIG_TILE(TMM3, 16, 32); + TC_CONFIG_TILE(TMM4, 16, 64); + TC_CONFIG_TILE(TMM5, 16, 64); + TC_CONFIG_TILE(TMM6, 16, 64); + TC_CONFIG_TILE(TMM7, 16, 64); + _tile_loadconfig(&tc); + } + + is_first_time = false; +} + +// we need an extra 16 * 4B (TILE_N * int32_t) for each NB/KB block for compensation. +// See the notes `s8s8 igemm compensation in avx512-vnni` for detail. +template +int get_tile_size() { + int tile_size = TILE_N * sizeof(TB); + if (do_compensate::value) { + tile_size += TILE_N * sizeof(int32_t); + } + if (std::is_same::value || + std::is_same::value) { + tile_size += TILE_N * 4; + } + if (std::is_same::value) { + tile_size += TILE_N * 2; + } + return tile_size; +} + +template +int get_row_size(int K) { + int KB = K / BLOCK_K; + int row_size = KB * sizeof(TB); + if (do_compensate::value) { + row_size += KB * sizeof(int32_t); + } + if (std::is_same::value || + std::is_same::value) { + row_size += KB * 4; + } + if (std::is_same::value) { + row_size += KB * 2; + } + return row_size; +} + +// vectorized dtype conversion +inline float FP16_TO_FP32(ggml_half val) { + __m256i v = _mm256_setr_epi16( + val, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0); + __m512 o = _mm512_cvtph_ps(v); + return _mm512_cvtss_f32(o); +} + +inline __m512 FP16_TO_FP32_VEC(ggml_half val) { + __m256i v = _mm256_set1_epi16(val); + return _mm512_cvtph_ps(v); +} + +// horizontal reduce +inline float _mm512_reduce_max_ps(const __m512 x) { + __m512 v = x; + __m512 v1 = _mm512_shuffle_f32x4(v, v, 0x4E); + v = _mm512_max_ps(v, v1); + v1 = _mm512_shuffle_f32x4(v, v, 0xB1); + v = _mm512_max_ps(v, v1); + v1 = _mm512_shuffle_ps(v, v, 0x4E); + v = _mm512_max_ps(v, v1); + v1 = _mm512_shuffle_ps(v, v, 0xB1); + v = _mm512_max_ps(v, v1); + return _mm512_cvtss_f32(v); +} + +// transpose utils +#define SHUFFLE_EPI32(a, b, mask) \ + _mm256_castps_si256(_mm256_shuffle_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b), mask)) +inline void transpose_8x8_32bit(__m256i * v, __m256i * v1) { + // unpacking and 32-bit elements + v1[0] = _mm256_unpacklo_epi32(v[0], v[1]); + v1[1] = _mm256_unpackhi_epi32(v[0], v[1]); + v1[2] = _mm256_unpacklo_epi32(v[2], v[3]); + v1[3] = _mm256_unpackhi_epi32(v[2], v[3]); + v1[4] = _mm256_unpacklo_epi32(v[4], v[5]); + v1[5] = _mm256_unpackhi_epi32(v[4], v[5]); + v1[6] = _mm256_unpacklo_epi32(v[6], v[7]); + v1[7] = _mm256_unpackhi_epi32(v[6], v[7]); + + // shuffling the 32-bit elements + v[0] = SHUFFLE_EPI32(v1[0], v1[2], 0x44); + v[1] = SHUFFLE_EPI32(v1[0], v1[2], 0xee); + v[2] = SHUFFLE_EPI32(v1[4], v1[6], 0x44); + v[3] = SHUFFLE_EPI32(v1[4], v1[6], 0xee); + v[4] = SHUFFLE_EPI32(v1[1], v1[3], 0x44); + v[5] = SHUFFLE_EPI32(v1[1], v1[3], 0xee); + v[6] = SHUFFLE_EPI32(v1[5], v1[7], 0x44); + v[7] = SHUFFLE_EPI32(v1[5], v1[7], 0xee); + + // shuffling 128-bit elements + v1[0] = _mm256_permute2f128_si256(v[2], v[0], 0x02); + v1[1] = _mm256_permute2f128_si256(v[3], v[1], 0x02); + v1[2] = _mm256_permute2f128_si256(v[6], v[4], 0x02); + v1[3] = _mm256_permute2f128_si256(v[7], v[5], 0x02); + v1[4] = _mm256_permute2f128_si256(v[2], v[0], 0x13); + v1[5] = _mm256_permute2f128_si256(v[3], v[1], 0x13); + v1[6] = _mm256_permute2f128_si256(v[6], v[4], 0x13); + v1[7] = _mm256_permute2f128_si256(v[7], v[5], 0x13); +} + +inline void transpose_16x4_32bit(__m512i * r, __m512i * d) { + + static const __m512i index1 = _mm512_set_epi32( + 0x0f, 0x0b, 0x07, 0x03, + 0x0e, 0x0a, 0x06, 0x02, + 0x0d, 0x09, 0x05, 0x01, + 0x0c, 0x08, 0x04, 0x00); + + d[0] = _mm512_permutexvar_epi32(index1, r[0]); + d[1] = _mm512_permutexvar_epi32(index1, r[1]); + d[2] = _mm512_permutexvar_epi32(index1, r[2]); + d[3] = _mm512_permutexvar_epi32(index1, r[3]); + + r[0] = _mm512_shuffle_i32x4(d[0], d[1], 0x44); + r[1] = _mm512_shuffle_i32x4(d[0], d[1], 0xee); + r[2] = _mm512_shuffle_i32x4(d[2], d[3], 0x44); + r[3] = _mm512_shuffle_i32x4(d[2], d[3], 0xee); + + d[0] = _mm512_shuffle_i32x4(r[0], r[2], 0x88); + d[1] = _mm512_shuffle_i32x4(r[0], r[2], 0xdd); + d[2] = _mm512_shuffle_i32x4(r[1], r[3], 0x88); + d[3] = _mm512_shuffle_i32x4(r[1], r[3], 0xdd); +} + +inline void transpose_16x16_32bit(__m512i * v) { + __m512i v1[16]; + v1[0] = _mm512_unpacklo_epi32(v[0], v[1]); + v1[1] = _mm512_unpackhi_epi32(v[0], v[1]); + v1[2] = _mm512_unpacklo_epi32(v[2], v[3]); + v1[3] = _mm512_unpackhi_epi32(v[2], v[3]); + v1[4] = _mm512_unpacklo_epi32(v[4], v[5]); + v1[5] = _mm512_unpackhi_epi32(v[4], v[5]); + v1[6] = _mm512_unpacklo_epi32(v[6], v[7]); + v1[7] = _mm512_unpackhi_epi32(v[6], v[7]); + v1[8] = _mm512_unpacklo_epi32(v[8], v[9]); + v1[9] = _mm512_unpackhi_epi32(v[8], v[9]); + v1[10] = _mm512_unpacklo_epi32(v[10], v[11]); + v1[11] = _mm512_unpackhi_epi32(v[10], v[11]); + v1[12] = _mm512_unpacklo_epi32(v[12], v[13]); + v1[13] = _mm512_unpackhi_epi32(v[12], v[13]); + v1[14] = _mm512_unpacklo_epi32(v[14], v[15]); + v1[15] = _mm512_unpackhi_epi32(v[14], v[15]); + + v[0] = _mm512_unpacklo_epi64(v1[0], v1[2]); + v[1] = _mm512_unpackhi_epi64(v1[0], v1[2]); + v[2] = _mm512_unpacklo_epi64(v1[1], v1[3]); + v[3] = _mm512_unpackhi_epi64(v1[1], v1[3]); + v[4] = _mm512_unpacklo_epi64(v1[4], v1[6]); + v[5] = _mm512_unpackhi_epi64(v1[4], v1[6]); + v[6] = _mm512_unpacklo_epi64(v1[5], v1[7]); + v[7] = _mm512_unpackhi_epi64(v1[5], v1[7]); + v[8] = _mm512_unpacklo_epi64(v1[8], v1[10]); + v[9] = _mm512_unpackhi_epi64(v1[8], v1[10]); + v[10] = _mm512_unpacklo_epi64(v1[9], v1[11]); + v[11] = _mm512_unpackhi_epi64(v1[9], v1[11]); + v[12] = _mm512_unpacklo_epi64(v1[12], v1[14]); + v[13] = _mm512_unpackhi_epi64(v1[12], v1[14]); + v[14] = _mm512_unpacklo_epi64(v1[13], v1[15]); + v[15] = _mm512_unpackhi_epi64(v1[13], v1[15]); + + v1[0] = _mm512_shuffle_i32x4(v[0], v[4], 0x88); + v1[1] = _mm512_shuffle_i32x4(v[1], v[5], 0x88); + v1[2] = _mm512_shuffle_i32x4(v[2], v[6], 0x88); + v1[3] = _mm512_shuffle_i32x4(v[3], v[7], 0x88); + v1[4] = _mm512_shuffle_i32x4(v[0], v[4], 0xdd); + v1[5] = _mm512_shuffle_i32x4(v[1], v[5], 0xdd); + v1[6] = _mm512_shuffle_i32x4(v[2], v[6], 0xdd); + v1[7] = _mm512_shuffle_i32x4(v[3], v[7], 0xdd); + v1[8] = _mm512_shuffle_i32x4(v[8], v[12], 0x88); + v1[9] = _mm512_shuffle_i32x4(v[9], v[13], 0x88); + v1[10] = _mm512_shuffle_i32x4(v[10], v[14], 0x88); + v1[11] = _mm512_shuffle_i32x4(v[11], v[15], 0x88); + v1[12] = _mm512_shuffle_i32x4(v[8], v[12], 0xdd); + v1[13] = _mm512_shuffle_i32x4(v[9], v[13], 0xdd); + v1[14] = _mm512_shuffle_i32x4(v[10], v[14], 0xdd); + v1[15] = _mm512_shuffle_i32x4(v[11], v[15], 0xdd); + + v[0] = _mm512_shuffle_i32x4(v1[0], v1[8], 0x88); + v[1] = _mm512_shuffle_i32x4(v1[1], v1[9], 0x88); + v[2] = _mm512_shuffle_i32x4(v1[2], v1[10], 0x88); + v[3] = _mm512_shuffle_i32x4(v1[3], v1[11], 0x88); + v[4] = _mm512_shuffle_i32x4(v1[4], v1[12], 0x88); + v[5] = _mm512_shuffle_i32x4(v1[5], v1[13], 0x88); + v[6] = _mm512_shuffle_i32x4(v1[6], v1[14], 0x88); + v[7] = _mm512_shuffle_i32x4(v1[7], v1[15], 0x88); + v[8] = _mm512_shuffle_i32x4(v1[0], v1[8], 0xdd); + v[9] = _mm512_shuffle_i32x4(v1[1], v1[9], 0xdd); + v[10] = _mm512_shuffle_i32x4(v1[2], v1[10], 0xdd); + v[11] = _mm512_shuffle_i32x4(v1[3], v1[11], 0xdd); + v[12] = _mm512_shuffle_i32x4(v1[4], v1[12], 0xdd); + v[13] = _mm512_shuffle_i32x4(v1[5], v1[13], 0xdd); + v[14] = _mm512_shuffle_i32x4(v1[6], v1[14], 0xdd); + v[15] = _mm512_shuffle_i32x4(v1[7], v1[15], 0xdd); +} + +void quantize_row_q8_K_vnni(const float * RESTRICT x, void * RESTRICT vy, int64_t k) { + assert(k % QK_K == 0); + const int KB = k / QK_K; + constexpr int kVecs = QK_K / 16; + + block_q8_K * y = reinterpret_cast(vy); + + // hold 16 float vecs from x + __m512 v[kVecs]; + + // hold the quants vecs + __m512i vq[kVecs / 4]; + + // hold the packed quants vecs + __m512i vq_packed[kVecs / 4]; + + const __m512 signBit = _mm512_set1_ps(-0.f); + + for (int i = 0; i < KB; ++i) { + // Compute max(abs(e)) for the block + __m512 vamax = _mm512_set1_ps(0.f); + for (int j = 0; j < kVecs; ++j) { + v[j] = _mm512_loadu_ps(x); x += 16; + vamax = _mm512_max_ps(vamax, _mm512_andnot_ps(signBit, v[j])); + } + const float amax = _mm512_reduce_max_ps(vamax); + + // Quantize these floats + const float iscale = 127.f / amax; + y[i].d = GGML_FP32_TO_FP16(1 / iscale); + const float id = ( amax != 0.0f ) ? iscale : 0.f; + const __m512 vscale = _mm512_set1_ps(id); + + // Apply multiplier and round to nearest integer + for (int j = 0; j < kVecs; ++j) { + v[j] = _mm512_mul_ps(v[j], vscale); + v[j] = _mm512_roundscale_ps(v[j], (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC)); + } + + // Pack to epi8 vecs + for (int j = 0; j < kVecs / 4; ++j) { + __m128i q8_0 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 0])); + __m128i q8_1 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 1])); + __m128i q8_2 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 2])); + __m128i q8_3 = _mm512_cvtepi32_epi8(_mm512_cvtps_epi32(v[j * 4 + 3])); + + __m256i q8_01 = _mm256_insertf128_si256(_mm256_castsi128_si256(q8_0), (q8_1), 1); + __m256i q8_23 = _mm256_insertf128_si256(_mm256_castsi128_si256(q8_2), (q8_3), 1); + + vq[j] = _mm512_inserti32x8(_mm512_castsi256_si512(q8_01), q8_23, 1); + _mm512_storeu_si512((__m512i *)(y[i].qs + j * 64), vq[j]); + } + + // Compute the bsums with vnni + transpose_16x4_32bit(vq, vq_packed); + + const __m512i one = _mm512_set1_epi8(1); + __m512i sum = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + sum = _mm512_dpbusd_epi32(sum, one, vq_packed[k]); + } + _mm256_storeu_si256((__m256i *)(y[i].bsums), _mm512_cvtepi32_epi16(sum)); + } +} + +// quantize A from float to `vec_dot_type` +template +inline void from_float(const float * x, char * vy, int64_t k); + +template <> +inline void from_float(const float * x, char * vy, int64_t k) { + quantize_row_q8_0(x, vy, k); +} + +template <> +inline void from_float(const float * x, char * vy, int64_t k) { + quantize_row_q8_1(x, vy, k); +} + +template <> +inline void from_float(const float * x, char * vy, int64_t k) { +#if 1 + // TODO: this is reference impl! + quantize_row_q8_K(x, vy, k); +#else + quantize_row_q8_K_vnni(x, vy, k); +#endif +} + +// load A from memory to array when nrows can not fill in whole tile +void unpack_A(int8_t * RESTRICT tile, const block_q8_0 * RESTRICT A, int lda, int nr) { + assert(nr != TILE_M); + for (int m = 0; m < nr; ++m) { + const __m256i v = _mm256_loadu_si256((const __m256i *)(A[m * lda].qs)); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), v); + } +} + +void unpack_A(int8_t * RESTRICT tile, const block_q8_1 * RESTRICT A, int lda, int nr) { + assert(nr != TILE_M); + for (int m = 0; m < nr; ++m) { + const __m256i v = _mm256_loadu_si256((const __m256i *)(A[m * lda].qs)); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), v); + } +} + +template +void unpack_A(int8_t * RESTRICT tile, const block_q8_K * RESTRICT A, int lda, int k, int nr) { + assert(nr <= TILE_M); + for (int m = 0; m < nr; ++m) { + const __m256i v = _mm256_loadu_si256((const __m256i *)(A[m * lda].qs + k * 32)); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), v); + } +} + +template <> +void unpack_A(int8_t * RESTRICT tile, const block_q8_K * RESTRICT A, int lda, int k, int nr) { + assert(nr <= TILE_M); + // zero padding k from 16 to 32, so that we don't have to re-config amx + const __m128i zero = _mm_setzero_si128(); + for (int m = 0; m < nr; ++m) { + const __m128i v = _mm_loadu_si128((const __m128i *)(A[m * lda].qs + k * 16)); + const __m256i r = _mm256_insertf128_si256(_mm256_castsi128_si256(v), zero, 1); + _mm256_storeu_si256((__m256i *)(tile + m * TILE_K), r); + } +} + +#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) +inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { + const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); + const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); + const __m256i lowMask = _mm256_set1_epi8(0xF); + return _mm256_and_si256(lowMask, bytes); +} + +// used for block_q4_K +inline __m512i bytes_from_nibbles_64(const uint8_t * rsi) { + const __m256i tmp = _mm256_loadu_si256((const __m256i *)rsi); + const __m256i lowMask = _mm256_set1_epi8(0xF); + const __m256i q4l = _mm256_and_si256(tmp, lowMask); + const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(tmp, 4), lowMask); + return _mm512_inserti32x8(_mm512_castsi256_si512(q4l), q4h, 1); +} + +// used for block_q5_K +inline __m512i bytes_from_nibbles_64(const uint8_t * qs, const uint8_t * qh, int k) { + const __m256i lowMask = _mm256_set1_epi8(0xF); + __m256i hmask = _mm256_set1_epi8(1); + hmask = _mm256_slli_epi16(hmask, k); + + const __m256i q5bits = _mm256_loadu_si256((const __m256i *)qs); + const __m256i hbits = _mm256_loadu_si256((const __m256i *)qh); + + const __m256i q5l_0 = _mm256_and_si256(q5bits, lowMask); + const __m256i q5h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), k + 0), 4); + const __m256i q5_0 = _mm256_add_epi8(q5l_0, q5h_0); + hmask = _mm256_slli_epi16(hmask, 1); + + const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), lowMask); + const __m256i q5h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), k + 1), 4); + const __m256i q5_1 = _mm256_add_epi8(q5l_1, q5h_1); + + return _mm512_inserti32x8(_mm512_castsi256_si512(q5_0), q5_1, 1); +} + +// used for block_q6_K +inline void bytes_from_nibbles_128(__m512i& r0, __m512i& r1, const uint8_t * qs, const uint8_t * qh) { + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m256i m2 = _mm256_set1_epi8(0x3); + + const __m256i q6bits1 = _mm256_loadu_si256((const __m256i *)qs); + const __m256i q6bits2 = _mm256_loadu_si256((const __m256i *)(qs + 32)); + const __m256i q6bitsH = _mm256_loadu_si256((const __m256i *)qh); + + const __m256i q6h_0 = _mm256_slli_epi16(_mm256_and_si256( q6bitsH, m2), 4); + const __m256i q6h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q6bitsH, 2), m2), 4); + const __m256i q6h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q6bitsH, 4), m2), 4); + const __m256i q6h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q6bitsH, 6), m2), 4); + + const __m256i q6_0 = _mm256_or_si256(_mm256_and_si256(q6bits1, m4), q6h_0); + const __m256i q6_1 = _mm256_or_si256(_mm256_and_si256(q6bits2, m4), q6h_1); + const __m256i q6_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q6bits1, 4), m4), q6h_2); + const __m256i q6_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q6bits2, 4), m4), q6h_3); + + r0 = _mm512_inserti32x8(_mm512_castsi256_si512(q6_0), q6_1, 1); + r1 = _mm512_inserti32x8(_mm512_castsi256_si512(q6_2), q6_3, 1); +} + +inline __m512i packNibbles(__m512i r0, __m512i r1) { + return _mm512_or_si512(r0, _mm512_slli_epi16(r1, 4)); +} + +template +inline void pack_qs(void * RESTRICT packed_B, const TB * RESTRICT B, int KB) { + int8_t tmp[8 * 64]; + __m256i v[8], v2[8]; + for (int n = 0; n < 8; ++n) { + v[n] = bytes_from_nibbles_32(B[n * KB].qs); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)(tmp + n * 64), v2[n]); + } + for (int n = 0; n < 8; ++n) { + v[n] = bytes_from_nibbles_32(B[(n + 8) * KB].qs); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)(tmp + n * 64 + 32), v2[n]); + } + + // pack again with 128 to fully utilize vector length + for (int n = 0; n < 8; n += 2) { + __m512i r0 = _mm512_loadu_si512((const __m512i *)(tmp + n * 64)); + __m512i r1 = _mm512_loadu_si512((const __m512i *)(tmp + n * 64 + 64)); + __m512i r1r0 = packNibbles(r0, r1); + _mm512_storeu_si512((__m512i *)((char *)packed_B + n * 32), r1r0); + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q8_0 * RESTRICT B, int KB) { + __m256i v[8], v2[8]; + for (int n = 0; n < 8; ++n) { + v[n] = _mm256_loadu_si256((const __m256i *)(B[n * KB].qs)); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)((char *)packed_B + n * 64), v2[n]); + } + for (int n = 0; n < 8; ++n) { + v[n] = _mm256_loadu_si256((const __m256i *)(B[(n + 8) * KB].qs)); + } + transpose_8x8_32bit(v, v2); + for (int n = 0; n < 8; ++n) { + _mm256_storeu_si256((__m256i *)((char *)packed_B + n * 64 + 32), v2[n]); + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q4_K * RESTRICT B, int KB) { + __m512i v[16]; + // QK_K 256 with 8 groups, handle 2 groups at a time + char * pb = (char *)packed_B; + for (int k = 0; k < QK_K / 64; ++k) { + // pack 2 groups { n, g, k} to {g, k/4, 4n} + // e.g. {16, 2, 32} to {2, 8, 64} + for (int n = 0; n < TILE_N; ++n) { + v[n] = bytes_from_nibbles_64(B[n * KB].qs + k * 32); + } + + transpose_16x16_32bit(v); + + // pack again with 128 to fully utilize vector length + for (int n = 0; n < TILE_N; n += 2) { + _mm512_storeu_si512((__m512i *)pb, packNibbles(v[n], v[n + 1])); + pb += 64; + } + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q5_K * RESTRICT B, int KB) { + __m512i v[16]; + const __m512i lowMask = _mm512_set1_epi8(0xF); + // QK_K 256 with 8 groups, handle 2 groups at a time + char * pb = (char *)packed_B; + char * ph = (char *)packed_B + (QK_K / 2) * TILE_N; + for (int k = 0; k < QK_K / 64; ++k) { + // pack 2 groups { n, g, k} to {g, k/4, 4n} + // e.g. {16, 2, 32} to {2, 8, 64} + for (int n = 0; n < TILE_N; ++n) { + v[n] = bytes_from_nibbles_64(B[n * KB].qs + k * 32, B[n * KB].qh, /* group */2 * k); + } + + transpose_16x16_32bit(v); + + // 1. pack lower 4bits with 2 groups + for (int n = 0; n < TILE_N; n += 2) { + // get lower 4 bits + const __m512i r0 = _mm512_and_si512(v[n], lowMask); + const __m512i r1 = _mm512_and_si512(v[n + 1], lowMask); + _mm512_storeu_si512((__m512i *)pb, packNibbles(r0, r1)); pb += 64; + } + + // 2. pack higher 1bit with 2 groups + const __m512i hmask = _mm512_set1_epi8(0x10); + for (int g = 0; g < 2; ++g) { + __m512i hbits = _mm512_setzero_si512(); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 0], hmask), 4)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 1], hmask), 3)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 2], hmask), 2)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 8 + 3], hmask), 1)); + hbits = _mm512_add_epi8(hbits, _mm512_and_si512(v[g * 8 + 4], hmask) ); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 8 + 5], hmask), 1)); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 8 + 6], hmask), 2)); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 8 + 7], hmask), 3)); + _mm512_storeu_si512((__m512i *)ph, hbits); ph += 64; + } + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_q6_K * RESTRICT B, int KB) { + __m512i v[32]; + const __m512i lowMask = _mm512_set1_epi8(0xF); + // QK_K 256 with 8 groups, handle 4 groups at a time + char * pb = (char *)packed_B; + char * ph = (char *)packed_B + (QK_K / 2) * TILE_N; + for (int k = 0; k < QK_K / 128; ++k) { + for (int n = 0; n < TILE_N; ++n) { + bytes_from_nibbles_128(v[n], v[n + 16], B[n * KB].ql + k * 64, B[n * KB].qh + k * 32); + } + + // top half: group 0,1 or 4,5; bottom half: group 2,3 or 6,7 + transpose_16x16_32bit(v); + transpose_16x16_32bit(v + 16); + + // 1. pack lower 4bits with 4 groups + for (int n = 0; n < 32; n += 2) { + const __m512i r0 = _mm512_and_si512(v[n], lowMask); + const __m512i r1 = _mm512_and_si512(v[n + 1], lowMask); + _mm512_storeu_si512((__m512i *)pb, packNibbles(r0, r1)); pb += 64; + } + + // 2. pack higher 2bit with 4 groups + const __m512i hmask = _mm512_set1_epi8(0x30); + for (int g = 0; g < 8; ++g) { + __m512i hbits = _mm512_setzero_si512(); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 4 + 0], hmask), 4)); + hbits = _mm512_add_epi8(hbits, _mm512_srli_epi16(_mm512_and_si512(v[g * 4 + 1], hmask), 2)); + hbits = _mm512_add_epi8(hbits, _mm512_and_si512(v[g * 4 + 2], hmask) ); + hbits = _mm512_add_epi8(hbits, _mm512_slli_epi16(_mm512_and_si512(v[g * 4 + 3], hmask), 2)); + _mm512_storeu_si512((__m512i *)ph, hbits); ph += 64; + } + } +} + +template <> +inline void pack_qs(void * RESTRICT packed_B, const block_iq4_xs * RESTRICT B, int KB) { + __m512i v[16]; + char * pb = (char *)packed_B; + for (int k = 0; k < QK_K / 64; ++k) { + for (int n = 0; n < TILE_N; ++n) { + __m256i r0 = bytes_from_nibbles_32(B[n * KB].qs + k * 32 + 0); + __m256i r1 = bytes_from_nibbles_32(B[n * KB].qs + k * 32 + 16); + v[n] = _mm512_inserti32x8(_mm512_castsi256_si512(r0), r1, 1); + } + + transpose_16x16_32bit(v); + + // pack again with 128 to fully utilize vector length + for (int n = 0; n < TILE_N; n += 2) { + _mm512_storeu_si512((__m512i *)pb, packNibbles(v[n], v[n + 1])); + pb += 64; + } + } +} + +// pack B to vnni formats in 4bits or 8 bits +void pack_B(void * RESTRICT packed_B, const block_q4_0 * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + ggml_half * d0 = reinterpret_cast((char *)packed_B + TILE_N * TILE_K / 2); + for (int n = 0; n < TILE_N; ++n) { + d0[n] = B[n * KB].d; + } +} + +void pack_B(void * RESTRICT packed_B, const block_q4_1 * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + ggml_half * d0 = reinterpret_cast((char *)packed_B + TILE_N * TILE_K / 2); + ggml_half * m0 = d0 + TILE_N; + for (int n = 0; n < TILE_N; ++n) { + d0[n] = B[n * KB].d; + m0[n] = B[n * KB].m; + } +} + +inline void s8s8_compensation(void * RESTRICT packed_B) { + // packed_B layout: + // quants {TILE_N, TILEK} int8_t + // d0 {TILE_N} ggml_half + // comp {TILE_N} int32_t + const int offset = TILE_N * TILE_K + TILE_N * sizeof(ggml_half); + __m512i vcomp = _mm512_setzero_si512(); + const __m512i off = _mm512_set1_epi8(static_cast(0x80)); + for (int k = 0; k < 8; ++k) { + __m512i vb = _mm512_loadu_si512((const __m512i *)((const char *)packed_B + k * 64)); + vcomp = _mm512_dpbusd_epi32(vcomp, off, vb); + } + _mm512_storeu_si512((__m512i *)((char *)(packed_B) + offset), vcomp); +} + +void pack_B(void * RESTRICT packed_B, const block_q8_0 * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + ggml_half * d0 = reinterpret_cast((char *)packed_B + TILE_N * TILE_K); + for (int n = 0; n < TILE_N; ++n) { + d0[n] = B[n * KB].d; + } + s8s8_compensation(packed_B); +} + +// convert 8 * {min, scale} from int6 to int8 +inline void unpack_mins_and_scales(const uint8_t * scales, uint32_t * utmp) { + const uint32_t kmask1 = 0x3f3f3f3f; + const uint32_t kmask2 = 0x0f0f0f0f; + const uint32_t kmask3 = 0x03030303; + + memcpy(utmp, scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; +} + +// packed_B layout: +// quants {8, TILE_N, 16} uint8 +// scales {8, TILE_N} uint8 +// mins {8, TILE_N} uint8 +// d {TILE_N} ggml_half +// dmin {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_q4_K * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + uint8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N); + uint8_t * mins = scales + 8 * TILE_N; + ggml_half * d = reinterpret_cast(mins + 8 * TILE_N); + ggml_half * dmin = d + TILE_N; + + union { + uint32_t u32[4]; + uint8_t u8[16]; + } s; + + for (int n = 0; n < TILE_N; ++n) { + unpack_mins_and_scales(B[n * KB].scales, s.u32); + for (int k = 0; k < 8; ++k) { + scales[k * TILE_N + n] = s.u8[k]; + mins[(k >> 1) * TILE_N * 2 + n * 2 + (k & 0x1)] = s.u8[k + 8]; + } + d[n] = B[n * KB].d; + dmin[n] = B[n * KB].dmin; + } +} + +// packed_B layout: +// quants {8, TILE_N, 16} uint8 +// qh {8, TILE_N, 4} uint8 +// scales {8, TILE_N} uint8 +// mins {8, TILE_N} uint8 +// d {TILE_N} ggml_half +// dmin {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_q5_K * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + uint8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N); + uint8_t * mins = scales + 8 * TILE_N; + ggml_half * d = reinterpret_cast(mins + 8 * TILE_N); + ggml_half * dmin = d + TILE_N; + + union { + uint32_t u32[4]; + uint8_t u8[16]; + } s; + + for (int n = 0; n < TILE_N; ++n) { + unpack_mins_and_scales(B[n * KB].scales, s.u32); + for (int k = 0; k < 8; ++k) { + scales[k * TILE_N + n] = s.u8[k]; + mins[(k >> 1) * TILE_N * 2 + n * 2 + (k & 0x1)] = s.u8[k + 8]; + } + d[n] = B[n * KB].d; + dmin[n] = B[n * KB].dmin; + } +} + +// packed_B layout: +// quants {16, TILE_N, 8} uint8 +// qh {16, TILE_N, 4} uint8 +// scales {16, TILE_N} uint8 +// d {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_q6_K * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + uint8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N); + ggml_half * d = reinterpret_cast(scales + 16 * TILE_N); + for (int n = 0; n < TILE_N; ++n) { + const int8_t * ps = B[n * KB].scales; + for (int k = 0; k < 16; ++k) { + scales[k * TILE_N + n] = ps[k]; + } + d[n] = B[n * KB].d; + } +} + +// packed_B layout: +// quants {8, TILE_N, 16} uint8 +// scales {8, TILE_N} int8 +// d {TILE_N} ggml_half +void pack_B(void * RESTRICT packed_B, const block_iq4_xs * RESTRICT B, int KB) { + pack_qs(packed_B, B, KB); + + int8_t * scales = reinterpret_cast((char *)packed_B + (QK_K / 2) * TILE_N); + ggml_half * d = reinterpret_cast(scales + 8 * TILE_N); + + // pack the scales + for (int n = 0; n < TILE_N; ++n) { + uint16_t sh = B[n * KB].scales_h; + for (int k = 0; k < 8; k += 2) { + const int16_t ls1 = ((B[n * KB].scales_l[k / 2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((B[n * KB].scales_l[k / 2] >> 4) | ((sh << 2) & 0x30)) - 32; + scales[(k + 0) * TILE_N + n] = ls1; + scales[(k + 1) * TILE_N + n] = ls2; + sh >>= 4; + } + d[n] = B[n * KB].d; + } +} + +template> +void unpack_B(packed_B_t * RESTRICT tile, const void * RESTRICT packed_B) { + GGML_UNUSED(tile); + GGML_UNUSED(packed_B); +}; + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B) { + const __m512i off = _mm512_set1_epi8(8); + const __m512i lowMask = _mm512_set1_epi8(0xF); + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)((const char *)packed_B + n * 32)); + const __m512i r0 = _mm512_sub_epi8(_mm512_and_si512(bytes, lowMask), off); + const __m512i r1 = _mm512_sub_epi8(_mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask), off); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template <> +void unpack_B(uint8_t * RESTRICT tile, const void * RESTRICT packed_B) { + const __m512i lowMask = _mm512_set1_epi8(0xF); + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)((const char *)packed_B + n * 32)); + const __m512i r0 = _mm512_and_si512(bytes, lowMask); + const __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +// packed_B_t for QKK is int8_t +template +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + const int packed_B_group_size = QK_K / 2 * TILE_N / 8; + const char * packed_B_group = (const char *)packed_B + k * packed_B_group_size; + const __m512i lowMask = _mm512_set1_epi8(0xF); + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512(packed_B_group + n * 32); + const __m512i r0 = _mm512_and_si512(bytes, lowMask); + const __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + // lower 4bits, stride 256 bytes + const int packed_l4_group_size = QK_K / 2 * TILE_N / 8; + const char * pb = (const char *)packed_B + k * packed_l4_group_size; + + // higher 1bit, stride 64 bytes + const int packed_h1_group_size = QK_K / 8 * TILE_N / 8; + const char * ph = (const char *)packed_B + (QK_K / 2) * TILE_N + k * packed_h1_group_size; + const __m512i hbits = _mm512_loadu_si512(ph); + + const __m512i lowMask = _mm512_set1_epi8(0xF); + __m512i hmask0 = _mm512_set1_epi8(0x1); + __m512i hmask1 = _mm512_set1_epi8(0x2); + + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512(pb + n * 32); + __m512i r0 = _mm512_and_si512(bytes, lowMask); + __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + __m512i h0 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask0), n), 4); + __m512i h1 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask1), n + 1), 4); + + hmask0 = _mm512_slli_epi16(hmask0, 2); + hmask1 = _mm512_slli_epi16(hmask1, 2); + r0 = _mm512_add_epi8(r0, h0); + r1 = _mm512_add_epi8(r1, h1); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + // lower 4bits, stride 128 bytes + const int packed_l4_group_size = QK_K / 2 * TILE_N / 16; + const char * pb = (const char *)packed_B + k * packed_l4_group_size; + + // higher 2bits, stride 64 bytes + const int packed_h2_group_size = QK_K / 4 * TILE_N / 16; + const char * ph = (const char *)packed_B + (QK_K / 2) * TILE_N + k * packed_h2_group_size; + const __m512i hbits = _mm512_loadu_si512(ph); + + const __m512i off = _mm512_set1_epi8(32); + const __m512i lowMask = _mm512_set1_epi8(0xF); + __m512i hmask0 = _mm512_set1_epi8(0x3); // 0011 + __m512i hmask1 = _mm512_set1_epi8(0xC); // 1100 + + // notes: skip zero padding from row4 to row7 as we have done so in `unpack_A` + __m512i bytes = _mm512_loadu_si512(pb); + __m512i r0 = _mm512_and_si512(bytes, lowMask); + __m512i r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + __m512i h0 = _mm512_slli_epi16(_mm512_and_si512(hbits, hmask0), 4); + __m512i h1 = _mm512_slli_epi16(_mm512_and_si512(hbits, hmask1), 2); + _mm512_storeu_si512((__m512i *)(tile + 0), _mm512_sub_epi8(_mm512_add_epi8(r0, h0), off)); + _mm512_storeu_si512((__m512i *)(tile + 64), _mm512_sub_epi8(_mm512_add_epi8(r1, h1), off)); + + hmask0 = _mm512_slli_epi16(hmask0, 4); + hmask1 = _mm512_slli_epi16(hmask1, 4); + + bytes = _mm512_loadu_si512(pb + 64); + r0 = _mm512_and_si512(bytes, lowMask); + r1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + h0 = _mm512_and_si512(hbits, hmask0); + h1 = _mm512_srli_epi16(_mm512_and_si512(hbits, hmask1), 2); + _mm512_storeu_si512((__m512i *)(tile + 128), _mm512_sub_epi8(_mm512_add_epi8(r0, h0), off)); + _mm512_storeu_si512((__m512i *)(tile + 192), _mm512_sub_epi8(_mm512_add_epi8(r1, h1), off)); +} + +template <> +void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B, int k) { + static const __m512i values128 = _mm512_set_epi8( + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127 + ); + + const int packed_B_group_size = QK_K / 2 * TILE_N / 8; + const char * pb = (const char *)packed_B + k * packed_B_group_size; + const __m512i lowMask = _mm512_set1_epi8(0xF); + + for (int n = 0; n < 8; n += 2) { + __m512i bytes = _mm512_loadu_si512(pb + n * 32); + const __m512i r0 = _mm512_shuffle_epi8(values128, _mm512_and_si512(bytes, lowMask)); + const __m512i r1 = _mm512_shuffle_epi8(values128, _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask)); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 0), r0); + _mm512_storeu_si512((__m512i *)(tile + n * 64 + 64), r1); + } +} + +template +struct acc_C {}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_0 * A, int lda, const void * packed_B, int nr) { + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset))); + + for (int m = 0; m < nr; ++m) { + const __m512 vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].d)); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + vsum = _mm512_fmadd_ps(vtile, _mm512_mul_ps(vd0, vd1), vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_1 * A, int lda, const void * packed_B, int nr) { + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset))); + const __m512 vm0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset + TILE_N * sizeof(ggml_half)))); + + for (int m = 0; m < nr; ++m) { + const __m512 vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].d)); + const __m512 vs1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].s)); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + vsum = _mm512_fmadd_ps(vtile, _mm512_mul_ps(vd0, vd1), vsum); + vsum = _mm512_fmadd_ps(vm0, vs1, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_0 * A, int lda, const void * packed_B, int nr) { + const int offset = TILE_N * TILE_K; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)((const char *)packed_B + offset))); + + for (int m = 0; m < nr; ++m) { + const __m512 vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[m * lda].d)); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + vsum = _mm512_fmadd_ps(vtile, _mm512_mul_ps(vd0, vd1), vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N); + const uint8_t * mins = scales + 8 * TILE_N; + const ggml_half * d0 = reinterpret_cast(mins + 8 * TILE_N); + const ggml_half * dmin = d0 + TILE_N; + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)dmin)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vdm = _mm512_mul_ps(_mm512_set1_ps(-d1), vdmin); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[m * lda].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, _mm512_castsi128_si512(q8s)); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + vsum = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc_m), vdm, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N); + const uint8_t * mins = scales + 8 * TILE_N; + const ggml_half * d0 = reinterpret_cast(mins + 8 * TILE_N); + const ggml_half * dmin = d0 + TILE_N; + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)dmin)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vdm = _mm512_mul_ps(_mm512_set1_ps(-d1), vdmin); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[m * lda].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, _mm512_castsi128_si512(q8s)); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + vsum = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc_m), vdm, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N); + const ggml_half * d0 = reinterpret_cast(scales + 16 * TILE_N); + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template +struct acc_C { + static void apply(float * RESTRICT C, int ldc, const int32_t * RESTRICT tile, const block_q8_K * A, int lda, const void * packed_B, int nr) { + const int8_t * scales = reinterpret_cast((const char *)packed_B + (QK_K / 2) * TILE_N); + const ggml_half * d0 = reinterpret_cast(scales + 8 * TILE_N); + + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)d0)); + + for (int m = 0; m < nr; ++m) { + const float d1 = A[m * lda].d; + const __m512 vd = _mm512_mul_ps(_mm512_set1_ps(d1), vd0); + const __m512 vtile = _mm512_cvtepi32_ps(_mm512_loadu_si512(tile + m * TILE_N)); + + __m512 vsum; + if (is_acc) { + vsum = _mm512_loadu_ps(C + m * ldc); + } else { + vsum = _mm512_set1_ps(0.f); + } + + vsum = _mm512_fmadd_ps(vtile, vd, vsum); + _mm512_storeu_ps(C + m * ldc, vsum); + } + } +}; + +template constexpr int get_quants_size(); +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N; } +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N; } +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N; } +template <> constexpr int get_quants_size() { return (QK_K / 2) * TILE_N; } + +// used for QKK format +template ::value, int>::type = 0> +inline void scale_C(const int32_t * RESTRICT tile, int32_t * RESTRICT sumi, const void * packed_B, int k, int nr) { + const uint8_t * scales = reinterpret_cast((const char *)packed_B + get_quants_size()); + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(scales + k * TILE_N))); + + for (int m = 0; m < nr; ++m) { + __m512i vsumi; + if (is_acc) { + vsumi = _mm512_loadu_si512(sumi + m * TILE_N); + } else { + vsumi = _mm512_setzero_si512(); + } + __m512i vtile = _mm512_loadu_si512(tile + m * TILE_N); + vsumi = _mm512_add_epi32(vsumi, _mm512_mullo_epi32(vtile, vscale)); + _mm512_storeu_si512((__m512i *)(sumi + m * TILE_N), vsumi); + } +} + +template +struct tinygemm_kernel_avx { + static void apply(int K, const TA * RESTRICT A, const TB * RESTRICT B, TC * RESTRICT C, int ldc) { + GGML_UNUSED(K); + GGML_UNUSED(A); + GGML_UNUSED(B); + GGML_UNUSED(C); + GGML_UNUSED(ldc); + } +}; + +template +struct tinygemm_kernel_avx { + static void apply(int K, const float * RESTRICT A, const ggml_fp16_t * RESTRICT B, float * RESTRICT C, int ldc) { + constexpr int ROWS = BLOCK_M; + constexpr int COLS = BLOCK_N; + assert(BLOCK_K == 16); + + __m512 va; + __m512 vb[COLS]; + __m512 vc[ROWS * COLS]; + + auto loadc = [&](int idx) { + vc[idx] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int idx, int k) { + // TODO: use `constexpr` here to get rid of interger div + // when upgraded to C++17 + const int row = idx / COLS; + const int col = idx % COLS; + + if (col == 0) { + va = _mm512_loadu_ps(A + row * K + k); + } + if (row == 0) { + vb[col] = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(B + col * K + k))); + } + vc[idx] = _mm512_fmadd_ps(va, vb[col], vc[idx]); + }; + + for (int k = 0; k < K; k += 16) { + Unroll{}(compute, k); + } + + auto storec = [&](int idx) { + const int row = idx / COLS; + const int col = idx % COLS; + C[row * ldc + col] = _mm512_reduce_add_ps(vc[idx]); + }; + Unroll{}(storec); + } +}; + +#define LAUNCH_TINYGEMM_KERNEL_AVX(MB_SIZE, NB_SIZE) \ + tinygemm_kernel_avx::apply( \ + K, (const float *)src1->data + mb_start * K, \ + (const type *)src0->data + nb_start * K, \ + (float *)dst->data + mb_start * ldc + nb_start, ldc); + + +// re-organize in the format {NB, KB, TILE_SIZE}: +#define PACKED_INDEX(n, k, KB, tile_size) (n * KB + k) * tile_size + +template +void convert_B_packed_format(void * RESTRICT packed_B, const TB * RESTRICT B, int N, int K, int n_threads) { + const int NB = N / TILE_N; + const int KB = K / BLOCK_K; + const int TILE_SIZE = get_tile_size(); + + // parallel on NB should be enough + parallel_for(n_threads, NB, [&](int begin, int end) { + for (int n = begin; n < end; ++n) { + for (int k = 0; k < KB; ++k) { + int n0 = n * TILE_N; + pack_B((char *)packed_B + PACKED_INDEX(n, k, KB, TILE_SIZE), &B[n0 * KB + k], KB); + } + } + }); +} + +template +struct tinygemm_kernel_vnni {}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q4_0); + + const block_q8_0 * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + __m512i va[8]; + __m512 vc[COLS]; + __m512 vd1; + + // sum of offsets, shared across COLS + // + // avx512-vnni does not have `_mm512_dpbssd_epi32`, + // need to transfrom ss to us: + // a * (b - 8) is equavilent to b * a - 8 * a + // s u u u s u s + // + __m512i vcomp; + + const __m512i off = _mm512_set1_epi8(8); + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + // load a and compute compensation + if (col == 0) { + const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); + vcomp = _mm512_setzero_si512(); + for (int k = 0; k < 8; ++k) { + va[k] = _mm512_set1_epi32(a_ptr[k]); + vcomp = _mm512_dpbusd_epi32(vcomp, off, va[k]); + } + vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].d)); + } + + // load b + __m512i vsum = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + for (int k = 0; k < 8; k += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)(b_ptr + k * 32)); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va[k + 0]); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va[k + 1]); + } + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset))); + vsum = _mm512_sub_epi32(vsum, vcomp); + + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(vsum), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q4_1); + + const block_q8_1 * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + __m512i va[8]; + __m512i vb[8]; + __m512 vc[COLS]; + __m512 vd1, vs1; + + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + // load a + if (col == 0) { + const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); + for (int k = 0; k < 8; ++k) { + va[k] = _mm512_set1_epi32(a_ptr[k]); + } + vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].d)); + vs1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].s)); + } + + // load b + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + for (int k = 0; k < 8; k += 2) { + __m512i bytes = _mm512_loadu_si512((const __m512i *)(b_ptr + k * 32)); + vb[k + 0] = _mm512_and_si512(bytes, lowMask); + vb[k + 1] = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + } + const int offset = TILE_N * TILE_K / 2; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset))); + const __m512 vm0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset + TILE_N * sizeof(ggml_half)))); + + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; ++k) { + vsum = _mm512_dpbusd_epi32(vsum, vb[k], va[k]); + } + + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(vsum), _mm512_mul_ps(vd0, vd1), vc[col]); + vc[col] = _mm512_fmadd_ps(vm0, vs1, vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q8_0) + TILE_N * sizeof(int32_t); + + const block_q8_0 * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + __m512i va[8]; + __m512i vb[8]; + __m512 vc[COLS]; + __m512 vd1; + + // Notes: s8s8 igemm compensation in avx512-vnni + // change s8s8 to u8s8 with compensate + // a * b = (a + 128) * b - 128 * b + // s s u s u s + // + // (128 * b is pre-computed when packing B to vnni formats) + // + const __m512i off = _mm512_set1_epi8(static_cast(0x80)); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + // load a and add offset 128 + if (col == 0) { + const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); + for (int k = 0; k < 8; ++k) { + va[k] = _mm512_set1_epi32(a_ptr[k]); + va[k] = _mm512_add_epi8(va[k], off); + } + vd1 = _mm512_set1_ps(GGML_FP16_TO_FP32(A[0 * KB + i].d)); + } + + // load b + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + for (int k = 0; k < 8; ++k) { + vb[k] = _mm512_loadu_si512((const __m512i *)(b_ptr + k * 64)); + } + const int offset = TILE_N * TILE_K; + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset))); + const int offset2 = TILE_N * TILE_K + TILE_N * sizeof(ggml_half); + const __m512i vcomp = _mm512_loadu_si512((const __m512i *)(b_ptr + offset2)); + + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; ++k) { + vsum = _mm512_dpbusd_epi32(vsum, va[k], vb[k]); + } + vsum = _mm512_sub_epi32(vsum, vcomp); + + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(vsum), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q4_K) + TILE_N * 4; + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // a.qs: 8 groups, 32 bytes each group (m256i) + __m512i va[8]; + // a.bsum: 8 groups, 2 bytes each group (m128i) + __m512i va_bsum; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_scales = (QK_K / 2) * TILE_N; + const int offset_mins = (QK_K / 2) * TILE_N + 8 * TILE_N; + const int offset_d0 = (QK_K / 2) * TILE_N + 16 * TILE_N; + const int offset_dmin = (QK_K / 2) * TILE_N + 16 * TILE_N + TILE_N * sizeof(ggml_half); + + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + // Notes: vnni formats in QK_K + // a) quants vnni format + // int8 {k/4, n, 4}, viewed as 2d {k/4, 4n}, k = 32 + // from {16, 32} to {8, 64} + // + // b) min vnni format + // int16 {k/2, n, 2}, viewed as 2d {k/2, 2n}, k = 8 + // from {16, 8} to {4, 32} + // + auto compute = [&](int col, int i) { + // load a + if (col == 0) { + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + va[k_group] = _mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)(A[0 * KB + i].qs + k_group * 32))); + } + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + va_bsum = _mm512_castsi128_si512(q8s); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // step 1: accumultate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; k += 2) { + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 0), va[k_group]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 1), va[k_group]); + + __m512i bytes = _mm512_loadu_si512((const __m512i *)b_qs); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + + b_qs += 64; + } + // vacc += scale * (q8 @ q4) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + + // step 2: accumulate the mins + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, va_bsum); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_dmin))); + vc[col] = _mm512_fnmadd_ps(_mm512_cvtepi32_ps(acc_m), _mm512_mul_ps(vdmin, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q5_K) + TILE_N * 4; + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // a.qs: 8 groups, 32 bytes each group (m256i) + __m512i va[8]; + // a.bsum: 8 groups, 2 bytes each group (m128i) + __m512i va_bsum; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_qh = (QK_K / 2) * TILE_N; + const int offset_scales = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N; + const int offset_mins = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N + 8 * TILE_N; + const int offset_d0 = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N + 16 * TILE_N; + const int offset_dmin = (QK_K / 2) * TILE_N + (QK_K / 8) * TILE_N + 16 * TILE_N + TILE_N * sizeof(ggml_half); + + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + // Q5_K and Q4_K shares the same vnni formats, refer to notes above. + auto compute = [&](int col, int i) { + // load a + if (col == 0) { + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + va[k_group] = _mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)(A[0 * KB + i].qs + k_group * 32))); + } + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + va_bsum = _mm512_castsi128_si512(q8s); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // step 1: accumultate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + const char * b_qh = b_ptr + offset_qh; + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + __m512i vsum = _mm512_setzero_si512(); + __m512i hmask0 = _mm512_set1_epi8(0x1); + __m512i hmask1 = _mm512_set1_epi8(0x2); + __m512i hbits = _mm512_loadu_si512((const __m512i *)(b_qh + k_group * 64)); + for (int k = 0; k < 8; k += 2) { + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 0), va[k_group]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(k + 1), va[k_group]); + + __m512i bytes = _mm512_loadu_si512((const __m512i *)b_qs); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + + __m512i vh0 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask0), k), 4); + __m512i vh1 = _mm512_slli_epi16(_mm512_srli_epi16(_mm512_and_si512(hbits, hmask1), k + 1), 4); + + hmask0 = _mm512_slli_epi16(hmask0, 2); + hmask1 = _mm512_slli_epi16(hmask1, 2); + vb0 = _mm512_add_epi8(vb0, vh0); + vb1 = _mm512_add_epi8(vb1, vh1); + + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + + b_qs += 64; + } + // vacc += scale * (q8 @ q5) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + + // step 2: accumulate the mins + __m512i acc_m = _mm512_setzero_si512(); + for (int k = 0; k < 4; ++k) { + __m512i vmask = _mm512_set1_epi32(k); + __m512i va = _mm512_permutexvar_epi32(vmask, va_bsum); + __m512i vb = _mm512_cvtepi8_epi16(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_mins + k * 32))); + acc_m = _mm512_dpwssds_epi32(acc_m, va, vb); + } + const __m512 vdmin = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_dmin))); + vc[col] = _mm512_fnmadd_ps(_mm512_cvtepi32_ps(acc_m), _mm512_mul_ps(vdmin, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_q6_K); + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // load the 256 bytes from A to 4 avx512 vectors + __m512i va[4]; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_qh = (QK_K / 2) * TILE_N; + const int offset_scales = (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N; + const int offset_d0 = (QK_K / 2) * TILE_N + (QK_K / 4) * TILE_N + 16 * TILE_N; + + // compensation + __m512i vcomp; + + const __m512i m32s = _mm512_set1_epi32(32); + const __m512i lowMask = _mm512_set1_epi8(0xF); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + if (col == 0) { + // load a + va[0] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 0)); + va[1] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 64)); + va[2] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 128)); + va[3] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 192)); + + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + vcomp = _mm512_mullo_epi32(_mm512_cvtepi16_epi32(q8sums), m32s); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // accmulate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + const char * b_qh = b_ptr + offset_qh; + int mask = 0; + for (int k_group = 0; k_group < QK_K / 16; ++k_group) { + int r = k_group >> 2; + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + + __m512i vsum = _mm512_setzero_si512(); + __m512i hmask = _mm512_set1_epi8(0x3); + + __m512i bytes = _mm512_loadu_si512(b_qs); + __m512i hbits = _mm512_loadu_si512(b_qh); + __m512i vb0 = _mm512_and_si512(bytes, lowMask); + __m512i vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + __m512i vh0 = _mm512_slli_epi16(_mm512_and_si512(hbits, hmask), 4); + __m512i vh1 = _mm512_slli_epi16(_mm512_and_si512(hbits, _mm512_slli_epi16(hmask, 2)), 2); + + vb0 = _mm512_add_epi8(vb0, vh0); + vb1 = _mm512_add_epi8(vb1, vh1); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + b_qs += 64; + + va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + + bytes = _mm512_loadu_si512(b_qs); + vb0 = _mm512_and_si512(bytes, lowMask); + vb1 = _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask); + vh0 = _mm512_and_si512(hbits, _mm512_slli_epi16(hmask, 4)); + vh1 = _mm512_srli_epi16(_mm512_and_si512(hbits, _mm512_slli_epi16(hmask, 6)), 2); + vb0 = _mm512_add_epi8(vb0, vh0); + vb1 = _mm512_add_epi8(vb1, vh1); + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + b_qs += 64; + b_qh += 64; + + // B * A - 32 * A + __m512i vmask = _mm512_set1_epi32(k_group); + vsum = _mm512_sub_epi32(vsum, _mm512_permutexvar_epi32(vmask, vcomp)); + + // vacc += scale * (q8 @ q6) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +template +struct tinygemm_kernel_vnni { + static void apply(int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + + constexpr int COLS = BLOCK_N / 16; + const int TILE_SIZE = TILE_N * sizeof(block_iq4_xs) + TILE_N * 2; + + const block_q8_K * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + // load the 256 bytes from A to 4 avx512 vectors + __m512i va[4]; + __m512 vc[COLS]; + __m512 vd1; + + // packed_B: + const int offset_scales = (QK_K / 2) * TILE_N ; + const int offset_d0 = (QK_K / 2) * TILE_N + 8 * TILE_N; + + // compensation + __m512i vcomp; + + const __m256i m128s = _mm256_set1_epi16(128); + const __m512i lowMask = _mm512_set1_epi8(0xF); + + const __m512i values128 = _mm512_set_epi8( + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127, + 113, 89, 69, 53, 38, 25, 13, 1, -10, -22, -35, -49, -65, -83, -104, -127 + ); + const __m512i off = _mm512_set1_epi8(static_cast(0x80)); + const __m512i values256 = _mm512_add_epi8(values128, off); + + auto loadc = [&](int col) { + vc[col] = _mm512_setzero_ps(); + }; + Unroll{}(loadc); + + auto compute = [&](int col, int i) { + if (col == 0) { + // load a + va[0] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 0)); + va[1] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 64)); + va[2] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 128)); + va[3] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 192)); + + // compensation: 128 * A + const __m256i q8sums = _mm256_loadu_si256((const __m256i *)A[0 * KB + i].bsums); + vcomp = _mm512_castsi256_si512(_mm256_madd_epi16(q8sums, m128s)); + vd1 = _mm512_set1_ps(A[0 * KB + i].d); + } + + // accmulate the quants + __m512i acc = _mm512_setzero_si512(); + const char * b_ptr = B + PACKED_INDEX(col, i, KB, TILE_SIZE); + const char * b_qs = b_ptr; + int mask = 0; + for (int k_group = 0; k_group < QK_K / 32; ++k_group) { + int r = k_group >> 1; + __m512i vmask = _mm512_set1_epi32(k_group); + __m512i vsum = _mm512_setzero_si512(); + for (int k = 0; k < 8; k += 2) { + __m512i va0 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + __m512i va1 = _mm512_permutexvar_epi32(_mm512_set1_epi32(mask++), va[r]); + + __m512i bytes = _mm512_loadu_si512(b_qs); + __m512i vb0 = _mm512_shuffle_epi8(values256, _mm512_and_si512(bytes, lowMask)); + __m512i vb1 = _mm512_shuffle_epi8(values256, _mm512_and_si512(_mm512_srli_epi16(bytes, 4), lowMask)); + + vsum = _mm512_dpbusd_epi32(vsum, vb0, va0); + vsum = _mm512_dpbusd_epi32(vsum, vb1, va1); + b_qs += 64; + } + // (B + 128) * A - 128 * A + vsum = _mm512_sub_epi32(vsum, _mm512_permutexvar_epi32(vmask, vcomp)); + + // vacc += scale * (q8 @ q4) + const __m512i vscale = _mm512_cvtepi8_epi32(_mm_loadu_si128((const __m128i *)(b_ptr + offset_scales + k_group * TILE_N))); + acc = _mm512_add_epi32(acc, _mm512_mullo_epi32(vsum, vscale)); + } + const __m512 vd0 = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(b_ptr + offset_d0))); + vc[col] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(acc), _mm512_mul_ps(vd0, vd1), vc[col]); + }; + + for (int i = 0; i < KB; ++i) { + Unroll{}(compute, i); + } + + //store to C + auto storec = [&](int col) { + _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); + }; + Unroll{}(storec); + } +}; + +#define LAUNCH_TINYGEMM_KERNEL_VNNI(NB_SIZE) \ + tinygemm_kernel_vnni::apply( \ + KB, (const char *)wdata + 0 * row_size_A, \ + (const char *)src0->data + PACKED_INDEX(nb * kTilesN, 0, KB, TILE_SIZE), \ + (float *) dst->data + 0 * N + nb_start, ldc) + +template ::value, int>::type = 0> +void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const void * RESTRICT _B, TC * RESTRICT C, int ldc) { + using packed_B_t = packed_B_type; + const int TILE_SIZE = get_tile_size(); + const bool need_unpack = do_unpack::value; + + GGML_ASSERT(M <= 2 * TILE_M && N == 2 * TILE_N); + const TA * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + const int m0 = std::min(M, TILE_M); + const int m1 = std::max(M - TILE_M, 0); + const int lda = KB * sizeof(TA); + //const int ldb = KB * sizeof(TB); + + static thread_local packed_B_t Tile0[TILE_N * TILE_K]; + static thread_local packed_B_t Tile1[TILE_N * TILE_K]; + static thread_local int8_t Tile23[TILE_M * TILE_K]; + + static thread_local int32_t TileC0[TILE_M * TILE_N * 4]; + static thread_local int32_t TileC1[TILE_M * TILE_N * 4]; + + // double buffering C to interleave avx512 and amx + int32_t * C_cur = TileC0; + int32_t * C_pre = TileC1; + + auto Tile4 = [&](int32_t * base) { return base; }; + auto Tile5 = [&](int32_t * base) { return base + TILE_M * TILE_N; }; + auto Tile6 = [&](int32_t * base) { return base + 2 * TILE_M * TILE_N; }; + auto Tile7 = [&](int32_t * base) { return base + 3 * TILE_M * TILE_N; }; + + if (M == 2 * TILE_M) { + // i = 0 + const char * B_blk0 = B + PACKED_INDEX(0, 0, KB, TILE_SIZE); + const char * B_blk1 = B + PACKED_INDEX(1, 0, KB, TILE_SIZE); + if (need_unpack) { + unpack_B(Tile0, B_blk0); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM0, B_blk0, TILE_N * VNNI_BLK); + } + + _tile_zero(TMM4); + _tile_loadd(TMM2, A[0].qs, lda); + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_stored(TMM4, Tile4(C_pre), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM5); + _tile_loadd(TMM3, A[TILE_M * KB + 0].qs, lda); + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_stored(TMM5, Tile5(C_pre), TILE_N * sizeof(int32_t)); + + if (need_unpack) { + unpack_B(Tile1, B_blk0); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK); + } + + _tile_zero(TMM6); + _tile_dpbssd(TMM6, TMM2, TMM1); + _tile_stored(TMM6, Tile6(C_pre), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM7); + _tile_dpbssd(TMM7, TMM3, TMM1); + _tile_stored(TMM7, Tile7(C_pre), TILE_N * sizeof(int32_t)); + + for (int i = 1; i < KB; ++i) { + // index of previous iter + const int ii = i - 1; + const char * B_blk0 = B + PACKED_INDEX(0, i, KB, TILE_SIZE); + const char * B_blk1 = B + PACKED_INDEX(1, i, KB, TILE_SIZE); + GGML_DISPATCH_BOOL(ii > 0, is_acc, [&] { + if (need_unpack) { + unpack_B(Tile0, B_blk0); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM0, B_blk0, TILE_N * VNNI_BLK); + } + _tile_zero(TMM4); + _tile_loadd(TMM2, A[i].qs, lda); + acc_C::apply(C, ldc, Tile4(C_pre), &A[ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_stored(TMM4, Tile4(C_cur), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM5); + _tile_loadd(TMM3, A[TILE_M * KB + i].qs, lda); + acc_C::apply(C + TILE_M * ldc, ldc, Tile5(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_stored(TMM5, Tile5(C_cur), TILE_N * sizeof(int32_t)); + + if (need_unpack) { + unpack_B(Tile1, B_blk1); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK); + } + _tile_zero(TMM6); + acc_C::apply(C + TILE_N, ldc, Tile6(C_pre), &A[ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM6, TMM2, TMM1); + _tile_stored(TMM6, Tile6(C_cur), TILE_N * sizeof(int32_t)); + + _tile_zero(TMM7); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Tile7(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + + _tile_dpbssd(TMM7, TMM3, TMM1); + _tile_stored(TMM7, Tile7(C_cur), TILE_N * sizeof(int32_t)); + + std::swap(C_cur, C_pre); + }); + } + // final accumulation + { + int ii = KB - 1; + acc_C::apply(C, ldc, Tile4(C_pre), &A[ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + acc_C::apply(C + TILE_M * ldc, ldc, Tile5(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(0, ii, KB, TILE_SIZE), TILE_M); + acc_C::apply(C + TILE_N, ldc, Tile6(C_pre), &A[ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Tile7(C_pre), &A[TILE_M * KB + ii], KB, B + PACKED_INDEX(1, ii, KB, TILE_SIZE), TILE_M); + } + } else { + for (int i = 0; i < KB; ++i) { + _tile_zero(TMM4); + _tile_zero(TMM6); + if (m1 != 0) { + _tile_zero(TMM5); + _tile_zero(TMM7); + } + + const char * B_blk0 = B + PACKED_INDEX(0, i, KB, TILE_SIZE); + const char * B_blk1 = B + PACKED_INDEX(1, i, KB, TILE_SIZE); + if (need_unpack) { + unpack_B(Tile0, B_blk0); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM0, B_blk0, TILE_N * VNNI_BLK); + } + + if (need_unpack) { + unpack_B(Tile1, B_blk1); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + } else { + _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK); + } + + if (m0 == TILE_M) { + _tile_loadd(TMM2, A[i].qs, lda); + } else { + unpack_A(Tile23, &A[i], KB, m0); + _tile_loadd(TMM2, Tile23, TILE_K); + } + + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_dpbssd(TMM6, TMM2, TMM1); + + _tile_stored(TMM4, Tile4(C_cur), TILE_N * sizeof(int32_t)); + _tile_stored(TMM6, Tile6(C_cur), TILE_N * sizeof(int32_t)); + + GGML_DISPATCH_BOOL(i > 0, is_acc, [&] { + acc_C::apply(C, ldc, Tile4(C_cur), &A[i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m0); + acc_C::apply(C + TILE_N, ldc, Tile6(C_cur), &A[i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m0); + }); + + if (m1 != 0) { + unpack_A(Tile23, &A[TILE_M * KB + i], KB, m1); + _tile_loadd(TMM3, Tile23, TILE_K); + + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_dpbssd(TMM7, TMM3, TMM1); + _tile_stored(TMM5, Tile5(C_cur), TILE_N * sizeof(int32_t)); + _tile_stored(TMM7, Tile7(C_cur), TILE_N * sizeof(int32_t)); + GGML_DISPATCH_BOOL(i > 0, is_acc, [&] { + acc_C::apply(C + TILE_M * ldc, ldc, Tile5(C_cur), &A[TILE_M * KB + i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m1); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Tile7(C_cur), &A[TILE_M * KB + i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m1); + }); + } + } + } + return; +} + +template ::value, int>::type = 0> +void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const void * RESTRICT _B, float * RESTRICT C, int ldc) { + static_assert(std::is_same::value); + const int TILE_SIZE = get_tile_size(); + + GGML_ASSERT(M <= 2 * TILE_M && N == 2 * TILE_N); + const TA * RESTRICT A = static_cast(_A); + const char * RESTRICT B = static_cast(_B); + + const int m0 = std::min(M, TILE_M); + const int m1 = std::max(M - TILE_M, 0); + //const int lda = KB * sizeof(TA); + + static thread_local int8_t Tile0[TILE_N * TILE_K]; + static thread_local int8_t Tile1[TILE_N * TILE_K]; + static thread_local int8_t Tile23[TILE_M * TILE_K]; + + // mat mul result for each group + static thread_local int32_t Tile4[TILE_M * TILE_N]; + static thread_local int32_t Tile5[TILE_M * TILE_N]; + static thread_local int32_t Tile6[TILE_M * TILE_N]; + static thread_local int32_t Tile7[TILE_M * TILE_N]; + + // sum of each QK_K block, contains 8 groups, int32 + static thread_local int32_t Sumi4[TILE_M * TILE_N]; + static thread_local int32_t Sumi5[TILE_M * TILE_N]; + static thread_local int32_t Sumi6[TILE_M * TILE_N]; + static thread_local int32_t Sumi7[TILE_M * TILE_N]; + + const int k_group_size = std::is_same::value ? 16 : 32; + for (int i = 0; i < KB; ++i) { + // step 1: accumulate the quants across 8 groups, each group with 32 + for (int k = 0; k < QK_K / k_group_size; ++k) { + GGML_DISPATCH_BOOL(k > 0, is_acc, [&] { + _tile_zero(TMM4); + _tile_zero(TMM6); + + unpack_B(Tile0, B + PACKED_INDEX(0, i, KB, TILE_SIZE), k); + _tile_loadd(TMM0, Tile0, TILE_N * VNNI_BLK); + + unpack_B(Tile1, B + PACKED_INDEX(1, i, KB, TILE_SIZE), k); + _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); + + unpack_A(Tile23, &A[i], KB, k, m0); + _tile_loadd(TMM2, Tile23, TILE_K); + + _tile_dpbssd(TMM4, TMM2, TMM0); + _tile_dpbssd(TMM6, TMM2, TMM1); + + _tile_stored(TMM4, Tile4, TILE_N * sizeof(int32_t)); + _tile_stored(TMM6, Tile6, TILE_N * sizeof(int32_t)); + + scale_C(Tile4, Sumi4, B + PACKED_INDEX(0, i, KB, TILE_SIZE), k, m0); + scale_C(Tile6, Sumi6, B + PACKED_INDEX(1, i, KB, TILE_SIZE), k, m0); + + if (m1 != 0) { + _tile_zero(TMM5); + _tile_zero(TMM7); + + unpack_A(Tile23, &A[TILE_M * KB + i], KB, k, m1); + _tile_loadd(TMM3, Tile23, TILE_K); + + _tile_dpbssd(TMM5, TMM3, TMM0); + _tile_dpbssd(TMM7, TMM3, TMM1); + + _tile_stored(TMM5, Tile5, TILE_N * sizeof(int32_t)); + _tile_stored(TMM7, Tile7, TILE_N * sizeof(int32_t)); + + scale_C(Tile5, Sumi5, B + PACKED_INDEX(0, i, KB, TILE_SIZE), k, m1); + scale_C(Tile7, Sumi7, B + PACKED_INDEX(1, i, KB, TILE_SIZE), k, m1); + } + }); + } + + // step 2: accmulate the mins + GGML_DISPATCH_BOOL(i > 0, is_acc, [&] { + acc_C::apply(C, ldc, Sumi4, &A[i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m0); + acc_C::apply(C + TILE_N, ldc, Sumi6, &A[i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m0); + if (m1 != 0) { + acc_C::apply(C + TILE_M * ldc, ldc, Sumi5, &A[TILE_M * KB + i], KB, B + PACKED_INDEX(0, i, KB, TILE_SIZE), m1); + acc_C::apply(C + TILE_M * ldc + TILE_N, ldc, Sumi7, &A[TILE_M * KB + i], KB, B + PACKED_INDEX(1, i, KB, TILE_SIZE), m1); + } + }); + } + return; +} + +} // anonymous namespace + +// get the packed tensor size for quantized weights +size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor) { + const enum ggml_type TYPE = tensor->type; + + const int K = tensor->ne[0]; // ne0: in_features + const int N = tensor->ne[1]; // ne1: out_features + + auto get_tensor_size = [&] { + size_t row_size_B{0}; + GGML_DISPATCH_QTYPES(TYPE, [&] { + row_size_B = get_row_size(K); + }); + return N * row_size_B; + }; + + if (qtype_has_amx_kernels(TYPE)) { + return get_tensor_size(); + } else { + // for f16, bf16 we don't do packing + return ggml_nbytes(tensor); + } +} + +// pack weight to vnni format +void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + + size_t alloc_size = ggml_backend_amx_get_alloc_size(tensor); + GGML_ASSERT(alloc_size == size); + + const enum ggml_type TYPE = tensor->type; + + const int K = tensor->ne[0]; // ne0: in_features + const int N = tensor->ne[1]; // ne1: out_features + +#if defined(_OPENMP) + // the buffer ctx is not initialized when .set_tensor is called + int n_threads = omp_get_num_threads(); +#else + int n_threads = 1; +#endif + + GGML_DISPATCH_QTYPES(TYPE, [&] { + convert_B_packed_format((void *)((char *)tensor->data + offset), (const type *)data, N, K, n_threads); + }); +} + +// NB: mixed dtype gemm with Advanced Matrix Extensions (Intel AMX) +// +// src0: weight in shape of {N, K}, quantized +// src1: input in shape of {M, K}, float32 +// dst: output in shape of {M, N}, float32 +// +// the function performs: dst = src1 @ src0.T +// +void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { + struct ggml_tensor * src0 = dst->src[0]; + struct ggml_tensor * src1 = dst->src[1]; + + const enum ggml_type TYPE = src0->type; + + const int n_threads = ctx->n_threads; + + // f16 only has avx512 kernels for now, + // amx kernels will be added once 6th gen xeon is released. + const bool is_floating_type = TYPE == GGML_TYPE_F16; + + const int M = dst->ne[1]; + const int N = dst->ne[0]; + const int K = src0->ne[0]; + const int ldc = dst->nb[1] / dst->nb[0]; + + if (is_floating_type) { + constexpr int BLOCK_M = 4; + constexpr int BLOCK_N = 6; + const int MB = div_up(M, BLOCK_M); + const int NB = div_up(N, BLOCK_N); + + parallel_for(n_threads, MB * NB, [&](int begin, int end) { + GGML_DISPATCH_FLOATING_TYPES(TYPE, [&] { + for (int i = begin; i < end; ++i) { + int mb = i / NB; + int nb = i % NB; + + int mb_start = mb * BLOCK_M; + int mb_size = std::min(BLOCK_M, M - mb_start); + int nb_start = nb * BLOCK_N; + int nb_size = std::min(BLOCK_N, N - nb_start); + + switch (mb_size << 4 | nb_size) { + case 0x12: LAUNCH_TINYGEMM_KERNEL_AVX(1, 2); break; + case 0x14: LAUNCH_TINYGEMM_KERNEL_AVX(1, 4); break; + case 0x16: LAUNCH_TINYGEMM_KERNEL_AVX(1, 6); break; + case 0x22: LAUNCH_TINYGEMM_KERNEL_AVX(2, 2); break; + case 0x24: LAUNCH_TINYGEMM_KERNEL_AVX(2, 4); break; + case 0x26: LAUNCH_TINYGEMM_KERNEL_AVX(2, 6); break; + case 0x32: LAUNCH_TINYGEMM_KERNEL_AVX(3, 2); break; + case 0x34: LAUNCH_TINYGEMM_KERNEL_AVX(3, 4); break; + case 0x36: LAUNCH_TINYGEMM_KERNEL_AVX(3, 6); break; + case 0x42: LAUNCH_TINYGEMM_KERNEL_AVX(4, 2); break; + case 0x44: LAUNCH_TINYGEMM_KERNEL_AVX(4, 4); break; + case 0x46: LAUNCH_TINYGEMM_KERNEL_AVX(4, 6); break; + default: fprintf(stderr, "Unexpected block size!\n"); + } + } + }); + }); + return; + } + + // pointer to work space, used convert A from float to quantized type + void * wdata = nullptr; + + //TODO: performance improvement: merge quant A + GGML_DISPATCH_QTYPES(TYPE, [&] { + const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); + const size_t desired_wsize = M * row_size_A; + if (ctx->work_size < desired_wsize) { + ctx->work_data.reset(new char[desired_wsize]); + ctx->work_size = desired_wsize; + } + wdata = ctx->work_data.get(); + + // Q4_0, Q4_1, Q8_0 handles 1 TILE_K per blck_size + // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size + GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size); + + const float * A_data = static_cast(src1->data); + for (int m = 0; m < M; ++m) { + from_float(A_data + m * K, (char *)wdata + m * row_size_A, K); + } + }); + + if (M == 1) { + // MB = 1 and handle 8 tiles in each block + constexpr int kTilesN = 4; + constexpr int BLOCK_N = TILE_N * kTilesN; + const int NB = div_up(N, BLOCK_N); + + parallel_for(n_threads, NB, [&](int begin, int end) { + GGML_DISPATCH_QTYPES(TYPE, [&] { + const int KB = K / blck_size; + const int TILE_SIZE = get_tile_size(); + const int row_size_A = KB * sizeof(vec_dot_type); + for (int i = begin; i < end; ++i) { + int nb = i; + int nb_start = nb * BLOCK_N; + int nb_size = std::min(BLOCK_N, N - nb_start); // 32, 64, 96 + + switch (nb_size) { + //case 160: LAUNCH_TINYGEMM_KERNEL_VNNI(160); break; + case 128: LAUNCH_TINYGEMM_KERNEL_VNNI(128); break; + case 96: LAUNCH_TINYGEMM_KERNEL_VNNI(96); break; + case 64: LAUNCH_TINYGEMM_KERNEL_VNNI(64); break; + case 32: LAUNCH_TINYGEMM_KERNEL_VNNI(32); break; + default: fprintf(stderr, "Unexpected n block size!\n"); + } + } + }); + }); + return; + } + + // handle 4 tiles at a tile + constexpr int BLOCK_M = TILE_M * 2; + constexpr int BLOCK_N = TILE_N * 2; + const int MB = div_up(M, BLOCK_M); + const int NB = div_up(N, BLOCK_N); + + parallel_for(n_threads, MB * NB, [&](int begin, int end) { + // init tile config for each thread + ggml_tile_config_init(); + + GGML_DISPATCH_QTYPES(TYPE, [&] { + const int KB = K / blck_size; + const int TILE_SIZE = get_tile_size(); + const int row_size_A = KB * sizeof(vec_dot_type); + + for (int i = begin; i < end; ++i) { + int mb = i / NB; + int nb = i % NB; + + int mb_start = mb * BLOCK_M; + int mb_size = std::min(BLOCK_M, M - mb_start); + int nb_start = nb * BLOCK_N; + int nb_size = BLOCK_N; + + tinygemm_kernel_amx( + mb_size, nb_size, KB, + (const char *)wdata + mb_start * row_size_A, + (const char *)src0->data + PACKED_INDEX(nb * 2, 0, KB, TILE_SIZE), + (float *) dst->data + mb_start * N + nb_start, ldc); + } + }); + }); +} + +#else // if defined(__AMX_INT8__) + +void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { + fprintf(stderr, "GGML is not compiled with AMX support!\n"); + + GGML_UNUSED(ctx); + GGML_UNUSED(dst); +} + +#endif // if defined(__AMX_INT8__) diff --git a/ggml/src/ggml-amx/mmq.h b/ggml/src/ggml-amx/mmq.h new file mode 100644 index 000000000..cf0920620 --- /dev/null +++ b/ggml/src/ggml-amx/mmq.h @@ -0,0 +1,17 @@ +#pragma once +#include "common.h" +#include + +#ifdef __cplusplus +extern "C" { +#endif + +size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor); + +void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + +void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index a3bc79a46..1c17dde30 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -329,7 +329,6 @@ bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type if (backend->device) { return ggml_backend_dev_supports_buft(backend->device, buft); } - return backend->iface.supports_buft(backend, buft); } @@ -550,6 +549,14 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na #include "ggml-rpc.h" #endif +#ifndef __AMX_INT8__ +#undef GGML_USE_AMX +#endif + +#ifdef GGML_USE_AMX +# include "ggml-amx.h" +#endif + struct ggml_backend_registry { std::vector backends; std::vector devices; @@ -570,6 +577,9 @@ struct ggml_backend_registry { #ifdef GGML_USE_RPC register_backend(ggml_backend_rpc_reg()); #endif +#ifdef GGML_USE_AMX + register_backend(ggml_backend_amx_reg()); +#endif // TODO: sycl, kompute, cann diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 779b38d12..7e24313ed 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -23252,6 +23252,14 @@ int ggml_cpu_has_avx512_bf16(void) { #endif } +int ggml_cpu_has_amx_int8(void) { +#if defined(__AMX_INT8__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; diff --git a/src/llama.cpp b/src/llama.cpp index dcb015d12..0025e94b8 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -16,6 +16,14 @@ # include "ggml-cann.h" #endif +#ifndef __AMX_INT8__ +#undef GGML_USE_AMX +#endif + +#ifdef GGML_USE_AMX +# include "ggml-amx.h" +#endif + // TODO: replace with ggml API call #define QK_K 256 @@ -3533,6 +3541,7 @@ static size_t llama_get_device_memory(const llama_model & model, int device) { #else return 1; #endif + GGML_UNUSED(model); GGML_UNUSED(device); } @@ -7031,7 +7040,14 @@ static bool llm_load_tensors( // assign cpu layers for (int i = 0; i < i_gpu_start; ++i) { +#ifdef GGML_USE_AMX + model.buft_layer[i] = { + ggml_backend_amx_buffer_type(), + llama_default_buffer_type_cpu(model, true) + }; +#else model.buft_layer[i] = llama_default_buffer_type_cpu(model, true); +#endif } if (split_mode == LLAMA_SPLIT_MODE_LAYER) { @@ -21839,6 +21855,7 @@ const char * llama_print_system_info(void) { s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | "; s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; + s += "AMX_INT8 = " + std::to_string(ggml_cpu_has_amx_int8()) + " | "; s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; s += "SVE = " + std::to_string(ggml_cpu_has_sve()) + " | "; From 87421a23e8c60e00a7b227d501e8aab2a1aff7ce Mon Sep 17 00:00:00 2001 From: Ouadie EL FAROUKI Date: Fri, 18 Oct 2024 06:46:16 +0100 Subject: [PATCH 29/95] [SYCL] Add SYCL Backend registry, device and Event Interfaces (#9705) * implemented missing SYCL event APIs * sycl : Added device and backend reg interfaces * Restructured ggml-sycl.cpp --- examples/llama-bench/llama-bench.cpp | 2 +- ggml/include/ggml-sycl.h | 11 +- ggml/src/ggml-backend.cpp | 10 +- ggml/src/ggml-sycl.cpp | 2689 ++++++++++++++------------ src/llama.cpp | 61 +- 5 files changed, 1492 insertions(+), 1281 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c22bdedcf..60a7aef5b 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -151,7 +151,7 @@ static std::string get_gpu_info() { int count = ggml_backend_sycl_get_device_count(); for (int i = 0; i < count; i++) { char buf[128]; - ggml_sycl_get_device_description(i, buf, sizeof(buf)); + ggml_backend_sycl_get_device_description(i, buf, sizeof(buf)); id += buf; if (i < count - 1) { id += "/"; diff --git a/ggml/include/ggml-sycl.h b/ggml/include/ggml-sycl.h index 03b698e61..af521f599 100644 --- a/ggml/include/ggml-sycl.h +++ b/ggml/include/ggml-sycl.h @@ -19,6 +19,8 @@ extern "C" { // backend API GGML_API ggml_backend_t ggml_backend_sycl_init(int device); +GGML_API bool ggml_backend_is_sycl(ggml_backend_t backend); + // devide buffer GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); @@ -29,14 +31,19 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const fl GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); GGML_API void ggml_backend_sycl_print_sycl_devices(void); -GGML_API void ggml_sycl_get_gpu_list(int *id_list, int max_len); -GGML_API void ggml_sycl_get_device_description(int device, char *description, size_t description_size); +GGML_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len); +GGML_API void ggml_backend_sycl_get_device_description(int device, + char *description, + size_t description_size); GGML_API int ggml_backend_sycl_get_device_count(); GGML_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); // SYCL doesn't support registering host memory, keep here for reference // GGML_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); // GGML_API void ggml_backend_sycl_unregister_host_buffer(void * buffer); + +GGML_API ggml_backend_reg_t ggml_backend_sycl_reg(void); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 1c17dde30..81d09cd8b 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -537,6 +537,10 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na #include "ggml-metal.h" #endif +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + #ifdef GGML_USE_VULKAN #include "ggml-vulkan.h" #endif @@ -568,6 +572,9 @@ struct ggml_backend_registry { #ifdef GGML_USE_METAL register_backend(ggml_backend_metal_reg()); #endif +#ifdef GGML_USE_SYCL + register_backend(ggml_backend_sycl_reg()); +#endif #ifdef GGML_USE_VULKAN register_backend(ggml_backend_vk_reg()); #endif @@ -581,7 +588,7 @@ struct ggml_backend_registry { register_backend(ggml_backend_amx_reg()); #endif - // TODO: sycl, kompute, cann + // TODO: kompute, cann register_backend(ggml_backend_cpu_reg()); } @@ -2254,6 +2261,7 @@ ggml_backend_sched_t ggml_backend_sched_new( sched->backends[b] = backends[b]; sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]); GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b])); + if (sched->n_copies > 1) { for (int c = 0; c < sched->n_copies; c++) { sched->events[b][c] = ggml_backend_event_new(backends[b]->device); diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp index 4d3f1c5ce..4d91ee460 100644 --- a/ggml/src/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl.cpp @@ -40,17 +40,316 @@ #include "ggml-sycl/presets.hpp" #include "ggml-sycl/gemm.hpp" -bool ggml_sycl_loaded(void); -void ggml_sycl_free_data(struct ggml_tensor * tensor); -void ggml_sycl_copy_to_device(struct ggml_tensor * tensor); -void ggml_sycl_set_main_device(int main_device); -void ggml_sycl_set_mul_mat_q(bool mul_mat_q); -void ggml_sycl_get_device_description(int device, char * description, size_t description_size); -bool ggml_backend_is_sycl(ggml_backend_t backend); -int ggml_backend_sycl_get_device(ggml_backend_t backend); -static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer); -static inline int get_sycl_env(const char *env_name, int default_val); +static bool g_sycl_loaded = false; +static ggml_sycl_device_info ggml_sycl_init() { + ggml_sycl_device_info info = {}; + + info.device_count = dpct::dev_mgr::instance().device_count(); + if (info.device_count == 0) { + fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); + return info; + } + + GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES); + + int64_t total_vram = 0; +#if defined(GGML_SYCL_FORCE_MMQ) + fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); +#endif +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif + fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); + + for (int i = 0; i < info.device_count; ++i) { + info.devices[i].vmm = 0; + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(i)))); + + info.default_tensor_split[i] = total_vram; + total_vram += prop.get_global_mem_size(); + + info.devices[i].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + + info.max_work_group_sizes[i] = prop.get_max_work_group_size(); + } + + for (int id = 0; id < info.device_count; ++id) { + info.default_tensor_split[id] /= total_vram; + } + return info; +} + +const ggml_sycl_device_info & ggml_sycl_info() { + static ggml_sycl_device_info info = ggml_sycl_init(); + return info; +} + +void print_device_detail(int id, sycl::device &device, std::string device_type) { + + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::get_device_info(prop, device))); + + std::string version; + version += std::to_string(prop.get_major_version()); + version += "."; + version += std::to_string(prop.get_minor_version()); + + device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), ""); + std::string name = std::string(prop.get_name()); + name = std::regex_replace(name, std::regex("\\(R\\)"), ""); + name = std::regex_replace(name, std::regex("\\(TM\\)"), ""); + + auto global_mem_size = prop.get_global_mem_size()/1000000; + + fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), + name.c_str(), version.c_str(), prop.get_max_compute_units(), + prop.get_max_work_group_size(), prop.get_max_sub_group_size(), + global_mem_size, device.get_info().c_str()); +} + +void ggml_backend_sycl_print_sycl_devices() { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n"); + int device_count = dpct::dev_mgr::instance().device_count(); + std::map DeviceNums; + fprintf(stderr, "found %d SYCL devices:\n", device_count); + fprintf(stderr, "| | | | |Max | |Max |Global | |\n"); + fprintf(stderr, "| | | | |compute|Max work|sub |mem | |\n"); + fprintf(stderr, "|ID| Device Type| Name|Version|units |group |group|size | Driver version|\n"); + fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n"); + for (int id = 0; id < device_count; ++id) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + sycl::backend backend = device.get_backend(); + std::string backend_type = get_device_backend_and_type(device); + int type_id=DeviceNums[backend_type]++; + std::stringstream device_type; + device_type << "[" << backend_type << ":" << std::to_string(type_id) << "]"; + print_device_detail(id, device, device_type.str()); + } +} + +static inline int get_sycl_env(const char *env_name, int default_val) { + char *user_device_string = getenv(env_name); + int user_number = default_val; + + unsigned n; + if (user_device_string != NULL && + sscanf(user_device_string, " %u", &n) == 1) { + user_number = (int)n; + } else { + user_number = default_val; + } + return user_number; +} + +static void ggml_check_sycl() try { + static bool initialized = false; + + if (!initialized) { + fprintf(stderr, "[SYCL] call ggml_check_sycl\n"); + g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); + + fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); + +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); +#endif + +/* NOT REMOVE, keep it for next optimize for XMX. +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif +*/ + + if (CHECK_TRY_ERROR(g_all_sycl_device_count = + dpct::dev_mgr::instance().device_count()) != 0) { + initialized = true; + g_sycl_loaded = false; + return; + } + GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); + ggml_backend_sycl_print_sycl_devices(); + initialized = true; + g_sycl_loaded = true; + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +/* +device_index: device index from 0 to n (continue numbers). + It is used for device select/set in SYCL backend internal data structure. +*/ +inline void check_allow_gpu_index(const int device_index) { + if (device_index >= ggml_sycl_info().device_count) { + char error_buf[256]; + snprintf( + error_buf, + sizeof(error_buf), + "%s error: device_index:%d is out of range: [0-%d]", + __func__, + device_index, + ggml_sycl_info().device_count - 1); + fprintf(stderr, "%s\n", error_buf); + assert(false); + } +} + +GGML_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len) try { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_gpu_list\n"); + for(int i=0;i=max_len) break; + id_list[i] = i; + } + return; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +// sycl buffer + +struct ggml_backend_sycl_buffer_context { + int device; + void * dev_ptr = nullptr; + queue_ptr stream; + std::string name; + + ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) : + device(device), dev_ptr(dev_ptr), stream(stream) { + check_allow_gpu_index(device); + name = (GGML_SYCL_NAME + std::to_string(device)); + } + + + ~ggml_backend_sycl_buffer_context() { + if (dev_ptr != nullptr) { + ggml_sycl_set_device(device); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream))); + } + } +}; + +static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; + return ctx->name.c_str(); +} + +static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name; +} + +static void +ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_sycl_set_device(ctx->device); + + delete ctx; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + return ctx->dev_ptr; +} + +static void +ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; + + if (tensor->view_src != NULL && tensor->view_offs == 0) { + assert(tensor->view_src->buffer->buft == buffer->buft); + tensor->backend = tensor->view_src->backend; + tensor->extra = tensor->view_src->extra; + return; + } + + + if (ggml_is_quantized(tensor->type)) { + // initialize padding to 0 to avoid possible NaN values + size_t original_size = ggml_nbytes(tensor); + size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); + + if (padded_size > original_size && tensor->view_src == nullptr) { + SYCL_CHECK(CHECK_TRY_ERROR(ctx->stream->memset( + (char *)tensor->data + original_size, 0, + padded_size - original_size).wait())); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, + const void *data, size_t offset, + size_t size) try { + + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); + char* host_buf = (char*)malloc(size); + memcpy(host_buf, data, size); + SYCL_CHECK( + CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) + .wait())); + free(host_buf); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, + void *data, size_t offset, + size_t size) try { + + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + auto stream = dpct::dev_mgr::instance().get_device(ctx->device).default_queue(); + + SYCL_CHECK(CHECK_TRY_ERROR( + stream.memcpy(data, (const char *)tensor->data + offset, size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, const void *ptr_src, size_t size) { @@ -60,6 +359,850 @@ void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, free(host_buf); } +static bool +ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *src, + ggml_tensor *dst) try { + if (ggml_backend_buffer_is_sycl(src->buffer)) { + ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; + ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context; + + ggml_sycl_set_device(src_ctx->device); + /* + DPCT1009:198: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); + ggml_sycl_set_device(dst_ctx->device); + /* + DPCT1009:199: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); + /* + DPCT1009:200: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + + queue_ptr stream_dst = dst_ctx->stream; + queue_ptr stream_src = src_ctx->stream; + size_t size = ggml_nbytes(src); + + //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. + dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); + +//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove +#if 0 + SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( + (char *)dst->data, (const char *)src->data, size).wait())); + + /* + DPCT1009:201: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); +#endif + return true; + } + return false; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + +static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, + uint8_t value) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + queue_ptr stream = ctx->stream; + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + + SYCL_CHECK(CHECK_TRY_ERROR((*stream) + .memset(ctx->dev_ptr, value, buffer->size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static const ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { + /* .get_name = */ ggml_backend_sycl_buffer_get_name, + /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, + /* .memset_tensor = */ NULL, + /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, + /* .clear = */ ggml_backend_sycl_buffer_clear, + /* .reset = */ NULL, +}; + +// sycl buffer type +struct ggml_backend_sycl_buffer_type_context { + int device; + std::string name; + + // each buffer type has its own stream + queue_ptr stream = nullptr; +}; + +static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t +ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) try { + ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + ggml_sycl_set_device(buft_ctx->device); + const queue_ptr stream = buft_ctx->stream; + size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 + + void * dev_ptr; + SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( + size, *stream))); + if (!dev_ptr) { + fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, size); + return nullptr; + } + ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream); + return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + GGML_UNUSED(buft); +} + +static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return dpct::get_current_device().get_max_mem_alloc_size(); + + GGML_UNUSED(buft); +} + +static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); + int64_t ne0 = tensor->ne[0]; + + if (ggml_is_quantized(tensor->type)) { + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return size; + + GGML_UNUSED(buft); +} + +static const ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, + /* .is_host = */ NULL, +}; + +ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { + static std::mutex mutex; + std::lock_guard lock(mutex); + + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n"); + + auto dev_count = ggml_backend_sycl_get_device_count(); + + if (device>=dev_count or device<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device, dev_count-1); + GGML_ASSERT(devicedevice; + if (device>=ggml_sycl_info().device_count or device<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device, ggml_sycl_info().device_count-1); + GGML_ASSERT(devicestream(i, 0)}, + }; + } + ggml_backend_sycl_buffer_type_initialized = true; + } + return &ggml_backend_sycl_buffer_types[device]; +} + +// sycl split buffer + +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { + int64_t min_compute_capability = INT_MAX; + int64_t max_compute_capability = INT_MIN; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + if (tensor_split[i] < (i + 1 < ggml_sycl_info().device_count ? tensor_split[i + 1] : 1.0f)) { + if (min_compute_capability > ggml_sycl_info().devices[i].cc) { + min_compute_capability = ggml_sycl_info().devices[i].cc; + } + if (max_compute_capability < ggml_sycl_info().devices[i].cc) { + max_compute_capability = ggml_sycl_info().devices[i].cc; + } + } + } + + switch(type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + return 64; + case GGML_TYPE_F16: + case GGML_TYPE_F32: + return 1; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_IQ3_S: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_Q6_K: + return 64; + default: + GGML_ABORT("fatal error"); + } +} + +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; + if (id == ggml_sycl_info().device_count - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; + } +} + +static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); +} + +struct ggml_backend_sycl_split_buffer_type_context { + std::array tensor_split; +}; + +struct ggml_backend_sycl_split_buffer_context { + ~ggml_backend_sycl_split_buffer_context() try { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { + if (extra->events[i][is] != nullptr) { + /* + DPCT1009:206: SYCL uses exceptions to report errors and + does not use the error codes. The original code was + commented out and a warning string was inserted. You + need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::destroy_event(extra->events[i][is]))); + } + } + if (extra->data_device[i] != nullptr) { + /* + DPCT1009:207: SYCL uses exceptions to report errors and does + not use the error codes. The original code was commented out + and a warning string was inserted. You need to rewrite this + code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( + extra->data_device[i], *(streams[i])))); + } + } + delete extra; + } + } + catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + std::vector tensor_extras; + std::vector streams; +}; + +static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { + return GGML_SYCL_NAME "_Split"; + + GGML_UNUSED(buffer); +} + +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; +} + +static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + delete ctx; +} + +static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; + + GGML_UNUSED(buffer); +} + +static void +ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + + ctx->tensor_extras.push_back(extra); + ctx->streams.push_back(&(dpct::get_current_device().default_queue())); + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; + char * buf; + /* + DPCT1009:208: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( + size, *stream))); + if (!buf) { + char err_buf[1024]; + snprintf(err_buf, 1023, "%s: can't malloc %lu Bytes memory on device", __func__, size); + throw std::runtime_error(err_buf); + } + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + /* + DPCT1009:209: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memset(buf + original_size, 0, size - original_size) + .wait())); + } + + extra->data_device[i] = buf; + + for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { + /* + DPCT1009:210: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK( + CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); + } + } + tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void +ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, const void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + /* + DPCT1009:211: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memcpy(extra->data_device[i], buf_host, original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void +ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + /* + DPCT1009:212: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memcpy(buf_host, extra->data_device[i], original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + GGML_UNUSED(buffer); + GGML_UNUSED(value); +} + +static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_get_name, + /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, + /* .memset_tensor = */ NULL, + /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_sycl_split_buffer_clear, + /* .reset = */ NULL, +}; + +// sycl split buffer type + +static const char * ggml_backend_sycl_split_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Split"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); + + return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); +} + +static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + GGML_UNUSED(buft); +} + +static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; + + size_t total_size = 0; + + const int64_t ne0 = tensor->ne[0]; + + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + total_size += ggml_nbytes_split(tensor, nrows_split); + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return total_size; +} + +static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, +}; + +ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { + static std::mutex mutex; + std::lock_guard lock(mutex); + + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n"); + ggml_check_sycl(); + // FIXME: this is not thread safe + static std::map, struct ggml_backend_buffer_type> buft_map; + + std::array tensor_split_arr = {}; + + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = ggml_sycl_info().default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + tensor_split_arr[i] /= split_sum; + } + } + + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } + + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), 0), + /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, + }; + + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; +} + +// host buffer type + +static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Host"; + + GGML_UNUSED(buft); +} + +static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) { + return GGML_SYCL_NAME "_Host"; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_sycl_host_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr = ggml_sycl_host_malloc(size); + + if (ptr == nullptr) { + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + } + + // FIXME: this is a hack to avoid having to implement a new buffer type + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.get_name = ggml_backend_sycl_host_buffer_name; + buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n"); + static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_sycl_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_sycl_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), 0), + /* .context = */ nullptr, + }; + + return &ggml_backend_sycl_buffer_type_host; +} + +// buffer pool for sycl (legacy) +struct ggml_sycl_pool_leg : public ggml_sycl_pool { + static const int MAX_SYCL_BUFFERS = 256; + + int device; + queue_ptr qptr; + struct ggml_sycl_buffer { + void * ptr = nullptr; + size_t size = 0; + }; + + ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {}; + size_t pool_size = 0; + + explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : + qptr(qptr_), + device(device_) { + } + + ~ggml_sycl_pool_leg() { + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer & b = buffer_pool[i]; + if (b.ptr != nullptr) { + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr))); + pool_size -= b.size; + } + } + GGML_ASSERT(pool_size == 0); + } + + void * alloc(size_t size, size_t * actual_size) override { +#ifdef DEBUG_sycl_MALLOC + int nnz = 0; + size_t max_size = 0; +#endif + size_t best_diff = 1ull << 36; + int ibest = -1; + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer& b = buffer_pool[i]; + if (b.ptr != nullptr) { +#ifdef DEBUG_sycl_MALLOC + ++nnz; + if (b.size > max_size) max_size = b.size; +#endif + if (b.size >= size) { + size_t diff = b.size - size; + if (diff < best_diff) { + best_diff = diff; + ibest = i; + if (!best_diff) { + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + } + } + } + } + if (ibest >= 0) { + ggml_sycl_buffer& b = buffer_pool[ibest]; + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + void * ptr; + size_t look_ahead_size = (size_t) (1.05 * size); + + SYCL_CHECK( + CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( + look_ahead_size, *qptr))); + if (!ptr) { + fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, look_ahead_size); + return nullptr; + } + + *actual_size = look_ahead_size; + pool_size += look_ahead_size; + + #ifdef DEBUG_SYCL_MALLOC + fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, + (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); + #endif + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); + return ptr; + } + + void free(void * ptr, size_t size) override { + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer& b = buffer_pool[i]; + if (b.ptr == nullptr) { + b.ptr = ptr; + b.size = size; + return; + } + } + fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr))); + pool_size -= size; + } +}; + +std::unique_ptr ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) { + // TBD: NO VMM support + // if (ggml_sycl_info().devices[device].vmm) { + // return std::unique_ptr(new ggml_sycl_pool_vmm(device)); + // } + return std::unique_ptr(new ggml_sycl_pool_leg(qptr, device)); +} + +// TBD pool with virtual memory management +// struct ggml_sycl_pool_vmm : public ggml_sycl_pool + +/// kernels + typedef void (*cpy_kernel_t)(const char * cx, char * cdst); typedef void (*ggml_sycl_func_t)(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); typedef void (*ggml_sycl_op_mul_mat_t)( @@ -1706,296 +2849,6 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst, }); } -static bool g_sycl_loaded = false; - -bool ggml_sycl_loaded(void) { - return g_sycl_loaded; -} - -void print_device_detail(int id, sycl::device &device, std::string device_type) { - - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::get_device_info(prop, device))); - - std::string version; - version += std::to_string(prop.get_major_version()); - version += "."; - version += std::to_string(prop.get_minor_version()); - - device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), ""); - std::string name = std::string(prop.get_name()); - name = std::regex_replace(name, std::regex("\\(R\\)"), ""); - name = std::regex_replace(name, std::regex("\\(TM\\)"), ""); - - auto global_mem_size = prop.get_global_mem_size()/1000000; - - fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), - name.c_str(), version.c_str(), prop.get_max_compute_units(), - prop.get_max_work_group_size(), prop.get_max_sub_group_size(), - global_mem_size, device.get_info().c_str()); -} - -void ggml_backend_sycl_print_sycl_devices() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n"); - int device_count = dpct::dev_mgr::instance().device_count(); - std::map DeviceNums; - fprintf(stderr, "found %d SYCL devices:\n", device_count); - fprintf(stderr, "| | | | |Max | |Max |Global | |\n"); - fprintf(stderr, "| | | | |compute|Max work|sub |mem | |\n"); - fprintf(stderr, "|ID| Device Type| Name|Version|units |group |group|size | Driver version|\n"); - fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n"); - for (int id = 0; id < device_count; ++id) { - sycl::device device = dpct::dev_mgr::instance().get_device(id); - sycl::backend backend = device.get_backend(); - std::string backend_type = get_device_backend_and_type(device); - int type_id=DeviceNums[backend_type]++; - std::stringstream device_type; - device_type << "[" << backend_type << ":" << std::to_string(type_id) << "]"; - print_device_detail(id, device, device_type.str()); - } -} - -static inline int get_sycl_env(const char *env_name, int default_val) { - char *user_device_string = getenv(env_name); - int user_number = default_val; - - unsigned n; - if (user_device_string != NULL && - sscanf(user_device_string, " %u", &n) == 1) { - user_number = (int)n; - } else { - user_number = default_val; - } - return user_number; -} - -static void ggml_check_sycl() try { - static bool initialized = false; - - if (!initialized) { - fprintf(stderr, "[SYCL] call ggml_check_sycl\n"); - g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); - - fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); - -#if defined(GGML_SYCL_F16) - fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); -#endif - -/* NOT REMOVE, keep it for next optimize for XMX. -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif -*/ - - if (CHECK_TRY_ERROR(g_all_sycl_device_count = - dpct::dev_mgr::instance().device_count()) != 0) { - initialized = true; - g_sycl_loaded = false; - return; - } - GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); - ggml_backend_sycl_print_sycl_devices(); - initialized = true; - g_sycl_loaded = true; - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static ggml_sycl_device_info ggml_sycl_init() { - ggml_sycl_device_info info = {}; - - info.device_count = dpct::dev_mgr::instance().device_count(); - if (info.device_count == 0) { - fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); - return info; - } - - GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES); - - int64_t total_vram = 0; -#if defined(GGML_SYCL_FORCE_MMQ) - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); -#endif -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif - fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); - - for (int i = 0; i < info.device_count; ++i) { - info.devices[i].vmm = 0; - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(i)))); - - info.default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); - - info.devices[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - - info.max_work_group_sizes[i] = prop.get_max_work_group_size(); - } - - for (int id = 0; id < info.device_count; ++id) { - info.default_tensor_split[id] /= total_vram; - } - return info; -} - -const ggml_sycl_device_info & ggml_sycl_info() { - static ggml_sycl_device_info info = ggml_sycl_init(); - return info; -} - -/* -device_index: device index from 0 to n (continue numbers). - It is used for device select/set in SYCL backend internal data structure. -*/ -inline void check_allow_gpu_index(const int device_index) { - if (device_index >= ggml_sycl_info().device_count) { - char error_buf[256]; - snprintf( - error_buf, - sizeof(error_buf), - "%s error: device_index:%d is out of range: [0-%d]", - __func__, - device_index, - ggml_sycl_info().device_count - 1); - fprintf(stderr, "%s\n", error_buf); - assert(false); - } -} - -// buffer pool for sycl (legacy) -struct ggml_sycl_pool_leg : public ggml_sycl_pool { - static const int MAX_SYCL_BUFFERS = 256; - - int device; - queue_ptr qptr; - struct ggml_sycl_buffer { - void * ptr = nullptr; - size_t size = 0; - }; - - ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {}; - size_t pool_size = 0; - - explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : - qptr(qptr_), - device(device_) { - } - - ~ggml_sycl_pool_leg() { - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer & b = buffer_pool[i]; - if (b.ptr != nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr))); - pool_size -= b.size; - } - } - GGML_ASSERT(pool_size == 0); - } - - void * alloc(size_t size, size_t * actual_size) override { -#ifdef DEBUG_sycl_MALLOC - int nnz = 0; - size_t max_size = 0; -#endif - size_t best_diff = 1ull << 36; - int ibest = -1; - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer& b = buffer_pool[i]; - if (b.ptr != nullptr) { -#ifdef DEBUG_sycl_MALLOC - ++nnz; - if (b.size > max_size) max_size = b.size; -#endif - if (b.size >= size) { - size_t diff = b.size - size; - if (diff < best_diff) { - best_diff = diff; - ibest = i; - if (!best_diff) { - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - } - } - } - } - if (ibest >= 0) { - ggml_sycl_buffer& b = buffer_pool[ibest]; - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - void * ptr; - size_t look_ahead_size = (size_t) (1.05 * size); - - SYCL_CHECK( - CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( - look_ahead_size, *qptr))); - if (!ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, look_ahead_size); - return nullptr; - } - - *actual_size = look_ahead_size; - pool_size += look_ahead_size; - - #ifdef DEBUG_SYCL_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); - #endif - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); - return ptr; - } - - void free(void * ptr, size_t size) override { - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer& b = buffer_pool[i]; - if (b.ptr == nullptr) { - b.ptr = ptr; - b.size = size; - return; - } - } - fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr))); - pool_size -= size; - } -}; - -std::unique_ptr ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) { - // TBD: NO VMM support - // if (ggml_sycl_info().devices[device].vmm) { - // return std::unique_ptr(new ggml_sycl_pool_vmm(device)); - // } - return std::unique_ptr(new ggml_sycl_pool_leg(qptr, device)); -} - -// TBD pool with virtual memory management -// struct ggml_sycl_pool_vmm : public ggml_sycl_pool - static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, const struct ggml_tensor *src, int64_t i3, int64_t i2, @@ -2376,54 +3229,6 @@ inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor (void) src1_dd; } -static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { - int64_t min_compute_capability = INT_MAX; - int64_t max_compute_capability = INT_MIN; - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - if (tensor_split[i] < (i + 1 < ggml_sycl_info().device_count ? tensor_split[i + 1] : 1.0f)) { - if (min_compute_capability > ggml_sycl_info().devices[i].cc) { - min_compute_capability = ggml_sycl_info().devices[i].cc; - } - if (max_compute_capability < ggml_sycl_info().devices[i].cc) { - max_compute_capability = ggml_sycl_info().devices[i].cc; - } - } - } - - switch(type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return 64; - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return 1; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ4_NL: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_IQ3_S: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_Q6_K: - return 64; - default: - GGML_ABORT("fatal error"); - } - -} - inline void ggml_sycl_op_mul_mat_sycl( ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, @@ -2783,10 +3588,6 @@ static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) { peer_access_enabled = enable_peer_access; } -struct ggml_backend_sycl_split_buffer_type_context { - std::array tensor_split; -}; - static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, ggml_sycl_op_mul_mat_t op, @@ -3865,12 +4666,6 @@ static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * s (void) dst; } -static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); -} - void ggml_sycl_set_main_device(const int main_device) try { if (dpct::get_current_device_id() == main_device) return; check_allow_gpu_index(main_device); @@ -4038,39 +4833,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens return true; } -GGML_API void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { - GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n"); - for(int i=0;i=max_len) break; - id_list[i] = i; - } - return; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -int ggml_sycl_get_device_count() try { - int device_count; - if (CHECK_TRY_ERROR(device_count = - dpct::dev_mgr::instance().device_count()) != 0) { - return 0; - } - return device_count; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -GGML_API void ggml_sycl_get_device_description(int device, char *description, +GGML_API void ggml_backend_sycl_get_device_description(int device, char *description, size_t description_size) try { - GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n"); + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_description\n"); dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( prop, dpct::dev_mgr::instance().get_device(device)))); @@ -4108,801 +4873,9 @@ catch (sycl::exception const &exc) { //////////////////////////////////////////////////////////////////////////////// -// backend interface - -#define UNUSED GGML_UNUSED - -// sycl buffer - -struct ggml_backend_sycl_buffer_context { - int device; - void * dev_ptr = nullptr; - queue_ptr stream; - std::string name; - - ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) : - device(device), dev_ptr(dev_ptr), stream(stream) { - check_allow_gpu_index(device); - name = (GGML_SYCL_NAME + std::to_string(device)); - } - - - ~ggml_backend_sycl_buffer_context() { - if (dev_ptr != nullptr) { - ggml_sycl_set_device(device); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream))); - } - } -}; - -static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - -static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name; -} - -static void -ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - ggml_sycl_set_device(ctx->device); - - delete ctx; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - return ctx->dev_ptr; -} - -static void -ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - - if (tensor->view_src != NULL && tensor->view_offs == 0) { - assert(tensor->view_src->buffer->buft == buffer->buft); - tensor->backend = tensor->view_src->backend; - tensor->extra = tensor->view_src->extra; - return; - } - - - if (ggml_is_quantized(tensor->type)) { - // initialize padding to 0 to avoid possible NaN values - size_t original_size = ggml_nbytes(tensor); - size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); - - if (padded_size > original_size && tensor->view_src == nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR(ctx->stream->memset( - (char *)tensor->data + original_size, 0, - padded_size - original_size).wait())); - } - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor, - const void *data, size_t offset, - size_t size) try { - - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); - char* host_buf = (char*)malloc(size); - memcpy(host_buf, data, size); - SYCL_CHECK( - CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) - .wait())); - free(host_buf); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *tensor, - void *data, size_t offset, - size_t size) try { - - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - auto stream = dpct::dev_mgr::instance().get_device(ctx->device).default_queue(); - - SYCL_CHECK(CHECK_TRY_ERROR( - stream.memcpy(data, (const char *)tensor->data + offset, size) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static bool -ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *src, - ggml_tensor *dst) try { - if (ggml_backend_buffer_is_sycl(src->buffer)) { - ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; - ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context; - - ggml_sycl_set_device(src_ctx->device); - /* - DPCT1009:198: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); - ggml_sycl_set_device(dst_ctx->device); - /* - DPCT1009:199: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); - /* - DPCT1009:200: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - - queue_ptr stream_dst = dst_ctx->stream; - queue_ptr stream_src = src_ctx->stream; - size_t size = ggml_nbytes(src); - - //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. - dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); - -//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove -#if 0 - SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( - (char *)dst->data, (const char *)src->data, size).wait())); - - /* - DPCT1009:201: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); -#endif - return true; - } - return false; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - - -static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, - uint8_t value) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - queue_ptr stream = ctx->stream; - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); - - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memset(ctx->dev_ptr, value, buffer->size) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { - /* .get_name = */ ggml_backend_sycl_buffer_get_name, - /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, - /* .get_base = */ ggml_backend_sycl_buffer_get_base, - /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, - /* .clear = */ ggml_backend_sycl_buffer_clear, - /* .reset = */ NULL, -}; - -// sycl buffer type -struct ggml_backend_sycl_buffer_type_context { - int device; - std::string name; - - // each buffer type has its own stream - queue_ptr stream = nullptr; -}; - -static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) { - ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - - return ctx->name.c_str(); -} -static ggml_backend_buffer_t -ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, - size_t size) try { - ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - ggml_sycl_set_device(buft_ctx->device); - const queue_ptr stream = buft_ctx->stream; - size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 - - void * dev_ptr; - SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( - size, *stream))); - if (!dev_ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, size); - return nullptr; - } - ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream); - return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - UNUSED(buft); -} - -static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - return dpct::get_current_device().get_max_mem_alloc_size(); - - UNUSED(buft); -} - -static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; - - if (ggml_is_quantized(tensor->type)) { - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return size; - - UNUSED(buft); -} - -static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { - /* .get_name = */ ggml_backend_sycl_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, - /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, - /* .is_host = */ nullptr, -}; - -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n"); - - if (device>=ggml_sycl_info().device_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device, ggml_sycl_info().device_count-1); - GGML_ASSERT(devicedevice; - if (device>=ggml_sycl_info().device_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device, ggml_sycl_info().device_count-1); - GGML_ASSERT(devicestream(i, 0)}, - }; - } - ggml_backend_sycl_buffer_type_initialized = true; - } - return &ggml_backend_sycl_buffer_types[device]; -} - -// sycl split buffer type -static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { - const int64_t nrows = ggml_nrows(tensor); - const int64_t rounding = get_row_rounding(tensor->type, tensor_split); - - *row_low = id == 0 ? 0 : nrows*tensor_split[id]; - *row_low -= *row_low % rounding; - if (id == ggml_sycl_info().device_count - 1) { - *row_high = nrows; - } else { - *row_high = nrows*tensor_split[id + 1]; - *row_high -= *row_high % rounding; - } -} - -struct ggml_backend_sycl_split_buffer_context { - ~ggml_backend_sycl_split_buffer_context() try { - for (ggml_tensor_extra_gpu * extra : tensor_extras) { - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { - if (extra->events[i][is] != nullptr) { - /* - DPCT1009:206: SYCL uses exceptions to report errors and - does not use the error codes. The original code was - commented out and a warning string was inserted. You - need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::destroy_event(extra->events[i][is]))); - } - } - if (extra->data_device[i] != nullptr) { - /* - DPCT1009:207: SYCL uses exceptions to report errors and does - not use the error codes. The original code was commented out - and a warning string was inserted. You need to rewrite this - code. - */ - ggml_sycl_set_device(i); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( - extra->data_device[i], *(streams[i])))); - } - } - delete extra; - } - } - catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); - } - - std::vector tensor_extras; - std::vector streams; -}; - -static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { - return GGML_SYCL_NAME "_Split"; - - UNUSED(buffer); -} - -static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; -} - -static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - delete ctx; -} - -static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { - // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced - return (void *)0x1000; - - UNUSED(buffer); -} - -static void -ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - - ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; - - ctx->tensor_extras.push_back(extra); - ctx->streams.push_back(&(dpct::get_current_device().default_queue())); - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - // FIXME: do not crash if cudaMalloc fails - // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - char * buf; - /* - DPCT1009:208: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( - size, *stream))); - if (!buf) { - char err_buf[1024]; - snprintf(err_buf, 1023, "%s: can't malloc %lu Bytes memory on device", __func__, size); - throw std::runtime_error(err_buf); - } - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - /* - DPCT1009:209: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memset(buf + original_size, 0, size - original_size) - .wait())); - } - - extra->data_device[i] = buf; - - for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { - /* - DPCT1009:210: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ - SYCL_CHECK( - CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); - } - } - tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void -ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor, const void *data, - size_t offset, size_t size) try { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - const char * buf_host = (const char *)data + offset_split; - /* - DPCT1009:211: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memcpy(extra->data_device[i], buf_host, original_size) - .wait())); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void -ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *tensor, void *data, - size_t offset, size_t size) try { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf_host = (char *)data + offset_split; - /* - DPCT1009:212: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memcpy(buf_host, extra->data_device[i], original_size) - .wait())); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - UNUSED(buffer); - UNUSED(value); -} - -static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { - /* .get_name = */ ggml_backend_sycl_split_buffer_get_name, - /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, - /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, - /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_sycl_split_buffer_clear, - /* .reset = */ NULL, -}; - -static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_SYCL_NAME "_Split"; - - UNUSED(buft); -} - -static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point - // instead, we allocate them for each tensor separately in init_tensor - // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, - // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. - ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); - - return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); -} - -static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - UNUSED(buft); -} - -static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; - - size_t total_size = 0; - - const int64_t ne0 = tensor->ne[0]; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - total_size += ggml_nbytes_split(tensor, nrows_split); - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return total_size; -} - -static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - UNUSED(buft); -} - -static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { - /* .get_name = */ ggml_backend_sycl_split_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, -}; - -ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n"); - ggml_check_sycl(); - // FIXME: this is not thread safe - static std::map, struct ggml_backend_buffer_type> buft_map; - - std::array tensor_split_arr = {}; - - bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); - if (all_zero) { - tensor_split_arr = ggml_sycl_info().default_tensor_split; - } else { - float split_sum = 0.0f; - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - tensor_split_arr[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - tensor_split_arr[i] /= split_sum; - } - } - - auto it = buft_map.find(tensor_split_arr); - if (it != buft_map.end()) { - return &it->second; - } - - struct ggml_backend_buffer_type buft { - /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, - /* .device = */ nullptr, - /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, - }; - - auto result = buft_map.emplace(tensor_split_arr, buft); - return &result.first->second; -} - -// host buffer type - -static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_SYCL_NAME "_Host"; - - UNUSED(buft); -} - -static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) { - return GGML_SYCL_NAME "_Host"; - - UNUSED(buffer); -} - -static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_sycl_host_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_sycl_host_malloc(size); - - if (ptr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - // FIXME: this is a hack to avoid having to implement a new buffer type - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.get_name = ggml_backend_sycl_host_buffer_name; - buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n"); - static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_sycl_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_sycl_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ nullptr, - /* .context = */ nullptr, - }; - - return &ggml_backend_sycl_buffer_type_host; -} - // backend -static const char * ggml_backend_sycl_name(ggml_backend_t backend) { +static const char * ggml_backend_sycl_get_name(ggml_backend_t backend) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; @@ -4931,8 +4904,8 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); - SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy( - (char *)tensor->data + offset, data, size).wait())); + SYCL_CHECK(CHECK_TRY_ERROR( + (stream)->memcpy((char *)tensor->data + offset, data, size))); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -4987,7 +4960,7 @@ static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); SYCL_CHECK(CHECK_TRY_ERROR((stream)->wait())); - UNUSED(backend); + GGML_UNUSED(backend); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -5023,7 +4996,151 @@ static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_ return GGML_STATUS_SUCCESS; } -static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { +static void ggml_backend_sycl_event_record(ggml_backend_t backend, ggml_backend_event_t event) +try +{ + ggml_backend_sycl_context *sycl_ctx = + (ggml_backend_sycl_context *)backend->context; + sycl::event *sycl_event = static_cast(event->context); + + const queue_ptr &stream = sycl_ctx->stream(sycl_ctx->device, 0); + // Record the current state of the queue + SYCL_CHECK(CHECK_TRY_ERROR(*sycl_event = stream->ext_oneapi_submit_barrier())); +} +catch (sycl::exception const &exc) +{ + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_event_t event) try { + ggml_backend_sycl_context* sycl_ctx = static_cast(backend->context); + sycl::event* sycl_event = static_cast(event->context); + + if (ggml_backend_is_sycl(backend)) { + SYCL_CHECK(CHECK_TRY_ERROR(sycl_event->wait())); + } else + GGML_ABORT("fatal error"); +} catch (sycl::exception const& exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static ggml_backend_i ggml_backend_sycl_interface = { + /* .get_name = */ ggml_backend_sycl_get_name, + /* .free = */ ggml_backend_sycl_free, + /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, + /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, + /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, + /* .cpy_tensor_async = */ NULL, // ggml_backend_sycl_cpy_tensor_async, + // // TODO: update for the new + // interface + /* .synchronize = */ ggml_backend_sycl_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_sycl_graph_compute, + /* .supports_op = */ NULL, // moved to device + /* .supports_buft = */ NULL, // moved to device + /* .offload_op = */ NULL, // moved to device + /* .event_record = */ ggml_backend_sycl_event_record, + /* .event_wait = */ ggml_backend_sycl_event_wait, +}; + +static ggml_guid_t ggml_backend_sycl_guid() { + static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 }; + return &guid; +} + +bool ggml_backend_is_sycl(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); +} + +int ggml_backend_sycl_get_device_count() { + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n"); + return ggml_sycl_info().device_count; +} + + +// backend device + +struct ggml_backend_sycl_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_sycl_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_sycl_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_sycl_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + ggml_sycl_set_device(ctx->device); + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(ctx->device).get_memory_info(*free, *total))); +} + +static enum ggml_backend_dev_type ggml_backend_sycl_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; +} + +static void ggml_backend_sycl_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { + props->name = ggml_backend_sycl_device_get_name(dev); + props->description = ggml_backend_sycl_device_get_description(dev); + props->type = ggml_backend_sycl_device_get_type(dev); + ggml_backend_sycl_device_get_memory(dev, &props->memory_free, &props->memory_total); + + bool host_buffer = getenv("GGML_SYCL_NO_PINNED") == nullptr; +#ifdef GGML_SYCL_NO_PEER_COPY + bool events = false; +#else + bool events = true; +#endif + + props->caps = { + /* .async = */ true, + /* .host_buffer = */ host_buffer, + /* .buffer_from_host_ptr = */ false, + /* .events = */ events, + }; +} + +static ggml_backend_t ggml_backend_sycl_device_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ggml_backend_sycl_init(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_sycl_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + return ggml_backend_sycl_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_sycl_device_get_host_buffer_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return ggml_backend_sycl_host_buffer_type(); +} + +static ggml_backend_buffer_t ggml_backend_sycl_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { + GGML_UNUSED(dev); + GGML_UNUSED(ptr); + GGML_UNUSED(size); + GGML_UNUSED(max_tensor_size); + return nullptr; +} + +static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { switch (op->op) { case GGML_OP_CONV_TRANSPOSE_1D: { @@ -5167,47 +5284,173 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten return false; } - UNUSED(backend); + GGML_UNUSED(dev); } -static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) { - const int min_batch_size = 32; - return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID; - GGML_UNUSED(backend); -} - -static bool ggml_backend_sycl_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - if (buft->iface.get_name != ggml_backend_sycl_buffer_type_name) { +static bool ggml_backend_sycl_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_sycl_buffer_type_get_name) { return false; } ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + ggml_backend_sycl_device_context * sycl_ctx = (ggml_backend_sycl_device_context *)dev->context; return buft_ctx->device == sycl_ctx->device; } -static ggml_backend_i ggml_backend_sycl_interface = { - /* .get_name = */ ggml_backend_sycl_name, - /* .free = */ ggml_backend_sycl_free, - /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, - /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, - /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, - /* .cpy_tensor_async = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface - /* .synchronize = */ ggml_backend_sycl_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_sycl_graph_compute, - /* .supports_op = */ ggml_backend_sycl_supports_op, - /* .supports_buft = */ ggml_backend_sycl_supports_buft, - /* .offload_op = */ ggml_backend_sycl_offload_op, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, +static bool ggml_backend_sycl_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + const int min_batch_size = 32; + return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID; + GGML_UNUSED(dev); +} + +static ggml_backend_event_t +ggml_backend_sycl_device_event_new(ggml_backend_dev_t dev) { + +#ifdef GGML_SYCL_NO_PEER_COPY + return nullptr; +#else + sycl::event *event_ptr = new sycl::event(); + + return new ggml_backend_event{ + /* .device = */ dev, + /* .context = */ event_ptr, + }; +#endif +} + +static void ggml_backend_sycl_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) try { + GGML_UNUSED(dev); + if (event == nullptr) { + return; + } + + if (event->context != nullptr) { + sycl::event *sycl_event = static_cast(event->context); + delete sycl_event; + event->context = nullptr; + } + + delete event; +} catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + +static void ggml_backend_sycl_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) try { + GGML_UNUSED(dev); + + sycl::event *sycl_event = static_cast(event->context); + SYCL_CHECK(CHECK_TRY_ERROR(sycl_event->wait())); +} catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static const ggml_backend_device_i ggml_backend_sycl_device_interface = { + /* .get_name = */ ggml_backend_sycl_device_get_name, + /* .get_description = */ ggml_backend_sycl_device_get_description, + /* .get_memory = */ ggml_backend_sycl_device_get_memory, + /* .get_type = */ ggml_backend_sycl_device_get_type, + /* .get_props = */ ggml_backend_sycl_device_get_props, + /* .init_backend = */ ggml_backend_sycl_device_init, + /* .get_buffer_type = */ ggml_backend_sycl_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_sycl_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ ggml_backend_sycl_device_buffer_from_host_ptr, + /* .supports_op = */ ggml_backend_sycl_device_supports_op, + /* .supports_buft = */ ggml_backend_sycl_device_supports_buft, + /* .offload_op = */ ggml_backend_sycl_device_offload_op, + /* .event_new = */ ggml_backend_sycl_device_event_new, + /* .event_free = */ ggml_backend_sycl_device_event_free, + /* .event_synchronize = */ ggml_backend_sycl_device_event_synchronize, }; -static ggml_guid_t ggml_backend_sycl_guid() { - static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 }; - return &guid; +// backend reg + +struct ggml_backend_sycl_reg_context { + std::vector devices; +}; + +static const char * ggml_backend_sycl_reg_get_name(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return GGML_SYCL_NAME; +} + +static size_t ggml_backend_sycl_reg_get_device_count(ggml_backend_reg_t reg) { + ggml_backend_sycl_reg_context * ctx = (ggml_backend_sycl_reg_context *)reg->context; + return ctx->devices.size(); +} + +static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t reg, size_t index) { + ggml_backend_sycl_reg_context * ctx = (ggml_backend_sycl_reg_context *)reg->context; + GGML_ASSERT(index < ctx->devices.size()); + return ctx->devices[index]; +} + +static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) +{ + GGML_UNUSED(reg); + if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { + return (void *)ggml_backend_sycl_split_buffer_type; + } + // SYCL doesn't support registering host memory, left here for reference + // "ggml_backend_register_host_buffer" + // "ggml_backend_unregister_host_buffer" + return nullptr; +} + +static const ggml_backend_reg_i ggml_backend_sycl_reg_interface = { + /* .get_name = */ ggml_backend_sycl_reg_get_name, + /* .get_device_count = */ ggml_backend_sycl_reg_get_device_count, + /* .get_device_get = */ ggml_backend_sycl_reg_get_device, + /* .get_proc_address = */ ggml_backend_sycl_reg_get_proc_address, +}; + + +// backend registry + +ggml_backend_reg_t ggml_backend_sycl_reg() { + static ggml_backend_reg reg; + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + ggml_backend_sycl_reg_context * ctx = new ggml_backend_sycl_reg_context; + + for (int i = 0; i < ggml_sycl_info().device_count; i++) { + ggml_backend_sycl_device_context * dev_ctx = new ggml_backend_sycl_device_context; + dev_ctx->device = i; + dev_ctx->name = GGML_SYCL_NAME + std::to_string(i); + + ggml_sycl_set_device(i); + + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(i)))); + + dev_ctx->description = prop.get_name(); + + ggml_backend_dev_t dev = new ggml_backend_device { + /* .interface = */ ggml_backend_sycl_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx + }; + ctx->devices.push_back(dev); + } + + reg = ggml_backend_reg { + /* .interface = */ ggml_backend_sycl_reg_interface, + /* .context = */ ctx + }; + } + + initialized = true; + } + + return ® } ggml_backend_t ggml_backend_sycl_init(int device) { @@ -5225,18 +5468,10 @@ ggml_backend_t ggml_backend_sycl_init(int device) { ggml_backend_t sycl_backend = new ggml_backend { /* .guid = */ ggml_backend_sycl_guid(), /* .interface = */ ggml_backend_sycl_interface, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), device), /* .context = */ ctx }; return sycl_backend; } -bool ggml_backend_is_sycl(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); -} - -int ggml_backend_sycl_get_device_count() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n"); - return ggml_sycl_info().device_count; -} diff --git a/src/llama.cpp b/src/llama.cpp index 0025e94b8..10c975bf4 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -8,9 +8,7 @@ #include "ggml-alloc.h" #include "ggml-backend.h" -#if defined(GGML_USE_SYCL) -# include "ggml-sycl.h" -#elif defined(GGML_USE_KOMPUTE) +#if defined(GGML_USE_KOMPUTE) # include "ggml-kompute.h" #elif defined(GGML_USE_CANN) # include "ggml-cann.h" @@ -3422,9 +3420,11 @@ struct llama_lora_adapter { static int llama_get_device_count(const llama_model & model) { int count = (int) model.devices.size(); -#if defined(GGML_USE_SYCL) - count += ggml_backend_sycl_get_device_count(); -#elif defined(GGML_USE_CANN) +#if defined(GGML_USE_RPC) + count += (int) model.rpc_servers.size(); +#endif + +#if defined(GGML_USE_CANN) count += ggml_backend_cann_get_device_count(); #endif @@ -3445,11 +3445,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_mode } } -#if defined(GGML_USE_SYCL) - if (host_buffer) { - buft = ggml_backend_sycl_host_buffer_type(); - } -#elif defined(GGML_USE_CANN) +#if defined(GGML_USE_CANN) if (host_buffer) { buft = ggml_backend_cann_host_buffer_type(); } @@ -3473,9 +3469,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_ } device -= (int)model.devices.size(); -#if defined(GGML_USE_SYCL) - buft = ggml_backend_sycl_buffer_type(device); -#elif defined(GGML_USE_KOMPUTE) +#if defined(GGML_USE_KOMPUTE) buft = ggml_backend_kompute_buffer_type(device); #elif defined(GGML_USE_CANN) buft = ggml_backend_cann_buffer_type(device); @@ -3505,12 +3499,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_mo } } -#ifdef GGML_USE_SYCL - if (ggml_backend_sycl_get_device_count() > 1) { - buft = ggml_backend_sycl_split_buffer_type(tensor_split); - } -#endif - if (buft == nullptr) { buft = llama_default_buffer_type_offload(model, fallback_gpu); } @@ -3528,12 +3516,7 @@ static size_t llama_get_device_memory(const llama_model & model, int device) { return free; } -#if defined(GGML_USE_SYCL) - size_t total; - size_t free; - ggml_backend_sycl_get_device_memory(device, &free, &total); - return free; -#elif defined(GGML_USE_CANN) +#if defined(GGML_USE_CANN) size_t total; size_t free; ggml_backend_cann_get_device_memory(device, &free, &total); @@ -19096,7 +19079,7 @@ bool llama_supports_mlock(void) { } bool llama_supports_gpu_offload(void) { -#if defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) +#if defined(GGML_USE_KOMPUTE) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. return true; #else @@ -19428,29 +19411,7 @@ struct llama_context * llama_new_context_with_model( main_gpu -= (int)model->devices.size(); } -#if defined(GGML_USE_SYCL) - // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - ggml_backend_t backend = ggml_backend_sycl_init(main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, main_gpu); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } else { - // LLAMA_SPLIT_LAYER requires a backend for each GPU - for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) { - ggml_backend_t backend = ggml_backend_sycl_init(i); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d for No.%d backend\n", __func__, i, i); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } -#elif defined(GGML_USE_KOMPUTE) +#if defined(GGML_USE_KOMPUTE) if (model->n_gpu_layers > 0) { auto * backend = ggml_backend_kompute_init(main_gpu); if (backend == nullptr) { From afd9909a6481402844aecefa8a8908afdd7f52f1 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Fri, 18 Oct 2024 14:33:58 +0300 Subject: [PATCH 30/95] rpc : backend refactoring (#9912) * rpc : refactor backend Use structs for RPC request/response messages * rpc : refactor server --- ggml/src/ggml-rpc.cpp | 571 +++++++++++++++++++++++------------------- 1 file changed, 310 insertions(+), 261 deletions(-) diff --git a/ggml/src/ggml-rpc.cpp b/ggml/src/ggml-rpc.cpp index 13c7dd436..f95233284 100644 --- a/ggml/src/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc.cpp @@ -58,7 +58,7 @@ struct socket_t { }; // ggml_tensor is serialized into rpc_tensor -#pragma pack(push, 1) +#pragma pack(1) struct rpc_tensor { uint64_t id; uint32_t type; @@ -76,7 +76,6 @@ struct rpc_tensor { char padding[4]; }; -#pragma pack(pop) static_assert(sizeof(rpc_tensor) % 8 == 0, "rpc_tensor size must be multiple of 8"); @@ -96,6 +95,77 @@ enum rpc_cmd { RPC_CMD_COUNT, }; +#pragma pack(1) +struct rpc_msg_alloc_buffer_req { + uint64_t size; +}; + +#pragma pack(1) +struct rpc_msg_alloc_buffer_rsp { + uint64_t remote_ptr; + uint64_t remote_size; +}; + +#pragma pack(1) +struct rpc_msg_get_alignment_rsp { + uint64_t alignment; +}; + +#pragma pack(1) +struct rpc_msg_get_max_size_rsp { + uint64_t max_size; +}; + +#pragma pack(1) +struct rpc_msg_buffer_get_base_req { + uint64_t remote_ptr; +}; + +#pragma pack(1) +struct rpc_msg_buffer_get_base_rsp { + uint64_t base_ptr; +}; + +#pragma pack(1) +struct rpc_msg_free_buffer_req { + uint64_t remote_ptr; +}; + +#pragma pack(1) +struct rpc_msg_buffer_clear_req { + uint64_t remote_ptr; + uint8_t value; +}; + +#pragma pack(1) +struct rpc_msg_get_tensor_req { + rpc_tensor tensor; + uint64_t offset; + uint64_t size; +}; + +#pragma pack(1) +struct rpc_msg_copy_tensor_req { + rpc_tensor src; + rpc_tensor dst; +}; + +#pragma pack(1) +struct rpc_msg_copy_tensor_rsp { + uint8_t result; +}; + +#pragma pack(1) +struct rpc_msg_graph_compute_rsp { + uint8_t result; +}; + +#pragma pack(1) +struct rpc_msg_get_device_memory_rsp { + uint64_t free_mem; + uint64_t total_mem; +}; + // RPC data structures static ggml_guid_t ggml_backend_rpc_guid() { @@ -240,6 +310,38 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) { return true; } +static bool send_msg(sockfd_t sockfd, const void * msg, size_t msg_size) { + if (!send_data(sockfd, &msg_size, sizeof(msg_size))) { + return false; + } + return send_data(sockfd, msg, msg_size); +} + +static bool recv_msg(sockfd_t sockfd, void * msg, size_t msg_size) { + uint64_t size; + if (!recv_data(sockfd, &size, sizeof(size))) { + return false; + } + if (size != msg_size) { + return false; + } + return recv_data(sockfd, msg, msg_size); +} + +static bool recv_msg(sockfd_t sockfd, std::vector & input) { + uint64_t size; + if (!recv_data(sockfd, &size, sizeof(size))) { + return false; + } + try { + input.resize(size); + } catch (const std::bad_alloc & e) { + fprintf(stderr, "Failed to allocate input buffer of size %" PRIu64 "\n", size); + return false; + } + return recv_data(sockfd, input.data(), size); +} + static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) { size_t pos = endpoint.find(':'); if (pos == std::string::npos) { @@ -252,28 +354,27 @@ static bool parse_endpoint(const std::string & endpoint, std::string & host, int // RPC request : | rpc_cmd (1 byte) | request_size (8 bytes) | request_data (request_size bytes) | // RPC response: | response_size (8 bytes) | response_data (response_size bytes) | -static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const std::vector & input, std::vector & output) { +static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const void * input, size_t input_size, void * output, size_t output_size) { uint8_t cmd_byte = cmd; if (!send_data(sock->fd, &cmd_byte, sizeof(cmd_byte))) { return false; } - uint64_t input_size = input.size(); if (!send_data(sock->fd, &input_size, sizeof(input_size))) { return false; } - if (!send_data(sock->fd, input.data(), input.size())) { + if (!send_data(sock->fd, input, input_size)) { return false; } - uint64_t output_size; - if (!recv_data(sock->fd, &output_size, sizeof(output_size))) { + // TODO: currently the output_size is always known, do we need support for commands with variable output size? + // even if we do, we can skip sending output_size from the server for commands with known output size + uint64_t out_size; + if (!recv_data(sock->fd, &out_size, sizeof(out_size))) { return false; } - if (output_size == 0) { - output.clear(); - return true; + if (out_size != output_size) { + return false; } - output.resize(output_size); - if (!recv_data(sock->fd, output.data(), output_size)) { + if (!recv_data(sock->fd, output, output_size)) { return false; } return true; @@ -326,14 +427,9 @@ static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffe static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // input serialization format: | remote_ptr (8 bytes) | - std::vector input(sizeof(uint64_t), 0); - uint64_t remote_ptr = ctx->remote_ptr; - memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_FREE_BUFFER, input, output); + rpc_msg_free_buffer_req request = {ctx->remote_ptr}; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_FREE_BUFFER, &request, sizeof(request), nullptr, 0); GGML_ASSERT(status); - GGML_ASSERT(output.empty()); delete ctx; } @@ -342,20 +438,13 @@ static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { if (ctx->base_cache.find(buffer) != ctx->base_cache.end()) { return ctx->base_cache[buffer]; } - // input serialization format: | remote_ptr (8 bytes) | - std::vector input(sizeof(uint64_t), 0); - uint64_t remote_ptr = ctx->remote_ptr; - memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_GET_BASE, input, output); + rpc_msg_buffer_get_base_req request = {ctx->remote_ptr}; + rpc_msg_buffer_get_base_rsp response; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_GET_BASE, &request, sizeof(request), &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == sizeof(uint64_t)); - // output serialization format: | base_ptr (8 bytes) | - uint64_t base_ptr; - memcpy(&base_ptr, output.data(), sizeof(base_ptr)); - void * base = reinterpret_cast(base_ptr); - ctx->base_cache[buffer] = base; - return base; + void * base_ptr = reinterpret_cast(response.base_ptr); + ctx->base_cache[buffer] = base_ptr; + return base_ptr; } static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { @@ -405,26 +494,18 @@ static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggm memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), data, size); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR, input, output); + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR, input.data(), input.size(), nullptr, 0); GGML_ASSERT(status); } static void ggml_backend_rpc_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // input serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | - int input_size = sizeof(rpc_tensor) + 2*sizeof(uint64_t); - std::vector input(input_size, 0); - rpc_tensor rpc_tensor = serialize_tensor(tensor); - memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); - memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); - memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), &size, sizeof(size)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_GET_TENSOR, input, output); + rpc_msg_get_tensor_req request; + request.tensor = serialize_tensor(tensor); + request.offset = offset; + request.size = size; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_GET_TENSOR, &request, sizeof(request), data, size); GGML_ASSERT(status); - GGML_ASSERT(output.size() == size); - // output serialization format: | data (size bytes) | - memcpy(data, output.data(), size); } static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { @@ -437,30 +518,19 @@ static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con return false; } ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // input serialization format: | rpc_tensor src | rpc_tensor dst | - int input_size = 2*sizeof(rpc_tensor); - std::vector input(input_size, 0); - rpc_tensor rpc_src = serialize_tensor(src); - rpc_tensor rpc_dst = serialize_tensor(dst); - memcpy(input.data(), &rpc_src, sizeof(rpc_src)); - memcpy(input.data() + sizeof(rpc_src), &rpc_dst, sizeof(rpc_dst)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_COPY_TENSOR, input, output); + rpc_msg_copy_tensor_req request; + request.src = serialize_tensor(src); + request.dst = serialize_tensor(dst); + rpc_msg_copy_tensor_rsp response; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_COPY_TENSOR, &request, sizeof(request), &response, sizeof(response)); GGML_ASSERT(status); - // output serialization format: | result (1 byte) | - GGML_ASSERT(output.size() == 1); - return output[0]; + return response.result; } static void ggml_backend_rpc_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // serialization format: | bufptr (8 bytes) | value (1 byte) | - int input_size = sizeof(uint64_t) + sizeof(uint8_t); - std::vector input(input_size, 0); - memcpy(input.data(), &ctx->remote_ptr, sizeof(ctx->remote_ptr)); - memcpy(input.data() + sizeof(ctx->remote_ptr), &value, sizeof(value)); - std::vector output; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_CLEAR, input, output); + rpc_msg_buffer_clear_req request = {ctx->remote_ptr, value}; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_CLEAR, &request, sizeof(request), nullptr, 0); GGML_ASSERT(status); } @@ -484,25 +554,16 @@ static const char * ggml_backend_rpc_buffer_type_name(ggml_backend_buffer_type_t static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - // input serialization format: | size (8 bytes) | - int input_size = sizeof(uint64_t); - std::vector input(input_size, 0); - memcpy(input.data(), &size, sizeof(size)); - std::vector output; + rpc_msg_alloc_buffer_req request = {size}; + rpc_msg_alloc_buffer_rsp response; auto sock = get_socket(buft_ctx->endpoint); - bool status = send_rpc_cmd(sock, RPC_CMD_ALLOC_BUFFER, input, output); + bool status = send_rpc_cmd(sock, RPC_CMD_ALLOC_BUFFER, &request, sizeof(request), &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); - // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | - uint64_t remote_ptr; - memcpy(&remote_ptr, output.data(), sizeof(remote_ptr)); - size_t remote_size; - memcpy(&remote_size, output.data() + sizeof(uint64_t), sizeof(remote_size)); - if (remote_ptr != 0) { + if (response.remote_ptr != 0) { ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{sock, {}, remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"}, - remote_size); + new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"}, + response.remote_size); return buffer; } else { return nullptr; @@ -510,16 +571,10 @@ static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_back } static size_t get_alignment(const std::shared_ptr & sock) { - // input serialization format: | 0 bytes | - std::vector input; - std::vector output; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALIGNMENT, input, output); + rpc_msg_get_alignment_rsp response; + bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALIGNMENT, nullptr, 0, &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == sizeof(uint64_t)); - // output serialization format: | alignment (8 bytes) | - uint64_t alignment; - memcpy(&alignment, output.data(), sizeof(alignment)); - return alignment; + return response.alignment; } static size_t ggml_backend_rpc_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { @@ -528,16 +583,10 @@ static size_t ggml_backend_rpc_buffer_type_get_alignment(ggml_backend_buffer_typ } static size_t get_max_size(const std::shared_ptr & sock) { - // input serialization format: | 0 bytes | - std::vector input; - std::vector output; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_MAX_SIZE, input, output); + rpc_msg_get_max_size_rsp response; + bool status = send_rpc_cmd(sock, RPC_CMD_GET_MAX_SIZE, nullptr, 0, &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == sizeof(uint64_t)); - // output serialization format: | max_size (8 bytes) | - uint64_t max_size; - memcpy(&max_size, output.data(), sizeof(max_size)); - return max_size; + return response.max_size; } static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) { @@ -622,12 +671,11 @@ static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, g ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; std::vector input; serialize_graph(cgraph, input); - std::vector output; + rpc_msg_graph_compute_rsp response; auto sock = get_socket(rpc_ctx->endpoint); - bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_COMPUTE, input, output); + bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_COMPUTE, input.data(), input.size(), &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == 1); - return (enum ggml_status)output[0]; + return (enum ggml_status)response.result; } static ggml_backend_i ggml_backend_rpc_interface = { @@ -702,19 +750,11 @@ GGML_API bool ggml_backend_is_rpc(ggml_backend_t backend) { } static void get_device_memory(const std::shared_ptr & sock, size_t * free, size_t * total) { - // input serialization format: | 0 bytes | - std::vector input; - std::vector output; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_DEVICE_MEMORY, input, output); + rpc_msg_get_device_memory_rsp response; + bool status = send_rpc_cmd(sock, RPC_CMD_GET_DEVICE_MEMORY, nullptr, 0, &response, sizeof(response)); GGML_ASSERT(status); - GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); - // output serialization format: | free (8 bytes) | total (8 bytes) | - uint64_t free_mem; - memcpy(&free_mem, output.data(), sizeof(free_mem)); - uint64_t total_mem; - memcpy(&total_mem, output.data() + sizeof(uint64_t), sizeof(total_mem)); - *free = free_mem; - *total = total_mem; + *free = response.free_mem; + *total = response.total_mem; } GGML_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { @@ -734,16 +774,16 @@ public: rpc_server(ggml_backend_t backend) : backend(backend) {} ~rpc_server(); - bool alloc_buffer(const std::vector & input, std::vector & output); - void get_alignment(std::vector & output); - void get_max_size(std::vector & output); - bool buffer_get_base(const std::vector & input, std::vector & output); - bool free_buffer(const std::vector & input); - bool buffer_clear(const std::vector & input); + void alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response); + void get_alignment(rpc_msg_get_alignment_rsp & response); + void get_max_size(rpc_msg_get_max_size_rsp & response); + bool buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response); + bool free_buffer(const rpc_msg_free_buffer_req & request); + bool buffer_clear(const rpc_msg_buffer_clear_req & request); bool set_tensor(const std::vector & input); - bool get_tensor(const std::vector & input, std::vector & output); - bool copy_tensor(const std::vector & input, std::vector & output); - bool graph_compute(const std::vector & input, std::vector & output); + bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector & response); + bool copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response); + bool graph_compute(const std::vector & input, rpc_msg_graph_compute_rsp & response); private: ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor); @@ -757,80 +797,50 @@ private: std::unordered_set buffers; }; -bool rpc_server::alloc_buffer(const std::vector & input, std::vector & output) { - // input serialization format: | size (8 bytes) | - if (input.size() != sizeof(uint64_t)) { - return false; - } - uint64_t size; - memcpy(&size, input.data(), sizeof(size)); +void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); - ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); - uint64_t remote_ptr = 0; - uint64_t remote_size = 0; + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, request.size); + response.remote_ptr = 0; + response.remote_size = 0; if (buffer != nullptr) { - remote_ptr = reinterpret_cast(buffer); - remote_size = buffer->size; - GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size); + response.remote_ptr = reinterpret_cast(buffer); + response.remote_size = buffer->size; + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, request.size, response.remote_ptr, response.remote_size); buffers.insert(buffer); } else { - GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, size); + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size); } - // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | - output.resize(2*sizeof(uint64_t), 0); - memcpy(output.data(), &remote_ptr, sizeof(remote_ptr)); - memcpy(output.data() + sizeof(uint64_t), &remote_size, sizeof(remote_size)); - return true; } -void rpc_server::get_alignment(std::vector & output) { +void rpc_server::get_alignment(rpc_msg_get_alignment_rsp & response) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); size_t alignment = ggml_backend_buft_get_alignment(buft); GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment); - // output serialization format: | alignment (8 bytes) | - output.resize(sizeof(uint64_t), 0); - memcpy(output.data(), &alignment, sizeof(alignment)); + response.alignment = alignment; } -void rpc_server::get_max_size(std::vector & output) { +void rpc_server::get_max_size(rpc_msg_get_max_size_rsp & response) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); size_t max_size = ggml_backend_buft_get_max_size(buft); GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size); - // output serialization format: | max_size (8 bytes) | - output.resize(sizeof(uint64_t), 0); - memcpy(output.data(), &max_size, sizeof(max_size)); + response.max_size = max_size; } -bool rpc_server::buffer_get_base(const std::vector & input, std::vector & output) { - // input serialization format: | remote_ptr (8 bytes) | - if (input.size() != sizeof(uint64_t)) { - return false; - } - uint64_t remote_ptr; - memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); - ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); +bool rpc_server::buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response) { + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); if (buffers.find(buffer) == buffers.end()) { GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); return false; } void * base = ggml_backend_buffer_get_base(buffer); - // output serialization format: | base_ptr (8 bytes) | - uint64_t base_ptr = reinterpret_cast(base); - output.resize(sizeof(uint64_t), 0); - memcpy(output.data(), &base_ptr, sizeof(base_ptr)); + response.base_ptr = reinterpret_cast(base); return true; } -bool rpc_server::free_buffer(const std::vector & input) { - // input serialization format: | remote_ptr (8 bytes) | - if (input.size() != sizeof(uint64_t)) { - return false; - } - uint64_t remote_ptr; - memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); - ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); +bool rpc_server::free_buffer(const rpc_msg_free_buffer_req & request) { + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); if (buffers.find(buffer) == buffers.end()) { GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); return false; @@ -840,22 +850,14 @@ bool rpc_server::free_buffer(const std::vector & input) { return true; } -bool rpc_server::buffer_clear(const std::vector & input) { - // input serialization format: | remote_ptr (8 bytes) | value (1 byte) | - if (input.size() != sizeof(uint64_t) + sizeof(uint8_t)) { - return false; - } - uint64_t remote_ptr; - memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); - uint8_t value; - memcpy(&value, input.data() + sizeof(uint64_t), sizeof(value)); - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, remote_ptr, value); - ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); +bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) { + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, request.remote_ptr, request.value); + ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); if (buffers.find(buffer) == buffers.end()) { GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); return false; } - ggml_backend_buffer_clear(buffer, value); + ggml_backend_buffer_clear(buffer, request.value); return true; } @@ -930,74 +932,55 @@ bool rpc_server::set_tensor(const std::vector & input) { return true; } -bool rpc_server::get_tensor(const std::vector & input, std::vector & output) { - // serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | - if (input.size() != sizeof(rpc_tensor) + 2*sizeof(uint64_t)) { - return false; - } - const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); - uint64_t offset; - memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); - uint64_t size; - memcpy(&size, input.data() + sizeof(rpc_tensor) + sizeof(offset), sizeof(size)); - +bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector & response) { struct ggml_init_params params { /*.mem_size =*/ ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; struct ggml_context * ctx = ggml_init(params); - ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); + ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); if (tensor == nullptr) { GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__); ggml_free(ctx); return false; } - GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); + GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, request.offset, request.size); // sanitize tensor->data { const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer); const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer); - if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) { - GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); + if (request.tensor.data + request.offset < p0 || + request.tensor.data + request.offset >= p1 || + request.size > (p1 - request.tensor.data - request.offset)) { + GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); } } - // output serialization format: | data (size bytes) | - output.resize(size, 0); - ggml_backend_tensor_get(tensor, output.data(), offset, size); + response.resize(request.size, 0); + ggml_backend_tensor_get(tensor, response.data(), request.offset, request.size); ggml_free(ctx); return true; } -bool rpc_server::copy_tensor(const std::vector & input, std::vector & output) { - // serialization format: | rpc_tensor src | rpc_tensor dst | - if (input.size() != 2*sizeof(rpc_tensor)) { - return false; - } - const rpc_tensor * rpc_src = (const rpc_tensor *)input.data(); - const rpc_tensor * rpc_dst = (const rpc_tensor *)(input.data() + sizeof(rpc_src)); - +bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response) { struct ggml_init_params params { /*.mem_size =*/ 2*ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; struct ggml_context * ctx = ggml_init(params); - ggml_tensor * src = deserialize_tensor(ctx, rpc_src); - ggml_tensor * dst = deserialize_tensor(ctx, rpc_dst); + ggml_tensor * src = deserialize_tensor(ctx, &request.src); + ggml_tensor * dst = deserialize_tensor(ctx, &request.dst); if (src == nullptr || dst == nullptr) { GGML_PRINT_DEBUG("[%s] error deserializing tensors\n", __func__); ggml_free(ctx); return false; } GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer); - bool result = ggml_backend_buffer_copy_tensor(src, dst); - // output serialization format: | result (1 byte) | - output.resize(1, 0); - output[0] = result; + response.result = ggml_backend_buffer_copy_tensor(src, dst); ggml_free(ctx); return true; } @@ -1026,7 +1009,7 @@ ggml_tensor * rpc_server::create_node(uint64_t id, return result; } -bool rpc_server::graph_compute(const std::vector & input, std::vector & output) { +bool rpc_server::graph_compute(const std::vector & input, rpc_msg_graph_compute_rsp & response) { // serialization format: // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | if (input.size() < sizeof(uint32_t)) { @@ -1066,9 +1049,7 @@ bool rpc_server::graph_compute(const std::vector & input, std::vectornodes[i] = create_node(id, ctx, tensor_ptrs, tensor_map); } ggml_status status = ggml_backend_graph_compute(backend, graph); - // output serialization format: | status (1 byte) | - output.resize(1, 0); - output[0] = status; + response.result = status; ggml_free(ctx); return true; } @@ -1091,85 +1072,153 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre fprintf(stderr, "Unknown command: %d\n", cmd); break; } - std::vector input; - std::vector output; - uint64_t input_size; - if (!recv_data(sockfd, &input_size, sizeof(input_size))) { - break; - } - try { - input.resize(input_size); - } catch (const std::bad_alloc & e) { - fprintf(stderr, "Failed to allocate input buffer of size %" PRIu64 "\n", input_size); - break; - } - if (!recv_data(sockfd, input.data(), input_size)) { - break; - } - bool ok = true; switch (cmd) { case RPC_CMD_ALLOC_BUFFER: { - ok = server.alloc_buffer(input, output); + rpc_msg_alloc_buffer_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + rpc_msg_alloc_buffer_rsp response; + server.alloc_buffer(request, response); + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GET_ALIGNMENT: { - server.get_alignment(output); + if (!recv_msg(sockfd, nullptr, 0)) { + return; + } + rpc_msg_get_alignment_rsp response; + server.get_alignment(response); + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GET_MAX_SIZE: { - server.get_max_size(output); + if (!recv_msg(sockfd, nullptr, 0)) { + return; + } + rpc_msg_get_max_size_rsp response; + server.get_max_size(response); + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_BUFFER_GET_BASE: { - ok = server.buffer_get_base(input, output); + rpc_msg_buffer_get_base_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + rpc_msg_buffer_get_base_rsp response; + if (!server.buffer_get_base(request, response)) { + return; + } + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_FREE_BUFFER: { - ok = server.free_buffer(input); + rpc_msg_free_buffer_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + if (!server.free_buffer(request)) { + return; + } + if (!send_msg(sockfd, nullptr, 0)) { + return; + } break; } case RPC_CMD_BUFFER_CLEAR: { - ok = server.buffer_clear(input); + rpc_msg_buffer_clear_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + if (!server.buffer_clear(request)) { + return; + } + if (!send_msg(sockfd, nullptr, 0)) { + return; + } break; } case RPC_CMD_SET_TENSOR: { - ok = server.set_tensor(input); + std::vector input; + if (!recv_msg(sockfd, input)) { + return; + } + if (!server.set_tensor(input)) { + return; + } + if (!send_msg(sockfd, nullptr, 0)) { + return; + } break; } case RPC_CMD_GET_TENSOR: { - ok = server.get_tensor(input, output); + rpc_msg_get_tensor_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + std::vector response; + if (!server.get_tensor(request, response)) { + return; + } + if (!send_msg(sockfd, response.data(), response.size())) { + return; + } break; } case RPC_CMD_COPY_TENSOR: { - ok = server.copy_tensor(input, output); + rpc_msg_copy_tensor_req request; + if (!recv_msg(sockfd, &request, sizeof(request))) { + return; + } + rpc_msg_copy_tensor_rsp response; + if (!server.copy_tensor(request, response)) { + return; + } + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GRAPH_COMPUTE: { - ok = server.graph_compute(input, output); + std::vector input; + if (!recv_msg(sockfd, input)) { + return; + } + rpc_msg_graph_compute_rsp response; + if (!server.graph_compute(input, response)) { + return; + } + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } case RPC_CMD_GET_DEVICE_MEMORY: { - // output serialization format: | free (8 bytes) | total (8 bytes) | - output.resize(2*sizeof(uint64_t), 0); - memcpy(output.data(), &free_mem, sizeof(free_mem)); - memcpy(output.data() + sizeof(uint64_t), &total_mem, sizeof(total_mem)); + if (!recv_msg(sockfd, nullptr, 0)) { + return; + } + rpc_msg_get_device_memory_rsp response; + response.free_mem = free_mem; + response.total_mem = total_mem; + if (!send_msg(sockfd, &response, sizeof(response))) { + return; + } break; } default: { fprintf(stderr, "Unknown command: %d\n", cmd); - ok = false; + return; } } - if (!ok) { - break; - } - uint64_t output_size = output.size(); - if (!send_data(sockfd, &output_size, sizeof(output_size))) { - break; - } - if (!send_data(sockfd, output.data(), output_size)) { - break; - } } } From cda0e4b648dde8fac162b3430b14a99597d3d74f Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 18 Oct 2024 23:18:01 +0200 Subject: [PATCH 31/95] llama : remove all_pos_0, all_pos_1, all_seq_id from llama_batch (#9745) * refactor llama_batch_get_one * adapt all examples * fix simple.cpp * fix llama_bench * fix * fix context shifting * free batch before return * use common_batch_add, reuse llama_batch in loop * null terminated seq_id list * fix save-load-state example * fix perplexity * correct token pos in llama_batch_allocr --- common/common.cpp | 4 +- examples/batched-bench/batched-bench.cpp | 1 - .../cvector-generator/cvector-generator.cpp | 2 +- examples/eval-callback/eval-callback.cpp | 2 +- examples/imatrix/imatrix.cpp | 13 +- examples/infill/infill.cpp | 2 +- examples/llama-bench/llama-bench.cpp | 16 +- .../llama/src/main/cpp/llama-android.cpp | 3 - examples/llava/llava-cli.cpp | 2 +- examples/llava/llava.cpp | 38 ++++- examples/llava/minicpmv-cli.cpp | 2 +- examples/lookahead/lookahead.cpp | 4 +- examples/lookup/lookup.cpp | 4 +- examples/main/main.cpp | 4 +- examples/parallel/parallel.cpp | 1 - examples/perplexity/perplexity.cpp | 27 +++- examples/save-load-state/save-load-state.cpp | 30 +++- examples/server/server.cpp | 1 - examples/simple/simple.cpp | 4 +- examples/speculative/speculative.cpp | 6 +- include/llama.h | 20 +-- src/llama.cpp | 137 ++++++++++-------- 22 files changed, 205 insertions(+), 118 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index c08f01b42..2bc0b8800 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -955,7 +955,7 @@ struct common_init_result common_init_from_params(common_params & params) { } if (llama_model_has_encoder(model)) { - llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0)); + llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size())); llama_token decoder_start_token_id = llama_model_decoder_start_token(model); if (decoder_start_token_id == -1) { decoder_start_token_id = bos; @@ -964,7 +964,7 @@ struct common_init_result common_init_from_params(common_params & params) { tmp.push_back(decoder_start_token_id); } if (llama_model_has_decoder(model)) { - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch))); } llama_kv_cache_clear(lctx); llama_synchronize(lctx); diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 81c3220ad..a3b21ad6b 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -74,7 +74,6 @@ int main(int argc, char ** argv) { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp index 69e141ecb..d1731bba6 100644 --- a/examples/cvector-generator/cvector-generator.cpp +++ b/examples/cvector-generator/cvector-generator.cpp @@ -339,7 +339,7 @@ static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) { static bool get_hidden_layers(llama_context * ctx, std::vector & tokens) { llama_kv_cache_clear(ctx); - if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { fprintf(stderr, "%s : failed to eval\n", __func__); return false; } diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index fb52db4e1..c08e3e5f6 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -131,7 +131,7 @@ static bool run(llama_context * ctx, const common_params & params) { std::vector tokens = common_tokenize(ctx, params.prompt, add_bos); - if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { LOG_ERR("%s : failed to eval\n", __func__); return false; } diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index d1ff3e8bc..70ff47768 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -496,6 +496,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -508,9 +510,14 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); } - // TODO: use batch.logits to save computations instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return false; } @@ -523,6 +530,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index f82c614f5..f18362c91 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -396,7 +396,7 @@ int main(int argc, char ** argv) { LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 60a7aef5b..4a8ea9676 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -1428,7 +1428,7 @@ struct sql_printer : public printer { } }; -static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) { +static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); const llama_model * model = llama_get_model(ctx); @@ -1444,14 +1444,14 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat for (int i = 1; i < n_tokens; i++) { tokens[i] = std::rand() % n_vocab; } - llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0)); + llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens)); n_processed += n_tokens; } llama_synchronize(ctx); } -static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) { +static void test_gen(llama_context * ctx, int n_gen, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); const llama_model * model = llama_get_model(ctx); @@ -1460,7 +1460,7 @@ static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; for (int i = 0; i < n_gen; i++) { - llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0)); + llama_decode(ctx, llama_batch_get_one(&token, 1)); llama_synchronize(ctx); token = std::rand() % n_vocab; } @@ -1596,13 +1596,13 @@ int main(int argc, char ** argv) { fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count); } //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); - test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count); } - test_gen(ctx, 1, 0, t.n_threads); + test_gen(ctx, 1, t.n_threads); } for (int i = 0; i < params.reps; i++) { @@ -1614,13 +1614,13 @@ int main(int argc, char ** argv) { if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps); } - test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps); } - test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads); + test_gen(ctx, t.n_gen, t.n_threads); } uint64_t t_ns = get_time_ns() - t_start; diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp index f5ffd063f..b3858ddfb 100644 --- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -283,9 +283,6 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, nullptr, nullptr, nullptr, - 0, - 0, - 0, }; if (embd) { diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 5f9abe2b6..161098585 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -20,7 +20,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector n_batch) { n_eval = n_batch; } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); return false; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 2c96973c8..be6988540 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -401,6 +401,39 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co return true; } +struct llava_embd_batch { + std::vector pos; + std::vector n_seq_id; + std::vector seq_id_0; + std::vector seq_ids; + std::vector logits; + llama_batch batch; + llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { + pos .resize(n_tokens); + n_seq_id.resize(n_tokens); + seq_ids .resize(n_tokens + 1); + logits .resize(n_tokens); + seq_id_0.resize(1); + seq_id_0[0] = seq_id; + seq_ids [n_tokens] = nullptr; + batch = { + /*n_tokens =*/ n_tokens, + /*tokens =*/ nullptr, + /*embd =*/ embd, + /*pos =*/ pos.data(), + /*n_seq_id =*/ n_seq_id.data(), + /*seq_id =*/ seq_ids.data(), + /*logits =*/ logits.data(), + }; + for (int i = 0; i < n_tokens; i++) { + batch.pos [i] = pos_0 + i; + batch.n_seq_id[i] = 1; + batch.seq_id [i] = seq_id_0.data(); + batch.logits [i] = false; + } + } +}; + bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { int n_embd = llama_n_embd(llama_get_model(ctx_llama)); @@ -409,8 +442,9 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; - if (llama_decode(ctx_llama, batch)) { + float * embd = image_embed->embed+i*n_embd; + llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0); + if (llama_decode(ctx_llama, llava_batch.batch)) { LOG_ERR("%s : failed to eval\n", __func__); return false; } diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp index 6b666de1b..cbecec343 100644 --- a/examples/llava/minicpmv-cli.cpp +++ b/examples/llava/minicpmv-cli.cpp @@ -97,7 +97,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector n_batch) { n_eval = n_batch; } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); return false; } diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index f9e4aba81..3c0ccfea2 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -89,8 +89,8 @@ int main(int argc, char ** argv) { const auto t_enc_start = ggml_time_us(); // eval the prompt - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); for (int s = 1; s < W + G + 1; ++s) { llama_kv_cache_seq_cp(ctx, 0, s, -1, -1); diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 82fc7d466..a04728b18 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -89,8 +89,8 @@ int main(int argc, char ** argv){ const auto t_enc_start = ggml_time_us(); - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); const auto t_enc_end = ggml_time_us(); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 65483c45f..374ed47ad 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -528,7 +528,7 @@ int main(int argc, char ** argv) { int enc_input_size = embd_inp.size(); llama_token * enc_input_buf = embd_inp.data(); - if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) { + if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } @@ -648,7 +648,7 @@ int main(int argc, char ** argv) { LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { LOG_ERR("%s : failed to eval\n", __func__); return 1; } diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 20274c147..43c8f3ed5 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -308,7 +308,6 @@ int main(int argc, char ** argv) { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index efb41b80a..e803ff143 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -408,14 +408,21 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + //LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); - // TODO: use llama_batch.logits instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + if (llama_decode(ctx, batch)) { //LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return {tokens, -1, logit_history, prob_history}; } @@ -435,6 +442,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { @@ -704,7 +713,6 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector< batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); @@ -1791,6 +1799,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -1803,9 +1813,14 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); } - // TODO: use llama_batch.logits instead of relying on logits_all == true - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return; } @@ -1818,6 +1833,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 3866cfa27..5f60a86cb 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -48,9 +48,16 @@ int main(int argc, char ** argv) { // tokenize prompt auto tokens = common_tokenize(ctx, params.prompt, true); + // prepare the batch + llama_batch batch = llama_batch_init(tokens.size(), 0, 1); + for (size_t i = 0; i < tokens.size(); i++) { + common_batch_add(batch, tokens[i], i, {0}, false); + } + batch.logits[batch.n_tokens - 1] = true; // generate next token + // evaluate prompt - llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0)); - n_past += tokens.size(); + llama_decode(ctx, batch); + n_past += batch.n_tokens; // save state (rng, logits, embedding and kv_cache) to file { @@ -77,8 +84,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result0 += next_token_str; - if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx); llama_free_model(model); return 1; @@ -133,8 +144,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result1 += next_token_str; - if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx2, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx2); llama_free_model(model); return 1; @@ -221,8 +236,12 @@ int main(int argc, char ** argv) { printf("%s", next_token_str.c_str()); result2 += next_token_str; - if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {1}, true); + + if (llama_decode(ctx3, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); llama_free(ctx3); llama_free_model(model); return 1; @@ -236,6 +255,7 @@ int main(int argc, char ** argv) { llama_sampler_free(smpl2); llama_sampler_free(smpl3); + llama_batch_free(batch); llama_free(ctx3); llama_free_model(model); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8fd443878..3992108e7 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2326,7 +2326,6 @@ struct server_context { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index be91b2891..59760fe95 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -138,7 +138,7 @@ int main(int argc, char ** argv) { // prepare a batch for the prompt - llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size(), 0, 0); + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); // main loop @@ -175,7 +175,7 @@ int main(int argc, char ** argv) { fflush(stdout); // prepare the next batch with the sampled token - batch = llama_batch_get_one(&new_token_id, 1, n_pos, 0); + batch = llama_batch_get_one(&new_token_id, 1); n_decode += 1; } diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 5a7b3084f..b201bd714 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -155,9 +155,9 @@ int main(int argc, char ** argv) { const auto t_enc_start = ggml_time_us(); // eval the prompt with both models - llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); - llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input, 0, 0)); + llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1)); + llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input)); const auto t_enc_end = ggml_time_us(); diff --git a/include/llama.h b/include/llama.h index 1a13360c2..2558e9267 100644 --- a/include/llama.h +++ b/include/llama.h @@ -232,8 +232,11 @@ extern "C" { // - token : the token ids of the input (used when embd is NULL) // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) // - pos : the positions of the respective token in the sequence + // (if set to NULL, the token position will be tracked automatically by llama_decode) // - seq_id : the sequence to which the respective token belongs + // (if set to NULL, the sequence ID will be assumed to be 0) // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output + // (if set to NULL, only the logits for last token will be returned) // typedef struct llama_batch { int32_t n_tokens; @@ -244,15 +247,6 @@ extern "C" { int32_t * n_seq_id; llama_seq_id ** seq_id; int8_t * logits; // TODO: rename this to "output" - - // NOTE: helpers for smooth API transition - can be deprecated in the future - // for future-proof code, use the above fields instead and ignore everything below - // - // pos[i] = all_pos_0 + i*all_pos_1 - // - llama_pos all_pos_0; // used if pos == NULL - llama_pos all_pos_1; // used if pos == NULL - llama_seq_id all_seq_id; // used if seq_id == NULL } llama_batch; enum llama_model_kv_override_type { @@ -776,15 +770,15 @@ extern "C" { // Decoding // - // Return batch for single sequence of tokens starting at pos_0 + // Return batch for single sequence of tokens + // The sequence ID will be fixed to 0 + // The position of the tokens will be tracked automatically by llama_decode // // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it // LLAMA_API struct llama_batch llama_batch_get_one( llama_token * tokens, - int32_t n_tokens, - llama_pos pos_0, - llama_seq_id seq_id); + int32_t n_tokens); // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens // Each token can be assigned up to n_seq_max sequence ids diff --git a/src/llama.cpp b/src/llama.cpp index 10c975bf4..1813dd29b 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -2949,9 +2949,6 @@ struct llama_sbatch_seq { llama_seq_id * seq_id; size_t offset; size_t length; - - // helper for smoother batch API transition -- can be deprecated in the future - llama_seq_id all_seq_id; // used if seq_id == NULL }; // sequence-length-aware batch splitting @@ -3046,30 +3043,18 @@ struct llama_sbatch { } else { ubatch.embd = nullptr; } - // from here on, the else branches are deprecated; - // they are helpers for smoother batch API transition - if (batch->pos) { - if (ubatch.equal_seqs) { - for (size_t i = 0; i < length; ++i) { - ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; - } - } else { - // simple split - ubatch.pos = batch->pos + seq.offset; + if (ubatch.equal_seqs) { + for (size_t i = 0; i < length; ++i) { + ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; } } else { - for (size_t i = 0; i < length; ++i) { - llama_pos bi = ids[seq.offset + i]; - ubatch.pos[ubatch.n_tokens + i] = batch->all_pos_0 + (bi * batch->all_pos_1); - } + // simple split + ubatch.pos = batch->pos + seq.offset; } if (ubatch.equal_seqs) { ubatch.n_seq_id[ubatch.n_seqs] = seq.n_seq_id; if (seq.seq_id) { ubatch.seq_id[ubatch.n_seqs] = seq.seq_id; - } else { - GGML_ASSERT(seq.n_seq_id == 1); - ubatch.seq_id[ubatch.n_seqs] = &seq.all_seq_id; } } else { // simple split @@ -3082,10 +3067,6 @@ struct llama_sbatch { } if (batch->seq_id) { ubatch.seq_id = batch->seq_id + seq.offset; - } else { - for (size_t i = 0; i < length; ++i) { - ubatch.seq_id[ubatch.n_seqs + i] = &seq.all_seq_id; - } } } if (logits_all) { @@ -3204,7 +3185,6 @@ struct llama_sbatch { s.seq_id = nullptr; s.offset = 0; s.length = n_tokens; - s.all_seq_id = batch.all_seq_id; return; } std::sort(ids.begin(), ids.end(), @@ -3227,7 +3207,7 @@ struct llama_sbatch { if (batch.pos) { return batch.pos[a] < batch.pos[b]; } - // no pos, sort by id (assuming batch.all_pos_1 is positive) + // no pos, sort by id return a < b; } // shared prompts go first @@ -3237,30 +3217,25 @@ struct llama_sbatch { // init seq llama_sbatch_seq * last_seq = nullptr; - if (batch.n_seq_id != nullptr && batch.seq_id != nullptr) { - for (size_t i = 0; i < n_tokens; ++i) { - const size_t bi = ids[i]; - const int32_t n_seqs = batch.n_seq_id[bi]; - llama_seq_id * seq_ids = batch.seq_id[bi]; - if (last_seq != nullptr) { - bool same = n_seqs == last_seq->n_seq_id; - for (int32_t j = 0; same && j < n_seqs; ++j) { - if (seq_ids[j] != last_seq->seq_id[j]) { - same = false; - } - } - if (same) { - last_seq->length += 1; - continue; + for (size_t i = 0; i < n_tokens; ++i) { + const size_t bi = ids[i]; + const int32_t n_seqs = batch.n_seq_id[bi]; + llama_seq_id * seq_ids = batch.seq_id[bi]; + if (last_seq != nullptr) { + bool same = n_seqs == last_seq->n_seq_id; + for (int32_t j = 0; same && j < n_seqs; ++j) { + if (seq_ids[j] != last_seq->seq_id[j]) { + same = false; } } - llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1, batch.all_seq_id}; - seq.push_back(new_seq); - last_seq = &seq.back(); + if (same) { + last_seq->length += 1; + continue; + } } - } else { - llama_sbatch_seq new_seq = {1, nullptr, 0, n_tokens, batch.all_seq_id}; + llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1}; seq.push_back(new_seq); + last_seq = &seq.back(); } // keep shared prompts first at the end, then sort by length descending. std::sort(seq.begin(), seq.end(), @@ -21096,9 +21071,7 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) { struct llama_batch llama_batch_get_one( llama_token * tokens, - int32_t n_tokens, - llama_pos pos_0, - llama_seq_id seq_id) { + int32_t n_tokens) { return { /*n_tokens =*/ n_tokens, /*tokens =*/ tokens, @@ -21107,9 +21080,6 @@ struct llama_batch llama_batch_get_one( /*n_seq_id =*/ nullptr, /*seq_id =*/ nullptr, /*logits =*/ nullptr, - /*all_pos_0 =*/ pos_0, - /*all_pos_1 =*/ 1, - /*all_seq_id =*/ seq_id, }; } @@ -21122,9 +21092,6 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_ /*n_seq_id =*/ nullptr, /*seq_id =*/ nullptr, /*logits =*/ nullptr, - /*all_pos_0 =*/ 0, - /*all_pos_1 =*/ 0, - /*all_seq_id =*/ 0, }; if (embd) { @@ -21160,11 +21127,62 @@ void llama_batch_free(struct llama_batch batch) { if (batch.logits) free(batch.logits); } +// temporary allocate memory for the input batch if needed +static const llama_seq_id batch_default_seq_id = 0; +struct llama_batch_allocr { + std::array seq_id_0 = {batch_default_seq_id}; + std::vector pos; + std::vector n_seq_id; + std::vector seq_id; + std::vector logits; + struct llama_batch batch; + // optionally fulfill the batch returned by llama_batch_get_one + llama_batch_allocr(struct llama_context * ctx, struct llama_batch in_batch) { + batch = in_batch; + if (!batch.pos) { + // determine the last position in KV cache + llama_pos last_pos = -1; + for (const auto & cell : ctx->kv_self.cells) { + if (cell.has_seq_id(batch_default_seq_id)) { + last_pos = std::max(last_pos, cell.pos); + } + } + last_pos++; // next position + pos.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + pos[i] = i+last_pos; + } + batch.pos = pos.data(); + } + if (!batch.n_seq_id) { + n_seq_id.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + n_seq_id[i] = seq_id_0.size(); + } + batch.n_seq_id = n_seq_id.data(); + } + if (!batch.seq_id) { + seq_id.resize(batch.n_tokens + 1); + seq_id[batch.n_tokens] = NULL; + for (int32_t i = 0; i < batch.n_tokens; i++) { + seq_id[i] = seq_id_0.data(); + } + batch.seq_id = seq_id.data(); + } + if (!batch.logits) { + logits.resize(batch.n_tokens); + logits[logits.size() - 1] = true; + batch.logits = logits.data(); + } + } +}; + int32_t llama_encode( struct llama_context * ctx, struct llama_batch batch) { - const int ret = llama_encode_internal(*ctx, batch); - if (ret < 0) { + llama_batch_allocr batch_allocr(ctx, batch); + const int ret = llama_encode_internal(*ctx, batch_allocr.batch); + if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret); } @@ -21174,8 +21192,9 @@ int32_t llama_encode( int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch) { - const int ret = llama_decode_internal(*ctx, batch); - if (ret < 0) { + llama_batch_allocr batch_allocr(ctx, batch); + const int ret = llama_decode_internal(*ctx, batch_allocr.batch); + if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); } From 7cab2083c768dd92c20b105556c4165b59cd8a41 Mon Sep 17 00:00:00 2001 From: icppWorld <124377669+icppWorld@users.noreply.github.com> Date: Sun, 20 Oct 2024 12:01:34 -0400 Subject: [PATCH 32/95] readme : update infra list (#9942) llama_cpp_canister allows you to run llama.cpp as a Smart Contract on the Internet Computer. The smart contract runs as WebAssembly in a so-called 'canister'. --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 1088b3338..f1d8900c3 100644 --- a/README.md +++ b/README.md @@ -187,6 +187,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs +- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly **Games:** - [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you. From 45f097645efb11b6d09a5b4adbbfd7c312ac0126 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Carr=C3=A8re?= Date: Sun, 20 Oct 2024 18:25:41 +0200 Subject: [PATCH 33/95] readme : update bindings list (#9951) Update the binding list by adding LM-Kit.NET (C# & VB.NET) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f1d8900c3..06c32a2b4 100644 --- a/README.md +++ b/README.md @@ -122,6 +122,7 @@ Typically finetunes of the base models below are supported as well. - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) +- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html) - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) From 1db8c84fc62857e1e45c1c7ea93bcd5344cb3d31 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Mon, 21 Oct 2024 14:26:09 +0800 Subject: [PATCH 34/95] fix mul_mat_vec_q and *_vec_q error (#9939) Co-authored-by: arthw <14088817+arthw@users.noreply.github.com> --- ggml/src/ggml-sycl/mmvq.cpp | 136 ++++++++++++++++++------------------ 1 file changed, 69 insertions(+), 67 deletions(-) diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp index 1b96925e1..7b10cf688 100644 --- a/ggml/src/ggml-sycl/mmvq.cpp +++ b/ggml/src/ggml-sycl/mmvq.cpp @@ -1,6 +1,6 @@ #include "mmvq.hpp" #include "vecdotq.hpp" - +#include template static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, @@ -13,7 +13,8 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -37,7 +38,7 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -61,7 +62,8 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -85,7 +87,7 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -109,8 +111,8 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -133,7 +135,7 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -157,8 +159,8 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -181,7 +183,7 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -205,8 +207,8 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -229,7 +231,7 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -253,8 +255,8 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -277,7 +279,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -301,8 +303,8 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -325,7 +327,7 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -349,8 +351,8 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -373,7 +375,7 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -397,8 +399,8 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -421,7 +423,7 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -446,8 +448,8 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx, } const int blocks_per_row = ncols / qk; - const int blocks_per_warp = vdr * WARP_SIZE / qi; - + const int blocks_per_warp = vdr * QK_WARP_SIZE / qi; + assert(blocks_per_warp>0); // partial sum for each thread float tmp = 0.0f; @@ -470,7 +472,7 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx, // sum up partial sums and write back result #pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { + for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); } @@ -487,7 +489,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK4_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -495,7 +497,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -511,7 +513,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK4_1 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -519,7 +521,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -535,7 +537,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK5_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -543,7 +545,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -559,7 +561,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK5_1 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -567,7 +569,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -583,7 +585,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK8_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -591,7 +593,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -607,7 +609,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -615,7 +617,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -631,7 +633,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -639,7 +641,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -655,7 +657,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -663,7 +665,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -679,7 +681,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -687,7 +689,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -703,7 +705,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -711,7 +713,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q( vx, vy, dst, ncols, nrows, item_ct1); @@ -728,13 +730,13 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq2_xxs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -749,7 +751,7 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -759,7 +761,7 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq2_xs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -774,7 +776,7 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -784,7 +786,7 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq2_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -799,7 +801,7 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -809,7 +811,7 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq3_xxs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -824,7 +826,7 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -833,7 +835,7 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq3_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -848,7 +850,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { @@ -858,7 +860,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq1_s_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -873,13 +875,13 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq1_m_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -894,14 +896,14 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK4_NL == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq4_nl_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); @@ -916,14 +918,14 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy, GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); - const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE); { stream->submit([&](sycl::handler &cgh) { cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { + [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] { mul_mat_vec_q_iq4_xs_q8_1( vx, vy, dst, ncols, nrows, item_ct1); }); From bc219750845a59166d79f0d4ee3da1993b369b8a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 21 Oct 2024 09:37:12 +0300 Subject: [PATCH 35/95] speculative : fix handling of some input params (#9963) * speculative : fix batch sizes at initialization ggml-ci * speculative : handle params.n_predict == -1 * speculative : limit batch size to llama_n_batch --- examples/speculative/speculative.cpp | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index b201bd714..8a6475415 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -39,6 +39,11 @@ int main(int argc, char ** argv) { return 1; } + if (params.n_predict < -1) { + LOG_ERR("%s: --n-predict must be >= -1\n", __func__); + return 1; + } + common_init(); if (params.model_draft.empty()) { @@ -190,8 +195,8 @@ int main(int argc, char ** argv) { drafts[s].smpl = common_sampler_init(model_dft, params.sparams); } - llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1); - llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, n_seq_dft); + llama_batch batch_dft = llama_batch_init(llama_n_batch(ctx_dft), 0, 1); + llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, n_seq_dft); const auto t_dec_start = ggml_time_us(); @@ -441,7 +446,7 @@ int main(int argc, char ** argv) { ++n_past_dft; } - if (n_predict > params.n_predict || has_eos) { + if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) { break; } From 55e47786e373c90fc7803e718e3e1dd6d53c3db6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 21 Oct 2024 09:46:40 +0300 Subject: [PATCH 36/95] llama : default sampling changes + greedy update (#9897) * llama : deprecate softmax sampler + fix dist sampler ggml-ci * tests : replace macros with functions ggml-ci * sampling : change temperature sampler logic For t <= 0.0f, keep the max logit intact and set the rest to -inf * cont : no need for special "greedy" logic top-k == 1 is the same * tests : init prob correctly * llama : handle temp <= 0.0 in the temp_ext sampler too ggml-ci * cont : avoid extra loop in temperature sampler for sub-zero temp ggml-ci --- common/sampling.cpp | 88 +++--- .../llama.cpp.swift/LibLlama.swift | 1 - examples/save-load-state/save-load-state.cpp | 3 - examples/speculative/speculative.cpp | 3 - include/llama.h | 10 +- src/llama-sampling.cpp | 41 ++- tests/test-sampling.cpp | 274 ++++++++---------- 7 files changed, 202 insertions(+), 218 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index 56cd0df6b..4ab3eface 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -171,60 +171,46 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co params.penalize_nl, params.ignore_eos)); - if (params.temp > 0.0f) { - if (params.mirostat == 0) { - for (const auto & cnstr : params.samplers) { - switch (cnstr) { - case COMMON_SAMPLER_TYPE_TOP_K: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); - break; - case COMMON_SAMPLER_TYPE_TOP_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_MIN_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_XTC: - llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); - break; - case COMMON_SAMPLER_TYPE_TFS_Z: - llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_TYPICAL_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_TEMPERATURE: - llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); - break; - case COMMON_SAMPLER_TYPE_INFILL: - llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model)); - break; - default: - GGML_ASSERT(false && "unknown sampler type"); - } + if (params.mirostat == 0) { + for (const auto & cnstr : params.samplers) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_TOP_K: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + break; + case COMMON_SAMPLER_TYPE_TOP_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_MIN_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_XTC: + llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + break; + case COMMON_SAMPLER_TYPE_TFS_Z: + llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TYPICAL_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TEMPERATURE: + llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + break; + case COMMON_SAMPLER_TYPE_INFILL: + llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model)); + break; + default: + GGML_ASSERT(false && "unknown sampler type"); } - llama_sampler_chain_add(result->chain, llama_sampler_init_softmax()); - llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); - } else if (params.mirostat == 1) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); - } else if (params.mirostat == 2) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); - } else { - GGML_ASSERT(false && "unknown mirostat version"); } + llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); + } else if (params.mirostat == 1) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); + } else if (params.mirostat == 2) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); } else { - if (params.n_probs > 0) { - // some use cases require to sample greedily, but still obtain the probabilities of the top tokens - // ref: https://github.com/ggerganov/llama.cpp/pull/9605 - // - // the following will not produce exactly the same probs as applyging softmax to the full vocabulary, but - // it is much faster, since we avoid sorting all tokens and should give a good approximation - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k(params.n_probs)); - llama_sampler_chain_add(result->chain, llama_sampler_init_softmax()); - } - llama_sampler_chain_add(result->chain, llama_sampler_init_greedy()); + GGML_ASSERT(false && "unknown mirostat version"); } return result; diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index dcd9803a2..65cd4eb51 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -46,7 +46,6 @@ actor LlamaContext { let sparams = llama_sampler_chain_default_params() self.sampling = llama_sampler_chain_init(sparams) llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4)) - llama_sampler_chain_add(self.sampling, llama_sampler_init_softmax()) llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234)) } diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 5f60a86cb..8c49a52a6 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -42,7 +42,6 @@ int main(int argc, char ** argv) { llama_sampler * smpl = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl, llama_sampler_init_softmax()); llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed)); // tokenize prompt @@ -107,7 +106,6 @@ int main(int argc, char ** argv) { llama_sampler * smpl2 = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl2, llama_sampler_init_softmax()); llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed)); printf("\nsecond run: %s", params.prompt.c_str()); @@ -171,7 +169,6 @@ int main(int argc, char ** argv) { llama_sampler * smpl3 = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl3, llama_sampler_init_softmax()); llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed)); printf("\nsingle seq run: %s", params.prompt.c_str()); diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 8a6475415..a40e755a2 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -185,8 +185,6 @@ int main(int argc, char ** argv) { // target model sampling context (reuse the llama_context's sampling instance) struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams); - struct llama_sampler * softmax = llama_sampler_init_softmax(); - // draft sequence data std::vector drafts(n_seq_dft); @@ -629,7 +627,6 @@ int main(int argc, char ** argv) { common_sampler_free(drafts[s].smpl); } - llama_sampler_free(softmax); llama_batch_free(batch_dft); llama_free(ctx_tgt); diff --git a/include/llama.h b/include/llama.h index 2558e9267..d4059c8dd 100644 --- a/include/llama.h +++ b/include/llama.h @@ -217,6 +217,7 @@ extern "C" { typedef struct llama_token_data_array { // TODO: consider SoA + // NOTE: this pointer can be modified by the samplers llama_token_data * data; size_t size; int64_t selected; // this is the index in the data array (i.e. not the token id) @@ -1069,12 +1070,13 @@ extern "C" { // available samplers: - LLAMA_API struct llama_sampler * llama_sampler_init_greedy (void); - LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed); + LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void); + LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed); /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first. - LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void); + DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void), + "will be removed in the future (see https://github.com/ggerganov/llama.cpp/pull/9896#discussion_r1800920915)"); /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k); @@ -1090,6 +1092,8 @@ extern "C" { /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. LLAMA_API struct llama_sampler * llama_sampler_init_typical (float p, size_t min_keep); + + /// #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it's original value, the rest are set to -inf LLAMA_API struct llama_sampler * llama_sampler_init_temp (float t); /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772. diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index bd750c40e..d71516153 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -63,6 +63,30 @@ static void llama_log_softmax(float * array, size_t size) { } */ +static void llama_sampler_temp_impl(llama_token_data_array * cur_p, float temp) { + if (temp <= 0.0f) { + // find the token with the highest logit and set the rest to -inf + size_t max_i = 0; + float max_l = cur_p->data[0].logit; + + for (size_t i = 1; i < cur_p->size; ++i) { + if (cur_p->data[i ].logit > max_l) { + cur_p->data[max_i].logit = -INFINITY; + max_i = i; + max_l = cur_p->data[i].logit; + } else { + cur_p->data[i].logit = -INFINITY; + } + } + + return; + } + + for (size_t i = 0; i < cur_p->size; ++i) { + cur_p->data[i].logit /= temp; + } +} + static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) { GGML_ASSERT(cur_p->size > 0); @@ -427,6 +451,9 @@ static const char * llama_sampler_dist_name(const struct llama_sampler * /*smpl* static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { auto * ctx = (llama_sampler_dist *) smpl->ctx; + + llama_sampler_softmax_impl(cur_p); + cur_p->selected = llama_sample_dist(cur_p, ctx->rng); } @@ -912,9 +939,8 @@ static const char * llama_sampler_temp_name(const struct llama_sampler * /*smpl* static void llama_sampler_temp_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { const auto * ctx = (llama_sampler_temp *) smpl->ctx; - for (size_t i = 0; i < cur_p->size; ++i) { - cur_p->data[i].logit /= ctx->temp; - } + + llama_sampler_temp_impl(cur_p, ctx->temp); } static struct llama_sampler * llama_sampler_temp_clone(const struct llama_sampler * smpl) { @@ -961,6 +987,7 @@ static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_toke if (ctx->delta > 0) { const float min_temp = std::max(0.0f, ctx->temp - ctx->delta); const float max_temp = ctx->temp + ctx->delta; + float exponent_val = ctx->exponent; // no need to do anything if there is only one (or zero) candidates @@ -998,9 +1025,7 @@ static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_toke #endif // Apply the dynamically calculated temperature scaling - for (size_t i = 0; i < cur_p->size; ++i) { - cur_p->data[i].logit /= dyn_temp; - } + llama_sampler_temp_impl(cur_p, dyn_temp); // Re-compute softmax probabilities after scaling logits with dynamic temperature const double max_l_double = cur_p->data[0].logit; @@ -1024,9 +1049,7 @@ static void llama_sampler_temp_ext_apply(struct llama_sampler * smpl, llama_toke } #endif } else { - for (size_t i = 0; i < cur_p->size; ++i) { - cur_p->data[i].logit /= ctx->temp; - } + llama_sampler_temp_impl(cur_p, ctx->temp); } } diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 1372bdf13..05600e6f5 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -18,203 +18,176 @@ static void dump(const llama_token_data_array * cur_p) { #define DUMP(__cur_p) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__cur_p)); printf("-\n"); } while(0) -#define APPLY(__cnstr, __cur_p) do { \ - auto * cnstr = (__cnstr); \ - llama_sampler_apply(cnstr, (__cur_p)); \ - llama_sampler_free(cnstr); \ -} while(0) +struct sampler_tester { + sampler_tester(size_t n_vocab) { + cur.reserve(n_vocab); + for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { + const float logit = logf(token_id); + cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); + } -static void test_top_k(const std::vector & probs, const std::vector & expected_probs, int k) { - const size_t n_vocab = probs.size(); + cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false }; + } + + sampler_tester(const std::vector & probs, const std::vector & probs_expected) : probs_expected(probs_expected) { + cur.reserve(probs.size()); + for (llama_token token_id = 0; token_id < (llama_token)probs.size(); token_id++) { + const float logit = logf(probs[token_id]); + cur.emplace_back(llama_token_data{token_id, logit, probs[token_id]}); + } + + cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false }; + } + + void apply(llama_sampler * sampler) { + llama_sampler_apply(sampler, &cur_p); + llama_sampler_free(sampler); + } + + void check() { + GGML_ASSERT(cur_p.size == probs_expected.size()); + for (size_t i = 0; i < cur_p.size; i++) { + GGML_ASSERT(fabs(cur_p.data[i].p - probs_expected[i]) < 1e-5); + } + } + + llama_token_data_array cur_p; + +private: + const std::vector probs_expected; std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } +}; - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); - APPLY(llama_sampler_init_top_k(k), &cur_p); - DUMP(&cur_p); +static void test_temp(const std::vector & probs, const std::vector & probs_expected, float temp) { + sampler_tester tester(probs, probs_expected); - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-5); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_temp(temp)); + tester.apply(llama_sampler_init_dist(0)); + DUMP(&tester.cur_p); + + tester.check(); } -static void test_top_p(const std::vector & probs, const std::vector & expected_probs, float p) { - const size_t n_vocab = probs.size(); +static void test_temp_ext(const std::vector & probs, const std::vector & probs_expected, float temp, float delta, float exponent) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_temp_ext(temp, delta, exponent)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); - APPLY(llama_sampler_init_top_p(p, 1), &cur_p); - DUMP(&cur_p); - - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } + tester.check(); } -static void test_tfs(const std::vector & probs, const std::vector & expected_probs, float z) { - const size_t n_vocab = probs.size(); +static void test_top_k(const std::vector & probs, const std::vector & probs_expected, int k) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_top_k(k)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - DUMP(&cur_p); - APPLY(llama_sampler_init_tail_free(z, 1), &cur_p); - DUMP(&cur_p); - - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } + tester.check(); } -static void test_min_p(const std::vector & probs, const std::vector & expected_probs, float p) { - const size_t n_vocab = probs.size(); +static void test_top_p(const std::vector & probs, const std::vector & probs_expected, float p) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_top_p(p, 1)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - DUMP(&cur_p); - APPLY(llama_sampler_init_min_p(p, 1), &cur_p); - DUMP(&cur_p); - APPLY(llama_sampler_init_softmax(), &cur_p); - - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } + tester.check(); } -static void test_xtc(const std::vector & probs, const std::vector & expected_probs, float p, float t) { - const size_t n_vocab = probs.size(); +static void test_tfs(const std::vector & probs, const std::vector & probs_expected, float z) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_tail_free(z, 1)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); - APPLY(llama_sampler_init_xtc(p, t, 0, 0), &cur_p); - DUMP(&cur_p); - - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-5); - } + tester.check(); } -static void test_typical(const std::vector & probs, const std::vector & expected_probs, float p) { - const size_t n_vocab = probs.size(); +static void test_min_p(const std::vector & probs, const std::vector & probs_expected, float p) { + sampler_tester tester(probs, probs_expected); - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_min_p(p, 1)); + tester.apply(llama_sampler_init_dist (0)); + DUMP(&tester.cur_p); - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - DUMP(&cur_p); - APPLY(llama_sampler_init_typical(p, 1), &cur_p); - DUMP(&cur_p); + tester.check(); +} - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } +static void test_xtc(const std::vector & probs, const std::vector & probs_expected, float p, float t) { + sampler_tester tester(probs, probs_expected); + + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_xtc(p, t, 0, 0)); + DUMP(&tester.cur_p); + + tester.check(); +} + +static void test_typical(const std::vector & probs, const std::vector & probs_expected, float p) { + sampler_tester tester(probs, probs_expected); + + DUMP(&tester.cur_p); + tester.apply(llama_sampler_init_typical(p, 1)); + DUMP(&tester.cur_p); + + tester.check(); } static void test_penalties( const std::vector & probs, const std::vector & last_tokens, - const std::vector & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence + const std::vector & probs_expected, float repeat_penalty, float alpha_frequency, float alpha_presence ) { - GGML_ASSERT(probs.size() == expected_probs.size()); + GGML_ASSERT(probs.size() == probs_expected.size()); + + sampler_tester tester(probs, probs_expected); const size_t n_vocab = probs.size(); - - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(probs[token_id]); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } - - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; - auto * sampler = llama_sampler_init_penalties(n_vocab, LLAMA_TOKEN_NULL, LLAMA_TOKEN_NULL, last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence, false, false); for (size_t i = 0; i < last_tokens.size(); i++) { llama_sampler_accept(sampler, last_tokens[i]); } - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); - APPLY(sampler, &cur_p); - APPLY(llama_sampler_init_softmax(), &cur_p); - DUMP(&cur_p); + DUMP(&tester.cur_p); + tester.apply(sampler); + tester.apply(llama_sampler_init_dist(0)); + DUMP(&tester.cur_p); - GGML_ASSERT(cur_p.size == expected_probs.size()); - for (size_t i = 0; i < cur_p.size; i++) { - GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-3); - } + tester.check(); } static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p ) { - std::vector cur; - cur.reserve(n_vocab); - for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - const float logit = logf(token_id); - cur.emplace_back(llama_token_data{token_id, logit, 0.0f}); - } - - llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; + sampler_tester tester(n_vocab); llama_token min_token_id = 0; const llama_token max_token_id = n_vocab-1; for (auto s : samplers_sequence) { switch (s){ - case 'k': APPLY(llama_sampler_init_top_k(top_k), &cur_p); break; + case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break; case 'f': GGML_ABORT("tail_free test not implemented"); case 'y': GGML_ABORT("typical test not implemented"); - case 'p': APPLY(llama_sampler_init_top_p(top_p, 1), &cur_p); break; - case 'm': APPLY(llama_sampler_init_min_p(min_p, 1), &cur_p); break; + case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break; + case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); break; case 't': GGML_ABORT("temperature test not implemented"); default : GGML_ABORT("Unknown sampler"); } - APPLY(llama_sampler_init_softmax(), &cur_p); // make sure tokens are sorted for tests + tester.apply(llama_sampler_init_dist(0)); + + auto & cur_p = tester.cur_p; const int size = cur_p.size; @@ -307,21 +280,26 @@ static void test_perf() { BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32); BENCH(llama_sampler_init_typical (0.5f, 1), data, 32); BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32); - BENCH(llama_sampler_init_softmax (), data, 32); } int main(void) { ggml_time_init(); - test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1); - test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3); + test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f); + test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f); + + test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f, 0.0f, 1.0f); + test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0.0f, 0.0f, 1.0f); + + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 1); + test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 3); test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4); test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 0.8f); - test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 0); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f}, 0.7f); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 0.8f); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1.0f); test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.00f); test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.24f); From d5ebd79c76abd4887f0283cd6f6f9689122094d0 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Mon, 21 Oct 2024 13:35:40 +0300 Subject: [PATCH 37/95] rpc : pack only RPC structs (#9959) --- ggml/src/ggml-rpc.cpp | 17 +++-------------- 1 file changed, 3 insertions(+), 14 deletions(-) diff --git a/ggml/src/ggml-rpc.cpp b/ggml/src/ggml-rpc.cpp index f95233284..0e936b343 100644 --- a/ggml/src/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc.cpp @@ -57,8 +57,9 @@ struct socket_t { } }; +// all RPC structures must be packed +#pragma pack(push, 1) // ggml_tensor is serialized into rpc_tensor -#pragma pack(1) struct rpc_tensor { uint64_t id; uint32_t type; @@ -95,76 +96,64 @@ enum rpc_cmd { RPC_CMD_COUNT, }; -#pragma pack(1) struct rpc_msg_alloc_buffer_req { uint64_t size; }; -#pragma pack(1) struct rpc_msg_alloc_buffer_rsp { uint64_t remote_ptr; uint64_t remote_size; }; -#pragma pack(1) struct rpc_msg_get_alignment_rsp { uint64_t alignment; }; -#pragma pack(1) struct rpc_msg_get_max_size_rsp { uint64_t max_size; }; -#pragma pack(1) struct rpc_msg_buffer_get_base_req { uint64_t remote_ptr; }; -#pragma pack(1) struct rpc_msg_buffer_get_base_rsp { uint64_t base_ptr; }; -#pragma pack(1) struct rpc_msg_free_buffer_req { uint64_t remote_ptr; }; -#pragma pack(1) struct rpc_msg_buffer_clear_req { uint64_t remote_ptr; uint8_t value; }; -#pragma pack(1) struct rpc_msg_get_tensor_req { rpc_tensor tensor; uint64_t offset; uint64_t size; }; -#pragma pack(1) struct rpc_msg_copy_tensor_req { rpc_tensor src; rpc_tensor dst; }; -#pragma pack(1) struct rpc_msg_copy_tensor_rsp { uint8_t result; }; -#pragma pack(1) struct rpc_msg_graph_compute_rsp { uint8_t result; }; -#pragma pack(1) struct rpc_msg_get_device_memory_rsp { uint64_t free_mem; uint64_t total_mem; }; +#pragma pack(pop) // RPC data structures From f594bc80baf683818f29d8f5d6fb52daab99e572 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 21 Oct 2024 16:20:46 +0300 Subject: [PATCH 38/95] ggml : add asserts for type conversion in fattn kernels (#9971) ggml-ci --- common/common.cpp | 4 ++-- ggml/src/ggml.c | 6 +++++- src/llama.cpp | 2 +- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 2bc0b8800..a8eebb68b 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1035,7 +1035,7 @@ static ggml_type kv_cache_type_from_str(const std::string & s) { return GGML_TYPE_Q5_1; } - throw std::runtime_error("Invalid cache type: " + s); + throw std::runtime_error("Unsupported cache type: " + s); } struct llama_context_params common_context_params_to_llama(const common_params & params) { @@ -1047,7 +1047,7 @@ struct llama_context_params common_context_params_to_llama(const common_params & cparams.n_ubatch = params.n_ubatch; cparams.n_threads = params.cpuparams.n_threads; cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ? - params.cpuparams.n_threads : params.cpuparams_batch.n_threads; + params.cpuparams.n_threads : params.cpuparams_batch.n_threads; cparams.logits_all = params.logits_all; cparams.embeddings = params.embedding; cparams.rope_scaling_type = params.rope_scaling_type; diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 7e24313ed..b16c462fa 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -324,8 +324,9 @@ struct ggml_logger_state { static struct ggml_logger_state g_logger_state = {ggml_log_callback_default, NULL}; static void ggml_log_internal_v(enum ggml_log_level level, const char * format, va_list args) { - if (format == NULL) + if (format == NULL) { return; + } va_list args_copy; va_copy(args_copy, args); char buffer[128]; @@ -15723,6 +15724,9 @@ static void ggml_compute_forward_flash_attn_ext_f16( ggml_vec_dot_t const kq_vec_dot = type_traits[k->type].vec_dot; ggml_to_float_t const v_to_float = type_traits[v->type].to_float; + GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); + GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); + // loop over n_batch and n_head for (int ir = ir0; ir < ir1; ++ir) { // q indices diff --git a/src/llama.cpp b/src/llama.cpp index 1813dd29b..98ec123c1 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -19243,7 +19243,7 @@ struct llama_context * llama_new_context_with_model( params.flash_attn = false; } - if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) { + if (ggml_is_quantized(params.type_v) && !params.flash_attn) { LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__); return nullptr; } From dbd5f2f5736aec6ff8fd63df3b351dae23c43e2f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 21 Oct 2024 20:25:02 +0300 Subject: [PATCH 39/95] llama.vim : plugin for Neovim (#9787) --- examples/llama.vim | 706 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 706 insertions(+) create mode 100644 examples/llama.vim diff --git a/examples/llama.vim b/examples/llama.vim new file mode 100644 index 000000000..e75872cae --- /dev/null +++ b/examples/llama.vim @@ -0,0 +1,706 @@ +" LLM-based text completion using llama.cpp +" +" requires: +" +" - neovim +" - curl +" - llama.cpp server instance +" - FIM-compatible model +" +" sample config: +" +" - Tab - accept the current suggestion +" - Shift+Tab - accept just the first line of the segguestion +" - Ctrl+F - toggle FIM completion manually +" +" make symlink or copy this file to ~/.config/nvim/autoload/llama.vim +" +" start the llama.cpp server with a FIM-compatible model. for example: +" +" $ llama-server -m {model.gguf} --port 8012 -ngl 99 -fa -dt 0.1 --ubatch-size 512 --batch-size 1024 --cache-reuse 256 +" +" --batch-size [512, model max context] +" +" adjust the batch size to control how much of the provided local context will be used during the inference +" lower values will use smaller part of the context around the cursor, which will result in faster processing +" +" --ubatch-size [64, 2048] +" +" chunks the batch into smaller chunks for faster processing +" depends on the specific hardware. use llama-bench to profile and determine the best size +" +" --cache-reuse (ge:llama_config.n_predict, 1024] +" +" this should be either 0 (disabled) or strictly larger than g:llama_config.n_predict +" using non-zero value enables context reuse on the server side which dramatically improves the performance at +" large contexts. a value of 256 should be good for all cases +" +" run this once to initialise llama.vim: +" +" :call llama#init() +" +" more info: https://github.com/ggerganov/llama.cpp/pull/9787 +" + +" colors (adjust to your liking) +highlight llama_hl_hint guifg=#ff772f +highlight llama_hl_info guifg=#77ff2f + +" general parameters: +" +" endpoint: llama.cpp server endpoint +" n_prefix: number of lines before the cursor location to include in the local prefix +" n_suffix: number of lines after the cursor location to include in the local suffix +" n_predict: max number of tokens to predict +" t_max_prompt_ms: max alloted time for the prompt processing (TODO: not yet supported) +" t_max_predict_ms: max alloted time for the prediction +" show_info: show extra info about the inference (0 - disabled, 1 - statusline, 2 - inline) +" auto_fim: trigger FIM completion automatically on cursor movement +" max_line_suffix: do not auto-trigger FIM completion if there are more than this number of characters to the right of the cursor +" +" ring buffer of chunks, accumulated with time upon: +" +" - completion request +" - yank +" - entering a buffer +" - leaving a buffer +" - writing a file +" +" parameters for the ring-buffer with extra context: +" +" ring_n_chunks: max number of chunks to pass as extra context to the server (0 to disable) +" ring_chunk_size: max size of the chunks (in number of lines) +" note: adjust these numbers so that you don't overrun your context +" at ring_n_chunks = 64 and ring_chunk_size = 64 you need ~32k context +" ring_scope: the range around the cursor position (in number of lines) for gathering chunks after FIM +" ring_update_ms: how often to process queued chunks in normal mode +" +let s:default_config = { + \ 'endpoint': 'http://127.0.0.1:8012/infill', + \ 'n_prefix': 256, + \ 'n_suffix': 64, + \ 'n_predict': 128, + \ 't_max_prompt_ms': 500, + \ 't_max_predict_ms': 1000, + \ 'show_info': 2, + \ 'auto_fim': v:true, + \ 'max_line_suffix': 8, + \ 'ring_n_chunks': 64, + \ 'ring_chunk_size': 64, + \ 'ring_scope': 1024, + \ 'ring_update_ms': 1000, + \ } + +let g:llama_config = get(g:, 'llama_config', s:default_config) + +function! s:rand(i0, i1) abort + return a:i0 + rand() % (a:i1 - a:i0 + 1) +endfunction + +function! llama#init() + if !executable('curl') + echohl WarningMsg + echo 'llama.vim requires the "curl" command to be available' + echohl None + return + endif + + let s:pos_x = 0 " cursor position upon start of completion + let s:pos_y = 0 + + let s:line_cur = '' + + let s:line_cur_prefix = '' + let s:line_cur_suffix = '' + + let s:ring_chunks = [] " current set of chunks used as extra context + let s:ring_queued = [] " chunks that are queued to be sent for processing + let s:ring_n_evict = 0 + + let s:hint_shown = v:false + let s:pos_y_pick = -9999 " last y where we picked a chunk + let s:pos_dx = 0 + let s:content = [] + let s:can_accept = v:false + + let s:timer_fim = -1 + let s:t_fim_start = reltime() " used to measure total FIM time + let s:t_last_move = reltime() " last time the cursor moved + + let s:current_job = v:null + + augroup llama + autocmd! + autocmd InsertEnter * inoremap llama#fim_inline(v:false) + autocmd InsertLeavePre * call llama#fim_cancel() + + autocmd CursorMoved * call s:on_move() + autocmd CursorMovedI * call s:on_move() + autocmd CompleteChanged * call llama#fim_cancel() + + if g:llama_config.auto_fim + autocmd CursorMovedI * call llama#fim(v:true) + endif + + " gather chunks upon yanking + autocmd TextYankPost * if v:event.operator ==# 'y' | call s:pick_chunk(v:event.regcontents, v:false, v:true) | endif + + " gather chunks upon entering/leaving a buffer + autocmd BufEnter * call timer_start(100, {-> s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true)}) + autocmd BufLeave * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true) + + " gather chunk upon saving the file + autocmd BufWritePost * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true) + augroup END + + silent! call llama#fim_cancel() + + " init background update of the ring buffer + if g:llama_config.ring_n_chunks > 0 + call s:ring_update() + endif +endfunction + +" compute how similar two chunks of text are +" 0 - no similarity, 1 - high similarity +" TODO: figure out something better +function! s:chunk_sim(c0, c1) + let l:lines0 = len(a:c0) + let l:lines1 = len(a:c1) + + let l:common = 0 + + for l:line0 in a:c0 + for l:line1 in a:c1 + if l:line0 == l:line1 + let l:common += 1 + break + endif + endfor + endfor + + return 2.0 * l:common / (l:lines0 + l:lines1) +endfunction + +" pick a random chunk of size g:llama_config.ring_chunk_size from the provided text and queue it for processing +" +" no_mod - do not pick chunks from buffers with pending changes +" do_evict - evict chunks that are very similar to the new one +" +function! s:pick_chunk(text, no_mod, do_evict) + " do not pick chunks from buffers with pending changes or buffers that are not files + if a:no_mod && (getbufvar(bufnr('%'), '&modified') || !buflisted(bufnr('%')) || !filereadable(expand('%'))) + return + endif + + " if the extra context option is disabled - do nothing + if g:llama_config.ring_n_chunks <= 0 + return + endif + + " don't pick very small chunks + if len(a:text) < 3 + return + endif + + if len(a:text) + 1 < g:llama_config.ring_chunk_size + let l:chunk = a:text + else + let l:l0 = s:rand(0, max([0, len(a:text) - g:llama_config.ring_chunk_size/2])) + let l:l1 = min([l:l0 + g:llama_config.ring_chunk_size/2, len(a:text)]) + + let l:chunk = a:text[l:l0:l:l1] + endif + + let l:chunk_str = join(l:chunk, "\n") . "\n" + + " check if this chunk is already added + let l:exist = v:false + + for i in range(len(s:ring_chunks)) + if s:ring_chunks[i].data == l:chunk + let l:exist = v:true + break + endif + endfor + + for i in range(len(s:ring_queued)) + if s:ring_queued[i].data == l:chunk + let l:exist = v:true + break + endif + endfor + + if l:exist + return + endif + + " evict queued chunks that are very similar to the new one + for i in range(len(s:ring_queued) - 1, 0, -1) + if s:chunk_sim(s:ring_queued[i].data, l:chunk) > 0.9 + if a:do_evict + call remove(s:ring_queued, i) + let s:ring_n_evict += 1 + else + return + endif + endif + endfor + + " also from s:ring_chunks + for i in range(len(s:ring_chunks) - 1, 0, -1) + if s:chunk_sim(s:ring_chunks[i].data, l:chunk) > 0.9 + if a:do_evict + call remove(s:ring_chunks, i) + let s:ring_n_evict += 1 + else + return + endif + endif + endfor + + " TODO: become parameter ? + if len(s:ring_queued) == 16 + call remove(s:ring_queued, 0) + endif + + call add(s:ring_queued, {'data': l:chunk, 'str': l:chunk_str, 'time': reltime(), 'filename': expand('%')}) + + "let &statusline = 'extra context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued) +endfunction + +" picks a queued chunk, sends it for processing and adds it to s:ring_chunks +" called every g:llama_config.ring_update_ms +function! s:ring_update() + call timer_start(g:llama_config.ring_update_ms, {-> s:ring_update()}) + + " update only if in normal mode or if the cursor hasn't moved for a while + if mode() !=# 'n' && reltimefloat(reltime(s:t_last_move)) < 3.0 + return + endif + + if len(s:ring_queued) == 0 + return + endif + + " move the first queued chunk to the ring buffer + if len(s:ring_chunks) == g:llama_config.ring_n_chunks + call remove(s:ring_chunks, 0) + endif + + call add(s:ring_chunks, remove(s:ring_queued, 0)) + + "let &statusline = 'updated context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued) + + " send asynchronous job with the new extra context so that it is ready for the next FIM + let l:extra_context = [] + for l:chunk in s:ring_chunks + call add(l:extra_context, { + \ 'text': l:chunk.str, + \ 'time': l:chunk.time, + \ 'filename': l:chunk.filename + \ }) + endfor + + " no samplers needed here + let l:request = json_encode({ + \ 'input_prefix': "", + \ 'input_suffix': "", + \ 'input_extra': l:extra_context, + \ 'prompt': "", + \ 'n_predict': 1, + \ 'temperature': 0.0, + \ 'stream': v:false, + \ 'samplers': ["temperature"], + \ 'cache_prompt': v:true, + \ 't_max_prompt_ms': 1, + \ 't_max_predict_ms': 1 + \ }) + + let l:curl_command = printf( + \ "curl --silent --no-buffer --request POST --url %s --header \"Content-Type: application/json\" --data %s", + \ g:llama_config.endpoint, shellescape(l:request) + \ ) + + " no callbacks because we don't need to process the response + call jobstart(l:curl_command, {}) +endfunction + +" necessary for 'inoremap ' +function! llama#fim_inline(is_auto) abort + call llama#fim(a:is_auto) + return '' +endfunction + +" the main FIM call +" takes local context around the cursor and sends it together with the extra context to the server for completion +function! llama#fim(is_auto) abort + " we already have a suggestion for the current cursor position + if s:hint_shown && !a:is_auto + call llama#fim_cancel() + return + endif + + call llama#fim_cancel() + + " avoid sending repeated requests too fast + if reltimefloat(reltime(s:t_fim_start)) < 0.6 + if s:timer_fim != -1 + call timer_stop(s:timer_fim) + let s:timer_fim = -1 + endif + + let s:t_fim_start = reltime() + let s:timer_fim = timer_start(600, {-> llama#fim(v:true)}) + return + endif + + let s:t_fim_start = reltime() + + let s:content = [] + let s:can_accept = v:false + + let s:pos_x = col('.') - 1 + let s:pos_y = line('.') + let l:max_y = line('$') + + let l:lines_prefix = getline(max([1, s:pos_y - g:llama_config.n_prefix]), s:pos_y - 1) + let l:lines_suffix = getline(s:pos_y + 1, min([l:max_y, s:pos_y + g:llama_config.n_suffix])) + + let s:line_cur = getline('.') + + let s:line_cur_prefix = strpart(s:line_cur, 0, s:pos_x) + let s:line_cur_suffix = strpart(s:line_cur, s:pos_x) + + if a:is_auto && len(s:line_cur_suffix) > g:llama_config.max_line_suffix + return + endif + + let l:prefix = "" + \ . join(l:lines_prefix, "\n") + \ . "\n" + + let l:prompt = "" + \ . s:line_cur_prefix + + let l:suffix = "" + \ . s:line_cur_suffix + \ . "\n" + \ . join(l:lines_suffix, "\n") + \ . "\n" + + " prepare the extra context data + let l:extra_context = [] + for l:chunk in s:ring_chunks + call add(l:extra_context, { + \ 'text': l:chunk.str, + \ 'time': l:chunk.time, + \ 'filename': l:chunk.filename + \ }) + endfor + + " the indentation of the current line + let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*')) + + let l:request = json_encode({ + \ 'input_prefix': l:prefix, + \ 'input_suffix': l:suffix, + \ 'input_extra': l:extra_context, + \ 'prompt': l:prompt, + \ 'n_predict': g:llama_config.n_predict, + \ 'n_indent': l:indent, + \ 'top_k': 40, + \ 'top_p': 0.99, + \ 'stream': v:false, + \ 'samplers': ["top_k", "top_p", "infill"], + \ 'cache_prompt': v:true, + \ 't_max_prompt_ms': g:llama_config.t_max_prompt_ms, + \ 't_max_predict_ms': g:llama_config.t_max_predict_ms + \ }) + + let l:curl_command = printf( + \ "curl --silent --no-buffer --request POST --url %s --header \"Content-Type: application/json\" --data %s", + \ g:llama_config.endpoint, shellescape(l:request) + \ ) + + if s:current_job != v:null + call jobstop(s:current_job) + endif + + " send the request asynchronously + let s:current_job = jobstart(l:curl_command, { + \ 'on_stdout': function('s:fim_on_stdout'), + \ 'on_exit': function('s:fim_on_exit'), + \ 'stdout_buffered': v:true, + \ 'pos_x': s:pos_x, + \ 'pos_y': s:pos_y, + \ 'is_auto': a:is_auto + \ }) + + " TODO: per-file location + let l:delta_y = abs(s:pos_y - s:pos_y_pick) + + " gather some extra context nearby and process it in the background + " only gather chunks if the cursor has moved a lot + " TODO: something more clever? reranking? + if a:is_auto && l:delta_y > 32 + " expand the prefix even further + call s:pick_chunk(getline(max([1, s:pos_y - g:llama_config.ring_scope]), max([1, s:pos_y - g:llama_config.n_prefix])), v:false, v:false) + + " pick a suffix chunk + call s:pick_chunk(getline(min([l:max_y, s:pos_y + g:llama_config.n_suffix]), min([l:max_y, s:pos_y + g:llama_config.n_suffix + g:llama_config.ring_chunk_size])), v:false, v:false) + + let s:pos_y_pick = s:pos_y + endif +endfunction + +" if first_line == v:true accept only the first line of the response +function! llama#fim_accept(first_line) + " insert the suggestion at the cursor location + if s:can_accept && len(s:content) > 0 + call setline(s:pos_y, s:line_cur[:(s:pos_x - 1)] . s:content[0]) + if len(s:content) > 1 + if !a:first_line + call append(s:pos_y, s:content[1:-1]) + endif + endif + + " move the cursor to the end of the accepted text + if !a:first_line && len(s:content) > 1 + call cursor(s:pos_y + len(s:content) - 1, s:pos_x + s:pos_dx + 1) + else + call cursor(s:pos_y, s:pos_x + len(s:content[0])) + endif + endif + + call llama#fim_cancel() +endfunction + +function! llama#fim_cancel() + let s:hint_shown = v:false + + " clear the virtual text + let l:bufnr = bufnr('%') + + let l:id_vt_fim = nvim_create_namespace('vt_fim') + let l:id_vt_info = nvim_create_namespace('vt_info') + + call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1) + call nvim_buf_clear_namespace(l:bufnr, l:id_vt_info, 0, -1) + + " remove the mappings + silent! iunmap + silent! iunmap + silent! iunmap +endfunction + +function! s:on_move() + let s:t_last_move = reltime() + + call llama#fim_cancel() +endfunction + +" callback that processes the FIM result from the server and displays the suggestion +function! s:fim_on_stdout(job_id, data, event) dict + let l:raw = join(a:data, "\n") + if len(l:raw) == 0 + return + endif + + if self.pos_x != col('.') - 1 || self.pos_y != line('.') + return + endif + + " show the suggestion only in insert mode + if mode() !=# 'i' + return + endif + + let s:pos_x = self.pos_x + let s:pos_y = self.pos_y + + let s:can_accept = v:true + let l:has_info = v:false + + if s:can_accept && v:shell_error + if !self.is_auto + call add(s:content, "<| curl error: is the server on? |>") + endif + let s:can_accept = v:false + endif + + let l:n_prompt = 0 + let l:t_prompt_ms = 1.0 + let l:s_prompt = 0 + + let l:n_predict = 0 + let l:t_predict_ms = 1.0 + let l:s_predict = 0 + + " get the generated suggestion + if s:can_accept + let l:response = json_decode(l:raw) + + for l:part in split(get(l:response, 'content', ''), "\n", 1) + call add(s:content, l:part) + endfor + + " remove trailing new lines + while len(s:content) > 0 && s:content[-1] == "" + call remove(s:content, -1) + endwhile + + let l:generation_settings = get(l:response, 'generation_settings', {}) + let l:n_ctx = get(l:generation_settings, 'n_ctx', 0) + + let l:n_cached = get(l:response, 'tokens_cached', 0) + let l:truncated = get(l:response, 'truncated', v:false) + + " if response.timings is available + if len(get(l:response, 'timings', {})) > 0 + let l:has_info = v:true + let l:timings = get(l:response, 'timings', {}) + + let l:n_prompt = get(l:timings, 'prompt_n', 0) + let l:t_prompt_ms = get(l:timings, 'prompt_ms', 1) + let l:s_prompt = get(l:timings, 'prompt_per_second', 0) + + let l:n_predict = get(l:timings, 'predicted_n', 0) + let l:t_predict_ms = get(l:timings, 'predicted_ms', 1) + let l:s_predict = get(l:timings, 'predicted_per_second', 0) + endif + endif + + if len(s:content) == 0 + call add(s:content, "") + let s:can_accept = v:false + endif + + if len(s:content) == 0 + return + endif + + " NOTE: the following is logic for discarding predictions that repeat existing text + " the code is quite ugly and there is very likely a simpler and more canonical way to implement this + " + " still, I wonder if there is some better way that avoids having to do these special hacks? + " on one hand, the LLM 'sees' the contents of the file before we start editing, so it is normal that it would + " start generating whatever we have given it via the extra context. but on the other hand, it's not very + " helpful to re-generate the same code that is already there + + " truncate the suggestion if the first line is empty + if len(s:content) == 1 && s:content[0] == "" + let s:content = [""] + endif + + " ... and the next lines are repeated + if len(s:content) > 1 && s:content[0] == "" && s:content[1:] == getline(s:pos_y + 1, s:pos_y + len(s:content) - 1) + let s:content = [""] + endif + + " truncate the suggestion if it repeats the suffix + if len(s:content) == 1 && s:content[0] == s:line_cur_suffix + let s:content = [""] + endif + + " find the first non-empty line (strip whitespace) + let l:cmp_y = s:pos_y + 1 + while l:cmp_y < line('$') && getline(l:cmp_y) =~? '^\s*$' + let l:cmp_y += 1 + endwhile + + if (s:line_cur_prefix . s:content[0]) == getline(l:cmp_y) + " truncate the suggestion if it repeats the next line + if len(s:content) == 1 + let s:content = [""] + endif + + " ... or if the second line of the suggestion is the prefix of line l:cmp_y + 1 + if len(s:content) == 2 && s:content[-1] == getline(l:cmp_y + 1)[:len(s:content[-1]) - 1] + let s:content = [""] + endif + + " ... or if the middle chunk of lines of the suggestion is the same as [l:cmp_y + 1, l:cmp_y + len(s:content) - 1) + if len(s:content) > 2 && join(s:content[1:-1], "\n") == join(getline(l:cmp_y + 1, l:cmp_y + len(s:content) - 1), "\n") + let s:content = [""] + endif + endif + + " keep only lines that have the same or larger whitespace prefix as s:line_cur_prefix + "let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*')) + "for i in range(1, len(s:content) - 1) + " if strlen(matchstr(s:content[i], '^\s*')) < l:indent + " let s:content = s:content[:i - 1] + " break + " endif + "endfor + + let s:pos_dx = len(s:content[-1]) + + let s:content[-1] .= s:line_cur_suffix + + call llama#fim_cancel() + + " display virtual text with the suggestion + let l:bufnr = bufnr('%') + + let l:id_vt_fim = nvim_create_namespace('vt_fim') + let l:id_vt_info = nvim_create_namespace('vt_info') + + " construct the info message + if g:llama_config.show_info > 0 && l:has_info + " prefix the info string with whitespace in order to offset it to the right of the fim overlay + let l:prefix = repeat(' ', len(s:content[0]) - len(s:line_cur_suffix) + 3) + + if l:truncated + let l:info = printf("%s | WARNING: the context is full: %d / %d, increase the server context size or reduce g:llama_config.ring_n_chunks", + \ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim', + \ l:n_cached, l:n_ctx + \ ) + else + let l:info = printf("%s | c: %d / %d, r: %d / %d, e: %d, q: %d / 16 | p: %d (%.2f ms, %.2f t/s) | g: %d (%.2f ms, %.2f t/s) | t: %.2f ms", + \ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim', + \ l:n_cached, l:n_ctx, len(s:ring_chunks), g:llama_config.ring_n_chunks, s:ring_n_evict, len(s:ring_queued), + \ l:n_prompt, l:t_prompt_ms, l:s_prompt, + \ l:n_predict, l:t_predict_ms, l:s_predict, + \ 1000.0 * reltimefloat(reltime(s:t_fim_start)) + \ ) + endif + + if g:llama_config.show_info == 1 + "" display it in the statusline + let &statusline = l:info + elseif g:llama_config.show_info == 2 + " display it to the right of the current line + call nvim_buf_set_extmark(l:bufnr, l:id_vt_info, s:pos_y - 1, s:pos_x - 1, { + \ 'virt_text': [[l:info, 'llama_hl_info']], + \ 'virt_text_pos': 'eol', + \ }) + endif + endif + + " display the suggestion + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, { + \ 'virt_text': [[s:content[0], 'llama_hl_hint']], + \ 'virt_text_win_col': virtcol('.') - 1 + \ }) + + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, { + \ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}), + \ 'virt_text_win_col': virtcol('.') + \ }) + + " setup accept shortcuts + inoremap :call llama#fim_accept(v:false) + inoremap :call llama#fim_accept(v:true) + + let s:hint_shown = v:true +endfunction + +function! s:fim_on_exit(job_id, exit_code, event) dict + if a:exit_code != 0 + echom "Job failed with exit code: " . a:exit_code + endif + + let s:current_job = v:null +endfunction From 94008cc76075fb4a29ee371e7ac255378d1bce6c Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 21 Oct 2024 20:12:52 +0200 Subject: [PATCH 40/95] arg : fix attention non-causal arg value hint (#9985) This commit updates the argument value hint for the `--attention` argument to `non-causal`. The motivation for this change is that the only values for this argument are `causal` and `non-causal`. --- common/arg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index d6a8e1f6f..168c2b1f3 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1097,7 +1097,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING")); add_opt(common_arg( - {"--attention"}, "{causal,non,causal}", + {"--attention"}, "{causal,non-causal}", "attention type for embeddings, use model default if unspecified", [](common_params & params, const std::string & value) { /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; } From 994cfb1acb9144bc95be0ab319175f30737cc92b Mon Sep 17 00:00:00 2001 From: Asghar Ghorbani Date: Mon, 21 Oct 2024 20:20:59 +0200 Subject: [PATCH 41/95] readme : update UI list (#9972) add PocketPal AI app --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 06c32a2b4..eeb3975eb 100644 --- a/README.md +++ b/README.md @@ -173,6 +173,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL) - [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) - [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) +- [PocketPal AI - An iOS and Android App](https://github.com/a-ghorbani/pocketpal-ai) (MIT) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* From e01c67affe450638162a1a457e2e57859ef6ebf0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 21 Oct 2024 22:52:22 +0300 Subject: [PATCH 42/95] llama.vim : move info to the right of screen [no ci] (#9787) 'eol' messes up the rendering with nvim v0.10.2 for some reason --- examples/llama.vim | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/llama.vim b/examples/llama.vim index e75872cae..9af451fbd 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -668,13 +668,14 @@ function! s:fim_on_stdout(job_id, data, event) dict endif if g:llama_config.show_info == 1 - "" display it in the statusline + " display it in the statusline let &statusline = l:info elseif g:llama_config.show_info == 2 " display it to the right of the current line call nvim_buf_set_extmark(l:bufnr, l:id_vt_info, s:pos_y - 1, s:pos_x - 1, { \ 'virt_text': [[l:info, 'llama_hl_info']], - \ 'virt_text_pos': 'eol', + "\ 'virt_text_pos': 'eol', + \ 'virt_text_pos': 'right_align', \ }) endif endif From e94a138d644a9b34da61805f7aeb8af595c61b53 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 22 Oct 2024 00:35:25 +0300 Subject: [PATCH 43/95] llama.vim : fix info text display [no ci] (#9787) --- examples/llama.vim | 24 +++++++----------------- 1 file changed, 7 insertions(+), 17 deletions(-) diff --git a/examples/llama.vim b/examples/llama.vim index 9af451fbd..7a60442ad 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -482,11 +482,9 @@ function! llama#fim_cancel() " clear the virtual text let l:bufnr = bufnr('%') - let l:id_vt_fim = nvim_create_namespace('vt_fim') - let l:id_vt_info = nvim_create_namespace('vt_info') + let l:id_vt_fim = nvim_create_namespace('vt_fim') call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1) - call nvim_buf_clear_namespace(l:bufnr, l:id_vt_info, 0, -1) " remove the mappings silent! iunmap @@ -644,13 +642,11 @@ function! s:fim_on_stdout(job_id, data, event) dict " display virtual text with the suggestion let l:bufnr = bufnr('%') - let l:id_vt_fim = nvim_create_namespace('vt_fim') - let l:id_vt_info = nvim_create_namespace('vt_info') + let l:id_vt_fim = nvim_create_namespace('vt_fim') " construct the info message if g:llama_config.show_info > 0 && l:has_info - " prefix the info string with whitespace in order to offset it to the right of the fim overlay - let l:prefix = repeat(' ', len(s:content[0]) - len(s:line_cur_suffix) + 3) + let l:prefix = ' ' if l:truncated let l:info = printf("%s | WARNING: the context is full: %d / %d, increase the server context size or reduce g:llama_config.ring_n_chunks", @@ -668,21 +664,15 @@ function! s:fim_on_stdout(job_id, data, event) dict endif if g:llama_config.show_info == 1 - " display it in the statusline + " display the info in the statusline let &statusline = l:info - elseif g:llama_config.show_info == 2 - " display it to the right of the current line - call nvim_buf_set_extmark(l:bufnr, l:id_vt_info, s:pos_y - 1, s:pos_x - 1, { - \ 'virt_text': [[l:info, 'llama_hl_info']], - "\ 'virt_text_pos': 'eol', - \ 'virt_text_pos': 'right_align', - \ }) + let l:info = '' endif endif - " display the suggestion + " display the suggestion and append the info to the end of the first line call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, { - \ 'virt_text': [[s:content[0], 'llama_hl_hint']], + \ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']], \ 'virt_text_win_col': virtcol('.') - 1 \ }) From 674804a99617b4f90292b4080ecab450ea3d30ba Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 22 Oct 2024 09:40:02 +0200 Subject: [PATCH 44/95] arg : fix typo in embeddings argument help [no ci] (#9994) This commit fixes two typos in the help text for the `--embd-normalize` and `--embd-separator` arguments. It also updates common.h which contain the same typo in two comments. --- common/arg.cpp | 4 ++-- common/common.h | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 168c2b1f3..cd9d315dc 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1695,7 +1695,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_BENCH})); add_opt(common_arg( {"--embd-normalize"}, "N", - string_format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize), + string_format("normalisation for embeddings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize), [](common_params & params, int value) { params.embd_normalize = value; } @@ -1709,7 +1709,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); add_opt(common_arg( {"--embd-separator"}, "STRING", - "separator of embendings (default \\n) for example \"<#sep#>\"", + "separator of embeddings (default \\n) for example \"<#sep#>\"", [](common_params & params, const std::string & value) { params.embd_sep = value; } diff --git a/common/common.h b/common/common.h index 5ca8fd391..19d928777 100644 --- a/common/common.h +++ b/common/common.h @@ -274,9 +274,9 @@ struct common_params { // embedding bool embedding = false; // get only sentence embedding - int32_t embd_normalize = 2; // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) + int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix - std::string embd_sep = "\n"; // separator of embendings + std::string embd_sep = "\n"; // separator of embeddings bool reranking = false; // enable reranking support on server // server params From 6b8447352df3d662b56280c8fc38d7f092885787 Mon Sep 17 00:00:00 2001 From: leo-pony Date: Tue, 22 Oct 2024 16:16:01 +0800 Subject: [PATCH 45/95] [CANN] Adapt to dynamically loadable backends mechanism (#9970) * [CANN] Adapt to dynamically loadable backends mechanism * Fix the Bug: inference running result is garbled in debug running model for LM models who's type is Q4_0 class * Handle the review comments of this pull request --- ggml/include/ggml-cann.h | 2 + ggml/src/ggml-backend.cpp | 9 +- ggml/src/ggml-cann.cpp | 354 +++++++++++++++++++++++++++----------- src/llama.cpp | 51 +----- 4 files changed, 267 insertions(+), 149 deletions(-) diff --git a/ggml/include/ggml-cann.h b/ggml/include/ggml-cann.h index 95bdaf10d..528975493 100644 --- a/ggml/include/ggml-cann.h +++ b/ggml/include/ggml-cann.h @@ -34,6 +34,8 @@ extern "C" { */ #define GGML_CANN_MAX_DEVICES 16 +GGML_API ggml_backend_reg_t ggml_backend_cann_reg(void); + /** * @brief Initializes the CANN backend for a specified device. * diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 81d09cd8b..7d7b63a15 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -561,6 +561,10 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na # include "ggml-amx.h" #endif +#ifdef GGML_USE_CANN +#include "ggml-cann.h" +#endif + struct ggml_backend_registry { std::vector backends; std::vector devices; @@ -587,8 +591,11 @@ struct ggml_backend_registry { #ifdef GGML_USE_AMX register_backend(ggml_backend_amx_reg()); #endif +#ifdef GGML_USE_CANN + register_backend(ggml_backend_cann_reg()); +#endif - // TODO: kompute, cann + // TODO: kompute register_backend(ggml_backend_cpu_reg()); } diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann.cpp index ec3c0a688..af0fb603a 100644 --- a/ggml/src/ggml-cann.cpp +++ b/ggml/src/ggml-cann.cpp @@ -39,6 +39,8 @@ #include "ggml-common.h" +#define GGML_CANN_NAME "CANN" + /** * @brief Handles CANN errors by printing an error message and aborting. * @@ -851,13 +853,6 @@ static void ggml_backend_cann_buffer_set_tensor( void *transform_buffer = malloc(size); ggml_backend_cann_transform(tensor, data, transform_buffer); -#ifndef NDEBUG - void *check_buffer = malloc(size); - ggml_backend_cann_transform_back(tensor, transform_buffer, - check_buffer); - GGML_ASSERT(memcmp(data, check_buffer, size) == 0); - free(check_buffer); -#endif ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE)); @@ -969,7 +964,7 @@ static void ggml_backend_cann_buffer_clear( * This structure defines function pointers to operations that can be performed * on a CANN buffer within the backend. */ -static ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { +static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { /* .get_name = */ ggml_backend_cann_buffer_get_name, /* .free_buffer = */ ggml_backend_cann_buffer_free_buffer, /* .get_base = */ ggml_backend_cann_buffer_get_base, @@ -1105,19 +1100,25 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size( GGML_UNUSED(buft); } +static bool ggml_backend_cann_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + /** * @brief Interface for managing CANN buffer types in the GGML backend. * * Provides function pointers for allocating, querying properties, and managing * memory for CANN buffer types in the GGML backend. */ -static ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = { +static const ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = { /* .get_name = */ ggml_backend_cann_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cann_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cann_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cann_buffer_type_get_alloc_size, - /* .is_host = */ NULL, + /* .is_host = */ ggml_backend_cann_buffer_type_is_host, }; /** @@ -1148,7 +1149,7 @@ ggml_backend_cann_buffer_type(int32_t device) { for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) { ggml_backend_cann_buffer_types[i] = { /* .iface = */ ggml_backend_cann_buffer_type_interface, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), /* .context = */ new ggml_backend_cann_buffer_type_context{ i, "CANN" + std::to_string(i)}, @@ -1264,7 +1265,7 @@ ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() { /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0), /* .context = */ nullptr, }; @@ -1511,13 +1512,6 @@ static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend, void *transform_buffer = malloc(size); ggml_backend_cann_transform(tensor, data, transform_buffer); -#ifndef NDEBUG - void *check_buffer = malloc(size); - ggml_backend_cann_transform_back(tensor, transform_buffer, - check_buffer); - GGML_ASSERT(memcmp(data, check_buffer, size)); - free(check_buffer); -#endif ACL_CHECK(aclrtMemcpyAsync( (char *)tensor->data + offset, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE, cann_ctx->stream())); @@ -1692,7 +1686,7 @@ static enum ggml_status ggml_backend_cann_graph_compute( * @return bool Returns true if the operation is supported by the backend, * otherwise false. */ -static bool ggml_backend_cann_supports_op(ggml_backend_t backend, +static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_tensor* op) { switch (op->op) { case GGML_OP_UNARY: @@ -1783,7 +1777,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_t backend, return false; } - GGML_UNUSED(backend); + GGML_UNUSED(dev); } /** @@ -1801,31 +1795,6 @@ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) { return buft->iface.get_name == ggml_backend_cann_buffer_type_name; } -/** - * @brief Checks if the CANN backend supports a specific backend buffer type. - * - * This function determines whether the CANN backend supports the given backend - * buffer type by comparing the device context of the backend and buffer type. - * It returns true if the devices are same between the backend context and - * buffer type context. - * - * @param backend Pointer to the CANN backend. - * @param buft Pointer to the backend buffer type to check. - * @return bool Returns true if the CANN backend supports the buffer type, - * otherwise false. - */ -static bool ggml_backend_cann_supports_buft( - ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - if (ggml_backend_buft_is_cann(buft)) { - ggml_backend_cann_context * cann_ctx = - (ggml_backend_cann_context *)backend->context; - ggml_backend_cann_buffer_type_context * buft_ctx = - (ggml_backend_cann_buffer_type_context *)buft->context; - return buft_ctx->device == cann_ctx->device; - } - return false; -} - /** * @brief Determines if a tensor operation should be offloaded to the CANN * backend. @@ -1840,54 +1809,14 @@ static bool ggml_backend_cann_supports_buft( * @return bool Returns true if the operation should be offloaded, otherwise * false. */ -static bool ggml_backend_cann_offload_op(ggml_backend_t backend, +static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, const ggml_tensor* op) { const int min_batch_size = 32; - GGML_UNUSED(backend); + GGML_UNUSED(dev); return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; } -/** - * @brief Creates a new event for the CANN backend. - * - * This function initializes a new event for the CANN backend by setting the - * device and creating an ACL runtime event. The created event is then wrapped - * in a ggml_backend_event structure and returned. - * - * @param backend Pointer to the CANN backend. - * @return ggml_backend_event_t Returns a pointer to the new event structure. - */ -static ggml_backend_event_t ggml_backend_cann_event_new( - ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - - ggml_cann_set_device(cann_ctx->device); - - aclrtEvent event; - ACL_CHECK(aclrtCreateEvent(&event)); - - return new ggml_backend_event{ - /* .device = */ nullptr, - /* .context = */ event, - }; -} - -/** - * @brief Frees a CANN backend event. - * - * This function destroys the ACL runtime event associated with the given CANN - * backend event and then deletes the event structure itself. - * - * @param event Pointer to the event structure to be freed. - */ -static void ggml_backend_cann_event_free(ggml_backend_event_t event) { - ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context)); - - delete event; -} - /** * @brief Records an event on the CANN backend stream. * @@ -1924,17 +1853,6 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend, } } -/** - * @brief Synchronizes the given event on the CANN backend. - * - * This function waits for the specified event to complete on the ACL runtime. - * - * @param event Pointer to the event structure to be synchronized. - */ -static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) { - ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context)); -} - /** * @brief Structure defining the interface for the CANN backend. * @@ -1942,7 +1860,7 @@ static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) { * supported by the CANN backend, including name retrieval, memory * management, tensor operations, synchronization, and event handling. */ -static ggml_backend_i ggml_backend_cann_interface = { +static const ggml_backend_i ggml_backend_cann_interface = { /* .get_name = */ ggml_backend_cann_name, /* .free = */ ggml_backend_cann_free, /* .get_default_buffer_type = */ ggml_backend_cann_get_default_buffer_type, @@ -1955,9 +1873,9 @@ static ggml_backend_i ggml_backend_cann_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cann_graph_compute, - /* .supports_op = */ ggml_backend_cann_supports_op, - /* .supports_buft = */ ggml_backend_cann_supports_buft, - /* .offload_op = */ ggml_backend_cann_offload_op, + /* .supports_op = */ NULL, // moved to device + /* .supports_buft = */ NULL, // moved to device + /* .offload_op = */ NULL, // moved to device /* .event_record = */ ggml_backend_cann_event_record, /* .event_wait = */ ggml_backend_cann_event_wait, }; @@ -1976,6 +1894,234 @@ static ggml_guid_t ggml_backend_cann_guid() { return &guid; } +// backend device +struct ggml_backend_cann_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char* ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_get_device_memory(ctx->device, free, total); +} + +static enum ggml_backend_dev_type ggml_backend_cann_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; +} + +static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { + props->name = ggml_backend_cann_device_get_name(dev); + props->description = ggml_backend_cann_device_get_description(dev); + props->type = ggml_backend_cann_device_get_type(dev); + ggml_backend_cann_device_get_memory(dev, &props->memory_free, &props->memory_total); + + bool host_buffer = getenv("GGML_CANN_NO_PINNED") == nullptr; + + props->caps = { + /* .async = */ false, + /* .host_buffer = */ host_buffer, + /* .buffer_from_host_ptr = */ false, + /* .events = */ true, + }; +} + +static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ggml_backend_cann_init(ctx->device); +} + +/** + * @brief Checks if the CANN backend supports a specific backend buffer type. + * + * This function determines whether the CANN backend supports the given backend + * buffer type by comparing the device context of the backend and buffer type. + * It returns true if the devices are same between the backend context and + * buffer type context. + * + * @param backend Pointer to the CANN backend. + * @param buft Pointer to the backend buffer type to check. + * @return bool Returns true if the CANN backend supports the buffer type, + * otherwise false. + */ +static bool ggml_backend_cann_supports_buft( + ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (ggml_backend_buft_is_cann(buft)) { + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_buffer_type_context * buft_ctx = + (ggml_backend_cann_buffer_type_context *)buft->context; + return buft_ctx->device == dev_ctx->device; + } + return false; +} + +static ggml_backend_buffer_type_t ggml_backend_cann_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + return ggml_backend_cann_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return ggml_backend_cann_host_buffer_type(); +} + +/** + * @brief Creates a new event for the CANN backend device. + * + * This function initializes a new event for the CANN backend by setting the + * device and creating an ACL runtime event. The created event is then wrapped + * in a ggml_backend_event structure and returned. + * + * @param backend Pointer to the CANN backend. + * @return ggml_backend_event_t Returns a pointer to the new event structure. + */ +static ggml_backend_event_t ggml_backend_cann_device_event_new( + ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; + + ggml_cann_set_device(dev_ctx->device); + + aclrtEvent event; + ACL_CHECK(aclrtCreateEvent(&event)); + + return new ggml_backend_event{ + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), dev_ctx->device), + /* .context = */ event, + }; +} + +/** + * @brief Frees a CANN backend event. + * + * This function destroys the ACL runtime event associated with the given CANN + * backend event and then deletes the event structure itself. + * + * @param event Pointer to the event structure to be freed. + */ +static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { + ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context)); + + delete event; + GGML_UNUSED(dev); +} + +/** + * @brief Synchronizes the given event on the CANN backend. + * + * This function waits for the specified event to complete on the ACL runtime. + * + * @param event Pointer to the event structure to be synchronized. + */ +static void ggml_backend_cann_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { + ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context)); + + GGML_UNUSED(dev); +} + +static const ggml_backend_device_i ggml_backend_cann_device_interface = { + /* .get_name = */ ggml_backend_cann_device_get_name, + /* .get_description = */ ggml_backend_cann_device_get_description, + /* .get_memory = */ ggml_backend_cann_device_get_memory, + /* .get_type = */ ggml_backend_cann_device_get_type, + /* .get_props = */ ggml_backend_cann_device_get_props, + /* .init_backend = */ ggml_backend_cann_device_init, // called for every card + /* .get_buffer_type = */ ggml_backend_cann_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_cann_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ NULL, // not supported for CANN + /* .supports_op = */ ggml_backend_cann_supports_op, + /* .supports_buft = */ ggml_backend_cann_supports_buft, + /* .offload_op = */ ggml_backend_cann_offload_op, + /* .event_new = */ ggml_backend_cann_device_event_new, + /* .event_free = */ ggml_backend_cann_device_event_free, + /* .event_synchronize = */ ggml_backend_cann_device_event_synchronize, +}; + + +// backend reg +struct ggml_backend_cann_reg_context { + std::vector devices; +}; + +static const char * ggml_backend_cann_reg_get_name(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return GGML_CANN_NAME; +} + +static size_t ggml_backend_cann_reg_get_device_count(ggml_backend_reg_t reg) { + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + return ctx->devices.size(); +} + +static ggml_backend_dev_t ggml_backend_cann_reg_get_device(ggml_backend_reg_t reg, size_t index) { + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + GGML_ASSERT(index < ctx->devices.size()); + return ctx->devices[index]; +} + +static void * ggml_backend_cann_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) { + GGML_UNUSED(reg); + GGML_UNUSED(name); + // reserved for future use + return nullptr; +} + +static const ggml_backend_reg_i ggml_backend_cann_reg_interface = { + /* .get_name = */ ggml_backend_cann_reg_get_name, + /* .get_device_count = */ ggml_backend_cann_reg_get_device_count, + /* .get_device_get = */ ggml_backend_cann_reg_get_device, + /* .get_proc_address = */ ggml_backend_cann_reg_get_proc_address, +}; + +// backend registry, called only once for cann backend +ggml_backend_reg_t ggml_backend_cann_reg() { + static ggml_backend_reg reg; + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + aclInit(nullptr); + ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context; + + for (int i = 0; i < ggml_cann_info().device_count; i++) { + ggml_backend_cann_device_context* dev_ctx = new ggml_backend_cann_device_context(); + dev_ctx->description = aclrtGetSocName(); + dev_ctx->device = i; + dev_ctx->name = GGML_CANN_NAME + std::to_string(i); + ggml_cann_set_device(i); + ggml_backend_dev_t dev = new ggml_backend_device { + /* .interface = */ ggml_backend_cann_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx + }; + ctx->devices.push_back(dev); + } + + reg = ggml_backend_reg { + /* .interface = */ ggml_backend_cann_reg_interface, + /* .context = */ ctx + }; + } + + initialized = true; + } + + return ® +} + ggml_backend_t ggml_backend_cann_init(int32_t device) { aclInit(nullptr); if (device < 0 || device >= ggml_backend_cann_get_device_count()) { @@ -1992,7 +2138,7 @@ ggml_backend_t ggml_backend_cann_init(int32_t device) { ggml_backend_t cann_backend = new ggml_backend{/* .guid = */ ggml_backend_cann_guid(), /* .interface = */ ggml_backend_cann_interface, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), /* .context = */ ctx}; return cann_backend; diff --git a/src/llama.cpp b/src/llama.cpp index 98ec123c1..e1ca478ec 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -10,8 +10,6 @@ #if defined(GGML_USE_KOMPUTE) # include "ggml-kompute.h" -#elif defined(GGML_USE_CANN) -# include "ggml-cann.h" #endif #ifndef __AMX_INT8__ @@ -3399,10 +3397,6 @@ static int llama_get_device_count(const llama_model & model) { count += (int) model.rpc_servers.size(); #endif -#if defined(GGML_USE_CANN) - count += ggml_backend_cann_get_device_count(); -#endif - return count; GGML_UNUSED(model); @@ -3420,11 +3414,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_mode } } -#if defined(GGML_USE_CANN) - if (host_buffer) { - buft = ggml_backend_cann_host_buffer_type(); - } -#elif defined(GGML_USE_CPU_HBM) +#if defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); #endif @@ -3446,8 +3436,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_ #if defined(GGML_USE_KOMPUTE) buft = ggml_backend_kompute_buffer_type(device); -#elif defined(GGML_USE_CANN) - buft = ggml_backend_cann_buffer_type(device); #endif if (buft == nullptr) { @@ -3491,14 +3479,13 @@ static size_t llama_get_device_memory(const llama_model & model, int device) { return free; } -#if defined(GGML_USE_CANN) - size_t total; - size_t free; - ggml_backend_cann_get_device_memory(device, &free, &total); - return free; -#else + if (model.devices.size() > 0) { + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(model.devices[0]); + LLAMA_LOG_WARN("%s: failed to get free memmory of device:%d of backend:%s, for device id is out of range.\n", __func__, device, ggml_backend_reg_name(reg)); + } else { + LLAMA_LOG_WARN("%s: failed to get free memmory of device, no devices in inputted model.\n", __func__); + } return 1; -#endif GGML_UNUSED(model); GGML_UNUSED(device); @@ -19396,30 +19383,6 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(backend); } -#elif defined(GGML_USE_CANN) - // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used - // TODO: ggml_backend_cann is not support split tensor now, just leave code here. - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - ggml_backend_t backend = ggml_backend_cann_init(main_gpu); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, main_gpu); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } else { - // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU - // TODO: currently, CANN can't use multi-gpus, just leave code here for further cann version. - for (int32_t device = 0; device < ggml_backend_cann_get_device_count(); ++device) { - ggml_backend_t backend = ggml_backend_cann_init(device); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, device); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } #endif // add other backends (such as BLAS) From 4ff7fe1fb36b04ddd158b2de881c348c5f0ff5e4 Mon Sep 17 00:00:00 2001 From: Molly Sophia Date: Tue, 22 Oct 2024 18:33:37 +0800 Subject: [PATCH 46/95] llama : add chat template for RWKV-World + fix EOT (#9968) * Add chat template for RWKV-World Signed-off-by: Molly Sophia * RWKV: Fix the chat template not being used Signed-off-by: Molly Sophia * RWKV v6: Set EOT token to ``\n\n`` Signed-off-by: Molly Sophia * readme: add rwkv into supported model list Signed-off-by: Molly Sophia --------- Signed-off-by: Molly Sophia --- README.md | 1 + convert_hf_to_gguf.py | 2 ++ src/llama.cpp | 9 +++++++++ tests/test-chat-template.cpp | 4 ++++ 4 files changed, 16 insertions(+) diff --git a/README.md b/README.md index eeb3975eb..8fe1f4b4b 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,7 @@ Typically finetunes of the base models below are supported as well. - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a) - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat) - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) +- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) (instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md)) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index da5feb25b..e0b1b2bf9 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2864,6 +2864,8 @@ class Rwkv6Model(Model): self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) + special_vocab.chat_template = "rwkv-world" + special_vocab._set_special_token("eot", 261) special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): diff --git a/src/llama.cpp b/src/llama.cpp index e1ca478ec..73190c88f 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -21697,6 +21697,15 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "[|assistant|]"; } + } else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world") || tmpl_contains("'User: ' + message['content'] + '\n\nAssistant:'")) { + for (auto message : chat) { + std::string role(message->role); + if (role == "user") { + ss << "User: " << message->content << "\n\nAssistant:"; + } else { + ss << message->content << "\n\n"; + } + } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 6f046249f..fdc4a9bc3 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -65,6 +65,8 @@ int main(void) { u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", // DeepSeek-V2 "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", + // RWKV-World + "{% for message in messages %}{% if message['role'] == 'user' %}{{'User: ' + message['content'] + '\n\nAssistant:'}}{% else %}{{message['content'] + '\n\n'}}{% endif %}{% endfor %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -109,6 +111,8 @@ int main(void) { u8"You are a helpful assistant<用户>HelloHi there<用户>Who are youI am an assistant<用户>Another question", // DeepSeek-V2 u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:", + // RWKV-World + "You are a helpful assistant\n\nUser: Hello\n\nAssistant:Hi there\n\nUser: Who are you\n\nAssistant: I am an assistant \n\nUser: Another question\n\nAssistant:", }; std::vector formatted_chat(1024); int32_t res; From c421ac072d46172ab18924e1e8be53680b54ed3b Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 22 Oct 2024 13:08:41 +0200 Subject: [PATCH 47/95] lora : warn user if new token is added in the adapter (#9948) --- convert_lora_to_gguf.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index 439a78de1..bc68f68af 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -348,6 +348,9 @@ if __name__ == '__main__': if ".base_layer.weight" in name: continue logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor") + if ".embed_tokens.weight" in name or ".lm_head.weight" in name: + logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning") + logger.error("Hint: if you are using TRL, make sure not to call setup_chat_format()") sys.exit(1) if base_name in tensor_map: From 11d47057a51f3d9b9231e6b57d0ca36020c0ee99 Mon Sep 17 00:00:00 2001 From: Molly Sophia Date: Tue, 22 Oct 2024 21:22:26 +0800 Subject: [PATCH 48/95] Rwkv chat template fix (#10001) * llama: remove useless template matching for rwkv-world Signed-off-by: Molly Sophia * converter: Add comment about the hack for rwkv models Signed-off-by: Molly Sophia * Update src/llama.cpp Co-authored-by: Xuan Son Nguyen --------- Signed-off-by: Molly Sophia Co-authored-by: Xuan Son Nguyen --- convert_hf_to_gguf.py | 1 + src/llama.cpp | 3 ++- tests/test-chat-template.cpp | 4 ---- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index e0b1b2bf9..7e552a71b 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2865,6 +2865,7 @@ class Rwkv6Model(Model): self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) special_vocab.chat_template = "rwkv-world" + # hack: Add '\n\n' as the EOT token to make it chat normally special_vocab._set_special_token("eot", 261) special_vocab.add_to_gguf(self.gguf_writer) diff --git a/src/llama.cpp b/src/llama.cpp index 73190c88f..6a5c56a77 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -21697,7 +21697,8 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "[|assistant|]"; } - } else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world") || tmpl_contains("'User: ' + message['content'] + '\n\nAssistant:'")) { + } else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world")) { + // this template requires the model to have "\n\n" as EOT token for (auto message : chat) { std::string role(message->role); if (role == "user") { diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index fdc4a9bc3..6f046249f 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -65,8 +65,6 @@ int main(void) { u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", // DeepSeek-V2 "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", - // RWKV-World - "{% for message in messages %}{% if message['role'] == 'user' %}{{'User: ' + message['content'] + '\n\nAssistant:'}}{% else %}{{message['content'] + '\n\n'}}{% endif %}{% endfor %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -111,8 +109,6 @@ int main(void) { u8"You are a helpful assistant<用户>HelloHi there<用户>Who are youI am an assistant<用户>Another question", // DeepSeek-V2 u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:", - // RWKV-World - "You are a helpful assistant\n\nUser: Hello\n\nAssistant:Hi there\n\nUser: Who are you\n\nAssistant: I am an assistant \n\nUser: Another question\n\nAssistant:", }; std::vector formatted_chat(1024); int32_t res; From 19d900a7565b8f6b0a708836a57d26966cb9efe2 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 22 Oct 2024 15:31:06 +0200 Subject: [PATCH 49/95] llama : rename batch to ubatch (#9950) This commit renames the member field batch in llm_build_context to ubatch, and also the parameter batch in llama_build_graph, and llama_set_inputs to ubatch. The motivation for this change is to make the code more readable (considering there are the structs llama_batch and llama_sbatch), and consistent with other parts of the code base where parameters/fields of type llama_ubatch are named ubatch. --- src/llama.cpp | 218 +++++++++++++++++++++++++------------------------- 1 file changed, 109 insertions(+), 109 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 6a5c56a77..7a5a46dce 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -10017,7 +10017,7 @@ struct llm_build_context { llama_context & lctx; const llama_hparams & hparams; const llama_cparams & cparams; - const llama_ubatch & batch; + const llama_ubatch & ubatch; const llama_kv_cache & kv_self; const int64_t n_embd; @@ -10063,14 +10063,14 @@ struct llm_build_context { // TODO: consider making the entire interface noexcept llm_build_context( llama_context & lctx, - const llama_ubatch & batch, + const llama_ubatch & ubatch, const llm_build_cb & cb, bool worst_case) : model (lctx.model), lctx (lctx), hparams (model.hparams), cparams (lctx.cparams), - batch (batch), + ubatch (ubatch), kv_self (lctx.kv_self), n_embd (hparams.n_embd), n_layer (hparams.n_layer), @@ -10092,7 +10092,7 @@ struct llm_build_context { beta_slow (cparams.yarn_beta_slow), norm_eps (hparams.f_norm_eps), norm_rms_eps (hparams.f_norm_rms_eps), - n_tokens (batch.n_tokens), + n_tokens (ubatch.n_tokens), n_kv (worst_case ? kv_self.size : kv_self.n), n_outputs (worst_case ? n_tokens : lctx.n_outputs), n_outputs_enc (worst_case ? n_tokens : lctx.embd_enc.size() / hparams.n_embd), @@ -10461,7 +10461,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -10621,7 +10621,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr; @@ -10736,7 +10736,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -10840,7 +10840,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -10962,7 +10962,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // multiply by embedding_multiplier_scale of 78.38367176906169 inpL = ggml_scale(ctx0, inpL, 78.38367176906169f); @@ -11120,7 +11120,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11242,7 +11242,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -11345,7 +11345,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -11447,7 +11447,7 @@ struct llm_build_context { } // construct input embeddings (token, type, position) - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // token types are hardcoded to zero ("Sentence A") struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); @@ -11634,7 +11634,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -11736,7 +11736,7 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -11874,7 +11874,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12024,7 +12024,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12137,7 +12137,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12252,7 +12252,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12397,7 +12397,7 @@ struct llm_build_context { struct ggml_tensor * ffn_output; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12516,7 +12516,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12644,7 +12644,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12749,7 +12749,7 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12854,7 +12854,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -12964,7 +12964,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13082,7 +13082,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13209,7 +13209,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // scale the input embeddings inpL = ggml_scale(ctx0, inpL, scale_embd); @@ -13353,7 +13353,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // scale the input embeddings inpL = ggml_scale(ctx0, inpL, scale_embd); @@ -13554,7 +13554,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -13662,7 +13662,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd)); cb(inpL, "inp_scaled", -1); @@ -13800,7 +13800,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -13916,7 +13916,7 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); @@ -13928,7 +13928,7 @@ struct llm_build_context { LLM_NORM_RMS, cb, il); cb(cur, "attn_norm", il); - cur = llm_build_mamba(ctx0, lctx, batch, gf, cur, + cur = llm_build_mamba(ctx0, lctx, ubatch, gf, cur, state_copy, state_mask, kv_head, n_kv, cb, il); @@ -13974,7 +13974,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14131,7 +14131,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14259,7 +14259,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14378,7 +14378,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14505,7 +14505,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14650,7 +14650,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -14791,7 +14791,7 @@ struct llm_build_context { struct ggml_tensor * inpL; // {n_embd, n_tokens} - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15006,7 +15006,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15160,7 +15160,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); GGML_ASSERT(lctx.is_encoding); struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false); @@ -15292,7 +15292,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); GGML_ASSERT(!lctx.is_encoding); GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first"); @@ -15494,7 +15494,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); @@ -15586,7 +15586,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15700,7 +15700,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15824,7 +15824,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -15944,11 +15944,11 @@ struct llm_build_context { // Token shift state dimensions should be 2 * n_emb GGML_ASSERT(n_embd == hparams.n_embd_k_s() / 2); - const int64_t n_seqs = batch.n_seqs; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_seqs = ubatch.n_seqs; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(n_seqs != 0); - GGML_ASSERT(batch.equal_seqs); + GGML_ASSERT(ubatch.equal_seqs); GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs); struct ggml_tensor * cur; @@ -15956,7 +15956,7 @@ struct llm_build_context { struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1); for (int il = 0; il < n_layer; ++il) { @@ -16070,7 +16070,7 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -16266,7 +16266,7 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { static struct ggml_cgraph * llama_build_graph( llama_context & lctx, - const llama_ubatch & batch, + const llama_ubatch & ubatch, bool worst_case) { const auto & model = lctx.model; @@ -16288,7 +16288,7 @@ static struct ggml_cgraph * llama_build_graph( // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends // FIXME: fix in ggml_backend_sched const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer; - if (batch.n_tokens < 32 || full_offload) { + if (ubatch.n_tokens < 32 || full_offload) { if (il != -1 && strcmp(name, "norm") == 0) { for (auto * backend : lctx.backends) { if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) && @@ -16303,7 +16303,7 @@ static struct ggml_cgraph * llama_build_graph( struct ggml_cgraph * result = NULL; - struct llm_build_context llm(lctx, batch, cb, worst_case); + struct llm_build_context llm(lctx, ubatch, cb, worst_case); llm.init(); @@ -16554,7 +16554,7 @@ static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t return relative_bucket; } -static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { +static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) { // // set input data // @@ -16563,28 +16563,28 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { const auto & cparams = lctx.cparams; const auto & kv_self = lctx.kv_self; - if (batch.token) { - const int64_t n_tokens = batch.n_tokens; + if (ubatch.token) { + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); + ggml_backend_tensor_set(lctx.inp_tokens, ubatch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); } - if (batch.embd) { + if (ubatch.embd) { const int64_t n_embd = hparams.n_embd; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); + ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); } - if (batch.pos && lctx.inp_pos) { - const int64_t n_tokens = batch.n_tokens; + if (ubatch.pos && lctx.inp_pos) { + const int64_t n_tokens = ubatch.n_tokens; - ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); + ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); } if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) { GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs"); - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer)); int32_t * data = (int32_t *) lctx.inp_out_ids->data; @@ -16593,10 +16593,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int i = 0; i < n_tokens; ++i) { data[i] = i; } - } else if (batch.output) { + } else if (ubatch.output) { int32_t n_outputs = 0; for (int i = 0; i < n_tokens; ++i) { - if (batch.output[i]) { + if (ubatch.output[i]) { data[n_outputs++] = i; } } @@ -16621,9 +16621,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. if (cparams.causal_attn && !lctx.is_encoding) { const int64_t n_kv = kv_self.n; - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; float * data = nullptr; @@ -16640,14 +16640,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } // For causal attention, use only the previous KV cells - // of the correct sequence for each token of the batch. + // of the correct sequence for each token of the ubatch. // It's assumed that if a token in the batch has multiple sequences, they are equivalent. for (int h = 0; h < 1; ++h) { for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; for (int j = 0; j < n_seq_tokens; ++j) { - const llama_pos pos = batch.pos[s*n_seq_tokens + j]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + j]; for (int i = 0; i < n_kv; ++i) { float f; @@ -16693,9 +16693,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } } } else { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; // when using kv cache, the mask needs to match the kv cache size const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens; @@ -16705,7 +16705,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int s1 = 0; s1 < n_seqs; ++s1) { - const llama_seq_id seq_id = batch.seq_id[s1][0]; + const llama_seq_id seq_id = ubatch.seq_id[s1][0]; for (int j = 0; j < n_seq_tokens; ++j) { const int32_t tj = s1*n_seq_tokens + j; @@ -16715,10 +16715,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { const int32_t ti = s0*n_seq_tokens + i; float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[s0]; ++s) { - if (batch.seq_id[s0][s] == seq_id) { + for (int s = 0; s < ubatch.n_seq_id[s0]; ++s) { + if (ubatch.seq_id[s0][s] == seq_id) { if (hparams.use_alibi) { - f = -std::abs(batch.pos[ti] - batch.pos[tj]); + f = -std::abs(ubatch.pos[ti] - ubatch.pos[tj]); } else { f = 0.0f; } @@ -16740,9 +16740,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_mean); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); @@ -16753,12 +16753,12 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { std::vector sum(n_tokens, 0); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); - sum[seq_id] += batch.n_seq_tokens; + sum[seq_id] += ubatch.n_seq_tokens; } std::vector div(n_tokens, 0.0f); @@ -16770,7 +16770,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; for (int i = 0; i < n_seq_tokens; ++i) { data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id]; @@ -16781,9 +16781,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { if (cparams.embeddings && ( cparams.pooling_type == LLAMA_POOLING_TYPE_CLS || cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -16792,13 +16792,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls)); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK"); for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + i]; if (pos == 0) { data[seq_id] = s*n_seq_tokens + i; @@ -16808,9 +16808,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) { - const int64_t n_tokens = batch.n_tokens; - const int64_t n_seq_tokens = batch.n_seq_tokens; - const int64_t n_seqs = batch.n_seqs; + const int64_t n_tokens = ubatch.n_tokens; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + const int64_t n_seqs = ubatch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -16822,13 +16822,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { std::vector last_row(n_tokens, -1); for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = batch.seq_id[s][0]; + const llama_seq_id seq_id = ubatch.seq_id[s][0]; - // TODO: adapt limits to n_seqs when batch.equal_seqs is true + // TODO: adapt limits to n_seqs when ubatch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST"); for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + const llama_pos pos = ubatch.pos[s*n_seq_tokens + i]; if (pos >= last_pos[seq_id]) { last_pos[seq_id] = pos; @@ -16890,10 +16890,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } if (lctx.inp_pos_bucket) { - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer)); - GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing + GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing int32_t * data = (int32_t *) lctx.inp_pos_bucket->data; @@ -16902,7 +16902,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_kv; ++i) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); } } } @@ -16910,7 +16910,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int h = 0; h < 1; ++h) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_tokens; ++i) { - data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); + data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch.pos[i], ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding); } } } @@ -16926,10 +16926,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) { const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer)); - GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing + GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing float * data = (float *) lctx.inp_KQ_mask_cross->data; @@ -16937,8 +16937,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { for (int j = 0; j < n_tokens; ++j) { for (int i = 0; i < n_output_enc; ++i) { float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[j]; ++s) { - const llama_seq_id seq_id = batch.seq_id[j][s]; + for (int s = 0; s < ubatch.n_seq_id[j]; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[j][s]; if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) { f = 0.0f; } From c8c07d658a6cefc5a50cfdf6be7d726503612303 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 22 Oct 2024 16:59:02 +0200 Subject: [PATCH 50/95] llama : fix empty batch causing llama_batch_allocr to crash (#9966) * llama : fix empty batch cause llama_batch_allocr to crash * move batch_allocr inside decode/encode_internal * fix build * add GGML_ASSERT * Apply suggestions from code review Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- src/llama.cpp | 128 ++++++++++++++++++++++++++------------------------ 1 file changed, 67 insertions(+), 61 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 7a5a46dce..24e1f1f01 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -5177,6 +5177,57 @@ struct llama_model_loader { } }; +// temporary allocate memory for the input batch if needed +static const llama_seq_id batch_default_seq_id = 0; +struct llama_batch_allocr { + std::array seq_id_0 = {batch_default_seq_id}; + std::vector pos; + std::vector n_seq_id; + std::vector seq_id; + std::vector logits; + struct llama_batch batch; + // optionally fulfill the batch returned by llama_batch_get_one + llama_batch_allocr(llama_context & ctx, struct llama_batch in_batch) { + batch = in_batch; + GGML_ASSERT(batch.n_tokens > 0); + if (!batch.pos) { + // determine the last position in KV cache + llama_pos last_pos = -1; + for (const auto & cell : ctx.kv_self.cells) { + if (cell.has_seq_id(batch_default_seq_id)) { + last_pos = std::max(last_pos, cell.pos); + } + } + last_pos++; // next position + pos.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + pos[i] = i+last_pos; + } + batch.pos = pos.data(); + } + if (!batch.n_seq_id) { + n_seq_id.resize(batch.n_tokens); + for (int32_t i = 0; i < batch.n_tokens; i++) { + n_seq_id[i] = seq_id_0.size(); + } + batch.n_seq_id = n_seq_id.data(); + } + if (!batch.seq_id) { + seq_id.resize(batch.n_tokens + 1); + seq_id[batch.n_tokens] = NULL; + for (int32_t i = 0; i < batch.n_tokens; i++) { + seq_id[i] = seq_id_0.data(); + } + batch.seq_id = seq_id.data(); + } + if (!batch.logits) { + logits.resize(batch.n_tokens); + logits[logits.size() - 1] = true; + batch.logits = logits.data(); + } + } +}; + template<> bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) { uint32_t tmp; @@ -17095,16 +17146,20 @@ static void llama_graph_compute( // static int llama_decode_internal( llama_context & lctx, - llama_batch batch) { + llama_batch inp_batch) { lctx.is_encoding = false; - const uint32_t n_tokens_all = batch.n_tokens; - if (n_tokens_all == 0) { + if (inp_batch.n_tokens == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); return -1; } + // temporary allocate memory for the input batch if needed + llama_batch_allocr batch_allocr(lctx, inp_batch); + const llama_batch & batch = batch_allocr.batch; + const uint32_t n_tokens_all = batch.n_tokens; + const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -17409,17 +17464,20 @@ static int llama_decode_internal( // static int llama_encode_internal( llama_context & lctx, - llama_batch batch) { + llama_batch inp_batch) { lctx.is_encoding = true; - const uint32_t n_tokens = batch.n_tokens; - - if (n_tokens == 0) { + if (inp_batch.n_tokens == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); return -1; } + // temporary allocate memory for the input batch if needed + llama_batch_allocr batch_allocr(lctx, inp_batch); + const llama_batch & batch = batch_allocr.batch; + const uint32_t n_tokens = batch.n_tokens; + const auto & model = lctx.model; const auto & hparams = model.hparams; const auto & cparams = lctx.cparams; @@ -21090,61 +21148,10 @@ void llama_batch_free(struct llama_batch batch) { if (batch.logits) free(batch.logits); } -// temporary allocate memory for the input batch if needed -static const llama_seq_id batch_default_seq_id = 0; -struct llama_batch_allocr { - std::array seq_id_0 = {batch_default_seq_id}; - std::vector pos; - std::vector n_seq_id; - std::vector seq_id; - std::vector logits; - struct llama_batch batch; - // optionally fulfill the batch returned by llama_batch_get_one - llama_batch_allocr(struct llama_context * ctx, struct llama_batch in_batch) { - batch = in_batch; - if (!batch.pos) { - // determine the last position in KV cache - llama_pos last_pos = -1; - for (const auto & cell : ctx->kv_self.cells) { - if (cell.has_seq_id(batch_default_seq_id)) { - last_pos = std::max(last_pos, cell.pos); - } - } - last_pos++; // next position - pos.resize(batch.n_tokens); - for (int32_t i = 0; i < batch.n_tokens; i++) { - pos[i] = i+last_pos; - } - batch.pos = pos.data(); - } - if (!batch.n_seq_id) { - n_seq_id.resize(batch.n_tokens); - for (int32_t i = 0; i < batch.n_tokens; i++) { - n_seq_id[i] = seq_id_0.size(); - } - batch.n_seq_id = n_seq_id.data(); - } - if (!batch.seq_id) { - seq_id.resize(batch.n_tokens + 1); - seq_id[batch.n_tokens] = NULL; - for (int32_t i = 0; i < batch.n_tokens; i++) { - seq_id[i] = seq_id_0.data(); - } - batch.seq_id = seq_id.data(); - } - if (!batch.logits) { - logits.resize(batch.n_tokens); - logits[logits.size() - 1] = true; - batch.logits = logits.data(); - } - } -}; - int32_t llama_encode( struct llama_context * ctx, struct llama_batch batch) { - llama_batch_allocr batch_allocr(ctx, batch); - const int ret = llama_encode_internal(*ctx, batch_allocr.batch); + const int ret = llama_encode_internal(*ctx, batch); if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret); } @@ -21155,8 +21162,7 @@ int32_t llama_encode( int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch) { - llama_batch_allocr batch_allocr(ctx, batch); - const int ret = llama_decode_internal(*ctx, batch_allocr.batch); + const int ret = llama_decode_internal(*ctx, batch); if (ret != 0) { LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); } From 873279b1592e433c4d9eb5065091cc98473c7bee Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 20 Oct 2024 00:22:59 +0000 Subject: [PATCH 51/95] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/5633bcff0c6162b9e4b5f1264264611e950c8ec7?narHash=sha256-9UTxR8eukdg%2BXZeHgxW5hQA9fIKHsKCdOIUycTryeVw%3D' (2024-10-09) → 'github:NixOS/nixpkgs/4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0?narHash=sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c%2BcHUJwA%3D' (2024-10-18) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 702527028..1f8defab7 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1728492678, - "narHash": "sha256-9UTxR8eukdg+XZeHgxW5hQA9fIKHsKCdOIUycTryeVw=", + "lastModified": 1729256560, + "narHash": "sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c+cHUJwA=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "5633bcff0c6162b9e4b5f1264264611e950c8ec7", + "rev": "4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0", "type": "github" }, "original": { From 4c9388fb96ac2415fbb1239b7ba8346616606e2e Mon Sep 17 00:00:00 2001 From: Jun Hee Yoo Date: Wed, 23 Oct 2024 19:33:45 +0900 Subject: [PATCH 52/95] metal : add POOL2D and fix IM2COL (#9943) * add pool_2d Signed-off-by: Junhee Yoo * fix im2col and add unittest for N>=1024 Signed-off-by: Junhee Yoo * add tests for N % 1024 != 0 Signed-off-by: Junhee Yoo * remove trailing whitespaces Signed-off-by: Junhee Yoo * apply suggestions Signed-off-by: Junhee Yoo * apply more optimization - original IM2COL kernel + _ext with MIN() Signed-off-by: Junhee Yoo * apply review: change kernel name of pool_2d Signed-off-by: Junhee Yoo * apply review Signed-off-by: Junhee Yoo * fix more formatting and enhance readability Signed-off-by: Junhee Yoo --------- Signed-off-by: Junhee Yoo --- ggml/src/ggml-metal.m | 128 ++++++++++++++++++++++---- ggml/src/ggml-metal.metal | 178 +++++++++++++++++++++++++++++++++++++ tests/test-backend-ops.cpp | 10 +++ 3 files changed, 298 insertions(+), 18 deletions(-) diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m index 172a0f925..e9541441c 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal.m @@ -241,6 +241,8 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F32, + GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, + GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, GGML_METAL_KERNEL_TYPE_ARANGE_F32, @@ -272,6 +274,8 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_SIN, GGML_METAL_KERNEL_TYPE_COS, GGML_METAL_KERNEL_TYPE_SUM_ROWS, + GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, + GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, GGML_METAL_KERNEL_TYPE_COUNT }; @@ -685,6 +689,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, rope_neox_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, im2col_ext_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, im2col_ext_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); @@ -716,6 +722,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN, sin, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, pool_2d_avg_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, pool_2d_max_f32, true); } [metal_library release]; @@ -844,8 +852,8 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_OP_IM2COL: return op->src[0]->type == GGML_TYPE_F16; case GGML_OP_POOL_1D: - case GGML_OP_POOL_2D: return false; + case GGML_OP_POOL_2D: case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARANGE: @@ -2545,6 +2553,8 @@ static void ggml_metal_encode_node( } break; case GGML_OP_IM2COL: { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); @@ -2574,30 +2584,54 @@ static void ggml_metal_encode_node( const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; - id pipeline = nil; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; + + const bool is_gt_mttpt = ((size_t)(N * KH * KW)) > pipeline.maxTotalThreadsPerThreadgroup; switch (dst->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; + case GGML_TYPE_F32: { + pipeline = (is_gt_mttpt ? + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32].pipeline + : + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline); + } break; + case GGML_TYPE_F16: { + pipeline = (is_gt_mttpt ? + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16].pipeline + : + ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline); + } break; default: GGML_ABORT("fatal error"); }; [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; - [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; - [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; - [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; - [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; - [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; - [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; - [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; - [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; - [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ofs0 length:sizeof(int32_t) atIndex:2]; + [encoder setBytes:&ofs1 length:sizeof(int32_t) atIndex:3]; + [encoder setBytes:&IW length:sizeof(int32_t) atIndex:4]; + [encoder setBytes:&IH length:sizeof(int32_t) atIndex:5]; + [encoder setBytes:&CHW length:sizeof(int32_t) atIndex:6]; + [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:7]; + [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:8]; + [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:9]; + [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:10]; + [encoder setBytes:&d0 length:sizeof(int32_t) atIndex:11]; + [encoder setBytes:&d1 length:sizeof(int32_t) atIndex:12]; - [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + if (is_gt_mttpt) { + [encoder setBytes:&N length:sizeof(int32_t) atIndex:13]; + [encoder setBytes:&KH length:sizeof(int32_t) atIndex:14]; + [encoder setBytes:&KW length:sizeof(int32_t) atIndex:15]; + + const uint64_t n_threads = MIN(pipeline.maxTotalThreadsPerThreadgroup, (uint64_t)N); + + const int64_t quotient = N / n_threads + (N % n_threads > 0 ? 1 : 0); + + [encoder dispatchThreadgroups:MTLSizeMake(quotient * CHW, OH, OW) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; + } else { + [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + } } break; case GGML_OP_UPSCALE: { @@ -3001,6 +3035,64 @@ static void ggml_metal_encode_node( [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_POOL_2D: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0t == GGML_TYPE_F32 && src0t == dstt); + + const int32_t * opts = dst->op_params; + enum ggml_op_pool op = opts[0]; + + id pipeline = nil; + switch (src0t) { + case GGML_TYPE_F32: { + switch(op) { + case GGML_OP_POOL_AVG: + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32].pipeline; break; + case GGML_OP_POOL_MAX: + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + } + } break; + default: GGML_ASSERT(false && "not implemented"); + } + + const int32_t k0 = opts[1]; + const int32_t k1 = opts[2]; + const int32_t s0 = opts[3]; + const int32_t s1 = opts[4]; + const int32_t p0 = opts[5]; + const int32_t p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int64_t parallel_elements = N * OC * OH * OW; + const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements); + const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&k0 length:sizeof(int32_t) atIndex:2]; + [encoder setBytes:&k1 length:sizeof(int32_t) atIndex:3]; + [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:4]; + [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:5]; + [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:6]; + [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:7]; + [encoder setBytes:&IH length:sizeof(int64_t) atIndex:8]; + [encoder setBytes:&IW length:sizeof(int64_t) atIndex:9]; + [encoder setBytes:&OH length:sizeof(int64_t) atIndex:10]; + [encoder setBytes:&OW length:sizeof(int64_t) atIndex:11]; + [encoder setBytes:¶llel_elements length:sizeof(int64_t) atIndex:12]; + + [encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; + } break; default: { GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal index 2b2000323..71b58be1f 100644 --- a/ggml/src/ggml-metal.metal +++ b/ggml/src/ggml-metal.metal @@ -1933,6 +1933,85 @@ kernel void kernel_im2col( template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col; template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col; +typedef void (im2col_ext_t)( + device const float * x, + device char * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + constant int32_t & N, + constant int32_t & KH, + constant int32_t & KW, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template +kernel void kernel_im2col_ext( + device const float * x, + device char * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + constant int32_t & N, + constant int32_t & KH, + constant int32_t & KW, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1] + const int32_t KHW = KH * KW; // KHW == ntg[1] * ntg[2], KW == ntg[2] + + const int32_t d = tgpig[0] / CHW; + const int32_t chw = tgpig[0] % CHW; + const int32_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1) + const int32_t HW = tgpig[0] % KHW; + + const int32_t tpitg_0 = (d * ntg[0]) + tpitg[0]; + if (tpitg_0 >= N) { + return; + } + + const int32_t tpitg_1 = HW / KW; + const int32_t tpitg_2 = HW % KW; + + const int32_t iiw = tgpig[2] * s0 + tpitg_2 * d0 - p0; + const int32_t iih = tgpig[1] * s1 + tpitg_1 * d1 - p1; + + const int32_t offset_dst = + (tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + + (tgpig_0 * KHW + tpitg_1 * KW + tpitg_2); + + device T * pdst = (device T *) (dst); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + pdst[offset_dst] = 0.0f; + } else { + const int32_t offset_src = tpitg_0 * ofs0 + tgpig_0 * ofs1; + pdst[offset_dst] = x[offset_src + iih * IW + iiw]; + } +} + +template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext; +template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext; + kernel void kernel_upscale_f32( device const char * src0, device char * dst, @@ -6372,3 +6451,102 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; + +kernel void kernel_pool_2d_max_f32( + device const float * src0, + device float * dst, + constant int32_t & k0, + constant int32_t & k1, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int64_t & IH, + constant int64_t & IW, + constant int64_t & OH, + constant int64_t & OW, + constant int64_t & parallel_elements, + uint gid[[thread_position_in_grid]]) { + + if (gid >= parallel_elements) { + return; + } + + const int idx = gid; + const int I_HW = IH * IW; + const int O_HW = OH * OW; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / OW; + const int cur_ow = idx % O_HW % OW; + + device const float * i_ptr = src0 + nc * I_HW; + device float * o_ptr = dst + nc * O_HW; + + const int start_h = cur_oh * s1 - p1; + const int bh = MAX(0, start_h); + const int eh = MIN(IH, start_h + k1); + const int start_w = cur_ow * s0 - p0; + const int bw = MAX(0, start_w); + const int ew = MIN(IW, start_w + k0); + + float res = -INFINITY; + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { + res = MAX(res, i_ptr[i * IW + j]); + } + } + + o_ptr[cur_oh * OW + cur_ow] = res; +} + +kernel void kernel_pool_2d_avg_f32( + device const float * src0, + device float * dst, + constant int32_t & k0, + constant int32_t & k1, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int64_t & IH, + constant int64_t & IW, + constant int64_t & OH, + constant int64_t & OW, + constant int64_t & parallel_elements, + uint gid[[thread_position_in_grid]]) { + + if (gid >= parallel_elements) { + return; + } + + const int idx = gid; + const int I_HW = IH * IW; + const int O_HW = OH * OW; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / OW; + const int cur_ow = idx % O_HW % OW; + + device const float * i_ptr = src0 + nc * I_HW; + device float * o_ptr = dst + nc * O_HW; + + const int start_h = cur_oh * s1 - p1; + const int bh = MAX(0, start_h); + const int eh = MIN(IH, start_h + k1); + const int start_w = cur_ow * s0 - p0; + const int bw = MAX(0, start_w); + const int ew = MIN(IW, start_w + k0); + // const float scale = 1. / ((eh - bh) * (ew - bw)); + const float scale = 1. / (k0 * k1); + + float res = 0; + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { + float cur = i_ptr[i * IW + j]; + res += cur * scale; + } + } + + o_ptr[cur_oh * OW + cur_ow] = res; +} diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ee1a8877e..e087f7ba5 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -3316,6 +3316,16 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); + // test cases for 2D im2col + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 32}, {3, 3, 1, 32}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 32}, {3, 3, 2, 32}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 1024}, {3, 3, 1, 1024}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 1024}, {3, 3, 2, 1024}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 2048}, {3, 3, 1, 2048}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 2048}, {3, 3, 2, 2048}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 2560}, {3, 3, 1, 2560}, 1, 1, 1, 1, 1, 1, true)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 2560}, {3, 3, 2, 2560}, 1, 1, 1, 1, 1, 1, true)); + // sycl backend will limit task global_range < MAX_INT // test cases for 2D im2col with large input W and H (occurs in stable-diffusion) // however these cases need to alloc more memory which may fail in some devices (Intel Arc770, etc.) From ac113a0feee0935b2018312f7bc8d7a646b117ed Mon Sep 17 00:00:00 2001 From: Michael Coppola Date: Wed, 23 Oct 2024 07:09:26 -0400 Subject: [PATCH 53/95] llama.vim : add classic vim support (#9995) * added classic vim support * fixed ring update, removed blank line * minor * minor * minor doc update * removed uneeded var * minor * minor * fixed job_start creating new scratch buffers * fixed job_start creating new scratch buffers * fixed ghost text indenting when expandtab is on * removed unused code * minor * unified fim_on_exit * minor * vim ghost text rendering now uses pos_x and pos_y parameters * renamed *_hlgroup to hlgroup_* * renamed *_ghost_text to ghost_text_*, moved nvim/vim detection to llama#init() * minor --------- Co-authored-by: Michael Coppola --- examples/llama.vim | 168 ++++++++++++++++++++++++++++++++++----------- 1 file changed, 127 insertions(+), 41 deletions(-) diff --git a/examples/llama.vim b/examples/llama.vim index 7a60442ad..4bc26d4e9 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -2,7 +2,7 @@ " " requires: " -" - neovim +" - neovim or vim " - curl " - llama.cpp server instance " - FIM-compatible model @@ -10,7 +10,7 @@ " sample config: " " - Tab - accept the current suggestion -" - Shift+Tab - accept just the first line of the segguestion +" - Shift+Tab - accept just the first line of the suggestion " - Ctrl+F - toggle FIM completion manually " " make symlink or copy this file to ~/.config/nvim/autoload/llama.vim @@ -43,8 +43,8 @@ " " colors (adjust to your liking) -highlight llama_hl_hint guifg=#ff772f -highlight llama_hl_info guifg=#77ff2f +highlight llama_hl_hint guifg=#ff772f ctermfg=202 +highlight llama_hl_info guifg=#77ff2f ctermfg=119 " general parameters: " @@ -93,6 +93,18 @@ let s:default_config = { let g:llama_config = get(g:, 'llama_config', s:default_config) +function! s:get_indent(str) + let l:count = 0 + for i in range(len(a:str)) + if a:str[i] == "\t" + let l:count += &tabstop - 1 + else + break + endif + endfor + return l:count +endfunction + function! s:rand(i0, i1) abort return a:i0 + rand() % (a:i1 - a:i0 + 1) endfunction @@ -129,6 +141,21 @@ function! llama#init() let s:current_job = v:null + let s:ghost_text_nvim = exists('*nvim_buf_get_mark') + let s:ghost_text_vim = has('textprop') + + if s:ghost_text_vim + let s:hlgroup_hint = 'llama_hl_hint' + let s:hlgroup_info = 'llama_hl_info' + + if empty(prop_type_get(s:hlgroup_hint)) + call prop_type_add(s:hlgroup_hint, {'highlight': s:hlgroup_hint}) + endif + if empty(prop_type_get(s:hlgroup_info)) + call prop_type_add(s:hlgroup_info, {'highlight': s:hlgroup_info}) + endif + endif + augroup llama autocmd! autocmd InsertEnter * inoremap llama#fim_inline(v:false) @@ -317,13 +344,22 @@ function! s:ring_update() \ 't_max_predict_ms': 1 \ }) - let l:curl_command = printf( - \ "curl --silent --no-buffer --request POST --url %s --header \"Content-Type: application/json\" --data %s", - \ g:llama_config.endpoint, shellescape(l:request) - \ ) + let l:curl_command = [ + \ "curl", + \ "--silent", + \ "--no-buffer", + \ "--request", "POST", + \ "--url", g:llama_config.endpoint, + \ "--header", "Content-Type: application/json", + \ "--data", l:request + \ ] " no callbacks because we don't need to process the response - call jobstart(l:curl_command, {}) + if s:ghost_text_nvim + call jobstart(l:curl_command, {}) + elseif s:ghost_text_vim + call job_start(l:curl_command, {}) + endif endfunction " necessary for 'inoremap ' @@ -418,24 +454,37 @@ function! llama#fim(is_auto) abort \ 't_max_predict_ms': g:llama_config.t_max_predict_ms \ }) - let l:curl_command = printf( - \ "curl --silent --no-buffer --request POST --url %s --header \"Content-Type: application/json\" --data %s", - \ g:llama_config.endpoint, shellescape(l:request) - \ ) + let l:curl_command = [ + \ "curl", + \ "--silent", + \ "--no-buffer", + \ "--request", "POST", + \ "--url", g:llama_config.endpoint, + \ "--header", "Content-Type: application/json", + \ "--data", l:request + \ ] if s:current_job != v:null - call jobstop(s:current_job) + if s:ghost_text_nvim + call jobstop(s:current_job) + elseif s:ghost_text_vim + call job_stop(s:current_job) + endif endif " send the request asynchronously - let s:current_job = jobstart(l:curl_command, { - \ 'on_stdout': function('s:fim_on_stdout'), - \ 'on_exit': function('s:fim_on_exit'), - \ 'stdout_buffered': v:true, - \ 'pos_x': s:pos_x, - \ 'pos_y': s:pos_y, - \ 'is_auto': a:is_auto - \ }) + if s:ghost_text_nvim + let s:current_job = jobstart(l:curl_command, { + \ 'on_stdout': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]), + \ 'on_exit': function('s:fim_on_exit'), + \ 'stdout_buffered': v:true + \ }) + elseif s:ghost_text_vim + let s:current_job = job_start(l:curl_command, { + \ 'out_cb': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]), + \ 'exit_cb': function('s:fim_on_exit') + \ }) + endif " TODO: per-file location let l:delta_y = abs(s:pos_y - s:pos_y_pick) @@ -482,9 +531,13 @@ function! llama#fim_cancel() " clear the virtual text let l:bufnr = bufnr('%') - let l:id_vt_fim = nvim_create_namespace('vt_fim') - - call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1) + if s:ghost_text_nvim + let l:id_vt_fim = nvim_create_namespace('vt_fim') + call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1) + elseif s:ghost_text_vim + call prop_remove({'type': s:hlgroup_hint, 'all': v:true}) + call prop_remove({'type': s:hlgroup_info, 'all': v:true}) + endif " remove the mappings silent! iunmap @@ -499,13 +552,18 @@ function! s:on_move() endfunction " callback that processes the FIM result from the server and displays the suggestion -function! s:fim_on_stdout(job_id, data, event) dict - let l:raw = join(a:data, "\n") +function! s:fim_on_stdout(pos_x, pos_y, is_auto, job_id, data, event = v:null) + if s:ghost_text_nvim + let l:raw = join(a:data, "\n") + elseif s:ghost_text_vim + let l:raw = a:data + endif + if len(l:raw) == 0 return endif - if self.pos_x != col('.') - 1 || self.pos_y != line('.') + if a:pos_x != col('.') - 1 || a:pos_y != line('.') return endif @@ -514,14 +572,14 @@ function! s:fim_on_stdout(job_id, data, event) dict return endif - let s:pos_x = self.pos_x - let s:pos_y = self.pos_y + let s:pos_x = a:pos_x + let s:pos_y = a:pos_y let s:can_accept = v:true let l:has_info = v:false if s:can_accept && v:shell_error - if !self.is_auto + if !a:is_auto call add(s:content, "<| curl error: is the server on? |>") endif let s:can_accept = v:false @@ -642,7 +700,9 @@ function! s:fim_on_stdout(job_id, data, event) dict " display virtual text with the suggestion let l:bufnr = bufnr('%') - let l:id_vt_fim = nvim_create_namespace('vt_fim') + if s:ghost_text_nvim + let l:id_vt_fim = nvim_create_namespace('vt_fim') + endif " construct the info message if g:llama_config.show_info > 0 && l:has_info @@ -671,15 +731,41 @@ function! s:fim_on_stdout(job_id, data, event) dict endif " display the suggestion and append the info to the end of the first line - call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, { - \ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']], - \ 'virt_text_win_col': virtcol('.') - 1 - \ }) + if s:ghost_text_nvim + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, { + \ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']], + \ 'virt_text_win_col': virtcol('.') - 1 + \ }) - call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, { - \ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}), - \ 'virt_text_win_col': virtcol('.') - \ }) + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, { + \ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}), + \ 'virt_text_win_col': virtcol('.') + \ }) + elseif s:ghost_text_vim + let l:new_suffix = s:content[0] + if !empty(l:new_suffix) + call prop_add(s:pos_y, s:pos_x + 1, { + \ 'type': s:hlgroup_hint, + \ 'text': l:new_suffix + \ }) + endif + for line in s:content[1:] + call prop_add(s:pos_y, 0, { + \ 'type': s:hlgroup_hint, + \ 'text': line, + \ 'text_padding_left': s:get_indent(line), + \ 'text_align': 'below' + \ }) + endfor + if !empty(l:info) + call prop_add(s:pos_y, 0, { + \ 'type': s:hlgroup_info, + \ 'text': l:info, + \ 'text_padding_left': col('$'), + \ 'text_wrap': 'truncate' + \ }) + endif + endif " setup accept shortcuts inoremap :call llama#fim_accept(v:false) @@ -688,7 +774,7 @@ function! s:fim_on_stdout(job_id, data, event) dict let s:hint_shown = v:true endfunction -function! s:fim_on_exit(job_id, exit_code, event) dict +function! s:fim_on_exit(job_id, exit_code, event = v:null) if a:exit_code != 0 echom "Job failed with exit code: " . a:exit_code endif From c19af0acb1fe6d0fdbecadd8483c1fbe5d68d095 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 16 Oct 2024 20:10:01 +0200 Subject: [PATCH 54/95] ggml : remove redundant set of contexts used field (ggml/978) This commit removes the setting of the `used` field of the contexts in the global state (g_state) in `ggml_init`. The motivation for this change is that I believe that this additional initialization might not be required after the changes in Commit 45fc4fed0b9fb5b1af4a8525cbebb95e11208732 ("sync : latest changes from whisper.cpp"), which changed the initialization of the contexts field from `{ 0 }` to `{ { 0 } }`: ```console g_state = (struct ggml_state) { - /*.contexts =*/ { 0 }, + /*.contexts =*/ { { 0 } }, }; ``` My understanding is that the `{0}` initialization might not have zero-initialized all the nested fields in every array element because of compiler differences, and might have been the reason for having the explicit setting of the `used` fields to false. --- ggml/src/ggml.c | 4 ---- 1 file changed, 4 deletions(-) diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index b16c462fa..1741d3338 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -3852,10 +3852,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { }, }; - for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) { - g_state.contexts[i].used = false; - } - const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); From 80273a306d07ed95059d6130389deacb3b2d7196 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 18 Oct 2024 09:24:44 +0200 Subject: [PATCH 55/95] CUDA: fix 1D im2col, add tests (ggml/993) --- ggml/src/ggml-cuda.cu | 1 - ggml/src/ggml-cuda/im2col.cu | 6 +++--- tests/test-backend-ops.cpp | 36 +++++++++++++++++++++++++++++++----- 3 files changed, 34 insertions(+), 9 deletions(-) diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 1338bd458..fa280b529 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -3141,7 +3141,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_ROPE: return ggml_is_contiguous(op->src[0]); case GGML_OP_IM2COL: - return op->src[0]->type == GGML_TYPE_F16; case GGML_OP_POOL_2D: case GGML_OP_SUM: case GGML_OP_SUM_ROWS: diff --git a/ggml/src/ggml-cuda/im2col.cu b/ggml/src/ggml-cuda/im2col.cu index 16463ab0f..86a54e42b 100644 --- a/ggml/src/ggml-cuda/im2col.cu +++ b/ggml/src/ggml-cuda/im2col.cu @@ -91,9 +91,9 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const int64_t OH = is_2D ? dst->ne[2] : 1; const int64_t OW = dst->ne[1]; - const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - const int64_t batch = src1->ne[3]; - const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 + const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + const int64_t batch = src1->ne[is_2D ? 3 : 2]; + const size_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 if(dst->type == GGML_TYPE_F16) { im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index e087f7ba5..7e769a91a 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -3308,15 +3308,41 @@ static std::vector> make_test_cases_eval() { } } - test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32)); - test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32)); - test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16)); - // test cases for 1D im2col + // im2col 1D test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false)); + for (int s0 : {1, 3}) { + for (int p0 : {0, 3}) { + for (int d0 : {1, 3}) { + test_cases.emplace_back(new test_im2col( + GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {20, 2, 2, 1}, {3, 2, 2, 1}, + s0, 0, p0, 0, d0, 0, false)); + } + } + } - // test cases for 2D im2col + // im2col 2D + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16)); + for (int s0 : {1, 3}) { + for (int s1 : {1, 3}) { + for (int p0 : {0, 3}) { + for (int p1 : {0, 3}) { + for (int d0 : {1, 3}) { + for (int d1 : {1, 3}) { + test_cases.emplace_back(new test_im2col( + GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {20, 20, 2, 2}, {3, 3, 2, 2}, + s0, s1, p0, p1, d0, d1, true)); + } + } + } + } + } + } + + // extra tests for im2col 2D test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 32}, {3, 3, 1, 32}, 1, 1, 1, 1, 1, 1, true)); test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 32}, {3, 3, 2, 32}, 1, 1, 1, 1, 1, 1, true)); test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 1024}, {3, 3, 1, 1024}, 1, 1, 1, 1, 1, 1, true)); From 2d3aba9ee8da9c026d54e8a912a1d64f56809be3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 23 Oct 2024 17:16:56 +0300 Subject: [PATCH 56/95] llama.vim : bump generation time limit to 3s [no ci] --- examples/llama.vim | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llama.vim b/examples/llama.vim index 4bc26d4e9..57eb2a977 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -81,7 +81,7 @@ let s:default_config = { \ 'n_suffix': 64, \ 'n_predict': 128, \ 't_max_prompt_ms': 500, - \ 't_max_predict_ms': 1000, + \ 't_max_predict_ms': 3000, \ 'show_info': 2, \ 'auto_fim': v:true, \ 'max_line_suffix': 8, From 190a37d7977eb5bd6a729299bd1e371208c87149 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 23 Oct 2024 17:23:55 +0300 Subject: [PATCH 57/95] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 6d31b21b9..7f689f632 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -2327bda7a55ac6b72614ac5ebd5c5a5e02553b9b +6dccc647264f5429df2624f36138f601e7ce23e5 From 0a1c750c80147687df267114c81956757cc14382 Mon Sep 17 00:00:00 2001 From: wwoodsTM <104587230+wwoodsTM@users.noreply.github.com> Date: Wed, 23 Oct 2024 13:27:51 -0600 Subject: [PATCH 58/95] server : samplers accept the prompt correctly (#10019) --- examples/server/server.cpp | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3992108e7..51f30ffea 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2163,17 +2163,10 @@ struct server_context { GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); } - common_sampler_reset(slot.smpl); - if (slot.params.cache_prompt) { // reuse any previously computed tokens that are common with the new prompt slot.n_past = longest_common_prefix(slot.cache_tokens, prompt_tokens); - // push the prompt into the sampling context (do not apply grammar) - for (int i = 0; i < slot.n_past; ++i) { - common_sampler_accept(slot.smpl, slot.cache_tokens[i], false); - } - // reuse chunks from the cached prompt by shifting their KV cache in the new position if (params.n_cache_reuse > 0) { size_t head_c = slot.n_past; // cache @@ -2206,8 +2199,6 @@ struct server_context { for (size_t i = 0; i < n_match; i++) { slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i]; - common_sampler_accept(slot.smpl, slot.cache_tokens[head_p + i], false); - slot.n_past++; } @@ -2259,8 +2250,6 @@ struct server_context { // there is no common part left slot.n_past = 0; - - common_sampler_reset(slot.smpl); } SLT_INF(slot, "kv cache rm [%d, end)\n", slot.n_past); @@ -2288,6 +2277,13 @@ struct server_context { GGML_ASSERT(batch.n_tokens > 0); + common_sampler_reset(slot.smpl); + + // Process all prompt tokens through sampler system + for (int i = 0; i < slot.n_prompt_tokens; ++i) { + common_sampler_accept(slot.smpl, prompt_tokens[i], false); + } + // extract the logits only for the last token batch.logits[batch.n_tokens - 1] = true; From c39665f589091903396a442a6ee56613303e0350 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 24 Oct 2024 11:09:36 +0200 Subject: [PATCH 59/95] CUDA: fix MMQ for non-contiguous src0, add tests (#10021) * CUDA: fix MMQ for non-contiguous src0, add tests * revise test code --- ggml/src/ggml-cuda.cu | 18 +++++---- ggml/src/ggml-cuda/mmq.cu | 4 +- ggml/src/ggml.c | 2 +- tests/test-backend-ops.cpp | 78 +++++++++++++++++++++++++++++--------- 4 files changed, 73 insertions(+), 29 deletions(-) diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index fa280b529..4a0329a63 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -1151,8 +1151,8 @@ static cudaError_t ggml_cuda_cpy_tensor_2d( void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) { GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer)); - char * src_ptr = (char *) src->data; - char * dst_ptr = (char *) dst; + const char * src_ptr = (const char *) src->data; + char * dst_ptr = (char *) dst; const int64_t ne0 = src->ne[0]; const int64_t nb0 = src->nb[0]; @@ -1162,7 +1162,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d( const enum ggml_type type = src->type; const int64_t ts = ggml_type_size(type); const int64_t bs = ggml_blck_size(type); - int64_t i1_diff = i1_high - i1_low; + const int64_t i1_diff = i1_high - i1_low; const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3; if (nb0 == ts && nb1 == ts*ne0/bs) { @@ -1479,13 +1479,17 @@ static void ggml_cuda_op_mul_mat( if (src0_is_contiguous) { dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data; } else { - dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0)); + // If src0 is not contiguous it will be copied to a temporary buffer, it may then be necessary to clear padding. + const size_t nbytes_data = ggml_nbytes(src0); + const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); + dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding); + CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream)); } - // If src0 is on a temporary compute buffers (partial offloading) there may be some padding that needs to be cleared: + // If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared: if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) { - const int64_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00); - const int64_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); + const size_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00); + const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream)); } diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index 4935f8818..ae5c68ab3 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -8,8 +8,6 @@ void ggml_cuda_op_mul_mat_q( const int64_t ne00 = src0->ne[0]; - const int64_t nb01 = src0->nb[1]; - const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; GGML_ASSERT(ne10 % QK8_1 == 0); @@ -17,7 +15,7 @@ void ggml_cuda_op_mul_mat_q( const int64_t ne0 = dst->ne[0]; const int64_t row_diff = row_high - row_low; - const int64_t stride00 = nb01 / ggml_type_size(src0->type); + const int64_t stride00 = ne00 / ggml_blck_size(src0->type); int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 1741d3338..66df9a9c1 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -3464,7 +3464,7 @@ int64_t ggml_nrows(const struct ggml_tensor * tensor) { size_t ggml_nbytes(const struct ggml_tensor * tensor) { size_t nbytes; - size_t blck_size = ggml_blck_size(tensor->type); + const size_t blck_size = ggml_blck_size(tensor->type); if (blck_size == 1) { nbytes = ggml_type_size(tensor->type); for (int i = 0; i < GGML_MAX_DIMS; ++i) { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 7e769a91a..2e3ad79f0 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1650,11 +1650,12 @@ struct test_mul_mat : public test_case { const int64_t m; const int64_t n; const int64_t k; - const std::array bs; // dims 3 and 4 - const std::array nr; // repeat in dims 3 and 4 + const std::array bs; // dims 3 and 4 + const std::array nr; // repeat in dims 3 and 4 + const std::array per; // permutation of dimensions std::string vars() override { - return VARS_TO_STR7(type_a, type_b, m, n, k, bs, nr); + return VARS_TO_STR8(type_a, type_b, m, n, k, bs, nr, per); } double max_nmse_err() override { @@ -1669,17 +1670,44 @@ struct test_mul_mat : public test_case { test_mul_mat(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32, int64_t m = 32, int64_t n = 32, int64_t k = 32, std::array bs = {10, 10}, - std::array nr = {2, 2}) - : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr) {} + std::array nr = {2, 2}, + std::array per = {0, 1, 2, 3}) + : type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per) {} ggml_tensor * build_graph(ggml_context * ctx) override { // C^T = A * B^T: (k, m) * (k, n) => (m, n) - ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0] , bs[1]); - ggml_tensor * b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]); - ggml_set_param(ctx, a); - ggml_set_param(ctx, b); - ggml_set_name(a, "a"); - ggml_set_name(b, "b"); + ggml_tensor * a; + ggml_tensor * b; + + const int npermuted = (per[0] != 0) + (per[1] != 1) + (per[2] != 2) + (per[3] != 3); + if (npermuted > 0) { + GGML_ASSERT(npermuted == 2); + GGML_ASSERT(!ggml_is_quantized(type_a) || per[0] == 0); + GGML_ASSERT(!ggml_is_quantized(type_b) || per[0] == 0); + + // Create tensors with the permuted dimensions, then permute them back to the dimensions given by m,n,k. + const int64_t ne_a[4] = {k, m, bs[0], bs[1]}; + const int64_t ne_b[4] = {k, n, bs[0]*nr[0], bs[1]*nr[1]}; + + a = ggml_new_tensor_4d(ctx, type_a, ne_a[per[0]], ne_a[per[1]], ne_a[per[2]], ne_a[per[3]]); + b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]); + ggml_set_param(ctx, a); + ggml_set_param(ctx, b); + ggml_set_name(a, "a"); + ggml_set_name(b, "b"); + + a = ggml_permute(ctx, a, per[0], per[1], per[2], per[3]); + b = ggml_permute(ctx, b, per[0], per[1], per[2], per[3]); + ggml_set_name(a, "a_permuted"); + ggml_set_name(b, "b_permuted"); + } else { + a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]); + b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]); + ggml_set_param(ctx, a); + ggml_set_param(ctx, b); + ggml_set_name(a, "a"); + ggml_set_name(b, "b"); + } ggml_tensor * out = ggml_mul_mat(ctx, a, b); ggml_set_name(out, "out"); @@ -3478,13 +3506,14 @@ static std::vector> make_test_cases_eval() { #if 1 for (ggml_type type_a : base_types) { for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) { - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2})); - test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2})); + // test cases without permutation + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, { 1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 1}, {1, 1})); @@ -3493,6 +3522,19 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 2})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2})); + + // test cases with permutation + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 1, 3, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 3, 2, 1})); + + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 1, 3, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 3, 2, 1})); + + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 1, 3, 2})); + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 3, 2, 1})); } } for (ggml_type type_a : other_types) { From 167a515651a4b065a16225ffc69564c5674f3d0f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 24 Oct 2024 14:40:23 +0200 Subject: [PATCH 60/95] CUDA: fix insufficient buffer clearing for MMQ (#10032) --- ggml/src/ggml-cuda.cu | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 4a0329a63..21c9f5e38 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -1479,11 +1479,12 @@ static void ggml_cuda_op_mul_mat( if (src0_is_contiguous) { dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data; } else { - // If src0 is not contiguous it will be copied to a temporary buffer, it may then be necessary to clear padding. + // If src0 is not contiguous it will be copied to a temporary buffer. + // This buffer needs to be cleared entirely because multiple regions will function as padding. const size_t nbytes_data = ggml_nbytes(src0); const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding); - CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream)); + CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd, 0, nbytes_data + nbytes_padding, stream)); } // If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared: From 40f2555797f97314de749873cdc29dc102be66e2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 24 Oct 2024 21:23:33 +0300 Subject: [PATCH 61/95] ci : fix cmake flags for SYCL --- ci/run.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/run.sh b/ci/run.sh index e06778219..dc26d94ee 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -53,7 +53,7 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then exit 1 fi - CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON" fi if [ ! -z ${GG_BUILD_VULKAN} ]; then From 958367bf530d943a902afa1ce1c342476098576b Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 24 Oct 2024 21:51:22 +0200 Subject: [PATCH 62/95] server : refactor slot input data, move tokenizer to HTTP thread (#10023) * server : refactor slot input data, move tokenizer to HTTP thread * move prompt_tokens.empty() check * fix incorrect if branch * fix infinite generation loop * bring back infill validation * add infill test * try fixing format_infill * fix test * remove redundant code * rename completion to inference * update docs * use llama_tokens everywhere --- examples/server/README.md | 12 + examples/server/server.cpp | 466 +++++------------- examples/server/tests/features/infill.feature | 36 ++ examples/server/tests/features/steps/steps.py | 46 ++ examples/server/utils.hpp | 256 +++++++++- 5 files changed, 468 insertions(+), 348 deletions(-) create mode 100644 examples/server/tests/features/infill.feature diff --git a/examples/server/README.md b/examples/server/README.md index 09f1aa249..8f00fcc79 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -319,6 +319,18 @@ node index.js - The prompt is a string or an array with the first element given as a string - The model's `tokenizer.ggml.add_bos_token` metadata is `true` + These input shapes and data type are allowed for `prompt`: + + - Single string: `"string"` + - Single sequence of tokens: `[12, 34, 56]` + - Mixed tokens and strings: `[12, 34, "string", 56, 78]` + + Multiple prompts are also supported. In this case, the completion result will be an array. + + - Only strings: `["string1", "string2"]` + - Strings and sequences of tokens: `["string1", [12, 34, 56]]` + - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]` + `temperature`: Adjust the randomness of the generated text. Default: `0.8` `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 51f30ffea..58f93694f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -43,21 +43,6 @@ #include #include -#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) - -#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) - -#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) - using json = nlohmann::ordered_json; enum stop_type { @@ -68,6 +53,7 @@ enum stop_type { // state diagram: https://github.com/ggerganov/llama.cpp/pull/9283 enum slot_state { SLOT_STATE_IDLE, + SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future SLOT_STATE_PROCESSING_PROMPT, SLOT_STATE_DONE_PROMPT, SLOT_STATE_GENERATING, @@ -79,7 +65,7 @@ enum server_state { }; enum server_task_type { - SERVER_TASK_TYPE_COMPLETION, + SERVER_TASK_TYPE_INFERENCE, SERVER_TASK_TYPE_CANCEL, SERVER_TASK_TYPE_NEXT_RESPONSE, SERVER_TASK_TYPE_METRICS, @@ -89,21 +75,22 @@ enum server_task_type { SERVER_TASK_TYPE_SET_LORA, }; -enum server_task_cmpl_type { - SERVER_TASK_CMPL_TYPE_NORMAL, - SERVER_TASK_CMPL_TYPE_EMBEDDING, - SERVER_TASK_CMPL_TYPE_RERANK, - SERVER_TASK_CMPL_TYPE_INFILL, +enum server_task_inf_type { + SERVER_TASK_INF_TYPE_COMPLETION, + SERVER_TASK_INF_TYPE_EMBEDDING, + SERVER_TASK_INF_TYPE_RERANK, + SERVER_TASK_INF_TYPE_INFILL, }; struct server_task { int id = -1; // to be filled by server_queue int id_target = -1; // used by SERVER_TASK_TYPE_CANCEL + llama_tokens prompt_tokens; server_task_type type; json data; - server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL; + server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION; // utility function static std::unordered_set get_list_id(const std::vector & tasks) { @@ -161,26 +148,20 @@ struct server_slot { int32_t i_batch = -1; int32_t n_predict = -1; // TODO: disambiguate from params.n_predict + // n_prompt_tokens may not be equal to prompt_tokens.size(), because prompt maybe truncated int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_processed = 0; - json prompt; // can be either a string, array of strings or array of token ids - - json input_prefix; - json input_suffix; - json input_extra; - - // when a task is submitted, we first tokenize the prompt and store it here - std::vector prompt_tokens; - std::vector extra_tokens; + // input prompt tokens + llama_tokens prompt_tokens; size_t last_nl_pos = 0; std::string generated_text; - std::vector cache_tokens; + llama_tokens cache_tokens; std::vector generated_token_probs; - server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL; + server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION; bool has_next_token = true; bool has_new_line = false; @@ -229,7 +210,7 @@ struct server_slot { n_past = 0; n_sent_text = 0; n_sent_token_probs = 0; - cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL; + inf_type = SERVER_TASK_INF_TYPE_COMPLETION; generated_token_probs.clear(); } @@ -734,42 +715,6 @@ struct server_context { metrics.init(); } - std::vector tokenize(const json & json_prompt, bool add_special, bool parse_special) const { - // If `add_bos` is true, we only add BOS, when json_prompt is a string, - // or the first element of the json_prompt array is a string. - std::vector prompt_tokens; - - if (json_prompt.is_array()) { - bool first = true; - for (const auto & p : json_prompt) { - if (p.is_string()) { - auto s = p.template get(); - - std::vector p; - if (first) { - p = common_tokenize(ctx, s, add_special, parse_special); - first = false; - } else { - p = common_tokenize(ctx, s, false, parse_special); - } - - prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); - } else { - if (first) { - first = false; - } - - prompt_tokens.push_back(p.template get()); - } - } - } else { - auto s = json_prompt.template get(); - prompt_tokens = common_tokenize(ctx, s, add_special, parse_special); - } - - return prompt_tokens; - } - server_slot * get_slot_by_id(int id) { for (server_slot & slot : slots) { if (slot.id == id) { @@ -794,22 +739,16 @@ struct server_context { continue; } - // skip the slot if it does not contains prompt - if (!slot.prompt.is_string()) { + // skip the slot if it does not contains cached tokens + if (slot.prompt_tokens.empty()) { continue; } - // current slot's prompt - std::string slot_prompt = slot.prompt.get(); - - // length of the current slot's prompt - int slot_prompt_len = slot_prompt.size(); - // length of the Longest Common Prefix between the current slot's prompt and the input prompt - int lcp_len = longest_common_prefix(slot_prompt, prompt); + int lcp_len = longest_common_prefix(slot.cache_tokens, slot.prompt_tokens); // fraction of the common substring length compared to the current slot's prompt length - similarity = static_cast(lcp_len) / slot_prompt_len; + similarity = static_cast(lcp_len) / static_cast(slot.prompt_tokens.size()); // select the current slot if the criteria match if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) { @@ -914,57 +853,6 @@ struct server_context { SLT_WRN(slot, "n_predict = %d exceeds server configuration, setting to %d", slot.n_predict, slot.n_predict); } - // infill - slot.input_prefix = json_value(data, "input_prefix", json()); - slot.input_suffix = json_value(data, "input_suffix", json()); - slot.input_extra = json_value(data, "input_extra", json()); - - SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.input_extra.size()); - for (const auto & chunk : slot.input_extra) { - // { "text": string, "filename": string } - if (!chunk.contains("text") || !chunk["text"].is_string()) { - send_error(task, "extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST); - return false; - } - - // filename is optional - if (chunk.contains("filename") && !chunk["filename"].is_string()) { - send_error(task, "extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST); - return false; - } - - SLT_DBG(slot, "extra_context chunk in file '%s':\n%s\n", chunk.value("filename", "").c_str(), chunk.value("text", "").c_str()); - } - - // get prompt - { - const auto & prompt = data.find("prompt"); - if (prompt == data.end()) { - send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST); - return false; - } - - if ((prompt->is_string()) || - (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) || - (prompt->is_array() && !prompt->empty() && prompt->at(0).is_number_integer())) { - slot.prompt = *prompt; - } else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) { - slot.prompt = prompt->at(0); - } else if (prompt->is_array() && prompt->size() > 1) { - // array of strings - for (const auto & el : *prompt) { - if (!el.is_string()) { - send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST); - return false; - } - } - slot.prompt = *prompt; - } else { - send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST); - return false; - } - } - { slot.sparams.logit_bias.clear(); @@ -1044,8 +932,7 @@ struct server_context { } } - slot.state = SLOT_STATE_PROCESSING_PROMPT; - slot.prompt_tokens.clear(); + slot.state = SLOT_STATE_STARTED; SLT_INF(slot, "%s", "processing task\n"); @@ -1297,7 +1184,7 @@ struct server_context { }; if (slot.sparams.n_probs > 0) { - const std::vector to_send_toks = common_tokenize(ctx, tkn.text_to_send, false); + const llama_tokens to_send_toks = common_tokenize(ctx, tkn.text_to_send, false); const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); @@ -1333,7 +1220,7 @@ struct server_context { {"tokens_predicted", slot.n_decoded}, {"tokens_evaluated", slot.n_prompt_tokens}, {"generation_settings", get_formated_generation(slot)}, - {"prompt", slot.prompt}, + {"prompt", common_detokenize(ctx, slot.prompt_tokens)}, {"has_new_line", slot.has_new_line}, {"truncated", slot.truncated}, {"stopped_eos", slot.stopped_eos}, @@ -1348,7 +1235,7 @@ struct server_context { if (slot.sparams.n_probs > 0) { std::vector probs; if (!slot.params.stream && slot.stopped_word) { - const std::vector stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); + const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); probs = std::vector( @@ -1457,19 +1344,17 @@ struct server_context { // Functions to create new task(s) and receive result(s) // - std::vector create_tasks_cmpl(json data, server_task_cmpl_type cmpl_type) { + // break the input "prompt" into multiple tasks if needed, then format and tokenize the input prompt(s) + std::vector create_tasks_inference(json data, server_task_inf_type inf_type) { std::vector tasks; - auto create_task = [&](json & task_data, bool replace_prompt, json prompt) { + auto create_task = [&](json & task_data, llama_tokens & prompt_tokens) { + SRV_DBG("create task, n_tokens = %d\n", (int) prompt_tokens.size()); server_task task; - task.id = queue_tasks.get_new_id(); - task.cmpl_type = cmpl_type; - task.type = SERVER_TASK_TYPE_COMPLETION; - if (replace_prompt) { - task.data = task_data; - task.data["prompt"] = std::move(prompt); - } else { - task.data = std::move(task_data); - } + task.id = queue_tasks.get_new_id(); + task.inf_type = inf_type; + task.type = SERVER_TASK_TYPE_INFERENCE; + task.data = task_data; + task.prompt_tokens = std::move(prompt_tokens); tasks.push_back(std::move(task)); }; @@ -1478,41 +1363,49 @@ struct server_context { throw std::runtime_error(error_msg); } - json prompt = data.at("prompt"); - - // if the prompt is a singleton (i.e. a string or a list of tokens), we only need to create single task - if (prompt.is_string() || json_is_array_of_numbers(prompt)) { - data["index"] = 0; - create_task(data, false, nullptr); - } else if (prompt.is_array()) { - // otherwise, it's a multiple-prompt task, we break it into smaller tasks - std::vector prompts = prompt; - if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { - // prompts[0] is the question - // the rest are the answers/documents - SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) prompts.size() - 1); - for (size_t i = 1; i < prompts.size(); i++) { - json qd; - qd.push_back(prompts[0]); - qd.push_back(prompts[i]); - data["index"] = i - 1; - create_task(data, true, qd); - } - } else { - SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) prompts.size()); - for (size_t i = 0; i < prompts.size(); i++) { - const auto & e = prompts[i]; - if (e.is_string() || json_is_array_of_numbers(e)) { + // because llama_tokenize api is thread-safe, we can tokenize the prompt from HTTP thread + bool add_special = inf_type != SERVER_TASK_INF_TYPE_RERANK && inf_type != SERVER_TASK_INF_TYPE_INFILL; + std::vector tokenized_prompts = tokenize_input_prompts(ctx, data.at("prompt"), add_special, true); + switch (inf_type) { + case SERVER_TASK_INF_TYPE_RERANK: + { + // prompts[0] is the question + // the rest are the answers/documents + GGML_ASSERT(tokenized_prompts.size() > 1); + SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) tokenized_prompts.size() - 1); + for (size_t i = 1; i < tokenized_prompts.size(); i++) { + data["index"] = i - 1; + auto tokens = format_rerank(model, tokenized_prompts[0], tokenized_prompts[i]); + create_task(data, tokens); + } + } break; + case SERVER_TASK_INF_TYPE_INFILL: + { + SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); + for (size_t i = 0; i < tokenized_prompts.size(); i++) { data["index"] = i; - create_task(data, true, e); - } else { - throw std::runtime_error(error_msg); + auto tokens = format_infill( + ctx, + data.at("input_prefix"), + data.at("input_suffix"), + data.at("input_extra"), + params.n_batch, + params.n_predict, + slots[0].n_ctx, // TODO: there should be a better way + params.spm_infill, + tokenized_prompts[i] + ); + create_task(data, tokens); + } + } break; + default: + { + SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); + for (size_t i = 0; i < tokenized_prompts.size(); i++) { + data["index"] = i; + create_task(data, tokenized_prompts[i]); } } - } - } else { - // invalid case - throw std::runtime_error(error_msg); } return tasks; @@ -1534,7 +1427,7 @@ struct server_context { queue_tasks.post(cancel_tasks, true); } - // receive the results from task(s) created by create_tasks_cmpl + // receive the results from task(s) created by create_tasks_inference void receive_cmpl_results( const std::unordered_set & id_tasks, const std::function&)> & result_handler, @@ -1558,7 +1451,7 @@ struct server_context { result_handler(results); } - // receive the results from task(s) created by create_tasks_cmpl, in stream mode + // receive the results from task(s) created by create_tasks_inference, in stream mode void receive_cmpl_results_stream( const std::unordered_set & id_tasks, const std::function & result_handler, const @@ -1591,7 +1484,7 @@ struct server_context { void process_single_task(const server_task & task) { switch (task.type) { - case SERVER_TASK_TYPE_COMPLETION: + case SERVER_TASK_TYPE_INFERENCE: { const int id_slot = json_value(task.data, "id_slot", -1); @@ -1623,9 +1516,10 @@ struct server_context { slot->reset(); - slot->id_task = task.id; - slot->cmpl_type = task.cmpl_type; - slot->index = json_value(task.data, "index", 0); + slot->id_task = task.id; + slot->inf_type = task.inf_type; + slot->index = json_value(task.data, "index", 0); + slot->prompt_tokens = std::move(task.prompt_tokens); if (!launch_slot_with_task(*slot, task)) { SRV_ERR("failed to launch slot with task, id_task = %d\n", task.id); @@ -1658,7 +1552,7 @@ struct server_context { slot_data["id"] = slot.id; slot_data["id_task"] = slot.id_task; slot_data["state"] = slot.state; - slot_data["prompt"] = slot.prompt; + slot_data["prompt"] = common_detokenize(ctx, slot.prompt_tokens); slot_data["next_token"] = { {"has_next_token", slot.has_next_token}, {"has_new_line", slot.has_new_line}, @@ -1785,9 +1679,6 @@ struct server_context { } slot->cache_tokens.resize(token_count); - // TODO: maybe detokenize the slot->cache_tokens instead? - slot->prompt = string_format("[restored %d tokens from file]", (int) token_count); - const int64_t t_end = ggml_time_us(); const double t_restore_ms = (t_end - t_start) / 1000.0; @@ -1954,142 +1845,18 @@ struct server_context { if (params.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { // this slot still has a prompt to be processed - if (slot.state == SLOT_STATE_PROCESSING_PROMPT) { + if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { auto & prompt_tokens = slot.prompt_tokens; - // we haven't tokenized the prompt yet - do it now: - if (prompt_tokens.empty()) { - SLT_INF(slot, "tokenizing prompt, len = %d\n", (int) slot.prompt.size()); - + // TODO: maybe move branch to outside of this loop in the future + if (slot.state == SLOT_STATE_STARTED) { slot.t_start_process_prompt = ggml_time_us(); slot.t_start_generation = 0; - - switch (slot.cmpl_type) { - case SERVER_TASK_CMPL_TYPE_NORMAL: - case SERVER_TASK_CMPL_TYPE_EMBEDDING: - { - prompt_tokens = tokenize(slot.prompt, llama_add_bos_token(model), true); - } break; - case SERVER_TASK_CMPL_TYPE_RERANK: - { - // require slot.prompt to be array of 2 strings - if (!slot.prompt.is_array() || slot.prompt.size() != 2) { - SLT_ERR(slot, "%s", "invalid prompt for rerank task\n"); - slot.release(); - send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST); - continue; - } - - // prompt: [BOS]query[EOS][SEP]doc[EOS] - prompt_tokens.clear(); - prompt_tokens.push_back(llama_token_bos(model)); - { - const auto part = tokenize(slot.prompt[0], false, false); - prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end()); - } - prompt_tokens.push_back(llama_token_eos(model)); - prompt_tokens.push_back(llama_token_sep(model)); - { - const auto part = tokenize(slot.prompt[1], false, false); - prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end()); - } - prompt_tokens.push_back(llama_token_eos(model)); - } break; - case SERVER_TASK_CMPL_TYPE_INFILL: - { - // TODO: optimize this block by reducing memory allocations and movement - - // use FIM repo-level pattern: - // ref: https://arxiv.org/pdf/2409.12186 - // - // [FIM_REP]myproject - // [FIM_SEP]filename0 - // extra chunk 0 - // [FIM_SEP]filename1 - // extra chunk 1 - // ... - // [FIM_SEP]filename - // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt - // - auto tokens_prefix = tokenize(slot.input_prefix, false, false); - auto tokens_suffix = tokenize(slot.input_suffix, false, false); - auto tokens_prompt = tokenize(slot.prompt, false, false); - - slot.extra_tokens.clear(); - if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) { - static const auto k_fim_repo = tokenize("myproject\n", false, false); - - slot.extra_tokens.push_back(llama_token_fim_rep(model)); - slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); - } - - for (const auto & chunk : slot.input_extra) { - // { "text": string, "filename": string } - const std::string text = chunk.value("text", ""); - const std::string filename = chunk.value("filename", "tmp"); - - if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { - const auto k_fim_file = tokenize(filename + "\n", false, false); - - slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model)); - slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); - } else { - // chunk separator in binary form to avoid confusing the AI - static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; - static const auto k_chunk_prefix_tokens = tokenize(k_chunk_prefix_str, false, false); - - slot.extra_tokens.insert(slot.extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); - } - - const auto chunk_tokens = tokenize(text, false, false); - slot.extra_tokens.insert(slot.extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); - } - - if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { - // TODO: current filename - static const auto k_fim_file = tokenize("filename\n", false, false); - - slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model)); - slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); - } - - // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) - const int n_suffix_take = std::min(tokens_suffix.size(), (n_batch/4)); - const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4) - 3); - - // fill the rest of the context with extra chunks - const int n_extra_take = std::min(std::max(0, slot.n_ctx - (n_batch) - 2*slot.n_predict), slot.extra_tokens.size()); - - tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); - tokens_suffix.resize(n_suffix_take); - - tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model)); - tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); - tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model)); - - auto embd_inp = params.spm_infill ? tokens_suffix : tokens_prefix; - auto embd_end = params.spm_infill ? tokens_prefix : tokens_suffix; - - if (llama_add_bos_token(model)) { - embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); - } - - SLT_DBG(slot, "extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", slot.n_ctx, n_extra_take, (int) slot.extra_tokens.size()); - - // put the extra context before the FIM prefix - embd_inp.insert(embd_inp.begin(), slot.extra_tokens.end() - n_extra_take, slot.extra_tokens.end()); - - embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); - embd_inp.push_back(llama_token_fim_mid(model)); - - prompt_tokens = std::move(embd_inp); - } break; - } - slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); + slot.state = SLOT_STATE_PROCESSING_PROMPT; - SLT_INF(slot, "prompt tokenized, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens); + SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens); // print prompt tokens (for debugging) if (1) { @@ -2114,7 +1881,7 @@ struct server_context { continue; } - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { + if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { // this prompt is too large to process - discard it if (slot.n_prompt_tokens > n_ubatch) { slot.release(); @@ -2144,7 +1911,7 @@ struct server_context { const int n_block_size = n_left / 2; const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - std::vector new_tokens( + llama_tokens new_tokens( prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); @@ -2225,7 +1992,7 @@ struct server_context { } // non-causal tasks require to fit the entire prompt in the physical batch - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { + if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { // cannot fit the prompt in the current batch - will try next iter if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { continue; @@ -2234,8 +2001,8 @@ struct server_context { // check that we are in the right batch_type, if not defer the slot const bool slot_type = - slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || - slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK ? 1 : 0; + slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || + slot.inf_type == SERVER_TASK_INF_TYPE_RERANK ? 1 : 0; if (batch_type == -1) { batch_type = slot_type; @@ -2353,7 +2120,7 @@ struct server_context { } if (slot.state == SLOT_STATE_DONE_PROMPT) { - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) { + if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING) { // prompt evaluated for embedding send_embedding(slot, batch_view); slot.release(); @@ -2361,7 +2128,7 @@ struct server_context { continue; // continue loop of slots } - if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) { + if (slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { send_rerank(slot, batch_view); slot.release(); slot.i_batch = -1; @@ -2915,13 +2682,13 @@ int main(int argc, char ** argv) { res_ok(res, {{ "success", true }}); }; - const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_cmpl_type cmpl_type, json & data, httplib::Response & res) { + const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_inf_type inf_type, json & data, httplib::Response & res) { if (ctx_server.params.embedding || ctx_server.params.reranking) { res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); return; } - std::vector tasks = ctx_server.create_tasks_cmpl(data, cmpl_type); + std::vector tasks = ctx_server.create_tasks_inference(data, inf_type); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -2967,10 +2734,11 @@ int main(int argc, char ** argv) { const auto handle_completions = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) { json data = json::parse(req.body); - return handle_completions_generic(SERVER_TASK_CMPL_TYPE_NORMAL, data, res); + return handle_completions_generic(SERVER_TASK_INF_TYPE_COMPLETION, data, res); }; const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) { + // check model compatibility std::string err; if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) { err += "prefix token is missing. "; @@ -2981,14 +2749,42 @@ int main(int argc, char ** argv) { if (llama_token_fim_mid(ctx_server.model) == LLAMA_TOKEN_NULL) { err += "middle token is missing. "; } - if (!err.empty()) { res_error(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED)); return; } json data = json::parse(req.body); - return handle_completions_generic(SERVER_TASK_CMPL_TYPE_INFILL, data, res); + + // validate input + if (!data.contains("input_prefix")) { + res_error(res, format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST)); + } + + if (!data.contains("input_suffix")) { + res_error(res, format_error_response("\"input_suffix\" is required", ERROR_TYPE_INVALID_REQUEST)); + } + + if (data.contains("input_extra") && !data.at("input_extra").is_array()) { + res_error(res, format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST)); + return; + } + json input_extra = json_value(data, "input_extra", json::array()); + for (const auto & chunk : input_extra) { + // { "text": string, "filename": string } + if (!chunk.contains("text") || !chunk.at("text").is_string()) { + res_error(res, format_error_response("extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST)); + return; + } + // filename is optional + if (chunk.contains("filename") && !chunk.at("filename").is_string()) { + res_error(res, format_error_response("extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + data["input_extra"] = input_extra; // default to empty array if it's not exist + + return handle_completions_generic(SERVER_TASK_INF_TYPE_INFILL, data, res); }; // TODO: maybe merge this function with "handle_completions_generic" @@ -3000,7 +2796,7 @@ int main(int argc, char ** argv) { json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template); - std::vector tasks = ctx_server.create_tasks_cmpl(data, SERVER_TASK_CMPL_TYPE_NORMAL); + std::vector tasks = ctx_server.create_tasks_inference(data, SERVER_TASK_INF_TYPE_COMPLETION); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -3073,7 +2869,7 @@ int main(int argc, char ** argv) { const bool add_special = json_value(body, "add_special", false); const bool with_pieces = json_value(body, "with_pieces", false); - std::vector tokens = ctx_server.tokenize(body.at("content"), add_special, true); + llama_tokens tokens = tokenize_mixed(ctx_server.ctx, body.at("content"), add_special, true); if (with_pieces) { for (const auto& token : tokens) { @@ -3110,7 +2906,7 @@ int main(int argc, char ** argv) { std::string content; if (body.count("tokens") != 0) { - const std::vector tokens = body.at("tokens"); + const llama_tokens tokens = body.at("tokens"); content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend()); } @@ -3144,7 +2940,7 @@ int main(int argc, char ** argv) { json responses = json::array(); bool error = false; { - std::vector tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_EMBEDDING); + std::vector tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_EMBEDDING); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -3221,7 +3017,7 @@ int main(int argc, char ** argv) { json responses = json::array(); bool error = false; { - std::vector tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_RERANK); + std::vector tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_RERANK); ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); diff --git a/examples/server/tests/features/infill.feature b/examples/server/tests/features/infill.feature new file mode 100644 index 000000000..a0bbfef77 --- /dev/null +++ b/examples/server/tests/features/infill.feature @@ -0,0 +1,36 @@ +@llama.cpp +@infill +Feature: llama.cpp server + + # The current model is made by adding FIM tokens to the existing stories260K + # We may want to use a better model in the future, maybe something like SmolLM 360M + + Background: Server startup + Given a server listening on localhost:8080 + And a model file tinyllamas/stories260K-infill.gguf from HF repo ggml-org/models + And a model file test-model-infill.gguf + And a model alias tinyllama-infill + And 42 as server seed + And 1024 as batch size + And 1024 as ubatch size + And 2048 KV cache size + And 64 max tokens to predict + And 0.0 temperature + Then the server is starting + Then the server is healthy + + Scenario: Infill without input_extra + Given a prompt "Complete this" + And an infill input extra none none + And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" + And an infill input suffix "}\n" + And an infill request with no api error + Then 64 tokens are predicted matching One|day|she|saw|big|scary|bird + + Scenario: Infill with input_extra + Given a prompt "Complete this" + And an infill input extra "llama.h" "LLAMA_API int32_t llama_n_threads();\n" + And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" + And an infill input suffix "}\n" + And an infill request with no api error + Then 64 tokens are predicted matching cuts|Jimmy|mom|came|into|the|room" diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 540a2ecd5..2e418d8aa 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -80,6 +80,11 @@ def step_server_config(context, server_fqdn: str, server_port: str): context.lora_file = None context.disable_ctx_shift = False + # infill + context.infill_input_extra = None + context.infill_input_suffix = '' + context.infill_input_prefix = '' + context.tasks_result = [] context.concurrent_tasks = [] context.prompts = [] @@ -291,6 +296,28 @@ async def step_request_completion(context, api_error: Literal['raised'] | str): assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}" +@step('an infill request with {api_error} api error') +@async_run_until_complete +async def step_request_completion(context, api_error: Literal['raised'] | str): + if api_error != 'no': + raise ValueError(f'api_error={api_error} is not yet implemented') + payload = { + "prompt": context.prompts[0], + "input_suffix": context.infill_input_suffix, + "input_prefix": context.infill_input_prefix, + "n_predict": context.n_predict, + "seed": context.seed, + "temperature": context.temperature, + } + if context.infill_input_extra is not None: + payload['input_extra'] = context.infill_input_extra + async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: + async with session.post(f'{context.base_url}/infill', + json=payload) as response: + assert response.status == 200 + context.tasks_result = [await response.json()] + + @step('{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): context.completion = context.tasks_result.pop() @@ -539,6 +566,25 @@ def step_a_prompt_prompt(context, prompt): context.n_prompts = len(context.prompts) +# TODO: allow this to be repeated +@step('an infill input extra {filename} {text}') +def step_infill_input_extra(context, filename, text): + if filename == 'none': + context.infill_input_extra = None + else: + context.infill_input_extra = [{'filename': filename, 'text': text}] + + +@step('an infill input suffix {text}') +def step_infill_input_suffix(context, text): + context.infill_input_suffix = text + + +@step('an infill input prefix {text}') +def step_infill_input_prefix(context, text): + context.infill_input_prefix = text + + @step('{num_prompts:d} prompts {prompt} with seed {seed:d}') def step_many_prompts(context, num_prompts, prompt, seed): if context.seed is None: diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 69519ef95..811242062 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -24,6 +24,22 @@ #define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" using json = nlohmann::ordered_json; +using llama_tokens = std::vector; + +#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) + +#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) + +#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) // https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 enum error_type { @@ -52,9 +68,235 @@ static T json_value(const json & body, const std::string & key, const T & defaul } // -// chat template utils +// tokenizer and input processing utils // +static bool json_is_array_of_numbers(const json & data) { + if (data.is_array()) { + for (const auto & e : data) { + if (!e.is_number_integer()) { + return false; + } + } + return true; + } + return false; +} + +// is array having BOTH numbers & strings? +static bool json_is_array_of_mixed_numbers_strings(const json & data) { + bool seen_string = false; + bool seen_number = false; + if (data.is_array()) { + for (const auto & e : data) { + seen_string |= e.is_string(); + seen_number |= e.is_number_integer(); + if (seen_number && seen_string) { + return true; + } + } + } + return false; +} + +/** + * this handles 2 cases: + * - only string, example: "string" + * - mixed string and tokens, example: [12, 34, "string", 56, 78] + */ +static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) { + // If `add_bos` is true, we only add BOS, when json_prompt is a string, + // or the first element of the json_prompt array is a string. + llama_tokens prompt_tokens; + + if (json_prompt.is_array()) { + bool first = true; + for (const auto & p : json_prompt) { + if (p.is_string()) { + auto s = p.template get(); + + llama_tokens p; + if (first) { + p = common_tokenize(ctx, s, add_special, parse_special); + first = false; + } else { + p = common_tokenize(ctx, s, false, parse_special); + } + + prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); + } else { + if (first) { + first = false; + } + + prompt_tokens.push_back(p.template get()); + } + } + } else { + auto s = json_prompt.template get(); + prompt_tokens = common_tokenize(ctx, s, add_special, parse_special); + } + + return prompt_tokens; +} + +/** + * break the input "prompt" object into multiple prompt if needed, then tokenize them + * this supports these cases: + * - "prompt": "string" + * - "prompt": [12, 34, 56] + * - "prompt": [12, 34, "string", 56, 78] + * and multiple prompts (multi-tasks): + * - "prompt": ["string1", "string2"] + * - "prompt": ["string1", [12, 34, 56]] + * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]] + */ +static std::vector tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) { + std::vector result; + if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) { + // string or mixed + result.push_back(tokenize_mixed(ctx, json_prompt, add_special, parse_special)); + } else if (json_is_array_of_numbers(json_prompt)) { + // array of tokens + result.push_back(json_prompt.get()); + } else if (json_prompt.is_array()) { + // array of prompts + result.reserve(json_prompt.size()); + for (const auto & p : json_prompt) { + if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) { + result.push_back(tokenize_mixed(ctx, p, add_special, parse_special)); + } else if (json_is_array_of_numbers(p)) { + // array of tokens + result.push_back(p.get()); + } else { + throw std::runtime_error("element of \"prompt\" must be a string, an list of tokens, or a list of mixed strings & tokens"); + } + } + } else { + throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts"); + } + return result; +} + +// +// template utils +// + +// format rerank task: [BOS]query[EOS][SEP]doc[EOS] +static llama_tokens format_rerank(const struct llama_model * model, const llama_tokens & query, const llama_tokens & doc) { + llama_tokens result; + result.reserve(doc.size() + query.size() + 4); + result.push_back(llama_token_bos(model)); + result.insert(result.end(), query.begin(), query.end()); + result.push_back(llama_token_eos(model)); + result.push_back(llama_token_sep(model)); + result.insert(result.end(), doc.begin(), doc.end()); + result.push_back(llama_token_eos(model)); + return result; +} + +// format infill task +static llama_tokens format_infill( + const llama_context * ctx, + const json & input_prefix, + const json & input_suffix, + const json & input_extra, + const int n_batch, + const int n_predict, + const int n_ctx, + const bool spm_infill, + const llama_tokens & tokens_prompt + ) { + // TODO: optimize this block by reducing memory allocations and movement + + // use FIM repo-level pattern: + // ref: https://arxiv.org/pdf/2409.12186 + // + // [FIM_REP]myproject + // [FIM_SEP]filename0 + // extra chunk 0 + // [FIM_SEP]filename1 + // extra chunk 1 + // ... + // [FIM_SEP]filename + // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt + // + llama_tokens extra_tokens; + extra_tokens.reserve(n_ctx); + + auto model = llama_get_model(ctx); + auto tokens_prefix = tokenize_mixed(ctx, input_prefix, false, false); + auto tokens_suffix = tokenize_mixed(ctx, input_suffix, false, false); + + if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) { + // TODO: make project name an input + static const auto k_fim_repo = common_tokenize(ctx, "myproject\n", false, false); + + extra_tokens.push_back(llama_token_fim_rep(model)); + extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); + } + for (const auto & chunk : input_extra) { + // { "text": string, "filename": string } + const std::string text = json_value(chunk, "text", std::string()); + const std::string filename = json_value(chunk, "filename", std::string("tmp")); + + if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { + const auto k_fim_file = common_tokenize(ctx, filename + "\n", false, false); + + extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } else { + // chunk separator in binary form to avoid confusing the AI + static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; + static const auto k_chunk_prefix_tokens = common_tokenize(ctx, k_chunk_prefix_str, false, false); + + extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); + } + + const auto chunk_tokens = common_tokenize(ctx, text, false, false); + extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); + } + + if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { + // TODO: current filename + static const auto k_fim_file = common_tokenize(ctx, "filename\n", false, false); + + extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } + + // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) + const int n_suffix_take = std::min(tokens_suffix.size(), (n_batch/4)); + const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4) - 3); + + // fill the rest of the context with extra chunks + const int n_extra_take = std::min(std::max(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size()); + + tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); + tokens_suffix.resize(n_suffix_take); + + tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model)); + tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); + tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model)); + + auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix; + auto embd_end = spm_infill ? tokens_prefix : tokens_suffix; + + if (llama_add_bos_token(model)) { + embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); + } + + SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size()); + + // put the extra context before the FIM prefix + embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end()); + + embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); + embd_inp.push_back(llama_token_fim_mid(model)); + + return embd_inp; +} + // Format given chat. If tmpl is empty, we take the template from model metadata inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { std::vector chat; @@ -229,18 +471,6 @@ static size_t find_partial_stop_string(const std::string &stop, const std::strin return std::string::npos; } -static bool json_is_array_of_numbers(const json & data) { - if (data.is_array()) { - for (const auto & e : data) { - if (!e.is_number()) { - return false; - } - } - return true; - } - return false; -} - // TODO: reuse llama_detokenize template static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { From bc5ba007b2c83ac95875e68724dabfc12159fc61 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 25 Oct 2024 10:13:46 +0300 Subject: [PATCH 63/95] server : check that the prompt fits in the slot's context (#10030) ggml-ci --- convert_hf_to_gguf.py | 3 +++ convert_hf_to_gguf_update.py | 1 + examples/server/server.cpp | 7 ++++++- 3 files changed, 10 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 7e552a71b..a34dabe23 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -573,6 +573,9 @@ class Model: if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 res = "bert-bge" + if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7": + # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5 + res = "bert-bge-large" if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": # ref: https://huggingface.co/mosaicml/mpt-7b res = "mpt" diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 022354a3b..28cd02e5a 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -72,6 +72,7 @@ models = [ {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 58f93694f..2821877b2 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1882,12 +1882,17 @@ struct server_context { } if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { - // this prompt is too large to process - discard it if (slot.n_prompt_tokens > n_ubatch) { slot.release(); send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); continue; } + + if (slot.n_prompt_tokens > slot.n_ctx) { + slot.release(); + send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_SERVER); + continue; + } } else { if (!params.ctx_shift) { // if context shift is disabled, we make sure prompt size is smaller than KV size From 2f8bd2b90133cf37ae752015e1bfd738cc6d0112 Mon Sep 17 00:00:00 2001 From: Srihari-mcw <96763064+Srihari-mcw@users.noreply.github.com> Date: Fri, 25 Oct 2024 12:57:41 +0530 Subject: [PATCH 64/95] llamafile : extend sgemm.cpp support for Q5_0 models (#10010) --- ggml/src/llamafile/sgemm.cpp | 57 ++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/ggml/src/llamafile/sgemm.cpp b/ggml/src/llamafile/sgemm.cpp index 0193a463a..9eead3f61 100644 --- a/ggml/src/llamafile/sgemm.cpp +++ b/ggml/src/llamafile/sgemm.cpp @@ -942,6 +942,36 @@ class tinyBLAS_Q0_AVX { return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)), _mm_set1_epi8(8)); } + inline __m256i load(const block_q5_0 *b) { + return _mm256_or_si256(denibble(b->qs), bittobyte(b->qh)); + } + + inline __m128i load0(const block_q5_0* b) { + const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); + uint32_t x32; + memcpy(&x32, b->qh, sizeof(uint32_t)); + __m128i qxl = _mm_and_si128(_mm_set1_epi8(15), x); + __m128i bytesl = _mm_cmpeq_epi8(_mm_set1_epi64x(-1), + _mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe), + _mm_shuffle_epi8(_mm_set1_epi32(x32), + _mm_set_epi64x(0x0101010101010101, 0x0000000000000000)))); + bytesl = _mm_andnot_si128(bytesl, _mm_set1_epi8((char)0xF0)); + return _mm_or_si128(qxl, bytesl); + } + + inline __m128i load1(const block_q5_0* b) { + const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); + uint32_t x32; + memcpy(&x32, b->qh, sizeof(uint32_t)); + __m128i qxh = _mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)); + __m128i bytesh = _mm_cmpeq_epi8(_mm_set1_epi64x(-1), + _mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe), + _mm_shuffle_epi8(_mm_set1_epi32(x32), + _mm_set_epi64x(0x0303030303030303, 0x0202020202020202)))); + bytesh = _mm_andnot_si128(bytesh, _mm_set1_epi8((char)0xF0)); + return _mm_or_si128(qxh, bytesh); + } + inline __m256i load(const block_iq4_nl *b) { return MM256_SET_M128I(load1(b), load0(b)); } @@ -973,6 +1003,17 @@ class tinyBLAS_Q0_AVX { _mm_srli_epi16(x, 4), 1)); } + static inline __m256i bittobyte(const uint8_t *p) { + uint32_t x32; + memcpy(&x32, p, sizeof(uint32_t)); + __m256i bytes = _mm256_cmpeq_epi8(_mm256_set1_epi64x(-1), + _mm256_or_si256(_mm256_set1_epi64x(0x7fbfdfeff7fbfdfe), + _mm256_shuffle_epi8(_mm256_set1_epi32(x32), + _mm256_set_epi64x(0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000)))); + return _mm256_andnot_si256(bytes, _mm256_set1_epi8((char)0xF0)); + } + const TA *const A; const TB *const B; TC *const C; @@ -1182,6 +1223,22 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda #endif } + case GGML_TYPE_Q5_0: { + if (Btype != GGML_TYPE_Q8_0) + return false; +#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) + tinyBLAS_Q0_AVX tb{ + k, (const block_q5_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n); + return true; +#else + return false; +#endif + } + case GGML_TYPE_IQ4_NL: { if (Btype != GGML_TYPE_Q8_0) return false; From d80fb71f8b8bf69ec095ba281f8248d136d21c76 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Fri, 25 Oct 2024 17:57:54 +0200 Subject: [PATCH 65/95] llama: string_split fix (#10022) * llama: Refactor string_split to use template specialization, fixes parsing strings with spaces * llama: Add static_assert in the string_split template to ensure the correct template specialization is used for std::string --- common/arg.cpp | 10 +++++----- common/common.cpp | 13 ------------- common/common.h | 19 +++++++++++++++++-- examples/server/server.cpp | 2 +- 4 files changed, 23 insertions(+), 21 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index cd9d315dc..608e46e02 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -128,13 +128,13 @@ static void common_params_handle_model_default(common_params & params) { } params.hf_file = params.model; } else if (params.model.empty()) { - params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); + params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); } } else if (!params.model_url.empty()) { if (params.model.empty()) { - auto f = string_split(params.model_url, '#').front(); - f = string_split(f, '?').front(); - params.model = fs_get_cache_file(string_split(f, '/').back()); + auto f = string_split(params.model_url, '#').front(); + f = string_split(f, '?').front(); + params.model = fs_get_cache_file(string_split(f, '/').back()); } } else if (params.model.empty()) { params.model = DEFAULT_MODEL_PATH; @@ -879,7 +879,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--samplers"}, "SAMPLERS", string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()), [](common_params & params, const std::string & value) { - const auto sampler_names = string_split(value, ';'); + const auto sampler_names = string_split(value, ';'); params.sparams.samplers = common_sampler_types_from_names(sampler_names, true); } ).set_sparam()); diff --git a/common/common.cpp b/common/common.cpp index a8eebb68b..faaa420d9 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -416,19 +416,6 @@ std::string string_format(const char * fmt, ...) { return std::string(buf.data(), size); } -std::vector string_split(std::string input, char separator) { - std::vector parts; - size_t separator_pos = input.find(separator); - while (separator_pos != std::string::npos) { - std::string part = input.substr(0, separator_pos); - parts.emplace_back(part); - input = input.substr(separator_pos + 1); - separator_pos = input.find(separator); - } - parts.emplace_back(input); - return parts; -} - std::string string_strip(const std::string & str) { size_t start = 0; size_t end = str.size(); diff --git a/common/common.h b/common/common.h index 19d928777..f9333395c 100644 --- a/common/common.h +++ b/common/common.h @@ -380,8 +380,6 @@ bool set_process_priority(enum ggml_sched_priority prio); LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) std::string string_format(const char * fmt, ...); -std::vector string_split(std::string input, char separator); - std::string string_strip(const std::string & str); std::string string_get_sortable_timestamp(); @@ -389,6 +387,7 @@ void string_replace_all(std::string & s, const std::string & search, const std:: template static std::vector string_split(const std::string & str, char delim) { + static_assert(!std::is_same::value, "Please use the specialized version for std::string"); std::vector values; std::istringstream str_stream(str); std::string token; @@ -401,6 +400,22 @@ static std::vector string_split(const std::string & str, char delim) { return values; } +template<> +std::vector string_split(const std::string & input, char separator) +{ + std::vector parts; + size_t begin_pos = 0; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(begin_pos, separator_pos - begin_pos); + parts.emplace_back(part); + begin_pos = separator_pos + 1; + separator_pos = input.find(separator, begin_pos); + } + parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos)); + return parts; +} + bool string_parse_kv_override(const char * data, std::vector & overrides); void string_process_escapes(std::string & input); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2821877b2..3c12ef6f0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2380,7 +2380,7 @@ int main(int argc, char ** argv) { auto middleware_server_state = [&res_error, &state](const httplib::Request & req, httplib::Response & res) { server_state current_state = state.load(); if (current_state == SERVER_STATE_LOADING_MODEL) { - auto tmp = string_split(req.path, '.'); + auto tmp = string_split(req.path, '.'); if (req.path == "/" || tmp.back() == "html") { res.set_content(reinterpret_cast(loading_html), loading_html_len, "text/html; charset=utf-8"); res.status = 503; From ff252ea48e90e6552010fd74584334fb41bdd387 Mon Sep 17 00:00:00 2001 From: wwoodsTM <104587230+wwoodsTM@users.noreply.github.com> Date: Fri, 25 Oct 2024 10:07:34 -0600 Subject: [PATCH 66/95] llama : add DRY sampler (#9702) * sampling : add DRY sampler (post-refactor) * DRY: Trying to fix coauthors, removed unneeded line * DRY: Fixed redundant code * DRY: Fixed crash issue due to DRY being in chain but uninitialized --------- Co-authored-by: l3utterfly Co-authored-by: pi6am <34464159+pi6am@users.noreply.github.com> --- common/arg.cpp | 61 ++++ common/common.cpp | 4 + common/common.h | 70 +++-- common/sampling.cpp | 17 ++ examples/main/README.md | 24 ++ examples/server/README.md | 15 + examples/server/public/index-new.html | 14 +- examples/server/public/index.html | 8 + examples/server/public/style.css | 0 examples/server/server.cpp | 87 ++++-- include/llama.h | 10 + src/llama-sampling.cpp | 391 ++++++++++++++++++++++++++ src/llama-sampling.h | 18 ++ src/llama-vocab.cpp | 16 ++ src/llama-vocab.h | 5 + src/llama.cpp | 4 + tests/test-sampling.cpp | 32 +++ 17 files changed, 713 insertions(+), 63 deletions(-) mode change 100755 => 100644 examples/server/public/style.css diff --git a/common/arg.cpp b/common/arg.cpp index 608e46e02..e1e933934 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -251,6 +251,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context for (auto & antiprompt : params.antiprompt) { string_process_escapes(antiprompt); } + for (auto & seq_breaker : params.sparams.dry_sequence_breakers) { + string_process_escapes(seq_breaker); + } } if (!params.kv_overrides.empty()) { @@ -997,6 +1000,64 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sparams.penalty_freq = std::stof(value); } ).set_sparam()); + add_opt(common_arg( + {"--dry-multiplier"}, "N", + string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sparams.dry_multiplier), + [](common_params & params, const std::string & value) { + params.sparams.dry_multiplier = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-base"}, "N", + string_format("set DRY sampling base value (default: %.2f)", (double)params.sparams.dry_base), + [](common_params & params, const std::string & value) { + float potential_base = std::stof(value); + if (potential_base >= 1.0f) + { + params.sparams.dry_base = potential_base; + } + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-allowed-length"}, "N", + string_format("set allowed length for DRY sampling (default: %d)", params.sparams.dry_allowed_length), + [](common_params & params, int value) { + params.sparams.dry_allowed_length = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-penalty-last-n"}, "N", + string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sparams.dry_penalty_last_n), + [](common_params & params, int value) { + params.sparams.dry_penalty_last_n = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-sequence-breaker"}, "STRING", + string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n", + params.sparams.dry_sequence_breakers.empty() ? "none" : + std::accumulate(std::next(params.sparams.dry_sequence_breakers.begin()), + params.sparams.dry_sequence_breakers.end(), + std::string("'") + (params.sparams.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sparams.dry_sequence_breakers[0]) + "'", + [](const std::string& a, const std::string& b) { + std::string formatted_b = (b == "\n") ? "\\n" : b; + return a + ", '" + formatted_b + "'"; + }).c_str()), + [](common_params & params, const std::string & value) { + static bool defaults_cleared = false; + + if (!defaults_cleared) { + params.sparams.dry_sequence_breakers.clear(); + defaults_cleared = true; + } + + if (value == "none") { + params.sparams.dry_sequence_breakers.clear(); + } else { + params.sparams.dry_sequence_breakers.emplace_back(value); + } + } + ).set_sparam()); add_opt(common_arg( {"--dynatemp-range"}, "N", string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range), diff --git a/common/common.cpp b/common/common.cpp index faaa420d9..ff8cc4076 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2006,6 +2006,10 @@ void yaml_dump_non_result_info(FILE * stream, const common_params & params, cons fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); + fprintf(stream, "dry_allowed_length: %d # default: 2\n", sparams.dry_allowed_length); + fprintf(stream, "dry_base: %.2f # default: 1.75\n", sparams.dry_base); + fprintf(stream, "dry_multiplier: %.1f # default: 0.0\n", sparams.dry_multiplier); + fprintf(stream, "dry_penalty_last_n: %d # default: -1 (0 = disable, -1 = context size)\n", sparams.dry_penalty_last_n); fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); diff --git a/common/common.h b/common/common.h index f9333395c..18b2121ed 100644 --- a/common/common.h +++ b/common/common.h @@ -84,14 +84,15 @@ enum llama_example { enum common_sampler_type { COMMON_SAMPLER_TYPE_NONE = 0, - COMMON_SAMPLER_TYPE_TOP_K = 1, - COMMON_SAMPLER_TYPE_TOP_P = 2, - COMMON_SAMPLER_TYPE_MIN_P = 3, - COMMON_SAMPLER_TYPE_TFS_Z = 4, - COMMON_SAMPLER_TYPE_TYPICAL_P = 5, - COMMON_SAMPLER_TYPE_TEMPERATURE = 6, - COMMON_SAMPLER_TYPE_XTC = 7, - COMMON_SAMPLER_TYPE_INFILL = 8, + COMMON_SAMPLER_TYPE_DRY = 1, + COMMON_SAMPLER_TYPE_TOP_K = 2, + COMMON_SAMPLER_TYPE_TOP_P = 3, + COMMON_SAMPLER_TYPE_MIN_P = 4, + COMMON_SAMPLER_TYPE_TFS_Z = 5, + COMMON_SAMPLER_TYPE_TYPICAL_P = 6, + COMMON_SAMPLER_TYPE_TEMPERATURE = 7, + COMMON_SAMPLER_TYPE_XTC = 8, + COMMON_SAMPLER_TYPE_INFILL = 9, }; // dimensionality reduction methods, used by cvector-generator @@ -104,32 +105,39 @@ enum dimre_method { struct common_sampler_params { uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler - int32_t n_prev = 64; // number of previous tokens to remember - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. - int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens - int32_t top_k = 40; // <= 0 to use vocab size - float top_p = 0.95f; // 1.0 = disabled - float min_p = 0.05f; // 0.0 = disabled - float xtc_probability = 0.00f; // 0.0 = disabled - float xtc_threshold = 0.10f; // > 0.5 disables XTC - float tfs_z = 1.00f; // 1.0 = disabled - float typ_p = 1.00f; // typical_p, 1.0 = disabled - float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities - float dynatemp_range = 0.00f; // 0.0 = disabled - float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler - int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float penalty_repeat = 1.00f; // 1.0 = disabled - float penalty_freq = 0.00f; // 0.0 = disabled - float penalty_present = 0.00f; // 0.0 = disabled - int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float mirostat_tau = 5.00f; // target entropy - float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = false; // consider newlines as a repeatable token - bool ignore_eos = false; - bool no_perf = false; // disable performance metrics + int32_t n_prev = 64; // number of previous tokens to remember + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens + int32_t top_k = 40; // <= 0 to use vocab size + float top_p = 0.95f; // 1.0 = disabled + float min_p = 0.05f; // 0.0 = disabled + float xtc_probability = 0.00f; // 0.0 = disabled + float xtc_threshold = 0.10f; // > 0.5 disables XTC + float tfs_z = 1.00f; // 1.0 = disabled + float typ_p = 1.00f; // typical_p, 1.0 = disabled + float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities + float dynatemp_range = 0.00f; // 0.0 = disabled + float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler + int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float penalty_repeat = 1.00f; // 1.0 = disabled + float penalty_freq = 0.00f; // 0.0 = disabled + float penalty_present = 0.00f; // 0.0 = disabled + float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition: + float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length) + int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty + int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate + bool penalize_nl = false; // consider newlines as a repeatable token + bool ignore_eos = false; + bool no_perf = false; // disable performance metrics + + std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY std::vector samplers = { + COMMON_SAMPLER_TYPE_DRY, COMMON_SAMPLER_TYPE_TOP_K, COMMON_SAMPLER_TYPE_TFS_Z, COMMON_SAMPLER_TYPE_TYPICAL_P, diff --git a/common/sampling.cpp b/common/sampling.cpp index 4ab3eface..48a9df8ba 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -130,9 +130,11 @@ std::string common_sampler_params::print() const { snprintf(result, sizeof(result), "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" + "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", penalty_last_n, penalty_repeat, penalty_freq, penalty_present, + dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, mirostat, mirostat_eta, mirostat_tau); @@ -174,6 +176,17 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co if (params.mirostat == 0) { for (const auto & cnstr : params.samplers) { switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: + { + std::vector c_breakers; + c_breakers.reserve(params.dry_sequence_breakers.size()); + for (const auto& str : params.dry_sequence_breakers) { + c_breakers.push_back(str.c_str()); + } + + llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + } + break; case COMMON_SAMPLER_TYPE_TOP_K: llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); break; @@ -358,6 +371,7 @@ std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return 'd'; case COMMON_SAMPLER_TYPE_TOP_K: return 'k'; case COMMON_SAMPLER_TYPE_TFS_Z: return 'f'; case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y'; @@ -372,6 +386,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return "dry"; case COMMON_SAMPLER_TYPE_TOP_K: return "top_k"; case COMMON_SAMPLER_TYPE_TFS_Z: return "tfs_z"; case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p"; @@ -386,6 +401,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { std::vector common_sampler_types_from_names(const std::vector & names, bool allow_alt_names) { std::unordered_map sampler_canonical_name_map { + { "dry", COMMON_SAMPLER_TYPE_DRY }, { "top_k", COMMON_SAMPLER_TYPE_TOP_K }, { "top_p", COMMON_SAMPLER_TYPE_TOP_P }, { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P }, @@ -434,6 +450,7 @@ std::vector common_sampler_types_from_names(const std::vect std::vector common_sampler_types_from_chars(const std::string & chars) { std::unordered_map sampler_name_map = { + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TFS_Z), COMMON_SAMPLER_TYPE_TFS_Z }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, diff --git a/examples/main/README.md b/examples/main/README.md index 7e192b9f2..c7c823171 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -187,6 +187,30 @@ Use the `--no-penalize-nl` option to disable newline penalization when applying Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl` +### DRY Repetition Penalty + +DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)). + +- `--dry-multiplier N`: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled). +- `--dry-base N`: Set the DRY sampling base value (default: 1.75). +- `--dry-allowed-length N`: Set the allowed length for DRY sampling (default: 2). +- `--dry-penalty-last-n N`: Set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size). +- `--dry-sequence-breaker STRING`: Add a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: `['\n', ':', '"', '*']`. If the string `"none"` is supplied, no sequence breakers are used. + +The `dry-multiplier` option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8. + +The `dry-base` option sets the base value for the exponential penalty calculation in DRY sampling. Higher values lead to more aggressive penalization of repetitions. + +The `dry-allowed-length` option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words. + +The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of -1 considers the entire context. Use a positive value to limit the consideration to a specific number of recent tokens. + +The `dry-sequence-breaker` option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied. + +DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence. + +Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n -1 --dry-sequence-breaker "—" --dry-sequence-breaker "##"` + ### Top-K Sampling - `--top-k N`: Limit the next token selection to the K most probable tokens (default: 40). diff --git a/examples/server/README.md b/examples/server/README.md index 8f00fcc79..bc737237e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -114,6 +114,11 @@ The project is under active development, and we are [looking for feedback and co | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | | `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | | `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | +| `--dry-multiplier N` | DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--dry-base N` | DRY sampling base value (default: 1.75) | +| `--dry-allowed-length N` | allowed length for DRY sampling (default: 2) | +| `--dry-penalty-last-n N` | DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | +| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers (`['\n', ':', '"', '*']`) in the process; use `"none"` to not use any sequence breakers | `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | | `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | | `--mirostat N` | use Mirostat sampling.
Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | @@ -369,6 +374,16 @@ node index.js `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled. + `dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled. + + `dry_base`: Set the DRY repetition penalty base value. Default: `1.75` + + `dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2` + + `dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size. + + `dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']` + `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0. `mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0` diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html index ad4183cd9..cb3995abe 100644 --- a/examples/server/public/index-new.html +++ b/examples/server/public/index-new.html @@ -40,6 +40,10 @@ repeat_last_n: 0, // 0 = disable penalty, -1 = context size repeat_penalty: 1.0, // 1.0 = disabled penalize_nl: false, // true only useful for infinite completion + dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well + dry_base: 1.75, // 0.0 = disabled + dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well + dry_penalty_last_n: -1, // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) top_k: 0, // <= 0 to use vocab size top_p: 1.0, // 1.0 = disabled min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4 @@ -833,13 +837,17 @@ return html`
${IntField({ label: "Top-K", title: "Limits the selection of the next token to the K most probable tokens. 1 means no randomness = greedy sampling. If set to 0, it means the entire vocabulary size is considered.", max: 100, min: 0, step: 1, name: "top_k", value: params.value.top_k })} ${IntField({ label: "Penalize Last N", title: "The last n tokens that are taken into account to penalise repetitions. A value of 0 means that this function is deactivated and -1 means that the entire size of the context is taken into account.", max: 2048, min: 0, step: 16, name: "repeat_last_n", value: params.value.repeat_last_n })} - ${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Presence Penalty", title: "A penalty that is applied if certain tokens appear repeatedly in the generated text. A higher value leads to fewer repetitions.", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} - ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} + ${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })} ${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })} + ${FloatField({ label: "DRY Penalty Multiplier", title: "Set the DRY repetition penalty multiplier. Default is 0.0, which disables DRY.", max: 5.0, min: 0.0, name: "dry_multiplier", step: 0.01, value: params.value.dry_multiplier })} + ${FloatField({ label: "DRY Base", title: "Set the DRY repetition penalty base value. Default is 1.75", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })} + ${IntField({ label: "DRY Allowed Length", title: "Tokens that extend repetition beyond this receive exponentially increasing penalty. Default is 2", max: 10, min: 1, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })} + ${IntField({ label: "DRY Penalty Last N", title: "How many tokens to scan for repetitions. Default is -1, where 0 is disabled and -1 is context size", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })} + ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
@@ -1144,6 +1152,8 @@ document.addEventListener('DOMContentLoaded', (event) => { repeat_penalty: { snapValue: 1.0, snapRangeMultiplier: 4 }, presence_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 }, frequency_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 }, + dry_multiplier: { snapValue: 0.0, snapRangeMultiplier: 4 }, + dry_base: { snapValue: 1.75, snapRangeMultiplier: 4 }, }; // add an event listener for each slider Object.keys(snapSettings).forEach(sliderName => { diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 88065705f..7f9b02bfb 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -304,6 +304,10 @@ repeat_last_n: 256, // 0 = disable penalty, -1 = context size repeat_penalty: 1.18, // 1.0 = disabled penalize_nl: false, + dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well + dry_base: 1.75, // 0.0 = disabled + dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well + dry_penalty_last_n: -1, // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled @@ -1015,6 +1019,10 @@ ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} + ${FloatField({ label: "DRY Penalty Multiplier", max: 5.0, min: 0.0, name: "dry_multiplier", step: 0.01, value: params.value.dry_multiplier })} + ${FloatField({ label: "DRY Base", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })} + ${IntField({ label: "DRY Allowed Length", max: 10, min: 2, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })} + ${IntField({ label: "DRY Penalty Last N", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })} ${FloatField({ label: "XTC probability", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })} ${FloatField({ label: "XTC threshold", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
diff --git a/examples/server/public/style.css b/examples/server/public/style.css old mode 100755 new mode 100644 diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3c12ef6f0..ff1d9b03c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -800,35 +800,58 @@ struct server_context { slot.oaicompat_model = ""; } - slot.params.stream = json_value(data, "stream", false); - slot.params.cache_prompt = json_value(data, "cache_prompt", false); - slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict)); - slot.params.n_indent = json_value(data, "n_indent", default_params.n_indent); - slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); - slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); - slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); - slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability); - slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold); - slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); - slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p); - slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); - slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); - slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); - slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); - slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); - slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); - slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot.params.n_keep = json_value(data, "n_keep", default_params.n_keep); - slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); - slot.sparams.seed = json_value(data, "seed", default_sparams.seed); - slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); - slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); - //slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", default_params.t_max_prompt_ms); // TODO: implement - slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", default_params.t_max_predict_ms); + slot.params.stream = json_value(data, "stream", false); + slot.params.cache_prompt = json_value(data, "cache_prompt", false); + slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict)); + slot.params.n_indent = json_value(data, "n_indent", default_params.n_indent); + slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); + slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability); + slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold); + slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); + slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p); + slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); + slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); + slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + slot.sparams.dry_multiplier = json_value(data, "dry_multiplier", default_sparams.dry_multiplier); + slot.sparams.dry_base = json_value(data, "dry_base", default_sparams.dry_base); + slot.sparams.dry_allowed_length = json_value(data, "dry_allowed_length", default_sparams.dry_allowed_length); + slot.sparams.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", default_sparams.dry_penalty_last_n); + slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot.params.n_keep = json_value(data, "n_keep", default_params.n_keep); + slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); + slot.sparams.seed = json_value(data, "seed", default_sparams.seed); + slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); + //slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", default_params.t_max_prompt_ms); // TODO: implement + slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", default_params.t_max_predict_ms); + + if (slot.sparams.dry_base < 1.0f) + { + slot.sparams.dry_base = default_sparams.dry_base; + } + + // sequence breakers for DRY + { + // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format + // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 + + if (data.contains("dry_sequence_breakers")) { + slot.sparams.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); + if (slot.sparams.dry_sequence_breakers.empty()) { + send_error(task, "Error: dry_sequence_breakers must be a non-empty array of strings", ERROR_TYPE_INVALID_REQUEST); + return false; + } + } + } // process "json_schema" and "grammar" if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) { @@ -1132,6 +1155,11 @@ struct server_context { {"repeat_penalty", slot.sparams.penalty_repeat}, {"presence_penalty", slot.sparams.penalty_present}, {"frequency_penalty", slot.sparams.penalty_freq}, + {"dry_multiplier", slot.sparams.dry_multiplier}, + {"dry_base", slot.sparams.dry_base}, + {"dry_allowed_length", slot.sparams.dry_allowed_length}, + {"dry_penalty_last_n", slot.sparams.dry_penalty_last_n}, + {"dry_sequence_breakers", slot.sparams.dry_sequence_breakers}, {"mirostat", slot.sparams.mirostat}, {"mirostat_tau", slot.sparams.mirostat_tau}, {"mirostat_eta", slot.sparams.mirostat_eta}, @@ -1970,7 +1998,6 @@ struct server_context { for (size_t i = 0; i < n_match; i++) { slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i]; - slot.n_past++; } diff --git a/include/llama.h b/include/llama.h index d4059c8dd..b2d1e7d5a 100644 --- a/include/llama.h +++ b/include/llama.h @@ -1141,6 +1141,16 @@ extern "C" { bool penalize_nl, // consider newlines as a repeatable token bool ignore_eos); // ignore the end-of-sequence token + /// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982 + LLAMA_API struct llama_sampler * llama_sampler_init_dry( + const struct llama_model * model, + float dry_multiplier, + float dry_base, + int32_t dry_allowed_length, + int32_t dry_penalty_last_n, + const char ** seq_breakers, + size_t num_breakers); + LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias( int32_t n_vocab, int32_t n_logit_bias, diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index d71516153..25536eb6c 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1683,6 +1683,397 @@ struct llama_sampler * llama_sampler_init_penalties( }; } +// DRY + +struct llama_sampler_dry { + int32_t total_context_size; + + const float dry_multiplier; + const float dry_base; + const int32_t dry_allowed_length; + const int32_t dry_penalty_last_n; + + std::unordered_multimap> dry_processed_breakers; + std::vector dry_repeat_count; + std::unordered_map dry_max_token_repeat; + ring_buffer last_tokens; +}; + +// Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am) +static void get_overlapping_token_sequences(const llama_vocab & vocab, const std::string& str, std::unordered_multimap>& token_sequences, int max_tail_len = -1) { + for (llama_token token_id = 0; token_id < (llama_token)vocab.n_vocab; token_id++) { + std::string word = llama_detokenize(vocab, {token_id}, true); + if (word.find(str) != std::string::npos) { + token_sequences.emplace(token_id, std::vector()); + } else { + size_t word_len = word.size(), str_len = str.size(); + size_t pos = -1; + while ((pos = word.find(str[0], pos + 1)) != std::string::npos) { + bool match = true; + size_t i; + for (i = 1; i < str_len && i + pos < word_len; ++i) { + if (word[pos + i] != str[i]) { + match = false; + break; + } + } + if (match) { + std::vector tokenization = llama_tokenize_internal(vocab, str.substr(i), false, false); + if (max_tail_len >= 0 && tokenization.size() > (size_t)max_tail_len) { + tokenization.resize(max_tail_len); + } + + // Ensure we don't already have a duplicate matching tokenization + auto its = token_sequences.equal_range(token_id); + bool found = false; + for (auto it = its.first; it != its.second; ++it) { + if (tokenization == it->second) { + found = true; + break; + } + } + if (!found) { + token_sequences.emplace(token_id, tokenization); + } + } + } + } + } +} + +static const char * llama_sampler_dry_name(const struct llama_sampler * /*smpl*/) { + return "dry"; +} + +static void llama_sampler_dry_accept(struct llama_sampler * smpl, llama_token token) { + auto * ctx = (llama_sampler_dry *) smpl->ctx; + if (ctx->dry_multiplier == 0.0f || ctx->dry_base < 1.0f || ctx->dry_penalty_last_n == 0) { + return; + } + + ctx->last_tokens.push_back(token); +} + +// Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am) +static void llama_sampler_dry_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_dry *) smpl->ctx; + + if (ctx->dry_multiplier == 0.0f || ctx->dry_base < 1.0f || ctx->dry_penalty_last_n == 0) { + return; + } + + int32_t effective_dry_penalty_last_n = (ctx->dry_penalty_last_n == -1) ? ctx->total_context_size : std::max(ctx->dry_penalty_last_n, 0); + int last_n_repeat = std::min(std::min((int)ctx->last_tokens.size(), effective_dry_penalty_last_n), ctx->total_context_size); + + if (last_n_repeat <= ctx->dry_allowed_length) { + return; + } + + ctx->dry_repeat_count.assign(last_n_repeat, 0); + ctx->dry_max_token_repeat.clear(); + + // Step 1: Look for restart sequences to limit the maximum repetition length. + // Work backwards through the context looking for any token that begins a restart sequence. + // + // The collection `restart_sequences` is a mapping from a "head" token to all "tail" + // sequences that together comprise a restart sequence. This allows us to quickly check + // whether each token is the head of a complete sequence. Most restart sequences are actually + // a single token, and for these the "tail" is an empty vector. + // + // If the token is a "head", test all restart sequences that begin with this token + // (there will often only be one sequence for each token, but if sequences like 'aaaq1' and + // 'aaa1' are used as restart strings, both could start with 'aaa' when tokenized). The + // longest matching sequence (if any) is used to limit the maximum repetition length. + // + // Note that in the case case of a short sequence contained in a longer one, this might fail to + // find the smallest value for `rep_limit`. For example, if 'amniotic' and 'ni' are both used as + // restart sequences, 'ni' will be found first, and since it's shorter it will fail to suppress + // 'otic'. This is a minor issue since fully contained restart sequences are likely to be rare. + // + // This is theoretically worst-case O(N^2) for arbitrary restart sequences, which is why we + // have already clamped the maximum tail sequence length when generating `restart_sequences`. + // With clamping, this scan is O(N) in the context length. + + int rep_limit = last_n_repeat; + for (int i = 0; i < last_n_repeat; ++i) { + llama_token token = ctx->last_tokens.rat(i); + auto its = ctx->dry_processed_breakers.equal_range(token); + if (its.first == ctx->dry_processed_breakers.end()) { + continue; + } + int longest_match = -1; + for (auto it = its.first; it != its.second; ++it) { + // Note that (*it) does not contain the head character, so seq_len will be + // the restart sequence length minus 1. + // In the common case of a single-token restart sequence, (*it) will be empty + // and we will trivially match. + int seq_len = (int)it->second.size(); + if (seq_len > longest_match && seq_len <= (int)i) { + bool match = true; + for (int offset = 0; offset < seq_len; ++offset) { + // The -1 when indexing `last_tokens` is because we already matched the head. + if (it->second[offset] != ctx->last_tokens.rat(i - offset - 1)) { + match = false; + break; + } + } + if (match) { + longest_match = seq_len; + } + } + } + if (longest_match >= 0) { + // We found a restart sequence starting `i` tokens from the end and continuing for + // `longest_match` tokens. + rep_limit = i - longest_match; + break; + } + } + if (rep_limit < ctx->dry_allowed_length) { + return; + } + + // Step 2: Iterate in reverse over the last N tokens of the context, using the "Z-algorithm" (in + // the reverse direction) to efficiently compute the positions and lengths of suffixes appearing + // elsewhere in the context. We limit the suffix length to `rep_limit` to respect restart sequences. + // + // This algorithm is not currently documented on Wikipedia, but there is a clear description here: + // https://ivanyu.me/blog/2014/10/15/z-algorithm/ + // + // The code below is adapted from the public domain implementation by the same author here: + // https://github.com/ivanyu/string-algorithms/blob/master/z_algorithm.py + // + // Example: + // Last N tokens: a b c c b c y a b c + // Repeat counts: 0 0 3 1 0 2 0 0 0 0 + // ^ + // This `3` means that the last three tokens of the context (a b c) also appear here. + // + // This step is worst case O(N) since the Z-algorithm is linear, despite the appearance of nested + // for/while loops. This can be seen by observing that the `lt` and `rt` bounds are set after each + // repeated suffix is detected (i.e. after each while loop when n > 0). These bound variables + // ensure that the inner while loops only examine each token in the context once as the outer + // for loop iterates over the context. + + { + const int last = last_n_repeat - 1; + int rt = 0, lt = 0; + + for (int k = 1; k < last_n_repeat; ++k) { + if (k > rt) { + // If k is outside the current Z-box, do naive computation. + int n = 0; + while (n + k < last_n_repeat && ctx->last_tokens.rat(n) == ctx->last_tokens.rat(n+k)) { + ++n; + } + ctx->dry_repeat_count[last - k] = std::min(n, rep_limit); + if (n > 0) { + lt = k; + rt = k+n-1; + } + } else { + // If k is inside the current Z-box, consider two cases. + + int p = k - lt; // Pair index. + int right_part_len = rt - k + 1; + + if (ctx->dry_repeat_count[last - p] < right_part_len) { + int n = std::min(ctx->dry_repeat_count[last - p], rep_limit); + ctx->dry_repeat_count[last - k] = n; + } else { + int i = rt + 1; + while (i < last_n_repeat && ctx->last_tokens.rat(i) == ctx->last_tokens.rat(i - k)) { + i += 1; + } + + int n = std::min(i - k, rep_limit); + ctx->dry_repeat_count[last - k] = n; + lt = k; + rt = i - 1; + } + } + } + } + + // Step 3: Iterate over dry_repeat_count and last_tokens, examining the maximum repeat length + // that would be generated by emitting each new token that would extend a sequence. + // + // Following the same example as above: + // Last N tokens: a b c c b c y a b c + // Repeat counts: 0 0 3 1 0 2 0 0 0 0 + // + // For each non-zero, look ahead one token. This token, if emitted, would extend the repetition. + // c: 3 -> 4 (from `a b c` to `a b c c`) + // b: 1 -> 2 (from `c` to `c b`) + // y: 2 -> 3 (from `b c` to `b c y`) + + for (int i = 0; i < last_n_repeat - 1; ++i) { + int repeat_len = ctx->dry_repeat_count[i]; + if (repeat_len >= ctx->dry_allowed_length) { + // This token ends a repeat, so the next token would continue one. + // By convention, the value of `repeat_len` only includes the tokens currently + // in the context, not the new token that would be added. + llama_token token = ctx->last_tokens.rat(last_n_repeat - 2 - i); + // Track the maximum sequence ending in this token. + const auto& it = ctx->dry_max_token_repeat.find(token); + if (it == ctx->dry_max_token_repeat.end() || it->second < repeat_len) { + ctx->dry_max_token_repeat[token] = repeat_len; + } + } + } + + // Step 4: Apply logit penalties based on the maximum repeat length for relevant tokens. + + // Prevent floating point overflow in `pow(penalty_base, exponent)` by clamping to `max_exponent`. + // Compute it from `penalty_base` and the approximate log of `std::numeric_limits::max()` + const float FLOAT_MAX_LOG = 88.7228391f; + int max_exponent = 0; + if (ctx->dry_base > 1.000001f) { + max_exponent = FLOAT_MAX_LOG / std::log(ctx->dry_base); + } + + for (size_t i = 0; i < cur_p->size; ++i) { + const auto& af_kvp = ctx->dry_max_token_repeat.find(cur_p->data[i].id); + if (af_kvp != ctx->dry_max_token_repeat.end()) { + // Check all sequence breakers starting with this token + auto range = ctx->dry_processed_breakers.equal_range(cur_p->data[i].id); + bool is_single_token_breaker = false; + + for (auto it = range.first; it != range.second; ++it) { + if (it->second.empty()) { + is_single_token_breaker = true; + break; + } + } + + // Apply penalty only if it's not a single-token sequence breaker + if (!is_single_token_breaker) { + int repeat_exp = af_kvp->second - ctx->dry_allowed_length; + if (max_exponent > 0 && repeat_exp > max_exponent) { + repeat_exp = max_exponent; + } + float penalty = ctx->dry_multiplier * std::pow(ctx->dry_base, repeat_exp); + cur_p->data[i].logit -= penalty; + } + } + } + + cur_p->sorted = false; +} + +static void llama_sampler_dry_reset(struct llama_sampler * smpl) { + auto * ctx = (llama_sampler_dry *) smpl->ctx; + ctx->last_tokens.clear(); + ctx->dry_repeat_count.clear(); + ctx->dry_max_token_repeat.clear(); +} + +static struct llama_sampler * llama_sampler_dry_clone(const struct llama_sampler * smpl) { + const auto * ctx = (llama_sampler_dry *) smpl->ctx; + + // nullptr is passed as vocab because it is only needed for raw sequence breaker processing, which we have already done and will be copying + auto * result = llama_sampler_init_dry(nullptr, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0); + // Copy the state, including the processed breakers + { + auto * result_ctx = (llama_sampler_dry *) result->ctx; + result_ctx->dry_processed_breakers = ctx->dry_processed_breakers; + result_ctx->dry_repeat_count = ctx->dry_repeat_count; + result_ctx->dry_max_token_repeat = ctx->dry_max_token_repeat; + result_ctx->last_tokens = ctx->last_tokens; + } + + return result; +} + +static void llama_sampler_dry_free(struct llama_sampler * smpl) { + delete (llama_sampler_dry *) smpl->ctx; +} + +static struct llama_sampler_i llama_sampler_dry_i = { + /* .name = */ llama_sampler_dry_name, + /* .accept = */ llama_sampler_dry_accept, + /* .apply = */ llama_sampler_dry_apply, + /* .reset = */ llama_sampler_dry_reset, + /* .clone = */ llama_sampler_dry_clone, + /* .free = */ llama_sampler_dry_free, +}; + +struct llama_sampler * llama_sampler_init_dry_impl(const struct llama_vocab & vocab, int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) { + int32_t effective_dry_penalty_last_n = (dry_penalty_last_n == -1) ? context_size : std::max(dry_penalty_last_n, 0); + std::unordered_multimap> processed_breakers; + const int MAX_CHAR_LEN = 40; + const int MAX_SEQ_LEN = 20; + + const bool dry_enabled = (dry_multiplier != 0.0f && dry_base >= 1.0f && dry_penalty_last_n != 0); + + if (dry_enabled && seq_breakers != nullptr && num_breakers > 0) { + // Process sequence breakers + for (size_t i = 0; i < num_breakers; ++i) { + if (seq_breakers[i] == nullptr || std::strlen(seq_breakers[i]) == 0) { + LLAMA_LOG_WARN("skipping null or empty DRY sequence breaker at index %zu\n", i); + continue; + } + + std::string sequence_break(seq_breakers[i]); + if (sequence_break.empty()) { + LLAMA_LOG_WARN("skipping empty DRY sequence breaker\n"); + continue; + } + + if (sequence_break.size() > MAX_CHAR_LEN) { + LLAMA_LOG_WARN("truncating DRY sequence breaker to %d characters\n", MAX_CHAR_LEN); + sequence_break.resize(MAX_CHAR_LEN); + } + + get_overlapping_token_sequences(vocab, sequence_break, processed_breakers, MAX_SEQ_LEN); + } + } + + return new llama_sampler { + /* .iface = */ &llama_sampler_dry_i, + /* .ctx = */ new llama_sampler_dry { + /* .total_context_size = */ context_size, + /* .dry_multiplier = */ dry_multiplier, + /* .dry_base = */ dry_base, + /* .dry_allowed_length = */ dry_allowed_length, + /* .dry_penalty_last_n = */ dry_penalty_last_n, + /* .dry_processed_breakers = */ std::move(processed_breakers), + /* .dry_repeat_count = */ dry_enabled ? std::vector(effective_dry_penalty_last_n, 0) : std::vector{}, + /* .dry_max_token_repeat = */ {}, + /* .last_tokens = */ dry_enabled ? ring_buffer(effective_dry_penalty_last_n) : ring_buffer(0), + }, + }; +} + +// wrapper for test-sampling.cpp +struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector>& seq_breakers) { + llama_vocab dummy_vocab; + auto * result = llama_sampler_init_dry_impl(dummy_vocab, context_size, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, NULL, 0); + auto * ctx = (llama_sampler_dry *) result->ctx; + + // Process the token-based sequence breakers + ctx->dry_processed_breakers.clear(); + if (seq_breakers.empty()) { + LLAMA_LOG_WARN("empty DRY sequence breakers list in llama_sampler_init_dry_testing\n"); + } else { + for (const auto& breaker : seq_breakers) { + if (breaker.empty()) { + LLAMA_LOG_WARN("skipping DRY empty sequence breaker\n"); + continue; + } + llama_token head_token = breaker[0]; + std::vector tail_tokens(breaker.begin() + 1, breaker.end()); + ctx->dry_processed_breakers.emplace(head_token, std::move(tail_tokens)); + } + + if (ctx->dry_processed_breakers.empty()) { + LLAMA_LOG_WARN("no valid DRY sequence breakers processed in llama_sampler_init_dry_testing\n"); + } + } + + return result; +} + // logit-bias struct llama_sampler_logit_bias { diff --git a/src/llama-sampling.h b/src/llama-sampling.h index 2683f1b92..919f6fdfc 100644 --- a/src/llama-sampling.h +++ b/src/llama-sampling.h @@ -28,3 +28,21 @@ struct llama_sampler * llama_sampler_init_grammar_impl( struct llama_sampler * llama_sampler_init_infill_impl( const struct llama_vocab & vocab); + +struct llama_sampler * llama_sampler_init_dry_impl( + const struct llama_vocab & vocab, + int32_t context_size, + float dry_multiplier, + float dry_base, + int32_t dry_allowed_length, + int32_t dry_penalty_last_n, + const char ** seq_breakers, + size_t num_breakers); + +struct llama_sampler * llama_sampler_init_dry_testing( + int32_t context_size, + float dry_multiplier, + float dry_base, + int32_t dry_allowed_length, + int32_t dry_penalty_last_n, + const std::vector>& seq_breakers); diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 0a49ddbe3..d1dc96276 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1966,3 +1966,19 @@ int32_t llama_detokenize_impl( return total <= text_len_max ? total : -total; } + +std::string llama_detokenize(const struct llama_vocab & vocab, const std::vector & tokens, bool special) { + std::string text; + text.resize(std::max(text.capacity(), tokens.size())); + int32_t n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + if (n_chars < 0) { + text.resize(-n_chars); + n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization + } + + text.resize(n_chars); + + // NOTE: the original tokenizer decodes bytes after collecting the pieces. + return text; +} diff --git a/src/llama-vocab.h b/src/llama-vocab.h index d958d0073..4bb16d2e4 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -163,3 +163,8 @@ int32_t llama_detokenize_impl( int32_t text_len_max, bool remove_special, bool unparse_special); + +std::string llama_detokenize( + const struct llama_vocab & vocab, + const std::vector & tokens, + bool special); diff --git a/src/llama.cpp b/src/llama.cpp index 24e1f1f01..50eebc2c2 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -21775,6 +21775,10 @@ struct llama_sampler * llama_sampler_init_infill(const struct llama_model * mode return llama_sampler_init_infill_impl(model->vocab); } +struct llama_sampler * llama_sampler_init_dry(const struct llama_model * model, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) { + return llama_sampler_init_dry_impl(model->vocab, llama_n_ctx_train(model), dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers, num_breakers); +} + // // model split // diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 05600e6f5..eb39661c3 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -10,6 +10,8 @@ #include #include +extern struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector>& seq_breakers); + static void dump(const llama_token_data_array * cur_p) { for (size_t i = 0; i < cur_p->size; i++) { printf("%d: %f (%f)\n", cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit); @@ -167,6 +169,29 @@ static void test_penalties( tester.check(); } +static void test_dry( + const std::vector & probs, const std::vector & last_tokens, + const std::vector & expected_probs, float dry_multiplier, float dry_base, + int dry_allowed_length, int dry_penalty_last_n, + const std::vector> & seq_breakers +) { + GGML_ASSERT(probs.size() == expected_probs.size()); + + sampler_tester tester(probs, expected_probs); + + auto * sampler = llama_sampler_init_dry_testing(1024, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers); + + for (size_t i = 0; i < last_tokens.size(); i++) { + llama_sampler_accept(sampler, last_tokens[i]); + } + + DUMP(&tester.cur_p); + tester.apply(sampler); + tester.apply(llama_sampler_init_dist(0)); + DUMP(&tester.cur_p); + tester.check(); +} + static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p ) { sampler_tester tester(n_vocab); @@ -333,6 +358,13 @@ int main(void) { test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f); test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f); + + test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1}, {0.25f, 0.25f, 0.25f, 0.25f}, 1.0f, 1.1f, 2, 4, {}); + test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.296923f, 0.109232f}, 1.0f, 1.1f, 2, 5, {}); + test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 2, 6, {{3}}); + test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.241818f, 0.241818f, 0.032727f}, 2.0f, 1.1f, 2, 5, {}); + test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {}); + test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f); test_sampler_queue(10000, "k", 1, 1.0f, 1.0f); test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f); From 668750357e66bfa3d1504b65699f5a0dfe3cb7cb Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 25 Oct 2024 22:26:15 +0300 Subject: [PATCH 67/95] metal : support permuted matrix multiplicaions (#10033) * metal : support permuted matrix multiplicaions ggml-ci * cont : use nb01 directly for row steps ggml-ci * cont : add comments [no ci] * metal : minor refactor * metal : minor --- ggml/src/ggml-metal.m | 75 ++--- ggml/src/ggml-metal.metal | 578 +++++++++++++++++++++++++------------- 2 files changed, 423 insertions(+), 230 deletions(-) diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m index e9541441c..80c08f15b 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal.m @@ -1015,19 +1015,21 @@ static void ggml_metal_encode_node( id id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil; id id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; - //GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); - //if (src0) { - // GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, - // ggml_is_contiguous(src0), src0->name); - //} - //if (src1) { - // GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, - // ggml_is_contiguous(src1), src1->name); - //} - //if (dst) { - // GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, - // dst->name); - //} +#if 0 + GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); + if (src0) { + GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, + ggml_is_contiguous(src0), src0->name); + } + if (src1) { + GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13, + ggml_is_contiguous(src1), src1->name); + } + if (dst) { + GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, ne3, nb0, nb1, nb2, nb3, + dst->name); + } +#endif id device = ctx_dev->mtl_device; @@ -1810,14 +1812,16 @@ static void ggml_metal_encode_node( [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:13]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:14]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:7]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:9]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:10]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:11]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:12]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:15]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:16]; [encoder setThreadgroupMemoryLength:8192 atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; } else { @@ -1986,20 +1990,22 @@ static void ggml_metal_encode_node( [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:13]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:14]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:15]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:16]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; + [encoder setBytes:&r2 length:sizeof(r2) atIndex:19]; + [encoder setBytes:&r3 length:sizeof(r3) atIndex:20]; if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || - src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || - src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { + src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || + src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { @@ -2048,6 +2054,9 @@ static void ggml_metal_encode_node( GGML_ASSERT(src1t == GGML_TYPE_F32); + GGML_ASSERT(ne03 == 1); + GGML_ASSERT(ne13 == 1); + // find the break-even point where the matrix-matrix kernel becomes more efficient compared // to the matrix-vector kernel // ne20 = n_used_experts diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal index 71b58be1f..defde6246 100644 --- a/ggml/src/ggml-metal.metal +++ b/ggml/src/ggml-metal.metal @@ -777,10 +777,10 @@ kernel void kernel_ssm_conv_f32( const int64_t i3 = tgpig.z; const int64_t nc = ne10; - const int64_t ncs = ne00; - const int64_t nr = ne01; - const int64_t n_t = ne1; - const int64_t n_s = ne2; + //const int64_t ncs = ne00; + //const int64_t nr = ne01; + //const int64_t n_t = ne1; + //const int64_t n_s = ne2; device const float * s = (device const float *) ((device const char *) src0 + ir*nb01 + i2*nb00 + i3*nb02); device const float * c = (device const float *) ((device const char *) src1 + ir*nb11); @@ -834,9 +834,9 @@ kernel void kernel_ssm_scan_f32( const int64_t i3 = tgpig.y; const int64_t nc = d_state; - const int64_t nr = d_inner; + //const int64_t nr = d_inner; const int64_t n_t = n_seq_tokens; - const int64_t n_s = n_seqs; + //const int64_t n_s = n_seqs; for (int64_t i2 = 0; i2 < n_t; ++i2) { device const float * s0 = (device const float *) ((device const char *) src0 + ir*nb01 + i3*nb02); @@ -1064,17 +1064,18 @@ kernel void kernel_group_norm( inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl, int il) { float d = qb_curr->d; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - device const uint16_t * qs = ((device const uint16_t *)qb_curr + 1 + il/2); + device const uint16_t * qs = ((device const uint16_t *) qb_curr + 1 + il/2); - for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F) - + yl[i + 1] * (qs[i / 2] & 0x0F00); - acc[1] += yl[i + 8] * (qs[i / 2] & 0x00F0) - + yl[i + 9] * (qs[i / 2] & 0xF000); + for (int i = 0; i < 8; i += 2) { + acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F); + acc[1] += yl[i + 1] * (qs[i / 2] & 0x0F00); + acc[2] += yl[i + 8] * (qs[i / 2] & 0x00F0); + acc[3] += yl[i + 9] * (qs[i / 2] & 0xF000); } - return d * (sumy * -8.f + acc[0] + acc[1]); + + return d * (sumy * -8.f + acc[0] + acc[1] + acc[2] + acc[3]); } // function for calculate inner product between half a q4_1 block and 16 floats (yl), sumy is SUM(yl[i]) @@ -1085,17 +1086,18 @@ inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thre float d = qb_curr->d; float m = qb_curr->m; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2); + device const uint16_t * qs = ((device const uint16_t *) qb_curr + 2 + il/2); for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F) - + yl[i + 1] * (qs[i / 2] & 0x0F00); - acc[1] += yl[i + 8] * (qs[i / 2] & 0x00F0) - + yl[i + 9] * (qs[i / 2] & 0xF000); + acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F); + acc[1] += yl[i + 1] * (qs[i / 2] & 0x0F00); + acc[2] += yl[i + 8] * (qs[i / 2] & 0x00F0); + acc[3] += yl[i + 9] * (qs[i / 2] & 0xF000); } - return d * (acc[0] + acc[1]) + sumy * m; + + return d * (acc[0] + acc[1] + acc[2] + acc[3]) + sumy * m; } // function for calculate inner product between half a q5_0 block and 16 floats (yl), sumy is SUM(yl[i]) @@ -1105,18 +1107,19 @@ inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thre inline float block_q_n_dot_y(device const block_q5_0 * qb_curr, float sumy, thread float * yl, int il) { float d = qb_curr->d; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; device const uint16_t * qs = ((device const uint16_t *)qb_curr + 3 + il/2); const uint32_t qh = *((device const uint32_t *)qb_curr->qh); for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)) - + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); - acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)) - + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); + acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)); + acc[1] += yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); + acc[2] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)); + acc[3] += yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); } - return d * (sumy * -16.f + acc[0] + acc[1]); + + return d * (sumy * -16.f + acc[0] + acc[1] + acc[2] + acc[3]); } // function for calculate inner product between half a q5_1 block and 16 floats (yl), sumy is SUM(yl[i]) @@ -1127,18 +1130,19 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre float d = qb_curr->d; float m = qb_curr->m; - float2 acc = 0.f; + float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; device const uint16_t * qs = ((device const uint16_t *)qb_curr + 4 + il/2); const uint32_t qh = *((device const uint32_t *)qb_curr->qh); for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)) - + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); - acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)) - + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); + acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)); + acc[1] += yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); + acc[2] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)); + acc[3] += yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); } - return d * (acc[0] + acc[1]) + sumy * m; + + return d * (acc[0] + acc[1] + acc[2] + acc[3]) + sumy * m; } // putting them in the kernel cause a significant performance penalty @@ -1156,14 +1160,22 @@ void mul_vec_q_n_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, uint r3, threadgroup int8_t * shared_values, - uint3 tgpig, uint tiisg, uint sgitg) { + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK4_0; const int r0 = tgpig.x; @@ -1175,10 +1187,19 @@ void mul_vec_q_n_f32_impl( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + //const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_q_type * x = (device const block_q_type *) src0 + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + //device const block_q_type * x = (device const block_q_type *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); + + // pointers to src0 rows + device const block_q_type * ax[nr]; + for (int row = 0; row < nr; ++row) { + const uint offset0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + + ax[row] = (device const block_q_type *) ((device char *) src0 + offset0); + } float yl[16]; // src1 vector cache float sumf[nr] = {0.f}; @@ -1190,19 +1211,22 @@ void mul_vec_q_n_f32_impl( // each thread in a SIMD group deals with half a block. for (int ib = ix; ib < nb; ib += nw/2) { - float sumy = 0; - for (int i = 0; i < 8; i += 2) { - sumy += yb[i] + yb[i+1]; - yl[i+0] = yb[i+ 0]; - yl[i+1] = yb[i+ 1]/256.f; + float sumy[2] = { 0.f, 0.f }; - sumy += yb[i+16] + yb[i+17]; - yl[i+8] = yb[i+16]/16.f; - yl[i+9] = yb[i+17]/4096.f; +#pragma unroll + for (int i = 0; i < 8; i += 2) { + sumy[0] += yb[i + 0] + yb[i + 1]; + yl[i + 0] = yb[i + 0]; + yl[i + 1] = yb[i + 1]/256.f; + + sumy[1] += yb[i + 16] + yb[i + 17]; + yl[i + 8] = yb[i + 16]/16.f; + yl[i + 9] = yb[i + 17]/4096.f; } +#pragma unroll for (int row = 0; row < nr; row++) { - sumf[row] += block_q_n_dot_y(x+ib+row*nb, sumy, yl, il); + sumf[row] += block_q_n_dot_y(ax[row] + ib, sumy[0] + sumy[1], yl, il); } yb += QK4_0 * 16; @@ -1226,12 +1250,14 @@ kernel void kernel_mul_mv_q4_0_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1239,7 +1265,7 @@ kernel void kernel_mul_mv_q4_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q4_1_f32( @@ -1252,12 +1278,14 @@ kernel void kernel_mul_mv_q4_1_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1265,7 +1293,7 @@ kernel void kernel_mul_mv_q4_1_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q5_0_f32( @@ -1278,12 +1306,14 @@ kernel void kernel_mul_mv_q5_0_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1291,7 +1321,7 @@ kernel void kernel_mul_mv_q5_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q5_1_f32( @@ -1304,12 +1334,14 @@ kernel void kernel_mul_mv_q5_1_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1317,7 +1349,7 @@ kernel void kernel_mul_mv_q5_1_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } @@ -1330,8 +1362,14 @@ void kernel_mul_mv_q8_0_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -1354,10 +1392,19 @@ void kernel_mul_mv_q8_0_f32_impl( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + //const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + //device const block_q8_0 * x = (device const block_q8_0 *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); + + // pointers to src0 rows + device const block_q8_0 * ax[nr]; + for (int row = 0; row < nr; ++row) { + const uint offset0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + + ax[row] = (device const block_q8_0 *) ((device char *) src0 + offset0); + } float yl[NB_Q8_0]; float sumf[nr]={0.f}; @@ -1374,12 +1421,12 @@ void kernel_mul_mv_q8_0_f32_impl( } for (int row = 0; row < nr; row++) { - device const int8_t * qs = x[ib+row*nb].qs + NB_Q8_0*il; + device const int8_t * qs = ax[row][ib].qs + NB_Q8_0*il; float sumq = 0.f; for (int iq = 0; iq < NB_Q8_0; ++iq) { sumq += qs[iq] * yl[iq]; } - sumf[row] += sumq*x[ib+row*nb].d; + sumf[row] += sumq*ax[row][ib].d; } yb += NB_Q8_0 * nw; @@ -1404,12 +1451,14 @@ kernel void kernel_mul_mv_q8_0_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1417,7 +1466,7 @@ kernel void kernel_mul_mv_q8_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } #define N_MV_T_T 4 @@ -1433,12 +1482,14 @@ void kernel_mul_mv_impl( uint64_t nb00, uint64_t nb01, uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne11, int64_t ne12, uint64_t nb10, uint64_t nb11, uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -1452,7 +1503,7 @@ void kernel_mul_mv_impl( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02; + const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; device const T0 * x = (device const T0 *) (src0 + offset0); @@ -1463,7 +1514,9 @@ void kernel_mul_mv_impl( break; } - device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12); + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + + device const T1 * y = (device const T1 *) (src1 + offset1); float sumf = 0; for (int i = tiisg; i < ne00; i += 32) { @@ -1483,7 +1536,9 @@ void kernel_mul_mv_impl( break; } - device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12); + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + + device const T1 * y = (device const T1 *) (src1 + offset1); device const T14 * y4 = (device const T14 *) y; float sumf = 0; @@ -1511,12 +1566,14 @@ kernel void kernel_mul_mv( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1533,12 +1590,14 @@ kernel void kernel_mul_mv( nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, + nb13, ne0, ne1, r2, @@ -1564,12 +1623,14 @@ kernel void kernel_mul_mv_1row( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1584,10 +1645,11 @@ kernel void kernel_mul_mv_1row( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02; + const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; device const T * x = (device const T *) (src0 + offset0); - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float * y = (device const float *) (src1 + offset1); float sumf = 0; if (ne00 < 128) { @@ -1631,12 +1693,14 @@ kernel void kernel_mul_mv_l4( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -1651,12 +1715,14 @@ kernel void kernel_mul_mv_l4( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02; + const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; device const T4 * x4 = (device const T4 *) (src0 + offset0); for (int r1 = 0; r1 < nrows; ++r1) { - device const float4 * y4 = (device const float4 *) (src1 + r1*nb11 + im*nb12); + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + + device const float4 * y4 = (device const float4 *) (src1 + offset1); float sumf = 0; for (int i = tiisg; i < ne00/4; i += 32) { @@ -3416,8 +3482,14 @@ void kernel_mul_mv_q2_K_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -3433,21 +3505,19 @@ void kernel_mul_mv_q2_K_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_q2_K * x = (device const block_q2_K *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q2_K * x = (device const block_q2_K *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; - const int step = sizeof(block_q2_K) * nb; - const int ix = tiisg/8; // 0...3 const int it = tiisg%8; // 0...7 const int iq = it/4; // 0 or 1 @@ -3492,9 +3562,9 @@ void kernel_mul_mv_q2_K_f32_impl( (acc1[3] + 1.f/256.f * acc2[3]) * (sc[6] & 0xF) * 1.f/64.f) - dmin * (sumy[0] * (sc[0] & 0xF0) + sumy[1] * (sc[2] & 0xF0) + sumy[2] * (sc[4] & 0xF0) + sumy[3] * (sc[6] & 0xF0)); - qs += step/2; - sc += step; - dh += step/2; + qs += nb01/2; + sc += nb01; + dh += nb01/2; } y4 += 4 * QK_K; @@ -3519,12 +3589,14 @@ kernel void kernel_mul_mv_q2_K_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -3533,7 +3605,7 @@ kernel void kernel_mul_mv_q2_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q3_K_f32_impl( @@ -3543,8 +3615,14 @@ void kernel_mul_mv_q3_K_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -3565,10 +3643,11 @@ void kernel_mul_mv_q3_K_f32_impl( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_q3_K * x = (device const block_q3_K *) src0 + first_row*nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q3_K * x = (device const block_q3_K *) ((device char *) src0 + offset0); + device const float * yy = (device const float *) ((device char *) src1 + offset1); float yl[32]; @@ -3608,8 +3687,6 @@ void kernel_mul_mv_q3_K_f32_impl( const int q_offset = 32*ip + l0; const int y_offset = 128*ip + 32*il + l0; - const int step = sizeof(block_q3_K) * nb / 2; - device const float * y1 = yy + ix*QK_K + y_offset; uint32_t scales32, aux32; @@ -3619,7 +3696,6 @@ void kernel_mul_mv_q3_K_f32_impl( float sumf1[2] = {0.f}; float sumf2[2] = {0.f}; for (int i = ix; i < nb; i += 4) { - for (int l = 0; l < 8; ++l) { yl[l+ 0] = y1[l+ 0]; yl[l+ 8] = y1[l+16]; @@ -3633,7 +3709,6 @@ void kernel_mul_mv_q3_K_f32_impl( device const half * dh = &x[i].d; for (int row = 0; row < 2; ++row) { - const float d_all = (float)dh[0]; scales16[0] = a[4]; @@ -3673,15 +3748,13 @@ void kernel_mul_mv_q3_K_f32_impl( sumf1[row] += d1 * (scales[1] - 32); sumf2[row] += d2 * (scales[3] - 32); - q += step; - h += step; - a += step; - dh += step; - + q += nb01/2; + h += nb01/2; + a += nb01/2; + dh += nb01/2; } y1 += 4 * QK_K; - } for (int row = 0; row < 2; ++row) { @@ -3706,12 +3779,14 @@ kernel void kernel_mul_mv_q3_K_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -3720,7 +3795,7 @@ kernel void kernel_mul_mv_q3_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q4_K_f32_impl( @@ -3730,8 +3805,14 @@ void kernel_mul_mv_q4_K_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -3756,29 +3837,26 @@ void kernel_mul_mv_q4_K_f32_impl( const int im = tgpig.z; //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; const int first_row = r0 * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q4_K * x = (device const block_q4_K *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[16]; float yh[16]; float sumf[N_DST]={0.f}, all_sum; - const int step = sizeof(block_q4_K) * nb / 2; - device const float * y4 = y + ix * QK_K + 64 * iq + 8 * ir; uint16_t sc16[4]; thread const uint8_t * sc8 = (thread const uint8_t *)sc16; for (int ib = ix; ib < nb; ib += 4) { - float4 sumy = {0.f, 0.f, 0.f, 0.f}; for (int i = 0; i < 8; ++i) { yl[i+0] = y4[i+ 0]; sumy[0] += yl[i+0]; @@ -3792,7 +3870,6 @@ void kernel_mul_mv_q4_K_f32_impl( device const half * dh = &x[ib].d; for (int row = 0; row < N_DST; row++) { - sc16[0] = sc[0] & kmask1; sc16[1] = sc[2] & kmask1; sc16[2] = ((sc[4] >> 0) & kmask2) | ((sc[0] & kmask3) >> 2); @@ -3821,9 +3898,9 @@ void kernel_mul_mv_q4_K_f32_impl( (acc2[2] + 1.f/256.f * acc2[3]) * sc8[5] * 1.f/16.f) - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - q1 += step; - sc += step; - dh += step; + q1 += nb01/2; + sc += nb01/2; + dh += nb01/2; } y4 += 4 * QK_K; @@ -3848,12 +3925,14 @@ kernel void kernel_mul_mv_q4_K_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -3862,7 +3941,7 @@ kernel void kernel_mul_mv_q4_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q5_K_f32_impl( @@ -3872,8 +3951,14 @@ void kernel_mul_mv_q5_K_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -3894,15 +3979,14 @@ void kernel_mul_mv_q5_K_f32_impl( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_q5_K * x = (device const block_q5_K *) src0 + first_row*nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q5_K * x = (device const block_q5_K *) ((device char *) src0 + offset0); + device const float * yy = (device const float *) ((device char *) src1 + offset1); float sumf[2]={0.f}; - const int step = sizeof(block_q5_K) * nb; - float yl[16], yh[16]; const uint16_t kmask1 = 0x3f3f; @@ -3930,7 +4014,6 @@ void kernel_mul_mv_q5_K_f32_impl( device const float * y1 = yy + ix*QK_K + y_offset; for (int i = ix; i < nb; i += 4) { - device const uint8_t * q1 = x[i].qs + q_offset; device const uint8_t * qh = x[i].qh + l0; device const half * dh = &x[i].d; @@ -3946,7 +4029,6 @@ void kernel_mul_mv_q5_K_f32_impl( } for (int row = 0; row < 2; ++row) { - device const uint8_t * q2 = q1 + 64; sc16[0] = a[0] & kmask1; @@ -3975,15 +4057,13 @@ void kernel_mul_mv_q5_K_f32_impl( sc8[5] * (acc1[3]/16.f + 16.f*acc2[3])) - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - q1 += step; - qh += step; - dh += step/2; - a += step/2; - + q1 += nb01; + qh += nb01; + dh += nb01/2; + a += nb01/2; } y1 += 4 * QK_K; - } for (int row = 0; row < 2; ++row) { @@ -4005,12 +4085,14 @@ kernel void kernel_mul_mv_q5_K_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4019,7 +4101,7 @@ kernel void kernel_mul_mv_q5_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q6_K_f32_impl( @@ -4029,8 +4111,14 @@ void kernel_mul_mv_q6_K_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4056,10 +4144,11 @@ void kernel_mul_mv_q6_K_f32_impl( const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_q6_K * x = (device const block_q6_K *) src0 + row * nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_q6_K * x = (device const block_q6_K *) ((device char *) src0 + offset0); + device const float * yy = (device const float *) ((device char *) src1 + offset1); float sumf = 0; @@ -4115,12 +4204,14 @@ kernel void kernel_mul_mv_q6_K_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4129,7 +4220,7 @@ kernel void kernel_mul_mv_q6_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } // ======================= "True" 2-bit @@ -4141,8 +4232,14 @@ void kernel_mul_mv_iq2_xxs_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4158,15 +4255,15 @@ void kernel_mul_mv_iq2_xxs_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_iq2_xxs * x = (device const block_iq2_xxs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq2_xxs * x = (device const block_iq2_xxs *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4219,8 +4316,8 @@ void kernel_mul_mv_iq2_xxs_f32_impl( } sumf[row] += d * sum; - dh += nb*sizeof(block_iq2_xxs)/2; - q2 += nb*sizeof(block_iq2_xxs)/2; + dh += nb01/2; + q2 += nb01/2; } y4 += 32 * 32; @@ -4245,12 +4342,14 @@ kernel void kernel_mul_mv_iq2_xxs_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4260,7 +4359,7 @@ kernel void kernel_mul_mv_iq2_xxs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } void kernel_mul_mv_iq2_xs_f32_impl( @@ -4270,8 +4369,14 @@ void kernel_mul_mv_iq2_xs_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4287,15 +4392,15 @@ void kernel_mul_mv_iq2_xs_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_iq2_xs * x = (device const block_iq2_xs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq2_xs * x = (device const block_iq2_xs *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4357,9 +4462,9 @@ void kernel_mul_mv_iq2_xs_f32_impl( } sumf[row] += d1 * sum1 + d2 * sum2; - dh += nb*sizeof(block_iq2_xs)/2; - q2 += nb*sizeof(block_iq2_xs)/2; - sc += nb*sizeof(block_iq2_xs); + dh += nb01/2; + q2 += nb01/2; + sc += nb01; } y4 += 32 * 32; @@ -4384,12 +4489,14 @@ kernel void kernel_mul_mv_iq2_xs_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4399,7 +4506,7 @@ kernel void kernel_mul_mv_iq2_xs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } void kernel_mul_mv_iq3_xxs_f32_impl( @@ -4409,8 +4516,14 @@ void kernel_mul_mv_iq3_xxs_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4426,15 +4539,15 @@ void kernel_mul_mv_iq3_xxs_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_iq3_xxs * x = (device const block_iq3_xxs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq3_xxs * x = (device const block_iq3_xxs *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4489,9 +4602,9 @@ void kernel_mul_mv_iq3_xxs_f32_impl( } sumf[row] += d * (sum[0] + sum[1]); - dh += nb*sizeof(block_iq3_xxs)/2; - q3 += nb*sizeof(block_iq3_xxs); - gas += nb*sizeof(block_iq3_xxs)/2; + dh += nb01/2; + q3 += nb01; + gas += nb01/2; } y4 += 32 * 32; @@ -4516,12 +4629,14 @@ kernel void kernel_mul_mv_iq3_xxs_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4531,7 +4646,7 @@ kernel void kernel_mul_mv_iq3_xxs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } void kernel_mul_mv_iq3_s_f32_impl( @@ -4541,8 +4656,14 @@ void kernel_mul_mv_iq3_s_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4558,15 +4679,15 @@ void kernel_mul_mv_iq3_s_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_iq3_s * x = (device const block_iq3_s *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq3_s * x = (device const block_iq3_s *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4619,11 +4740,11 @@ void kernel_mul_mv_iq3_s_f32_impl( } sumf[row] += d * (sum[0] + sum[1]); - dh += nb*sizeof(block_iq3_s)/2; - qs += nb*sizeof(block_iq3_s); - qh += nb*sizeof(block_iq3_s); - sc += nb*sizeof(block_iq3_s); - signs += nb*sizeof(block_iq3_s); + dh += nb01/2; + qs += nb01; + qh += nb01; + sc += nb01; + signs += nb01; } y4 += 32 * 32; @@ -4648,12 +4769,14 @@ kernel void kernel_mul_mv_iq3_s_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4663,7 +4786,7 @@ kernel void kernel_mul_mv_iq3_s_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } void kernel_mul_mv_iq2_s_f32_impl( @@ -4673,8 +4796,14 @@ void kernel_mul_mv_iq2_s_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4690,15 +4819,15 @@ void kernel_mul_mv_iq2_s_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - device const block_iq2_s * x = (device const block_iq2_s *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + device const block_iq2_s * x = (device const block_iq2_s *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4752,11 +4881,11 @@ void kernel_mul_mv_iq2_s_f32_impl( } sumf[row] += d1 * sum[0] + d2 * sum[1]; - dh += nb*sizeof(block_iq2_s)/2; - qs += nb*sizeof(block_iq2_s); - qh += nb*sizeof(block_iq2_s); - sc += nb*sizeof(block_iq2_s); - signs += nb*sizeof(block_iq2_s); + dh += nb01/2; + qs += nb01; + qh += nb01; + sc += nb01; + signs += nb01; } y4 += 32 * 32; @@ -4781,12 +4910,14 @@ kernel void kernel_mul_mv_iq2_s_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -4796,7 +4927,7 @@ kernel void kernel_mul_mv_iq2_s_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } void kernel_mul_mv_iq1_s_f32_impl( @@ -4806,8 +4937,14 @@ void kernel_mul_mv_iq1_s_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4823,14 +4960,15 @@ void kernel_mul_mv_iq1_s_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + + device const block_iq1_s * x = (device const block_iq1_s *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4873,9 +5011,9 @@ void kernel_mul_mv_iq1_s_f32_impl( } sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1); - dh += nb*sizeof(block_iq1_s)/2; - qs += nb*sizeof(block_iq1_s); - qh += nb*sizeof(block_iq1_s)/2; + dh += nb01/2; + qs += nb01; + qh += nb01/2; } y4 += 32 * 32; @@ -4896,8 +5034,14 @@ void kernel_mul_mv_iq1_m_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -4913,14 +5057,15 @@ void kernel_mul_mv_iq1_m_f32_impl( const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq1_m * x = (device const block_iq1_m *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + + device const block_iq1_m * x = (device const block_iq1_m *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -4972,9 +5117,9 @@ void kernel_mul_mv_iq1_m_f32_impl( sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) + (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1)); - sc += nb*sizeof(block_iq1_m)/2; - qs += nb*sizeof(block_iq1_m); - qh += nb*sizeof(block_iq1_m); + sc += nb01/2; + qs += nb01; + qh += nb01; } y4 += 32 * 32; @@ -4995,8 +5140,14 @@ void kernel_mul_mv_iq4_nl_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -5012,14 +5163,15 @@ void kernel_mul_mv_iq4_nl_f32_impl( const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * 2 + sgitg) * 2; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq4_nl * x = (device const block_iq4_nl *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + + device const block_iq4_nl * x = (device const block_iq4_nl *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); const int ix = tiisg/2; // 0...15 const int it = tiisg%2; // 0 or 1 @@ -5089,8 +5241,14 @@ void kernel_mul_mv_iq4_xs_f32_impl( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -5106,14 +5264,15 @@ void kernel_mul_mv_iq4_xs_f32_impl( const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * 2 + sgitg) * 2; - const int ib_row = first_row * nb; const uint i12 = im%ne12; const uint i13 = im/ne12; - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - device const block_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + + device const block_iq4_xs * x = (device const block_iq4_xs *) ((device char *) src0 + offset0); + device const float * y = (device const float *) ((device char *) src1 + offset1); const int ix = tiisg/16; // 0 or 1 const int it = tiisg%16; // 0...15 @@ -5188,12 +5347,14 @@ kernel void kernel_mul_mv_iq1_s_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -5202,7 +5363,7 @@ kernel void kernel_mul_mv_iq1_s_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq1_m_f32")]] @@ -5216,12 +5377,14 @@ kernel void kernel_mul_mv_iq1_m_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -5230,7 +5393,7 @@ kernel void kernel_mul_mv_iq1_m_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_nl_f32")]] @@ -5244,12 +5407,14 @@ kernel void kernel_mul_mv_iq4_nl_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -5259,7 +5424,7 @@ kernel void kernel_mul_mv_iq4_nl_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_xs_f32")]] @@ -5273,12 +5438,14 @@ kernel void kernel_mul_mv_iq4_xs_f32( constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -5288,7 +5455,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } //============================= templates and their specializations ============================= @@ -5833,10 +6000,12 @@ kernel void kernel_mul_mm(device const uchar * src0, constant int64_t & ne02, constant uint64_t & nb01, constant uint64_t & nb02, + constant uint64_t & nb03, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, + constant uint64_t & nb13, constant int64_t & ne0, constant int64_t & ne1, constant uint & r2, @@ -5873,12 +6042,13 @@ kernel void kernel_mul_mm(device const uchar * src0, const uint i12 = im%ne12; const uint i13 = im/ne12; - uint offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02); + uint offset0 = (i12/r2)*nb02 + (i13/r3)*nb03; ushort offset1 = il/nl; device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1; device const float * y = (device const float *)(src1 - + nb12 * im + + nb13 * i13 + + nb12 * i12 + nb11 * (r1 * BLOCK_SIZE_N + thread_col) + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); @@ -6257,12 +6427,14 @@ typedef void (kernel_mul_mv_impl_t)( uint64_t nb00, uint64_t nb01, uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne11, int64_t ne12, uint64_t nb10, uint64_t nb11, uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -6277,8 +6449,14 @@ typedef void (kernel_mul_mv2_impl_t)( int64_t ne00, int64_t ne01, int64_t ne02, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne12, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint r2, @@ -6299,6 +6477,7 @@ void mmv_fn( uint64_t nb00, uint64_t nb01, uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne11, int64_t ne12, @@ -6306,6 +6485,7 @@ void mmv_fn( uint64_t nb10, uint64_t nb11, uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint64_t nb1, @@ -6316,7 +6496,7 @@ void mmv_fn( uint tiitg, uint tiisg, uint sgitg) { - impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,ne10,ne11,ne12,nb10,nb11,nb12,ne0,ne1,r2,r3,tgpig,tiisg); + impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,nb03,ne10,ne11,ne12,nb10,nb11,nb12,nb13,ne0,ne1,r2,r3,tgpig,tiisg); } template @@ -6330,6 +6510,7 @@ void mmv_fn( uint64_t nb00, uint64_t nb01, uint64_t nb02, + uint64_t nb03, int64_t ne10, int64_t ne11, int64_t ne12, @@ -6337,6 +6518,7 @@ void mmv_fn( uint64_t nb10, uint64_t nb11, uint64_t nb12, + uint64_t nb13, int64_t ne0, int64_t ne1, uint64_t nb1, @@ -6347,7 +6529,7 @@ void mmv_fn( uint tiitg, uint tiisg, uint sgitg) { - impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg); + impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg); } typedef decltype(mmv_fn>) mul_mv_impl_fn_t; @@ -6396,8 +6578,8 @@ kernel void kernel_mul_mv_id( const int64_t i2 = i12; device const char * src0_cur = src0s + i02*nb02; - device const char * src1_cur = src1 + i11*nb11 + i12*nb12; - device float * dst_cur = dst + i1*ne0 + i2*ne1*ne0; + device const char * src1_cur = src1 + i11*nb11 + i12*nb12; + device float * dst_cur = dst + i1*ne0 + i2*ne1*ne0; impl_fn( /* src0 */ src0_cur, @@ -6405,19 +6587,21 @@ kernel void kernel_mul_mv_id( /* dst */ dst_cur, /* ne00 */ ne00, /* ne01 */ ne01, - /* ne02 */ 1,//ne02, + /* ne02 */ 1, // ne02, /* nb00 */ nb00, /* nb01 */ nb01, /* nb02 */ nb02, + /* nb03 */ nb02, // ne02 == 1 /* ne10 */ ne10, - /* ne11 */ 1,//ne11, - /* ne12 */ 1,//ne12, - /* ne13 */ 1,//ne13, + /* ne11 */ 1, // ne11, + /* ne12 */ 1, // ne12, + /* ne13 */ 1, // ne13, /* nb10 */ nb10, /* nb11 */ nb11, /* nb12 */ nb12, + /* ne13 */ nb12, // ne12 == 1 /* ne0 */ ne0, - /* ne1 */ 1,//ne1, + /* ne1 */ 1, // ne1, /* nb1 */ nb1, /* r2 */ 1, /* r3 */ 1, From 9e4a2563eadf34e9432d248224d4f43e8495e8fe Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 26 Oct 2024 10:33:31 +0300 Subject: [PATCH 68/95] scripts : fix amx sync [no ci] --- scripts/sync-ggml-am.sh | 7 +++++++ scripts/sync-ggml.sh | 3 +++ 2 files changed, 10 insertions(+) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index ffce2aab0..fba29b935 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -76,6 +76,7 @@ while read c; do src/ggml*.m \ src/ggml*.metal \ src/ggml*.cu \ + src/ggml-amx/* \ src/ggml-cann/* \ src/ggml-cuda/* \ src/ggml-sycl/* \ @@ -121,6 +122,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-aarch64.c -> ggml/src/ggml-aarch64.c # src/ggml-aarch64.h -> ggml/src/ggml-aarch64.h # src/ggml-alloc.c -> ggml/src/ggml-alloc.c + # src/ggml-amx/* -> ggml/src/ggml-amx/ + # src/ggml-amx.cpp -> ggml/src/ggml-amx.cpp # src/ggml-backend-impl.h -> ggml/src/ggml-backend-impl.h # src/ggml-backend.cpp -> ggml/src/ggml-backend.cpp # src/ggml-cann/* -> ggml/src/ggml-cann/ @@ -141,6 +144,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # # include/ggml.h -> ggml/include/ggml.h # include/ggml-alloc.h -> ggml/include/ggml-alloc.h + # include/ggml-amx.h -> ggml/include/ggml-amx.h # include/ggml-backend.h -> ggml/include/ggml-backend.h # include/ggml-blas.h -> ggml/include/ggml-blas.h # include/ggml-cann.h -> ggml/include/ggml-cann.h @@ -168,6 +172,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.c/\1ggml\/src\/ggml-aarch64.c/g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.h/\1ggml\/src\/ggml-aarch64.h/g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-alloc\.c/\1ggml\/src\/ggml-alloc.c/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-amx\//\1ggml\/src\/ggml-amx\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-amx\.cpp/\1ggml\/src\/ggml-amx.cpp/g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-backend-impl\.h/\1ggml\/src\/ggml-backend-impl.h/g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-backend\.cpp/\1ggml\/src\/ggml-backend.cpp/g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\//\1ggml\/src\/ggml-cann\//g' \ @@ -187,6 +193,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/([[:space:]]|[ab]\/)src\/vulkan-shaders\//\1ggml\/src\/vulkan-shaders\//g' \ -e 's/([[:space:]]|[ab]\/)include\/ggml\.h/\1ggml\/include\/ggml.h/g' \ -e 's/([[:space:]]|[ab]\/)include\/ggml-alloc\.h/\1ggml\/include\/ggml-alloc.h/g' \ + -e 's/([[:space:]]|[ab]\/)include\/ggml-amx\.h/\1ggml\/include\/ggml-amx.h/g' \ -e 's/([[:space:]]|[ab]\/)include\/ggml-backend\.h/\1ggml\/include\/ggml-backend.h/g' \ -e 's/([[:space:]]|[ab]\/)include\/ggml-blas\.h/\1ggml\/include\/ggml-blas.h/g' \ -e 's/([[:space:]]|[ab]\/)include\/ggml-cann\.h/\1ggml\/include\/ggml-cann.h/g' \ diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index f6ff5e683..f5d87324a 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -8,6 +8,8 @@ cp -rpv ../ggml/src/ggml.c ./ggml/src/ggml.c cp -rpv ../ggml/src/ggml-aarch64.c ./ggml/src/ggml-aarch64.c cp -rpv ../ggml/src/ggml-aarch64.h ./ggml/src/ggml-aarch64.h cp -rpv ../ggml/src/ggml-alloc.c ./ggml/src/ggml-alloc.c +cp -rpv ../ggml/src/ggml-amx/* ./ggml/src/ggml-amx/ +cp -rpv ../ggml/src/ggml-amx.cpp ./ggml/src/ggml-amx.cpp cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml/src/ggml-backend-impl.h cp -rpv ../ggml/src/ggml-backend.cpp ./ggml/src/ggml-backend.cpp cp -rpv ../ggml/src/ggml-cann/* ./ggml/src/ggml-cann/ @@ -29,6 +31,7 @@ cp -rpv ../ggml/src/vulkan-shaders/* ./ggml/src/vulkan-shaders/ cp -rpv ../ggml/include/ggml.h ./ggml/include/ggml.h cp -rpv ../ggml/include/ggml-alloc.h ./ggml/include/ggml-alloc.h +cp -rpv ../ggml/include/ggml-amx.h ./ggml/include/ggml-amx.h cp -rpv ../ggml/include/ggml-backend.h ./ggml/include/ggml-backend.h cp -rpv ../ggml/include/ggml-blas.h ./ggml/include/ggml-blas.h cp -rpv ../ggml/include/ggml-cann.h ./ggml/include/ggml-cann.h From 8c60a8a46261ffb92b6d23a78acfac2fcb6fe233 Mon Sep 17 00:00:00 2001 From: bssrdf Date: Wed, 23 Oct 2024 14:34:00 -0400 Subject: [PATCH 69/95] increase cuda_cpy block size (ggml/996) Co-authored-by: bssrdf --- ggml/src/ggml-cuda/cpy.cuh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cuda/cpy.cuh b/ggml/src/ggml-cuda/cpy.cuh index 796167426..28b06cdda 100644 --- a/ggml/src/ggml-cuda/cpy.cuh +++ b/ggml/src/ggml-cuda/cpy.cuh @@ -1,6 +1,6 @@ #include "common.cuh" -#define CUDA_CPY_BLOCK_SIZE 32 +#define CUDA_CPY_BLOCK_SIZE 64 void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1); From cc2983d3753c94a630ca7257723914d4c4f6122b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 26 Oct 2024 10:34:08 +0300 Subject: [PATCH 70/95] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 7f689f632..da40927e1 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -6dccc647264f5429df2624f36138f601e7ce23e5 +162e232411ee98ceb0cccfa84886118d917d2123 From 8841ce3f439de6e770f70319b7e08b6613197ea7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Oct 2024 20:59:58 +0200 Subject: [PATCH 71/95] llama : switch KQ multiplication to F32 precision by default (#10015) ggml-ci --- src/llama.cpp | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 50eebc2c2..53979e83f 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -9618,20 +9618,16 @@ static struct ggml_tensor * llm_build_kqv( cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias, hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_GEMMA2) { - ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); - } + ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens); } else { struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2 || model.arch == LLM_ARCH_NEMOTRON || model.arch == LLM_ARCH_CHATGLM) { - // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs - // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 - ggml_mul_mat_set_prec(kq, GGML_PREC_F32); - } + // note: this op tends to require high floating point range + // while for some models F16 is enough, for others it is not, so we default to F32 here + ggml_mul_mat_set_prec(kq, GGML_PREC_F32); if (model.arch == LLM_ARCH_GROK) { // need to do the following: @@ -9640,9 +9636,6 @@ static struct ggml_tensor * llm_build_kqv( // kq = 30 * tanh(kq / 30) // before the softmax below - //try from phi2 - //ggml_mul_mat_set_prec(kq, GGML_PREC_F32); - kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f)); kq = ggml_scale(ctx, kq, 30); } From 8125e6cbfcf2b3b9066e4d923aca9295526730f5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 28 Oct 2024 08:49:32 +0200 Subject: [PATCH 72/95] server : don't overfill the batch during infill (#10018) ggml-ci --- examples/server/server.cpp | 1 + examples/server/utils.hpp | 6 ++++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ff1d9b03c..077c7ad1a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1880,6 +1880,7 @@ struct server_context { if (slot.state == SLOT_STATE_STARTED) { slot.t_start_process_prompt = ggml_time_us(); slot.t_start_generation = 0; + slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); slot.state = SLOT_STATE_PROCESSING_PROMPT; diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 811242062..562635555 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -266,8 +266,10 @@ static llama_tokens format_infill( } // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) - const int n_suffix_take = std::min(tokens_suffix.size(), (n_batch/4)); - const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4) - 3); + const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4)); + const int n_suffix_take = std::min(tokens_suffix.size(), std::max(0, (n_batch/4) - (2 + tokens_prompt.size()))); + + SRV_DBG("n_prefix_take = %d, n_suffix_take = %d, total = %d\n", n_prefix_take, n_suffix_take, (n_prefix_take + n_suffix_take)); // fill the rest of the context with extra chunks const int n_extra_take = std::min(std::max(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size()); From 524afeec9dad7d765ce91f5cf30c73703867cb47 Mon Sep 17 00:00:00 2001 From: R0CKSTAR Date: Mon, 28 Oct 2024 17:02:48 +0800 Subject: [PATCH 73/95] musa: workaround for Guilty Lockup in cleaning src0 (#10042) Signed-off-by: Xiaodong Ye --- ggml/src/ggml-cuda.cu | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 21c9f5e38..217df968a 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -1484,14 +1484,19 @@ static void ggml_cuda_op_mul_mat( const size_t nbytes_data = ggml_nbytes(src0); const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding); + // TODO: remove this for MUSA once the Guilty Lockup issue is resolved +#ifndef GGML_USE_MUSA CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd, 0, nbytes_data + nbytes_padding, stream)); +#else // GGML_USE_MUSA + CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream)); +#endif // !GGML_USE_MUSA } // If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared: if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) { const size_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00); const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); - CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream)); + CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream)); } if (src1_on_device && src1_is_contiguous) { From 07028f9d74d895da2ca4a1956624e3f07e04e620 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 28 Oct 2024 17:41:24 +0200 Subject: [PATCH 74/95] flake.lock: Update (#10063) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0?narHash=sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c%2BcHUJwA%3D' (2024-10-18) → 'github:NixOS/nixpkgs/2768c7d042a37de65bb1b5b3268fc987e534c49d?narHash=sha256-AlcmCXJZPIlO5dmFzV3V2XF6x/OpNWUV8Y/FMPGd8Z4%3D' (2024-10-23) Co-authored-by: github-actions[bot] --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 1f8defab7..732c7539c 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1729256560, - "narHash": "sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c+cHUJwA=", + "lastModified": 1729665710, + "narHash": "sha256-AlcmCXJZPIlO5dmFzV3V2XF6x/OpNWUV8Y/FMPGd8Z4=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0", + "rev": "2768c7d042a37de65bb1b5b3268fc987e534c49d", "type": "github" }, "original": { From 61715d5cc83a28181df6a641846e4f6a740f3c74 Mon Sep 17 00:00:00 2001 From: arch-btw <57669023+arch-btw@users.noreply.github.com> Date: Mon, 28 Oct 2024 10:45:33 -0700 Subject: [PATCH 75/95] llama : Add IBM granite template (#10013) * Add granite template to llama.cpp * Add granite template to test-chat-template.cpp * Update src/llama.cpp Co-authored-by: Xuan Son Nguyen * Update tests/test-chat-template.cpp Co-authored-by: Xuan Son Nguyen * Added proper template and expected output * Small change to \n Small change to \n * Add code space & Co-authored-by: Xuan Son Nguyen * Fix spacing * Apply suggestions from code review * Update src/llama.cpp --------- Co-authored-by: Xuan Son Nguyen --- src/llama.cpp | 10 ++++++++++ tests/test-chat-template.cpp | 4 ++++ 2 files changed, 14 insertions(+) diff --git a/src/llama.cpp b/src/llama.cpp index 53979e83f..4cb669bcf 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -21706,6 +21706,16 @@ static int32_t llama_chat_apply_template_internal( ss << message->content << "\n\n"; } } + } else if (tmpl == "granite" || tmpl_contains("<|start_of_role|>")) { + // IBM Granite template + for (const auto & message : chat) { + std::string role(message->role); + ss << "<|start_of_role|>" << role << "<|end_of_role|>" + << message->content << "<|end_of_text|>\n"; + } + if (add_ass) { + ss << "<|start_of_role|>assistant<|end_of_role|>\n"; + } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 6f046249f..03e897e66 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -65,6 +65,8 @@ int main(void) { u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", // DeepSeek-V2 "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", + // ibm-granite/granite-3.0-8b-instruct + "{%- if tools %}\n {{- '<|start_of_role|>available_tools<|end_of_role|>\n' }}\n {%- for tool in tools %}\n {{- tool | tojson(indent=4) }}\n {%- if not loop.last %}\n {{- '\n\n' }}\n {%- endif %}\n {%- endfor %}\n {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n {{- '<|start_of_role|>system<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'user' %}\n {{- '<|start_of_role|>user<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant' %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant_tool_call' %}\n {{- '<|start_of_role|>assistant<|end_of_role|><|tool_call|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'tool_response' %}\n {{- '<|start_of_role|>tool_response<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- endif %}\n {%- if loop.last and add_generation_prompt %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' }}\n {%- endif %}\n{%- endfor %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -109,6 +111,8 @@ int main(void) { u8"You are a helpful assistant<用户>HelloHi there<用户>Who are youI am an assistant<用户>Another question", // DeepSeek-V2 u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:", + // ibm-granite/granite-3.0-8b-instruct + "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|> I am an assistant <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n", }; std::vector formatted_chat(1024); int32_t res; From 8d8ff715367480b856ad86ac3888e9742b13a6fa Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 29 Oct 2024 10:42:05 +0200 Subject: [PATCH 76/95] llama : remove Tail-Free sampling (#10071) ggml-ci --- common/arg.cpp | 9 +- common/common.cpp | 1 - common/common.h | 4 +- common/sampling.cpp | 13 +-- examples/main/README.md | 8 -- examples/server/README.md | 11 +-- examples/server/public/index-new.html | 3 - examples/server/public/index.html | 2 - examples/server/server.cpp | 2 - examples/server/themes/buttons-top/index.html | 2 - examples/server/themes/wild/index.html | 2 - examples/server/utils.hpp | 2 +- include/llama.h | 3 - scripts/run-with-preset.py | 2 +- src/llama-sampling.cpp | 97 +------------------ tests/test-sampling.cpp | 26 +---- 16 files changed, 15 insertions(+), 172 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index e1e933934..7c5c5e5cd 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -943,13 +943,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sparams.min_p = std::stof(value); } ).set_sparam()); - add_opt(common_arg( - {"--tfs"}, "N", - string_format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z), - [](common_params & params, const std::string & value) { - params.sparams.tfs_z = std::stof(value); - } - ).set_sparam()); add_opt(common_arg( {"--xtc-probability"}, "N", string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability), @@ -1074,7 +1067,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--mirostat"}, "N", - string_format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n" + string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n" "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat), [](common_params & params, int value) { params.sparams.mirostat = value; diff --git a/common/common.cpp b/common/common.cpp index ff8cc4076..7656843b1 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2090,7 +2090,6 @@ void yaml_dump_non_result_info(FILE * stream, const common_params & params, cons const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector); - fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency()); fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); diff --git a/common/common.h b/common/common.h index 18b2121ed..cd5a8e051 100644 --- a/common/common.h +++ b/common/common.h @@ -88,7 +88,7 @@ enum common_sampler_type { COMMON_SAMPLER_TYPE_TOP_K = 2, COMMON_SAMPLER_TYPE_TOP_P = 3, COMMON_SAMPLER_TYPE_MIN_P = 4, - COMMON_SAMPLER_TYPE_TFS_Z = 5, + //COMMON_SAMPLER_TYPE_TFS_Z = 5, COMMON_SAMPLER_TYPE_TYPICAL_P = 6, COMMON_SAMPLER_TYPE_TEMPERATURE = 7, COMMON_SAMPLER_TYPE_XTC = 8, @@ -113,7 +113,6 @@ struct common_sampler_params { float min_p = 0.05f; // 0.0 = disabled float xtc_probability = 0.00f; // 0.0 = disabled float xtc_threshold = 0.10f; // > 0.5 disables XTC - float tfs_z = 1.00f; // 1.0 = disabled float typ_p = 1.00f; // typical_p, 1.0 = disabled float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities float dynatemp_range = 0.00f; // 0.0 = disabled @@ -139,7 +138,6 @@ struct common_sampler_params { std::vector samplers = { COMMON_SAMPLER_TYPE_DRY, COMMON_SAMPLER_TYPE_TOP_K, - COMMON_SAMPLER_TYPE_TFS_Z, COMMON_SAMPLER_TYPE_TYPICAL_P, COMMON_SAMPLER_TYPE_TOP_P, COMMON_SAMPLER_TYPE_MIN_P, diff --git a/common/sampling.cpp b/common/sampling.cpp index 48a9df8ba..7922fde47 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -131,11 +131,11 @@ std::string common_sampler_params::print() const { snprintf(result, sizeof(result), "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" - "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" + "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", penalty_last_n, penalty_repeat, penalty_freq, penalty_present, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, - top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, + top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, mirostat, mirostat_eta, mirostat_tau); return std::string(result); @@ -199,9 +199,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co case COMMON_SAMPLER_TYPE_XTC: llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); break; - case COMMON_SAMPLER_TYPE_TFS_Z: - llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep)); - break; case COMMON_SAMPLER_TYPE_TYPICAL_P: llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); break; @@ -373,7 +370,6 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { switch (cnstr) { case COMMON_SAMPLER_TYPE_DRY: return 'd'; case COMMON_SAMPLER_TYPE_TOP_K: return 'k'; - case COMMON_SAMPLER_TYPE_TFS_Z: return 'f'; case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y'; case COMMON_SAMPLER_TYPE_TOP_P: return 'p'; case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; @@ -388,7 +384,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { switch (cnstr) { case COMMON_SAMPLER_TYPE_DRY: return "dry"; case COMMON_SAMPLER_TYPE_TOP_K: return "top_k"; - case COMMON_SAMPLER_TYPE_TFS_Z: return "tfs_z"; case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p"; case COMMON_SAMPLER_TYPE_TOP_P: return "top_p"; case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; @@ -406,7 +401,6 @@ std::vector common_sampler_types_from_names(const std::vect { "top_p", COMMON_SAMPLER_TYPE_TOP_P }, { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "min_p", COMMON_SAMPLER_TYPE_MIN_P }, - { "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z }, { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, { "xtc", COMMON_SAMPLER_TYPE_XTC }, { "infill", COMMON_SAMPLER_TYPE_INFILL }, @@ -423,8 +417,6 @@ std::vector common_sampler_types_from_names(const std::vect { "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "typ", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "min-p", COMMON_SAMPLER_TYPE_MIN_P }, - { "tfs-z", COMMON_SAMPLER_TYPE_TFS_Z }, - { "tfs", COMMON_SAMPLER_TYPE_TFS_Z }, { "temp", COMMON_SAMPLER_TYPE_TEMPERATURE }, }; @@ -452,7 +444,6 @@ std::vector common_sampler_types_from_chars(const std::stri std::unordered_map sampler_name_map = { { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K }, - { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TFS_Z), COMMON_SAMPLER_TYPE_TFS_Z }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, diff --git a/examples/main/README.md b/examples/main/README.md index c7c823171..5357ac2e2 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -235,14 +235,6 @@ The Min-P sampling method was designed as an alternative to Top-P, and aims to e Example usage: `--min-p 0.05` -### Tail-Free Sampling (TFS) - -- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled). - -Tail-free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks at how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens and thus disables the effect of TFS. - -Example usage: `--tfs 0.95` - ### Locally Typical Sampling - `--typical N`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled). diff --git a/examples/server/README.md b/examples/server/README.md index bc737237e..1629e456b 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -99,7 +99,7 @@ The project is under active development, and we are [looking for feedback and co | Argument | Explanation | | -------- | ----------- | -| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) | +| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: top_k;typ_p;top_p;min_p;temperature) | | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | @@ -108,7 +108,6 @@ The project is under active development, and we are [looking for feedback and co | `--top-k N` | top-k sampling (default: 40, 0 = disabled) | | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | | `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) | -| `--tfs N` | tail free sampling, parameter z (default: 1.0, 1.0 = disabled) | | `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | @@ -121,7 +120,7 @@ The project is under active development, and we are [looking for feedback and co | `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers (`['\n', ':', '"', '*']`) in the process; use `"none"` to not use any sequence breakers | `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | | `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | -| `--mirostat N` | use Mirostat sampling.
Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | +| `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | | `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | | `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | | `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | @@ -360,8 +359,6 @@ node index.js `stop`: Specify a JSON array of stopping strings. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]` - `tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled. - `typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled. `repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1` @@ -412,7 +409,7 @@ node index.js `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false` - `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values. + `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values. **Response format** @@ -738,7 +735,6 @@ Example: "repeat_penalty": 1.100000023841858, "samplers": [ "top_k", - "tfs_z", "typical_p", "top_p", "min_p", @@ -752,7 +748,6 @@ Example: "stream": false, "task_id": 0, "temperature": 0.0, - "tfs_z": 1.0, "top_k": 40, "top_p": 0.949999988079071, "typical_p": 1.0 diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html index cb3995abe..8bfa380e5 100644 --- a/examples/server/public/index-new.html +++ b/examples/server/public/index-new.html @@ -49,7 +49,6 @@ min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4 xtc_probability: 0.0, // 0 = disabled; xtc_threshold: 0.1, // > 0.5 disables XTC; - tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled frequency_penalty: 0.0, // 0.0 = disabled @@ -847,7 +846,6 @@ return html` ${FloatField({ label: "DRY Base", title: "Set the DRY repetition penalty base value. Default is 1.75", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })} ${IntField({ label: "DRY Allowed Length", title: "Tokens that extend repetition beyond this receive exponentially increasing penalty. Default is 2", max: 10, min: 1, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })} ${IntField({ label: "DRY Penalty Last N", title: "How many tokens to scan for repetitions. Default is -1, where 0 is disabled and -1 is context size", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })} - ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })} @@ -1147,7 +1145,6 @@ document.addEventListener('DOMContentLoaded', (event) => { xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 }, xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 }, top_p: { snapValue: 1.0, snapRangeMultiplier: 4 }, - tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 }, typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 }, repeat_penalty: { snapValue: 1.0, snapRangeMultiplier: 4 }, presence_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 }, diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 7f9b02bfb..a95f5c6df 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -313,7 +313,6 @@ min_p: 0.05, // 0 = disabled xtc_probability: 0.0, // 0 = disabled; xtc_threshold: 0.1, // > 0.5 disables XTC; - tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled frequency_penalty: 0.0, // 0.0 = disabled @@ -1015,7 +1014,6 @@
More options
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 077c7ad1a..7953b5065 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -809,7 +809,6 @@ struct server_context { slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability); slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold); - slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p); slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); @@ -1149,7 +1148,6 @@ struct server_context { {"min_p", slot.sparams.min_p}, {"xtc_probability", slot.sparams.xtc_probability}, {"xtc_threshold", slot.sparams.xtc_threshold}, - {"tfs_z", slot.sparams.tfs_z}, {"typical_p", slot.sparams.typ_p}, {"repeat_last_n", slot.sparams.penalty_last_n}, {"repeat_penalty", slot.sparams.penalty_repeat}, diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html index 8334bcde5..2797c37c9 100644 --- a/examples/server/themes/buttons-top/index.html +++ b/examples/server/themes/buttons-top/index.html @@ -226,7 +226,6 @@ top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled - tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled frequency_penalty: 0.0, // 0.0 = disabled @@ -788,7 +787,6 @@
More options
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html index 8361c5774..dbe23c402 100644 --- a/examples/server/themes/wild/index.html +++ b/examples/server/themes/wild/index.html @@ -229,7 +229,6 @@ top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled - tfs_z: 1.0, // 1.0 = disabled typical_p: 1.0, // 1.0 = disabled presence_penalty: 0.0, // 0.0 = disabled frequency_penalty: 0.0, // 0.0 = disabled @@ -791,7 +790,6 @@
More options
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 562635555..58f5a5684 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -607,7 +607,7 @@ static json oaicompat_completion_params_parse( } // Copy remaining properties to llama_params - // This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint. + // This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint. // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp for (const auto & item : body.items()) { // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens" diff --git a/include/llama.h b/include/llama.h index b2d1e7d5a..4076d34a7 100644 --- a/include/llama.h +++ b/include/llama.h @@ -1087,9 +1087,6 @@ extern "C" { /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841 LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep); - /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/. - LLAMA_API struct llama_sampler * llama_sampler_init_tail_free (float z, size_t min_keep); - /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. LLAMA_API struct llama_sampler * llama_sampler_init_typical (float p, size_t min_keep); diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py index 47cacb432..8f0bf8ca8 100755 --- a/scripts/run-with-preset.py +++ b/scripts/run-with-preset.py @@ -20,7 +20,7 @@ CLI_ARGS_LLAMA_CLI_PERPLEXITY = [ "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt", "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n", "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed", - "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical", + "simple-io", "tensor-split", "threads", "temp", "top-k", "top-p", "typical", "verbose-prompt" ] diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 25536eb6c..c2cfe0a77 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -113,7 +113,7 @@ static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) { } static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k) { - // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast + // TODO: move bucket sort to separate function so that top_p/typical/softmax first is equally fast // if (k >= (int32_t)cur_p->size) { // return; // } @@ -733,101 +733,6 @@ struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) { }; } -// tail-free - -struct llama_sampler_tail_free { - const float z; - const size_t min_keep; -}; - -static const char * llama_sampler_tail_free_name(const struct llama_sampler * /*smpl*/) { - return "tail-free"; -} - -static void llama_sampler_tail_free_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { - const auto * ctx = (llama_sampler_tail_free *) smpl->ctx; - - if (ctx->z >= 1.0f || cur_p->size <= 2) { - return; - } - - llama_sampler_softmax_impl(cur_p); - - // Compute the first and second derivatives - std::vector first_derivatives(cur_p->size - 1); - std::vector second_derivatives(cur_p->size - 2); - - for (size_t i = 0; i < first_derivatives.size(); ++i) { - first_derivatives[i] = cur_p->data[i].p - cur_p->data[i + 1].p; - } - for (size_t i = 0; i < second_derivatives.size(); ++i) { - second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1]; - } - - // Calculate absolute value of second derivatives - for (size_t i = 0; i < second_derivatives.size(); ++i) { - second_derivatives[i] = std::abs(second_derivatives[i]); - } - - // Normalize the second derivatives - { - const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f); - - if (second_derivatives_sum > 1e-6f) { - for (float & value : second_derivatives) { - value /= second_derivatives_sum; - } - } else { - for (float & value : second_derivatives) { - value = 1.0f / second_derivatives.size(); - } - } - } - - float cum_sum = 0.0f; - size_t last_idx = cur_p->size; - for (size_t i = 0; i < second_derivatives.size(); ++i) { - cum_sum += second_derivatives[i]; - - // Check if the running sum is greater than z or if we have kept at least min_keep tokens - if (cum_sum > ctx->z && i >= ctx->min_keep) { - last_idx = i; - break; - } - } - - // Resize the output vector to keep only the tokens above the tail location - cur_p->size = last_idx; -} - -static struct llama_sampler * llama_sampler_tail_free_clone(const struct llama_sampler * smpl) { - const auto * ctx = (const llama_sampler_tail_free *) smpl->ctx; - return llama_sampler_init_tail_free(ctx->z, ctx->min_keep); -} - -static void llama_sampler_tail_free_free(struct llama_sampler * smpl) { - delete (llama_sampler_tail_free *) smpl->ctx; -} - -static struct llama_sampler_i llama_sampler_tail_free_i = { - /* .name = */ llama_sampler_tail_free_name, - /* .accept = */ nullptr, - /* .apply = */ llama_sampler_tail_free_apply, - /* .reset = */ nullptr, - /* .clone = */ llama_sampler_tail_free_clone, - /* .free = */ llama_sampler_tail_free_free, -}; - -struct llama_sampler * llama_sampler_init_tail_free(float z, size_t min_keep) { - return new llama_sampler { - /* .iface = */ &llama_sampler_tail_free_i, - /* .ctx = */ new llama_sampler_tail_free { - /* .z = */ z, - /*. min_keep = */ min_keep, - }, - }; -} - // typical struct llama_sampler_typical { diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index eb39661c3..be370044d 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -105,16 +105,6 @@ static void test_top_p(const std::vector & probs, const std::vector & probs, const std::vector & probs_expected, float z) { - sampler_tester tester(probs, probs_expected); - - DUMP(&tester.cur_p); - tester.apply(llama_sampler_init_tail_free(z, 1)); - DUMP(&tester.cur_p); - - tester.check(); -} - static void test_min_p(const std::vector & probs, const std::vector & probs_expected, float p) { sampler_tester tester(probs, probs_expected); @@ -202,7 +192,6 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler for (auto s : samplers_sequence) { switch (s){ case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break; - case 'f': GGML_ABORT("tail_free test not implemented"); case 'y': GGML_ABORT("typical test not implemented"); case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break; case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); break; @@ -299,12 +288,11 @@ static void test_perf() { data.emplace_back(llama_token_data{i, logit, 0.0f}); } - BENCH(llama_sampler_init_top_k (40), data, 32); - BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32); - BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32); - BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32); - BENCH(llama_sampler_init_typical (0.5f, 1), data, 32); - BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32); + BENCH(llama_sampler_init_top_k (40), data, 32); + BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32); + BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32); + BENCH(llama_sampler_init_typical(0.5f, 1), data, 32); + BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32); } int main(void) { @@ -343,10 +331,6 @@ int main(void) { printf("XTC should not:\n"); test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.99f, 0.39f); - test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f); - test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f); - test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f); - test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f); test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f); From 8f275a7c4593aa34147595a90282cf950a853690 Mon Sep 17 00:00:00 2001 From: Changyeon Kim Date: Tue, 29 Oct 2024 17:52:56 +0900 Subject: [PATCH 77/95] ggml: Add POOL2D OP for GPU acceleration to the Vulkan backend in the MobileVLM model. (#9763) * ggml: Add POOL2D OP for GPU ACC to the Vulkan. - The MobileVLM model now supports inference acceleration through GPU by utilizing the Vulkan backend. - A GGML_OP_POOL_2D shader has been added. (Pooling) - The encoding performance of the CLIP model improved from 2.8s on the CPU to 0.7s on the GPU. Signed-off-by: Changyeon Kim * [fix] Correct the incorrect order of the parameters. fix casting to int. Signed-off-by: Changyeon Kim --------- Signed-off-by: Changyeon Kim --- ggml/src/ggml-vulkan.cpp | 72 ++++++++++++++++++ ggml/src/vulkan-shaders/pool2d.comp | 74 +++++++++++++++++++ .../src/vulkan-shaders/vulkan-shaders-gen.cpp | 4 + 3 files changed, 150 insertions(+) create mode 100644 ggml/src/vulkan-shaders/pool2d.comp diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp index e749bbe70..94175a782 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan.cpp @@ -213,6 +213,7 @@ struct vk_device_struct { vk_pipeline pipeline_sum_rows_f32; vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16; vk_pipeline pipeline_timestep_embedding_f32; + vk_pipeline pipeline_pool2d_f32; std::unordered_map pipelines; std::unordered_map pipeline_descriptor_set_requirements; @@ -403,6 +404,17 @@ struct vk_op_timestep_embedding_push_constants { uint32_t max_period; }; +struct vk_op_pool2d_push_constants { + uint32_t IW; uint32_t IH; + uint32_t OW; uint32_t OH; + uint32_t OC; + uint32_t pelements; + uint32_t op; + int32_t k0; int32_t k1; + int32_t s0; int32_t s1; + int32_t p0; int32_t p1; +}; + // Allow pre-recording command buffers struct vk_staging_memcpy { vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {} @@ -1803,6 +1815,8 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1); + for (auto &c : compiles) { c.wait(); } @@ -4234,6 +4248,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_timestep_embedding_f32; } return nullptr; + case GGML_OP_POOL_2D: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_pool2d_f32; + } + return nullptr; case GGML_OP_LEAKY_RELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_leaky_relu_f32; @@ -4464,6 +4483,14 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co uint32_t half_ceil = (dim + 1) / 2; elements = { half_ceil, (uint32_t)src0->ne[0], 1 }; } break; + case GGML_OP_POOL_2D: + { + const uint32_t N = dst->ne[3]; + const uint32_t OC = dst->ne[2]; + const uint32_t OH = dst->ne[1]; + const uint32_t OW = dst->ne[0]; + elements = { N * OC * OH * OW, 1, 1}; + } break; case GGML_OP_ADD: case GGML_OP_DIV: case GGML_OP_MUL: @@ -4914,6 +4941,34 @@ static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context }, dryrun); } +static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { + uint32_t op = static_cast(dst->op_params[0]); + const int32_t k1 = dst->op_params[1]; + const int32_t k0 = dst->op_params[2]; + const int32_t s1 = dst->op_params[3]; + const int32_t s0 = dst->op_params[4]; + const int32_t p1 = dst->op_params[5]; + const int32_t p0 = dst->op_params[6]; + + const uint32_t IH = src0->ne[1]; + const uint32_t IW = src0->ne[0]; + + const uint32_t N = dst->ne[3]; + + const uint32_t OC = dst->ne[2]; + const uint32_t OH = dst->ne[1]; + const uint32_t OW = dst->ne[0]; + + const uint32_t parallel_elements = N * OC * OH * OW; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_POOL_2D, { + IW, IH, OW, OH, OC, + parallel_elements, + op, + k0, k1, s0, s1, p0, p1, + }, dryrun); +} + static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const float * op_params = (const float *)dst->op_params; ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun); @@ -5792,6 +5847,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_SUM_ROWS: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: case GGML_OP_LEAKY_RELU: break; default: @@ -5927,6 +5983,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_TIMESTEP_EMBEDDING: ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node, dryrun); + break; + case GGML_OP_POOL_2D: + ggml_vk_pool_2d(ctx, compute_ctx, src0, node, dryrun); + break; case GGML_OP_LEAKY_RELU: ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun); @@ -6018,6 +6078,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * case GGML_OP_SUM_ROWS: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: case GGML_OP_LEAKY_RELU: case GGML_OP_REPEAT: buf = tensor->buffer; @@ -6821,6 +6882,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_SUM_ROWS: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: case GGML_OP_LEAKY_RELU: return true; default: @@ -7334,6 +7396,16 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) { const int32_t dim = tensor->op_params[0]; const int32_t max_period = tensor->op_params[1]; tensor_clone = ggml_timestep_embedding(ggml_ctx, src0_clone, dim, max_period); + } else if (tensor->op == GGML_OP_POOL_2D) { + enum ggml_op_pool op = static_cast(dst->op_params[0]); + const int32_t k0 = tensor->op_params[1]; + const int32_t k1 = tensor->op_params[2]; + const int32_t s0 = tensor->op_params[3]; + const int32_t s1 = tensor->op_params[4]; + const int32_t p0 = tensor->op_params[5]; + const int32_t p1 = tensor->op_params[6]; + + tensor_clone = ggml_pool_2d(ggml_ctx, src0_clone, op, k0, k1, s0, s1, p0, p1); } else if (tensor->op == GGML_OP_LEAKY_RELU) { const float * op_params = (const float *)tensor->op_params; tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false); diff --git a/ggml/src/vulkan-shaders/pool2d.comp b/ggml/src/vulkan-shaders/pool2d.comp new file mode 100644 index 000000000..b6124411a --- /dev/null +++ b/ggml/src/vulkan-shaders/pool2d.comp @@ -0,0 +1,74 @@ +#version 450 + +#include "types.comp" + +#extension GL_EXT_shader_16bit_storage : require + +layout(push_constant) uniform parameter { + uint IW; uint IH; + uint OW; uint OH; + uint OC; + uint pelements; + uint op; + int k0; int k1; + int s0; int s1; + int p0; int p1; +} p; + +#define BLOCK_SIZE 512 +#define FLT_MAX 3.402823466e+38F +#define OP_POOL_MAX 0u +#define OP_POOL_AVG 1u + +layout (local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout(binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + if (idx >= p.pelements) { + return; + } + + const uint O_HW = p.OW * p.OH; + + const uint nc = idx / O_HW; + const uint cur_oh = (idx % O_HW) / p.OW; + const uint cur_ow = (idx % O_HW) % p.OW; + + const int start_h = int(cur_oh) * p.s0 - p.p0; + const uint bh = max(start_h, 0); + const uint eh = min(start_h + p.k0, p.IH); + + const int start_w = int(cur_ow) * p.s1 - p.p1; + const uint bw = max(start_w, 0); + const uint ew = min(start_w + p.k1, p.IW); + + const float scale = 1.0 / float(p.k0 * p.k1); + float res; + + if (p.op == OP_POOL_AVG) { + res = 0.0; + } else if (p.op == OP_POOL_MAX) { + res = -FLT_MAX; + } else { + return; + } + + #pragma unroll + for (uint i = bh; i < eh; i++) { + #pragma unroll + for (uint j = bw; j < ew; j++) { + const float cur = D_TYPE(data_a[nc * p.IH * p.IW + i * p.IW + j]); + + if (p.op == OP_POOL_AVG) { + res += cur * scale; + } else if (p.op == OP_POOL_MAX) { + res = max(res, cur); + } + } + } + + data_d[nc * O_HW + cur_oh * p.OW + cur_ow] = res; +} diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp index 1bd1b6f67..49759c593 100644 --- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp @@ -493,6 +493,10 @@ void process_shaders(std::vector>& tasks) { tasks.push_back(std::async(std::launch::async, [=] { string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); })); + + tasks.push_back(std::async(std::launch::async, [=] { + string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + })); } void write_output_files() { From c5b0f4b5d90297f3e729fca7f78ddb25fcab5ddc Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Wed, 30 Oct 2024 02:01:23 +0100 Subject: [PATCH 78/95] llama : refactor model loader with backend registry (#10026) --- examples/llama-bench/llama-bench.cpp | 132 +- ggml/include/ggml-backend.h | 19 +- ggml/include/ggml-cuda.h | 2 +- ggml/src/ggml-amx.cpp | 33 +- ggml/src/ggml-backend-impl.h | 19 +- ggml/src/ggml-backend.cpp | 235 +- ggml/src/ggml-blas.cpp | 20 +- ggml/src/ggml-cann.cpp | 50 +- ggml/src/ggml-cuda.cu | 142 +- ggml/src/ggml-kompute.cpp | 15 - ggml/src/ggml-metal.m | 44 +- ggml/src/ggml-rpc.cpp | 20 +- ggml/src/ggml-sycl.cpp | 54 +- ggml/src/ggml-vulkan.cpp | 26 +- ggml/src/ggml.c | 4 +- include/llama.h | 7 +- scripts/compare-llama-bench.py | 2 +- src/llama.cpp | 3098 +++++++++++++------------- 18 files changed, 1903 insertions(+), 2019 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 4a8ea9676..e7873a143 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -21,12 +21,6 @@ #include "ggml.h" #include "llama.h" #include "common.h" -#include "ggml-cuda.h" -#include "ggml-sycl.h" - -#ifdef GGML_USE_CANN -#include "ggml-cann.h" -#endif #ifdef _WIN32 #define WIN32_LEAN_AND_MEAN @@ -82,95 +76,27 @@ static T stdev(const std::vector & v) { } static std::string get_cpu_info() { - std::string id; -#ifdef __linux__ - FILE * f = fopen("/proc/cpuinfo", "r"); - if (f) { - char buf[1024]; - while (fgets(buf, sizeof(buf), f)) { - if (strncmp(buf, "model name", 10) == 0) { - char * p = strchr(buf, ':'); - if (p) { - p++; - while (std::isspace(*p)) { - p++; - } - while (std::isspace(p[strlen(p) - 1])) { - p[strlen(p) - 1] = '\0'; - } - id = p; - break; - } - } - } - fclose(f); - } -#elif defined(_WIN32) - HKEY hKey; - if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, - TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"), - 0, - KEY_READ, - &hKey) != ERROR_SUCCESS) { - // fail to open registry key - return ""; - } - char cpu_brand[256]; - DWORD cpu_brand_size = sizeof(cpu_brand); - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - (LPBYTE)cpu_brand, - &cpu_brand_size) == ERROR_SUCCESS) { - id.assign(cpu_brand, cpu_brand_size); - if (id.find('\0') != std::string::npos) { - id.resize(id.find('\0')); + std::vector cpu_list; + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + auto * dev = ggml_backend_dev_get(i); + auto dev_type = ggml_backend_dev_type(dev); + if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU || dev_type == GGML_BACKEND_DEVICE_TYPE_ACCEL) { + cpu_list.push_back(ggml_backend_dev_description(dev)); } } - RegCloseKey(hKey); -#endif - // TODO: other platforms - return id; + return join(cpu_list, ", "); } static std::string get_gpu_info() { - std::string id; -#ifdef GGML_USE_CUDA - int count = ggml_backend_cuda_get_device_count(); - for (int i = 0; i < count; i++) { - char buf[128]; - ggml_backend_cuda_get_device_description(i, buf, sizeof(buf)); - id += buf; - if (i < count - 1) { - id += "/"; + std::vector gpu_list; + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + auto * dev = ggml_backend_dev_get(i); + auto dev_type = ggml_backend_dev_type(dev); + if (dev_type == GGML_BACKEND_DEVICE_TYPE_GPU) { + gpu_list.push_back(ggml_backend_dev_description(dev)); } } -#endif -#ifdef GGML_USE_SYCL - int count = ggml_backend_sycl_get_device_count(); - for (int i = 0; i < count; i++) { - char buf[128]; - ggml_backend_sycl_get_device_description(i, buf, sizeof(buf)); - id += buf; - if (i < count - 1) { - id += "/"; - } - } -#endif -#ifdef GGML_USE_CANN - uint32_t count = ggml_backend_cann_get_device_count(); - for (uint32_t i = 0; i < count; i++) { - char buf[128]; - ggml_backend_cann_get_device_description(i, buf, sizeof(buf)); - id += buf; - if (i < count - 1) { - id += "/"; - } - } -#endif - // TODO: other backends - return id; + return join(gpu_list, ", "); } // command line params @@ -938,29 +864,15 @@ struct test { } static std::string get_backend() { - if (cuda) { - return GGML_CUDA_NAME; + std::vector backends; + for (size_t i = 0; i < ggml_backend_reg_count(); i++) { + auto * reg = ggml_backend_reg_get(i); + std::string name = ggml_backend_reg_name(reg); + if (name != "CPU") { + backends.push_back(ggml_backend_reg_name(reg)); + } } - if (vulkan) { - return "Vulkan"; - } - if (kompute) { - return "Kompute"; - } - if (metal) { - return "Metal"; - } - if (sycl) { - return GGML_SYCL_NAME; - } - if (gpu_blas) { - return "GPU BLAS"; - } - if (blas) { - return "BLAS"; - } - - return "CPU"; + return backends.empty() ? "CPU" : join(backends, ","); } static const std::vector & get_fields() { diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index 5933b8e8f..c11eb4183 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -114,11 +114,12 @@ extern "C" { // enum ggml_backend_dev_type { + // CPU device using system memory GGML_BACKEND_DEVICE_TYPE_CPU, + // GPU device using dedicated memory GGML_BACKEND_DEVICE_TYPE_GPU, - // devices with full capabilities (excludes backends such as BLAS that only support matrix multiplication) - GGML_BACKEND_DEVICE_TYPE_CPU_FULL, - GGML_BACKEND_DEVICE_TYPE_GPU_FULL + // accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX) + GGML_BACKEND_DEVICE_TYPE_ACCEL }; // functionality supported by the device @@ -167,10 +168,14 @@ extern "C" { GGML_API ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index); GGML_API void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name); + // Common functions that may be obtained using ggml_backend_reg_get_proc_address - // Functions that may be obtained using ggml_backend_reg_get_proc_address - typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(const float *); - typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t, int); + // Split buffer type for tensor parallelism + typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split); + // Set the number of threads for the backend + typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads); + // Get additional buffer types provided by the device (returns a NULL-terminated array) + typedef ggml_backend_buffer_type_t * (*ggml_backend_dev_get_extra_bufts_t)(ggml_backend_dev_t device); // // Backend registry @@ -192,7 +197,7 @@ extern "C" { GGML_API ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params); // = ggml_backend_dev_init(ggml_backend_dev_by_type(type), params) GGML_API ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params); - // = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU_FULL) OR ggml_backend_dev_by_type(CPU_FULL), NULL) + // = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU) OR ggml_backend_dev_by_type(CPU), NULL) GGML_API ggml_backend_t ggml_backend_init_best(void); // diff --git a/ggml/include/ggml-cuda.h b/ggml/include/ggml-cuda.h index f44d8f4e6..305d0b636 100644 --- a/ggml/include/ggml-cuda.h +++ b/ggml/include/ggml-cuda.h @@ -28,7 +28,7 @@ GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend); GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); // split tensor buffer that splits matrices by rows across multiple devices -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); +GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); diff --git a/ggml/src/ggml-amx.cpp b/ggml/src/ggml-amx.cpp index ac6ec2342..144dc9d8a 100644 --- a/ggml/src/ggml-amx.cpp +++ b/ggml/src/ggml-amx.cpp @@ -16,12 +16,6 @@ #if defined(__AMX_INT8__) // AMX buffer interface -static const char * ggml_backend_amx_buffer_get_name(ggml_backend_buffer_t buffer) { - return "AMX"; - - GGML_UNUSED(buffer); -} - static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { free(buffer->context); } @@ -72,7 +66,6 @@ static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t } static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { - /* .get_name = */ ggml_backend_amx_buffer_get_name, /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, /* .get_base = */ ggml_backend_amx_buffer_get_base, /* .init_tensor = */ NULL, // no initialization required @@ -121,14 +114,14 @@ static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { /* .iface = */ { - /* .get_name = */ ggml_backend_amx_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_amx_buffer_type_is_host, + /* .get_name = */ ggml_backend_amx_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_amx_buffer_type_is_host, }, - /* .device = */ NULL, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), /* .context = */ NULL, }; @@ -149,12 +142,6 @@ static void ggml_backend_amx_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_amx_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_amx_buffer_type(); - - GGML_UNUSED(backend); -} - static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; @@ -187,7 +174,6 @@ static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, s static struct ggml_backend_i ggml_backend_amx_i = { /* .get_name = */ ggml_backend_amx_name, /* .free = */ ggml_backend_amx_free, - /* .get_default_buffer_type = */ ggml_backend_amx_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -197,9 +183,6 @@ static struct ggml_backend_i ggml_backend_amx_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_amx_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -279,7 +262,7 @@ static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t * } static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_CPU; + return GGML_BACKEND_DEVICE_TYPE_ACCEL; GGML_UNUSED(dev); } diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h index fd3deae00..fa8d5b7fb 100644 --- a/ggml/src/ggml-backend-impl.h +++ b/ggml/src/ggml-backend-impl.h @@ -22,7 +22,7 @@ extern "C" { size_t (*get_max_size) (ggml_backend_buffer_type_t buft); // (optional) data size needed to allocate the tensor, including padding (defaults to ggml_nbytes) size_t (*get_alloc_size)(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); - // (optional) check if tensor data is in host memory (defaults to false) + // (optional) check if tensor data is in host memory and uses standard ggml tensor layout (defaults to false) bool (*is_host) (ggml_backend_buffer_type_t buft); }; @@ -37,7 +37,6 @@ extern "C" { // struct ggml_backend_buffer_i { - const char * (*get_name) (ggml_backend_buffer_t buffer); // (optional) free the buffer void (*free_buffer) (ggml_backend_buffer_t buffer); // base address of the buffer @@ -88,19 +87,16 @@ extern "C" { void (*free)(ggml_backend_t backend); - // Will be moved to the device interface - // buffer allocation - ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend); - // (optional) asynchronous tensor data access void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); bool (*cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst); - // (optional) complete all pending operations + // (optional) complete all pending operations (required if the backend supports async operations) void (*synchronize)(ggml_backend_t backend); - // (optional) compute graph with a plan (not used currently) + // (optional) graph plans (not used currently) + // compute graph with a plan ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); // update the plan with a new graph - this should be faster than creating a new plan when the graph has the same topology @@ -111,13 +107,6 @@ extern "C" { // compute graph (always async if supported by the backend) enum ggml_status (*graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph); - // IMPORTANT: these functions have been moved to the device interface and will be removed from the backend interface - // new backends should implement the device interface instead - // These functions are being moved to the device interface - bool (*supports_op) (ggml_backend_t backend, const struct ggml_tensor * op); - bool (*supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft); - bool (*offload_op) (ggml_backend_t backend, const struct ggml_tensor * op); - // (optional) event synchronization // record an event on this stream void (*event_record)(ggml_backend_t backend, ggml_backend_event_t event); diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 7d7b63a15..fd574887f 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -34,6 +34,11 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { } ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + if (size == 0) { + // return a dummy buffer for zero-sized allocations + return ggml_backend_buffer_init(buft, {}, NULL, 0); + } + return buft->iface.alloc_buffer(buft, size); } @@ -89,7 +94,7 @@ ggml_backend_buffer_t ggml_backend_buffer_init( } const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name(buffer); + return ggml_backend_buft_name(ggml_backend_buffer_get_type(buffer)); } void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { @@ -108,6 +113,11 @@ size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) { } void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { + // get_base is optional if the buffer is zero-sized + if (buffer->size == 0) { + return NULL; + } + void * base = buffer->iface.get_base(buffer); GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL"); @@ -122,6 +132,15 @@ void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_t } } +void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + // clear is optional if the buffer is zero-sized + if (buffer->size == 0) { + return; + } + + buffer->iface.clear(buffer, value); +} + size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) { return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer)); } @@ -134,10 +153,6 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor); } -void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - buffer->iface.clear(buffer, value); -} - bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) { return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer)); } @@ -198,7 +213,7 @@ void ggml_backend_free(ggml_backend_t backend) { } ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) { - return backend->iface.get_default_buffer_type(backend); + return ggml_backend_dev_buffer_type(backend->device); } ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) { @@ -238,43 +253,42 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + if (size == 0) { + return; + } + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - if (!size) { - return; - } - buf->iface.set_tensor(buf, tensor, data, offset, size); } void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + if (size == 0) { + return; + } + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - if (!size) { - return; - } - buf->iface.get_tensor(buf, tensor, data, offset, size); } GGML_API void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - GGML_ASSERT(buf != NULL && "tensor buffer not set"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - - if (!size) { + if (size == 0) { return; } - GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not supported by backend buffer"); + GGML_ASSERT(buf != NULL && "tensor buffer not set"); + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); + GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); + GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not implemented by backend buffer"); buf->iface.memset_tensor(buf, tensor, value, offset, size); } @@ -316,32 +330,15 @@ enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct } bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - // helper to ease transition to device interface - if (backend->device) { - return ggml_backend_dev_supports_op(backend->device, op); - } - - return backend->iface.supports_op(backend, op); + return ggml_backend_dev_supports_op(backend->device, op); } bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - // helper to ease transition to device interface - if (backend->device) { - return ggml_backend_dev_supports_buft(backend->device, buft); - } - return backend->iface.supports_buft(backend, buft); + return ggml_backend_dev_supports_buft(backend->device, buft); } bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) { - // helper to ease transition to device interface - if (backend->device) { - return ggml_backend_dev_offload_op(backend->device, op); - } - - if (backend->iface.offload_op != NULL) { - return backend->iface.offload_op(backend, op); - } - return false; + return ggml_backend_dev_offload_op(backend->device, op); } ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) { @@ -582,6 +579,9 @@ struct ggml_backend_registry { #ifdef GGML_USE_VULKAN register_backend(ggml_backend_vk_reg()); #endif +#ifdef GGML_USE_CANN + register_backend(ggml_backend_cann_reg()); +#endif #ifdef GGML_USE_BLAS register_backend(ggml_backend_blas_reg()); #endif @@ -591,9 +591,6 @@ struct ggml_backend_registry { #ifdef GGML_USE_AMX register_backend(ggml_backend_amx_reg()); #endif -#ifdef GGML_USE_CANN - register_backend(ggml_backend_cann_reg()); -#endif // TODO: kompute @@ -701,9 +698,9 @@ ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const } ggml_backend_t ggml_backend_init_best(void) { - ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL); + ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU); if (!dev) { - dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU_FULL); + dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); } if (!dev) { return NULL; @@ -711,13 +708,7 @@ ggml_backend_t ggml_backend_init_best(void) { return ggml_backend_dev_init(dev, NULL); } -// backend CPU - -static const char * ggml_backend_cpu_buffer_get_name(ggml_backend_buffer_t buffer) { - return "CPU"; - - GGML_UNUSED(buffer); -} +// CPU backend - buffer static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { uintptr_t data = (uintptr_t)buffer->context; @@ -767,7 +758,6 @@ static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t } static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = { - /* .get_name = */ ggml_backend_cpu_buffer_get_name, /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, /* .get_base = */ ggml_backend_cpu_buffer_get_base, /* .init_tensor = */ NULL, // no initialization required @@ -780,7 +770,6 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = { }; static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = { - /* .get_name = */ ggml_backend_cpu_buffer_get_name, /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed /* .get_base = */ ggml_backend_cpu_buffer_get_base, /* .init_tensor = */ NULL, // no initialization required @@ -792,6 +781,8 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = { /* .reset = */ NULL, }; +// CPU backend - buffer type + static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "CPU"; @@ -799,19 +790,14 @@ static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_ty } static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - auto alloc_size = size; - if (alloc_size == 0) { - alloc_size = 1; - } - - void * data = ggml_aligned_malloc(alloc_size); + void * data = ggml_aligned_malloc(size); if (data == NULL) { - GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, alloc_size); + GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); return NULL; } - return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, alloc_size); + return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size); } static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { @@ -843,6 +829,29 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { return &ggml_backend_cpu_buffer_type; } +static const char * ggml_backend_cpu_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_Mapped"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_type_t ggml_backend_cpu_buffer_from_ptr_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_from_ptr_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type; +} + #ifdef GGML_USE_CPU_HBM // buffer type HBM @@ -855,18 +864,11 @@ static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffe GGML_UNUSED(buft); } -static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { - return "CPU_HBM"; - - GGML_UNUSED(buf); -} - static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { hbw_free(buffer->context); } static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - //void * ptr = hbw_malloc(size); void * ptr; int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); if (result != 0) { @@ -876,7 +878,6 @@ static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_ ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name; buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; return buffer; @@ -899,6 +900,21 @@ ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { } #endif +static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backend_dev_t device) { + static ggml_backend_buffer_type_t bufts[] = { +#ifdef GGML_USE_CPU_HBM + ggml_backend_cpu_hbm_buffer_type(), +#endif + NULL + }; + + return bufts; + + GGML_UNUSED(device); +} + +// CPU backend - backend (stream) + struct ggml_backend_cpu_context { int n_threads; ggml_threadpool_t threadpool; @@ -923,12 +939,6 @@ static void ggml_backend_cpu_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(backend); -} - struct ggml_backend_plan_cpu { struct ggml_cplan cplan; struct ggml_cgraph cgraph; @@ -998,7 +1008,6 @@ static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, s static const struct ggml_backend_i ggml_backend_cpu_i = { /* .get_name = */ ggml_backend_cpu_get_name, /* .free = */ ggml_backend_cpu_free, - /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -1008,9 +1017,6 @@ static const struct ggml_backend_i ggml_backend_cpu_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, /* .graph_compute = */ ggml_backend_cpu_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -1081,10 +1087,10 @@ void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_ ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); - return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size); + return ggml_backend_buffer_init(ggml_backend_cpu_buffer_from_ptr_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size); } -//////////////////////// +// CPU backend - device struct ggml_backend_cpu_device_context { std::string description = "CPU"; @@ -1171,7 +1177,7 @@ static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * } static enum ggml_backend_dev_type ggml_backend_cpu_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_CPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_CPU; GGML_UNUSED(dev); } @@ -1189,7 +1195,7 @@ static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggm }; } -static ggml_backend_t ggml_backend_cpu_device_init(ggml_backend_dev_t dev, const char * params) { +static ggml_backend_t ggml_backend_cpu_device_init_backend(ggml_backend_dev_t dev, const char * params) { return ggml_backend_cpu_init(); GGML_UNUSED(dev); @@ -1202,7 +1208,7 @@ static ggml_backend_buffer_type_t ggml_backend_cpu_device_get_buffer_type(ggml_b GGML_UNUSED(dev); } -static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { +static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { return ggml_backend_cpu_buffer_from_ptr(ptr, size); GGML_UNUSED(dev); @@ -1244,10 +1250,10 @@ static const struct ggml_backend_device_i ggml_backend_cpu_device_i = { /* .get_memory = */ ggml_backend_cpu_device_get_memory, /* .get_type = */ ggml_backend_cpu_device_get_type, /* .get_props = */ ggml_backend_cpu_device_get_props, - /* .init_backend = */ ggml_backend_cpu_device_init, + /* .init_backend = */ ggml_backend_cpu_device_init_backend, /* .get_buffer_type = */ ggml_backend_cpu_device_get_buffer_type, /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_ptr, + /* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_host_ptr, /* .supports_op = */ ggml_backend_cpu_device_supports_op, /* .supports_buft = */ ggml_backend_cpu_device_supports_buft, /* .offload_op = */ NULL, @@ -1256,7 +1262,7 @@ static const struct ggml_backend_device_i ggml_backend_cpu_device_i = { /* .event_synchronize = */ NULL, }; -//////////////////////// +// CPU backend - backend (reg) static const char * ggml_backend_cpu_reg_get_name(ggml_backend_reg_t reg) { return "CPU"; @@ -1287,6 +1293,10 @@ static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const ch if (strcmp(name, "ggml_backend_set_n_threads") == 0) { return (void *)ggml_backend_cpu_set_n_threads; } + if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) { + return (void *)ggml_backend_cpu_get_extra_bufts; + } + return NULL; GGML_UNUSED(reg); @@ -1315,12 +1325,6 @@ struct ggml_backend_multi_buffer_context { size_t n_buffers; }; -static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context; - - return ctx->buffers[0]->iface.get_name(ctx->buffers[0]); -} - static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context; for (size_t i = 0; i < ctx->n_buffers; i++) { @@ -1339,7 +1343,6 @@ static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_ } static const struct ggml_backend_buffer_i ggml_backend_multi_buffer_i = { - /* .get_name = */ ggml_backend_multi_buffer_get_name, /* .free_buffer = */ ggml_backend_multi_buffer_free_buffer, /* .get_base = */ NULL, /* .init_tensor = */ NULL, @@ -1368,7 +1371,7 @@ ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer } bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_multi_buffer_get_name; + return buffer->iface.free_buffer == ggml_backend_multi_buffer_free_buffer; } void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { @@ -1460,7 +1463,7 @@ struct ggml_backend_sched { char * context_buffer; size_t context_buffer_size; - bool debug; + int debug; }; #define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor) @@ -1500,7 +1503,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co return -1; } -#if 0 +#if 1 #define GGML_SCHED_MAX_SPLITS_DEBUG 4096 static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) @@ -1548,7 +1551,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st if (src == NULL) { continue; } - if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + // skip ROPE since the rope freqs tensor is too small to choose a backend based on it + // not an ideal solution + if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); // check if a backend with higher prio wants to offload the op if (src_backend_id == sched->n_backends - 1) { @@ -1595,19 +1600,21 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str if (ggml_is_view_op(node->op)) { continue; } - ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); - GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, - fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node)); - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - continue; + if (sched->debug > 1) { + ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); + GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, + fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node)); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); + GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name, + fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); } - ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); - GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name, - fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); + GGML_LOG_DEBUG("\n"); } - GGML_LOG_DEBUG("\n"); } } @@ -1899,11 +1906,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (src == NULL) { continue; } - // check if a weight is on a different backend + // check if a weight is on a different and incompatible backend // by starting a new split, the memory of the previously offloaded weights can be reused if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { int src_backend_id = tensor_backend_id(src); - if (src_backend_id != cur_backend_id) { + if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) { need_new_split = true; break; } @@ -1915,7 +1922,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg int src_backend_id = sched->hv_tensor_backend_ids[id]; bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id); if (src_backend_id != cur_backend_id && tensor_id_copy(id, cur_backend_id, 0) == NULL && !supported) { - //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name); need_new_split = true; break; } @@ -2240,7 +2246,8 @@ ggml_backend_sched_t ggml_backend_sched_new( struct ggml_backend_sched * sched = (ggml_backend_sched *) calloc(1, sizeof(struct ggml_backend_sched)); - sched->debug = getenv("GGML_SCHED_DEBUG") != NULL; + const char * GGML_SCHED_DEBUG = getenv("GGML_SCHED_DEBUG"); + sched->debug = GGML_SCHED_DEBUG ? atoi(GGML_SCHED_DEBUG) : 0; sched->n_backends = n_backends; sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1; diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas.cpp index 7875ec86d..8d96220b9 100644 --- a/ggml/src/ggml-blas.cpp +++ b/ggml/src/ggml-blas.cpp @@ -224,12 +224,6 @@ static void ggml_backend_blas_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_blas_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(backend); -} - static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context; @@ -265,7 +259,6 @@ static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, static struct ggml_backend_i blas_backend_i = { /* .get_name = */ ggml_backend_blas_get_name, /* .free = */ ggml_backend_blas_free, - /* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -275,9 +268,6 @@ static struct ggml_backend_i blas_backend_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_blas_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -356,7 +346,7 @@ static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * } static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_CPU; + return GGML_BACKEND_DEVICE_TYPE_ACCEL; GGML_UNUSED(dev); } @@ -374,7 +364,7 @@ static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct gg }; } -static ggml_backend_t ggml_backend_blas_device_init(ggml_backend_dev_t dev, const char * params) { +static ggml_backend_t ggml_backend_blas_device_init_backend(ggml_backend_dev_t dev, const char * params) { return ggml_backend_blas_init(); GGML_UNUSED(dev); @@ -387,7 +377,7 @@ static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_ GGML_UNUSED(dev); } -static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { +static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { return ggml_backend_cpu_buffer_from_ptr(ptr, size); GGML_UNUSED(dev); @@ -456,10 +446,10 @@ static const struct ggml_backend_device_i ggml_backend_blas_device_i = { /* .get_memory = */ ggml_backend_blas_device_get_memory, /* .get_type = */ ggml_backend_blas_device_get_type, /* .get_props = */ ggml_backend_blas_device_get_props, - /* .init_backend = */ ggml_backend_blas_device_init, + /* .init_backend = */ ggml_backend_blas_device_init_backend, /* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type, /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_ptr, + /* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_host_ptr, /* .supports_op = */ ggml_backend_blas_device_supports_op, /* .supports_buft = */ ggml_backend_blas_device_supports_buft, /* .offload_op = */ NULL, diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann.cpp index af0fb603a..f8ac11e41 100644 --- a/ggml/src/ggml-cann.cpp +++ b/ggml/src/ggml-cann.cpp @@ -489,23 +489,6 @@ struct ggml_backend_cann_buffer_context { ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); } }; -/** - * @brief Retrieve the name associated with a CANN buffer. - * - * This function returns the name of a CANN buffer, which is stored in the - * context of the buffer. - * - * @param buffer The CANN buffer whose name is to be retrieved. - * @return A pointer to a C-string containing the name of the buffer. - */ - -static const char* ggml_backend_cann_buffer_get_name( - ggml_backend_buffer_t buffer) { - return "CANN"; - - GGML_UNUSED(buffer); -} - /** * @brief Check if a buffer is a CANN buffer. * @@ -515,9 +498,10 @@ static const char* ggml_backend_cann_buffer_get_name( * @param buffer The buffer to check. * @return true if the buffer is a CANN buffer, false otherwise. */ +static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft); static bool ggml_backend_buffer_is_cann( ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cann_buffer_get_name; + return ggml_backend_buft_is_cann(buffer->buft); } /** @@ -965,7 +949,6 @@ static void ggml_backend_cann_buffer_clear( * on a CANN buffer within the backend. */ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { - /* .get_name = */ ggml_backend_cann_buffer_get_name, /* .free_buffer = */ ggml_backend_cann_buffer_free_buffer, /* .get_base = */ ggml_backend_cann_buffer_get_base, /* .init_tensor = */ ggml_backend_cann_buffer_init_tensor, @@ -999,9 +982,10 @@ struct ggml_backend_cann_buffer_type_context { */ static const char* ggml_backend_cann_buffer_type_name( ggml_backend_buffer_type_t buft) { - return "CANN"; + ggml_backend_cann_buffer_type_context* buft_ctx = + (ggml_backend_cann_buffer_type_context*)buft->context; - GGML_UNUSED(buft); + return buft_ctx->name.c_str(); } /** @@ -1465,24 +1449,6 @@ static void ggml_backend_cann_free(ggml_backend_t backend) { delete backend; } -/** - * @brief Retrieves the default buffer type associated with the CANN backend. - * - * This function returns the buffer type specific to the device associated - * with the CANN backend. It is used to allocate buffers for computations - * performed by the backend. - * - * @param backend Pointer to the CANN backend structure. - * @return Pointer to the buffer type structure for the CANN backend. - */ -static ggml_backend_buffer_type_t -ggml_backend_cann_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - - return ggml_backend_cann_buffer_type(cann_ctx->device); -} - /** * @brief Sets tensor data asynchronously in the CANN backend. * @@ -1863,7 +1829,6 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend, static const ggml_backend_i ggml_backend_cann_interface = { /* .get_name = */ ggml_backend_cann_name, /* .free = */ ggml_backend_cann_free, - /* .get_default_buffer_type = */ ggml_backend_cann_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_cann_set_tensor_async, /* .get_tensor_async = */ ggml_backend_cann_get_tensor_async, /* .cpy_tensor_async = */ ggml_backend_cann_cpy_tensor_async, @@ -1873,9 +1838,6 @@ static const ggml_backend_i ggml_backend_cann_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cann_graph_compute, - /* .supports_op = */ NULL, // moved to device - /* .supports_buft = */ NULL, // moved to device - /* .offload_op = */ NULL, // moved to device /* .event_record = */ ggml_backend_cann_event_record, /* .event_wait = */ ggml_backend_cann_event_wait, }; @@ -1918,7 +1880,7 @@ static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * static enum ggml_backend_dev_type ggml_backend_cann_device_get_type(ggml_backend_dev_t dev) { GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; } static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 217df968a..087091516 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -421,20 +421,15 @@ struct ggml_backend_cuda_buffer_context { } }; -static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - -static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name; -} - static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; delete ctx; } +static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { + return buffer->iface.free_buffer == ggml_backend_cuda_buffer_free_buffer; +} + static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; return ctx->dev_ptr; @@ -515,7 +510,6 @@ static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t } static const ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { - /* .get_name = */ ggml_backend_cuda_buffer_get_name, /* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer, /* .get_base = */ ggml_backend_cuda_buffer_get_base, /* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor, @@ -548,8 +542,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac ggml_cuda_set_device(buft_ctx->device); - size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 - void * dev_ptr; cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device); if (err != cudaSuccess) { @@ -657,7 +649,9 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl } struct ggml_backend_cuda_split_buffer_type_context { + int main_device; std::array tensor_split; + std::string name; }; struct ggml_backend_cuda_split_buffer_context { @@ -680,16 +674,6 @@ struct ggml_backend_cuda_split_buffer_context { std::vector tensor_extras; }; -static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) { - return GGML_CUDA_NAME "_Split"; - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name; - GGML_UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds -} static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; @@ -833,7 +817,6 @@ static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, u } static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { - /* .get_name = */ ggml_backend_cuda_split_buffer_get_name, /* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer, /* .get_base = */ ggml_backend_cuda_split_buffer_get_base, /* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor, @@ -848,9 +831,9 @@ static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { // cuda split buffer type static const char * ggml_backend_cuda_split_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return GGML_CUDA_NAME "_Split"; + ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; - GGML_UNUSED(buft); + return ctx->name.c_str(); } static bool ggml_backend_buft_is_cuda_split(ggml_backend_buffer_type_t buft) { @@ -915,11 +898,11 @@ static const ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_inte /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, }; -ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) { +ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split) { static std::mutex mutex; std::lock_guard lock(mutex); - static std::map, struct ggml_backend_buffer_type> buft_map; + static std::map>, struct ggml_backend_buffer_type> buft_map; std::array tensor_split_arr = {}; @@ -937,18 +920,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten } } - auto it = buft_map.find(tensor_split_arr); + auto it = buft_map.find({main_device, tensor_split_arr}); if (it != buft_map.end()) { return &it->second; } + auto * ctx = new ggml_backend_cuda_split_buffer_type_context{ + main_device, + tensor_split_arr, + GGML_CUDA_NAME + std::to_string(main_device) + "_Split", + }; struct ggml_backend_buffer_type buft { /* .iface = */ ggml_backend_cuda_split_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), 0), - /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr}, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), main_device), + /* .context = */ ctx, }; - auto result = buft_map.emplace(tensor_split_arr, buft); + auto result = buft_map.emplace(std::make_pair(main_device, tensor_split_arr), buft); return &result.first->second; } @@ -960,12 +948,6 @@ static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_ GGML_UNUSED(buft); } -static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) { - return GGML_CUDA_NAME "_Host"; - - GGML_UNUSED(buffer); -} - static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { CUDA_CHECK(cudaFreeHost(buffer->context)); } @@ -998,7 +980,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_cuda_host_buffer_name; buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; return buffer; @@ -1400,7 +1381,7 @@ static void ggml_cuda_op_mul_mat( const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer); + const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); GGML_ASSERT(!(split && ne02 > 1)); GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); @@ -1890,7 +1871,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co } static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer); + const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 @@ -2017,7 +1998,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * GGML_TENSOR_BINARY_OP_LOCALS - GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers"); + GGML_ASSERT(!ggml_backend_buft_is_cuda_split(src0->buffer->buft) && "mul_mat_id does not support split buffers"); cudaStream_t stream = ctx.stream(); @@ -2150,7 +2131,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) { // why is this here instead of mul_mat? - if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) { + if (dst->src[0] != nullptr && ggml_backend_buft_is_cuda_split(dst->src[0]->buffer->buft)) { ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device); } @@ -2371,12 +2352,6 @@ static void ggml_backend_cuda_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - return ggml_backend_cuda_buffer_type(cuda_ctx->device); -} - static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; @@ -2582,7 +2557,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, continue; } - if (node->src[0] && node->src[0]->buffer && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) { + if (node->src[0] && node->src[0]->buffer && ggml_backend_buft_is_cuda_split(node->src[0]->buffer->buft)) { use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture #ifndef NDEBUG GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__); @@ -2669,7 +2644,8 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { assert(node->src[j]->buffer); - assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer)); + assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || + ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft)); } } #endif @@ -2762,7 +2738,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); if (stat == cudaErrorGraphExecUpdateFailure) { #ifndef NDEBUG - GGML_LOG_ERROR("%s: CUDA graph update failed\n", __func__); + GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__); #endif // The pre-existing graph exec cannot be updated due to violated constraints // so instead clear error and re-instantiate @@ -2811,7 +2787,6 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev static const ggml_backend_i ggml_backend_cuda_interface = { /* .get_name = */ ggml_backend_cuda_get_name, /* .free = */ ggml_backend_cuda_free, - /* .get_default_buffer_type = */ ggml_backend_cuda_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async, /* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async, /* .cpy_tensor_async = */ ggml_backend_cuda_cpy_tensor_async, @@ -2821,9 +2796,6 @@ static const ggml_backend_i ggml_backend_cuda_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cuda_graph_compute, - /* .supports_op = */ NULL, // moved to device - /* .supports_buft = */ NULL, // moved to device - /* .offload_op = */ NULL, // moved to device /* .event_record = */ ggml_backend_cuda_event_record, /* .event_wait = */ ggml_backend_cuda_event_wait, }; @@ -2913,7 +2885,7 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) { GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; } static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { @@ -2937,7 +2909,7 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back }; } -static ggml_backend_t ggml_backend_cuda_device_init(ggml_backend_dev_t dev, const char * params) { +static ggml_backend_t ggml_backend_cuda_device_init_backend(ggml_backend_dev_t dev, const char * params) { GGML_UNUSED(params); ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context; return ggml_backend_cuda_init(ctx->device); @@ -2953,18 +2925,29 @@ static ggml_backend_buffer_type_t ggml_backend_cuda_device_get_host_buffer_type( return ggml_backend_cuda_host_buffer_type(); } -static ggml_backend_buffer_t ggml_backend_cuda_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - GGML_UNUSED(dev); - GGML_UNUSED(ptr); - GGML_UNUSED(size); - GGML_UNUSED(max_tensor_size); - return nullptr; -} - // TODO: move these functions here static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) dev->context; + // split buffers can only be used with GGML_OP_MUL_MAT + if (op->op != GGML_OP_MUL_MAT) { + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda_split(op->src[i]->buffer->buft)) { + return false; + } + } + } + + // check if all the sources are allocated on this device + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda(op->src[i]->buffer->buft)) { + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)op->src[i]->buffer->buft->context; + if (buft_ctx->device != dev_ctx->device) { + return false; + } + } + } + switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -3190,24 +3173,27 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g } static bool ggml_backend_cuda_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - if (ggml_backend_buft_is_cuda_split(buft)) { - return true; - } + return (ggml_backend_buft_is_cuda(buft) || ggml_backend_buft_is_cuda_split(buft)) && buft->device == dev; +} - if (ggml_backend_buft_is_cuda(buft)) { - ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *)dev->context; - ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - return buft_ctx->device == dev_ctx->device; +static int64_t get_op_batch_size(const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_GET_ROWS: + return 0; + case GGML_OP_MUL_MAT: + return op->ne[1]; + case GGML_OP_MUL_MAT_ID: + case GGML_OP_ROPE: + return op->ne[2]; + default: + return ggml_nrows(op); } - - return false; } static bool ggml_backend_cuda_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { const int min_batch_size = 32; - return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || - (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); + return get_op_batch_size(op) >= min_batch_size; GGML_UNUSED(dev); } @@ -3248,10 +3234,10 @@ static const ggml_backend_device_i ggml_backend_cuda_device_interface = { /* .get_memory = */ ggml_backend_cuda_device_get_memory, /* .get_type = */ ggml_backend_cuda_device_get_type, /* .get_props = */ ggml_backend_cuda_device_get_props, - /* .init_backend = */ ggml_backend_cuda_device_init, + /* .init_backend = */ ggml_backend_cuda_device_init_backend, /* .get_buffer_type = */ ggml_backend_cuda_device_get_buffer_type, /* .get_host_buffer_type = */ ggml_backend_cuda_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ ggml_backend_cuda_device_buffer_from_host_ptr, + /* .buffer_from_host_ptr = */ NULL, /* .supports_op = */ ggml_backend_cuda_device_supports_op, /* .supports_buft = */ ggml_backend_cuda_device_supports_buft, /* .offload_op = */ ggml_backend_cuda_device_offload_op, diff --git a/ggml/src/ggml-kompute.cpp b/ggml/src/ggml-kompute.cpp index 2c926aaee..1f2220234 100644 --- a/ggml/src/ggml-kompute.cpp +++ b/ggml/src/ggml-kompute.cpp @@ -1820,11 +1820,6 @@ static void ggml_backend_kompute_device_unref(ggml_backend_buffer_type_t buft) { } } -static const char * ggml_backend_kompute_buffer_get_name(ggml_backend_buffer_t buffer) { - auto * ctx = static_cast(buffer->buft->context); - return ctx->name.c_str(); -} - static void ggml_backend_kompute_buffer_free_buffer(ggml_backend_buffer_t buffer) { auto * memory = (ggml_vk_memory *)buffer->context; if (ggml_vk_has_device()) { @@ -1868,7 +1863,6 @@ static void ggml_backend_kompute_buffer_clear(ggml_backend_buffer_t buffer, uint } static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = { - /* .get_name = */ ggml_backend_kompute_buffer_get_name, /* .free_buffer = */ ggml_backend_kompute_buffer_free_buffer, /* .get_base = */ ggml_backend_kompute_buffer_get_base, /* .init_tensor = */ NULL, @@ -1953,11 +1947,6 @@ static void ggml_backend_kompute_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(ggml_backend_t backend) { - auto * ctx = static_cast(backend->context); - return ggml_backend_kompute_buffer_type(ctx->device); -} - static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { auto * ctx = static_cast(backend->context); ggml_vk_graph_compute(ctx, cgraph); @@ -1977,7 +1966,6 @@ static bool ggml_backend_kompute_supports_buft(ggml_backend_t backend, ggml_back static struct ggml_backend_i kompute_backend_i = { /* .get_name = */ ggml_backend_kompute_name, /* .free = */ ggml_backend_kompute_free, - /* .get_default_buffer_type = */ ggml_backend_kompute_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -1987,9 +1975,6 @@ static struct ggml_backend_i kompute_backend_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_kompute_graph_compute, - /* .supports_op = */ ggml_backend_kompute_supports_op, - /* .supports_buft = */ ggml_backend_kompute_supports_buft, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m index 80c08f15b..a2b4d49d5 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal.m @@ -3247,12 +3247,6 @@ static enum ggml_status ggml_metal_graph_compute( // backend interface -static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { - return "Metal"; - - UNUSED(buffer); -} - static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; @@ -3307,7 +3301,6 @@ static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_ } static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { - /* .get_name = */ ggml_backend_metal_buffer_get_name, /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, /* .get_base = */ ggml_backend_metal_buffer_get_base, /* .init_tensor = */ NULL, @@ -3432,6 +3425,29 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { return &ggml_backend_buffer_type_metal; } +static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { + return "Metal_Mapped"; + + UNUSED(buft); +} + +static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) { + static struct ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_metal = { + /* .iface = */ { + /* .get_name = */ ggml_backend_metal_buffer_from_ptr_type_get_name, + /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size, + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_metal_buffer_type_is_host, + }, + /* .device = */ &g_ggml_backend_metal_device, + /* .context = */ NULL, + }; + + return &ggml_backend_buffer_from_ptr_type_metal; +} + // TODO: obsoleted by ggml_backend_metal_device_buffer_from_ptr ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context)); @@ -3508,7 +3524,7 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz } } - return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); + return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size); } // backend @@ -3529,12 +3545,6 @@ static void ggml_backend_metal_free(ggml_backend_t backend) { free(backend); } -static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_metal_buffer_type(); - - UNUSED(backend); -} - static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return ggml_metal_graph_compute(backend, cgraph); } @@ -3601,7 +3611,6 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { static struct ggml_backend_i ggml_backend_metal_i = { /* .get_name = */ ggml_backend_metal_name, /* .free = */ ggml_backend_metal_free, - /* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -3611,9 +3620,6 @@ static struct ggml_backend_i ggml_backend_metal_i = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -3708,7 +3714,7 @@ static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t } static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; GGML_UNUSED(dev); } diff --git a/ggml/src/ggml-rpc.cpp b/ggml/src/ggml-rpc.cpp index 0e936b343..2778009e4 100644 --- a/ggml/src/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc.cpp @@ -178,7 +178,6 @@ struct ggml_backend_rpc_buffer_context { std::shared_ptr sock; std::unordered_map base_cache; uint64_t remote_ptr; - std::string name; }; // RPC helper functions @@ -409,11 +408,6 @@ static std::shared_ptr get_socket(const std::string & endpoint) { return sock; } -static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; rpc_msg_free_buffer_req request = {ctx->remote_ptr}; @@ -524,7 +518,6 @@ static void ggml_backend_rpc_buffer_clear(ggml_backend_buffer_t buffer, uint8_t } static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = { - /* .get_name = */ ggml_backend_rpc_buffer_get_name, /* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer, /* .get_base = */ ggml_backend_rpc_buffer_get_base, /* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor, @@ -551,7 +544,7 @@ static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_back if (response.remote_ptr != 0) { ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"}, + new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr}, response.remote_size); return buffer; } else { @@ -609,11 +602,6 @@ static void ggml_backend_rpc_free(ggml_backend_t backend) { delete backend; } -static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str()); -} - static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { UNUSED(backend); // this is no-op because we don't have any async operations @@ -670,7 +658,6 @@ static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, g static ggml_backend_i ggml_backend_rpc_interface = { /* .get_name = */ ggml_backend_rpc_name, /* .free = */ ggml_backend_rpc_free, - /* .get_default_buffer_type = */ ggml_backend_rpc_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, @@ -680,9 +667,6 @@ static ggml_backend_i ggml_backend_rpc_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_rpc_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -1278,7 +1262,7 @@ static void ggml_backend_rpc_device_get_memory(ggml_backend_dev_t dev, size_t * static enum ggml_backend_dev_type ggml_backend_rpc_device_get_type(ggml_backend_dev_t dev) { // TODO: obtain value from the server - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; UNUSED(dev); } diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp index 4d91ee460..a62c67f4f 100644 --- a/ggml/src/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl.cpp @@ -249,13 +249,10 @@ struct ggml_backend_sycl_buffer_context { } }; -static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - return ctx->name.c_str(); -} +static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_type_t buft); static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name; + return buffer->buft->iface.get_name == ggml_backend_sycl_buffer_type_get_name; } static void @@ -440,7 +437,6 @@ catch (sycl::exception const &exc) { } static const ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { - /* .get_name = */ ggml_backend_sycl_buffer_get_name, /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, /* .get_base = */ ggml_backend_sycl_buffer_get_base, /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, @@ -698,16 +694,6 @@ struct ggml_backend_sycl_split_buffer_context { std::vector streams; }; -static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { - return GGML_SYCL_NAME "_Split"; - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; -} - static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; delete ctx; @@ -915,7 +901,6 @@ static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, u } static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { - /* .get_name = */ ggml_backend_sycl_split_buffer_get_name, /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, @@ -935,6 +920,10 @@ static const char * ggml_backend_sycl_split_buffer_type_get_name(ggml_backend_bu GGML_UNUSED(buft); } +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { + return buffer->buft->iface.get_name == ggml_backend_sycl_split_buffer_type_get_name; +} + static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point // instead, we allocate them for each tensor separately in init_tensor @@ -1040,12 +1029,6 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_ GGML_UNUSED(buft); } -static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) { - return GGML_SYCL_NAME "_Host"; - - GGML_UNUSED(buffer); -} - static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_sycl_host_free(buffer->context); } @@ -1061,7 +1044,6 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_sycl_host_buffer_name; buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; return buffer; @@ -4889,12 +4871,6 @@ static void ggml_backend_sycl_free(ggml_backend_t backend) { delete backend; } - -static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - return ggml_backend_sycl_buffer_type(sycl_ctx->device); -} - static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, ggml_tensor *tensor, const void *data, size_t offset, @@ -5031,7 +5007,6 @@ static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_ev static ggml_backend_i ggml_backend_sycl_interface = { /* .get_name = */ ggml_backend_sycl_get_name, /* .free = */ ggml_backend_sycl_free, - /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, /* .cpy_tensor_async = */ NULL, // ggml_backend_sycl_cpy_tensor_async, @@ -5043,9 +5018,6 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, - /* .supports_op = */ NULL, // moved to device - /* .supports_buft = */ NULL, // moved to device - /* .offload_op = */ NULL, // moved to device /* .event_record = */ ggml_backend_sycl_event_record, /* .event_wait = */ ggml_backend_sycl_event_wait, }; @@ -5092,7 +5064,7 @@ static void ggml_backend_sycl_device_get_memory(ggml_backend_dev_t dev, size_t * static enum ggml_backend_dev_type ggml_backend_sycl_device_get_type(ggml_backend_dev_t dev) { GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; } static void ggml_backend_sycl_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { @@ -5388,12 +5360,14 @@ static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t re return ctx->devices[index]; } -static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) -{ +static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) { GGML_UNUSED(reg); - if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { - return (void *)ggml_backend_sycl_split_buffer_type; - } + + // TODO: update to the current function signature + //if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { + // return (void *)ggml_backend_sycl_split_buffer_type; + //} + // SYCL doesn't support registering host memory, left here for reference // "ggml_backend_register_host_buffer" // "ggml_backend_unregister_host_buffer" diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp index 94175a782..83c37ea9c 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan.cpp @@ -6247,13 +6247,8 @@ static void ggml_vk_get_device_description(int device, char * description, size_ // device backend -static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) { - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - return ctx->name.c_str(); -} - static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { - return buffer->iface.get_name == ggml_backend_vk_buffer_get_name; + return buffer->buft->iface.get_name == ggml_backend_vk_buffer_type_name; } static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { @@ -6317,7 +6312,6 @@ static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t v } static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { - /* .get_name = */ ggml_backend_vk_buffer_get_name, /* .free_buffer = */ ggml_backend_vk_buffer_free_buffer, /* .get_base = */ ggml_backend_vk_buffer_get_base, /* .init_tensor = */ ggml_backend_vk_buffer_init_tensor, @@ -6413,7 +6407,6 @@ static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_ ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_vk_host_buffer_name; buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer; return buffer; @@ -6646,7 +6639,6 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg static ggml_backend_i ggml_backend_vk_interface = { /* .get_name = */ ggml_backend_vk_name, /* .free = */ ggml_backend_vk_free, - /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type, /* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async, /* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async, /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, @@ -6656,9 +6648,6 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_vk_graph_compute, - /* .supports_op = */ NULL, - /* .supports_buft = */ NULL, - /* .offload_op = */ NULL, /* .event_record = */ NULL, /* .event_wait = */ NULL, }; @@ -6717,7 +6706,7 @@ void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total ////////////////////////// struct ggml_backend_vk_device_context { - int device; + size_t device; std::string name; std::string description; }; @@ -6749,7 +6738,7 @@ static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(gg static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) { UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU_FULL; + return GGML_BACKEND_DEVICE_TYPE_GPU; } static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { @@ -6758,9 +6747,10 @@ static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml props->type = ggml_backend_vk_device_get_type(dev); ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total); props->caps = { - /* async */ false, - /* host_buffer */ true, - /* events */ false, + /* .async = */ false, + /* .host_buffer = */ true, + /* .buffer_from_host_ptr = */ false, + /* .events = */ false, }; } @@ -6949,7 +6939,7 @@ static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg, static std::mutex mutex; std::lock_guard lock(mutex); if (!initialized) { - for (size_t i = 0; i < ggml_backend_vk_get_device_count(); i++) { + for (int i = 0; i < ggml_backend_vk_get_device_count(); i++) { ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context; char desc[256]; ggml_backend_vk_get_device_description(i, desc, sizeof(desc)); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 66df9a9c1..a8da10d79 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -4028,7 +4028,9 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_LOG_WARN("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size); - assert(false); +#ifndef NDEBUG + GGML_ABORT("not enough space in the context's memory pool"); +#endif return NULL; } diff --git a/include/llama.h b/include/llama.h index 4076d34a7..24005548d 100644 --- a/include/llama.h +++ b/include/llama.h @@ -205,7 +205,7 @@ extern "C" { enum llama_split_mode { LLAMA_SPLIT_MODE_NONE = 0, // single GPU LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs - LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs + LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported }; // TODO: simplify (https://github.com/ggerganov/llama.cpp/pull/9294#pullrequestreview-2286561979) @@ -274,10 +274,7 @@ extern "C" { int32_t n_gpu_layers; // number of layers to store in VRAM enum llama_split_mode split_mode; // how to split the model across multiple GPUs - // main_gpu interpretation depends on split_mode: - // LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model - // LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results - // LLAMA_SPLIT_MODE_LAYER: ignored + // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE int32_t main_gpu; // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index e45e83ce8..4ac6b5fc0 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -20,7 +20,7 @@ logger = logging.getLogger("compare-llama-bench") # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", - "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads", + "blas", "model_filename", "model_type", "n_batch", "n_ubatch", "embeddings", "n_threads", "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" ] diff --git a/src/llama.cpp b/src/llama.cpp index 4cb669bcf..ef1b8ee59 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -8,18 +8,6 @@ #include "ggml-alloc.h" #include "ggml-backend.h" -#if defined(GGML_USE_KOMPUTE) -# include "ggml-kompute.h" -#endif - -#ifndef __AMX_INT8__ -#undef GGML_USE_AMX -#endif - -#ifdef GGML_USE_AMX -# include "ggml-amx.h" -#endif - // TODO: replace with ggml API call #define QK_K 256 @@ -1558,44 +1546,52 @@ static llm_arch llm_arch_from_string(const std::string & name) { // std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias" // std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight" // +struct LLM_TN_IMPL { + const llm_arch arch; + const llm_tensor tensor; + const char * const suffix; + const int bid; + const int xid; + + std::string str() const { + if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { + return "__missing__"; + } + + std::string name = ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid); + + if (suffix != nullptr) { + name += "."; + name += suffix; + } + + return name; + } + + operator std::string() const { + return str(); + } + + friend bool operator==(const std::string & str, const LLM_TN_IMPL & tn) { + return str == tn.str(); + } + + friend bool operator!=(const std::string & str, const LLM_TN_IMPL & tn) { + return str != tn.str(); + } +}; + struct LLM_TN { LLM_TN(llm_arch arch) : arch(arch) {} llm_arch arch; - std::string operator()(llm_tensor tensor) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return LLM_TENSOR_NAMES.at(arch).at(tensor); + LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const { + return { arch, tensor, suffix, bid, xid }; } - std::string operator()(llm_tensor tensor, const char * suffix) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return std::string(LLM_TENSOR_NAMES.at(arch).at(tensor)) + "." + suffix; - } - - std::string operator()(llm_tensor tensor, int bid) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid); - } - - std::string operator()(llm_tensor tensor, const char * suffix, int bid) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid) + "." + suffix; - } - - std::string operator()(llm_tensor tensor, const char * suffix, int bid, int xid) const { - if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) { - return "__missing__"; - } - return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid) + "." + suffix; + LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const { + return { arch, tensor, nullptr, bid, xid }; } }; @@ -2587,6 +2583,11 @@ struct llama_cparams { // TODO: separate into "llama_layer_enc" and "llama_layer_dec" struct llama_layer { + llama_layer() { + // initialize all pointers to NULL + std::memset(this, 0, sizeof(*this)); + } + // normalization struct ggml_tensor * attn_norm; struct ggml_tensor * attn_norm_b; @@ -2667,9 +2668,9 @@ struct llama_layer { struct ggml_tensor * ffn_up_shexp; // ff bias - struct ggml_tensor * ffn_gate_b = nullptr; - struct ggml_tensor * ffn_down_b = nullptr; // b2 - struct ggml_tensor * ffn_up_b = nullptr; // b3 + struct ggml_tensor * ffn_gate_b; + struct ggml_tensor * ffn_down_b; // b2 + struct ggml_tensor * ffn_up_b; // b3 struct ggml_tensor * ffn_act; // mamba proj @@ -2860,22 +2861,21 @@ struct llama_model { llama_hparams hparams = {}; llama_vocab vocab; - // TODO: should init all tensors to nullptr - struct ggml_tensor * tok_embd; - struct ggml_tensor * type_embd; - struct ggml_tensor * pos_embd; - struct ggml_tensor * tok_norm; - struct ggml_tensor * tok_norm_b; + struct ggml_tensor * tok_embd = nullptr; + struct ggml_tensor * type_embd = nullptr; + struct ggml_tensor * pos_embd = nullptr; + struct ggml_tensor * tok_norm = nullptr; + struct ggml_tensor * tok_norm_b = nullptr; - struct ggml_tensor * output_norm; - struct ggml_tensor * output_norm_b; - struct ggml_tensor * output; - struct ggml_tensor * output_b; - struct ggml_tensor * output_norm_enc; + struct ggml_tensor * output_norm = nullptr; + struct ggml_tensor * output_norm_b = nullptr; + struct ggml_tensor * output = nullptr; + struct ggml_tensor * output_b = nullptr; + struct ggml_tensor * output_norm_enc = nullptr; // classifier - struct ggml_tensor * cls; - struct ggml_tensor * cls_b; + struct ggml_tensor * cls = nullptr; + struct ggml_tensor * cls_b = nullptr; struct ggml_tensor * cls_out = nullptr; struct ggml_tensor * cls_out_b = nullptr; @@ -2888,24 +2888,24 @@ struct llama_model { int main_gpu; int n_gpu_layers; + std::vector rpc_servers; + // list of devices used in this model std::vector devices; - std::vector rpc_servers; - // layer -> buffer type mapping - struct layer_buft { - layer_buft() : buft_matrix(nullptr), buft(nullptr) {} - layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {} - layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {} + // lists of buffer types used for each layer + using buft_list_t = std::vector>; + buft_list_t cpu_buft_list; + std::map gpu_buft_list; - ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication - ggml_backend_buffer_type_t buft; // everything else + struct layer_dev { + ggml_backend_dev_t dev; + buft_list_t * buft_list; }; - - layer_buft buft_input; - layer_buft buft_output; - std::vector buft_layer; + layer_dev dev_input = {}; + layer_dev dev_output = {}; + std::vector dev_layer; // contexts where the model tensors metadata is stored std::vector ctxs; @@ -3391,104 +3391,47 @@ struct llama_lora_adapter { }; static int llama_get_device_count(const llama_model & model) { - int count = (int) model.devices.size(); - -#if defined(GGML_USE_RPC) - count += (int) model.rpc_servers.size(); -#endif - - return count; - - GGML_UNUSED(model); + return (int) model.devices.size(); } -static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_model & model, bool host_buffer) { - ggml_backend_buffer_type_t buft = nullptr; +template +static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) { + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead()*8, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + throw std::runtime_error(format("failed to create ggml context")); + } - if (host_buffer) { - for (auto * dev : model.devices) { - buft = ggml_backend_dev_host_buffer_type(dev); - if (buft != nullptr) { - break; - } + ggml_backend_buffer_t buf = ggml_backend_buft_alloc_buffer(buft, 0); + ggml_tensor * op_tensor = fn(ctx); + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op_tensor->src[i] != nullptr) { + assert(op_tensor->src[i]->buffer == nullptr); + op_tensor->src[i]->buffer = buf; } } + bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor); -#if defined(GGML_USE_CPU_HBM) - buft = ggml_backend_cpu_hbm_buffer_type(); -#endif + ggml_free(ctx); + ggml_backend_buffer_free(buf); - if (buft == nullptr) { - buft = ggml_backend_cpu_buffer_type(); - } - return buft; - - GGML_UNUSED(host_buffer); + return op_supported; } -static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int device) { - ggml_backend_buffer_type_t buft = nullptr; - - if (device < (int)model.devices.size()) { - return ggml_backend_dev_buffer_type(model.devices[device]); - } - device -= (int)model.devices.size(); - -#if defined(GGML_USE_KOMPUTE) - buft = ggml_backend_kompute_buffer_type(device); -#endif - - if (buft == nullptr) { - buft = llama_default_buffer_type_cpu(model, true); - } - return buft; - - GGML_UNUSED(model); -} - -static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) { - ggml_backend_buffer_type_t buft = nullptr; - - // find a backend that supports split buffers - for (size_t i = 0; i < ggml_backend_reg_count(); ++i) { - ggml_backend_reg_t reg = ggml_backend_reg_get(i); - - auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type"); - if (ggml_backend_split_buffer_type_fn) { - buft = ggml_backend_split_buffer_type_fn(tensor_split); - if (buft != nullptr) { - break; - } +template +static ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) { + for (const auto & cur : buft_list) { + ggml_backend_dev_t cur_dev = cur.first; + ggml_backend_buffer_type_t cur_buft = cur.second; + if (buft_supported(cur_buft, cur_dev, fn)) { + return cur_buft; } } - - if (buft == nullptr) { - buft = llama_default_buffer_type_offload(model, fallback_gpu); - } - return buft; - - GGML_UNUSED(tensor_split); -} - -static size_t llama_get_device_memory(const llama_model & model, int device) { - if (device < (int)model.devices.size()) { - ggml_backend_dev_t dev = model.devices[device]; - size_t total; - size_t free; - ggml_backend_dev_memory(dev, &free, &total); - return free; - } - - if (model.devices.size() > 0) { - ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(model.devices[0]); - LLAMA_LOG_WARN("%s: failed to get free memmory of device:%d of backend:%s, for device id is out of range.\n", __func__, device, ggml_backend_reg_name(reg)); - } else { - LLAMA_LOG_WARN("%s: failed to get free memmory of device, no devices in inputted model.\n", __func__); - } - return 1; - - GGML_UNUSED(model); - GGML_UNUSED(device); + throw std::runtime_error(format("no suitable buffer type found")); } // @@ -3524,33 +3467,24 @@ static bool llama_kv_cache_init( cache.cells.clear(); cache.cells.resize(kv_size); - // count used buffer types - std::map buft_layer_count; - if (offload) { - for (int64_t i = 0; i < n_layer; ++i) { - buft_layer_count[model.buft_layer[i].buft]++; - } - } else { - buft_layer_count[llama_default_buffer_type_cpu(model, true)] = n_layer; - } - // create a context for each buffer type std::map ctx_map; - for (auto & it : buft_layer_count) { - int n_layers = it.second; - struct ggml_init_params params = { - /*.mem_size =*/ 2u*n_layers*ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - ggml_context * ctx = ggml_init(params); - if (!ctx) { - LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__); - return false; + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + if (ctx_map.count(buft) == 0) { + struct ggml_init_params params = { + /*.mem_size =*/ size_t(2u*n_layer*ggml_tensor_overhead()), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + return nullptr; + } + ctx_map[buft] = ctx; + cache.ctxs.push_back(ctx); } - ctx_map[it.first] = ctx; - cache.ctxs.push_back(ctx); - } + return ctx_map.at(buft); + }; cache.k_l.reserve(n_layer); cache.v_l.reserve(n_layer); @@ -3559,7 +3493,28 @@ static bool llama_kv_cache_init( const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(); - struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); + const llama_model::buft_list_t * buft_list; + if (offload) { + buft_list = model.dev_layer.at(i).buft_list; + } else { + buft_list = &model.cpu_buft_list; + } + ggml_backend_buffer_type_t buft = select_buft(*buft_list, + [&](ggml_context * ctx) { + ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); + if (hparams.rope_type == LLAMA_ROPE_TYPE_NONE) { + return k; + } + ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); + return ggml_rope(ctx, k, p, hparams.n_rot, hparams.rope_type); + }); + ggml_context * ctx = ctx_for_buft(buft); + + if (!ctx) { + LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__); + return false; + } + ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size); ggml_format_name(k, "cache_k_l%d", i); @@ -3570,8 +3525,9 @@ static bool llama_kv_cache_init( // allocate tensors and initialize the buffers to avoid NaNs in the padding for (auto it : ctx_map) { - ggml_backend_buffer_type_t buft = it.first; - ggml_context * ctx = it.second; + auto * buft = it.first; + auto * ctx = it.second; + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); if (!buf) { LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__); @@ -4765,10 +4721,10 @@ struct llama_model_loader { return weight->tensor; } - struct ggml_tensor * require_tensor_meta(const char * name) const { - struct ggml_tensor * tensor = get_tensor_meta(name); + struct ggml_tensor * require_tensor_meta(const std::string & name) const { + struct ggml_tensor * tensor = get_tensor_meta(name.c_str()); if (!tensor) { - throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name)); + throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } return tensor; } @@ -4777,19 +4733,6 @@ struct llama_model_loader { return get_tensor_meta(get_tensor_name(i)); } - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) { - struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); - ggml_set_name(tensor, ggml_get_name(cur)); - - if (duplicated) { - size_data += ggml_nbytes(cur); - } else { - n_created++; - } - - return tensor; - } - const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector & ne, bool required) const { const struct ggml_tensor * cur = get_tensor_meta(name.c_str()); @@ -4830,7 +4773,19 @@ struct llama_model_loader { return NULL; } - return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED); + bool duplicated = flags & TENSOR_DUPLICATED; + + struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); + ggml_set_name(tensor, ggml_get_name(cur)); + + if (duplicated) { + size_data += ggml_nbytes(cur); + } else { + n_created++; + } + + return tensor; + } struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list & ne, size_t offset, bool required = true) { @@ -4962,7 +4917,7 @@ struct llama_model_loader { std::vector events; std::vector host_ptrs; size_t buffer_idx = 0; // buffer to use for async loads - ggml_backend_t upload_backend = [&](const char * fn) -> ggml_backend_t { + ggml_backend_t upload_backend = [&](const char * func) -> ggml_backend_t { if (use_mmap || check_tensors) { return nullptr; } @@ -4970,20 +4925,20 @@ struct llama_model_loader { // First determine if the backend supports the necessary features for async uploads. auto * buf = bufs.count(0) ? bufs.at(0) : nullptr; if (!buf) { - LLAMA_LOG_DEBUG("%s: no buffer found for async uploads\n", fn); + LLAMA_LOG_DEBUG("%s: no buffer found for async uploads\n", func); return nullptr; } auto * buft = ggml_backend_buffer_get_type(buf); auto * dev = ggml_backend_buft_get_device(buft); if (!dev) { - LLAMA_LOG_DEBUG("%s: no device found for buffer type %s for async uploads\n", fn, + LLAMA_LOG_DEBUG("%s: no device found for buffer type %s for async uploads\n", func, ggml_backend_buft_name(buft)); return nullptr; } if (buft != ggml_backend_dev_buffer_type(dev)) { - LLAMA_LOG_DEBUG("%s: buffer type %s is not the default buffer type for device %s for async uploads\n", fn, + LLAMA_LOG_DEBUG("%s: buffer type %s is not the default buffer type for device %s for async uploads\n", func, ggml_backend_buft_name(buft), ggml_backend_dev_name(dev)); return nullptr; } @@ -4991,14 +4946,14 @@ struct llama_model_loader { ggml_backend_dev_props props; ggml_backend_dev_get_props(dev, &props); if (!props.caps.async || !props.caps.host_buffer || !props.caps.events) { - LLAMA_LOG_DEBUG("%s: device %s does not support async, host buffers or events\n", fn, + LLAMA_LOG_DEBUG("%s: device %s does not support async, host buffers or events\n", func, ggml_backend_dev_name(dev)); return nullptr; } auto * host_buft = ggml_backend_dev_host_buffer_type(dev); if (!host_buft) { - LLAMA_LOG_DEBUG("%s: no host buffer type found for device %s\n", fn, + LLAMA_LOG_DEBUG("%s: no host buffer type found for device %s\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -5007,7 +4962,7 @@ struct llama_model_loader { for (size_t idx = 0; idx < n_buffers; ++idx) { auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size); if (!buf) { - LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", fn, + LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -5017,7 +4972,7 @@ struct llama_model_loader { auto * event = ggml_backend_event_new(dev); if (!event) { - LLAMA_LOG_DEBUG("%s: failed to create event for async uploads for device %s\n", fn, + LLAMA_LOG_DEBUG("%s: failed to create event for async uploads for device %s\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -5027,7 +4982,7 @@ struct llama_model_loader { ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); if (!backend) { - LLAMA_LOG_DEBUG("%s: failed to initialize backend for device %s for async uploads\n", fn, + LLAMA_LOG_DEBUG("%s: failed to initialize backend for device %s for async uploads\n", func, ggml_backend_dev_name(dev)); return nullptr; } @@ -7000,6 +6955,338 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { } } +enum llm_tensor_layer { + LLM_TENSOR_LAYER_INPUT, + LLM_TENSOR_LAYER_REPEATING, + LLM_TENSOR_LAYER_OUTPUT, +}; + +struct llm_tensor_info { + llm_tensor_layer layer; + ggml_op op; +}; + +static const std::map llm_tensor_info_mapping = { + {LLM_TENSOR_TOKEN_EMBD, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_POS_EMBD, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_TOKEN_EMBD_NORM, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_TOKEN_TYPES, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_OUTPUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CLS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CLS_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_ENC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_ROPE_FREQS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}}, + {LLM_TENSOR_ROPE_FACTORS_LONG, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}}, + {LLM_TENSOR_ROPE_FACTORS_SHORT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}}, + {LLM_TENSOR_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_QKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_A_MQA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_QKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_DOWN_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_UP_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_A_MQA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ATTN_KV_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_CROSS_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DEC_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_ENC_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_INP_SHEXP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_GATE_INP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_IN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_DT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_SSM_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_W1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_W2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_DECAY_W1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_DECAY_W2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_KEY, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_VALUE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_RECEPTANCE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_TIME_MIX_OUTPUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CHANNEL_MIX_KEY, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CHANNEL_MIX_VALUE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_FFN_ACT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_DIV}}, + {LLM_TENSOR_SSM_CONV1D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}}, + {LLM_TENSOR_SSM_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_SCAN}}, + {LLM_TENSOR_SSM_D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_TIME_MIX_LERP_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_TIME_MIX_LN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_CHANNEL_MIX_LERP_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_CHANNEL_MIX_LERP_R, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_TIME_MIX_LERP_W, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_R, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_LERP_G, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_DECAY, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_TIME_MIX_FIRST, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_RWKV_WKV}}, + {LLM_TENSOR_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_NORM_2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_OUT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_NORM_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_K_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_LAYER_OUT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_Q_A_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_KV_A_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_SUB_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_SUB_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_CROSS_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_FFN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ENC_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ENC_FFN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_DEC_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_ENC_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_FFN_DOWN_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + {LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + {LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + // this tensor is loaded for T5, but never used + {LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}}, +}; + +// checks if the weight tensor can be used with the specified buffer type and device +static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) { + GGML_ASSERT(w != nullptr); + + if (op == GGML_OP_NONE) { + return true; + } + + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead()*8, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + throw std::runtime_error(format("failed to create ggml context")); + } + + ggml_tensor * op_tensor = nullptr; + + switch (op) { + case GGML_OP_GET_ROWS: + { + ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); + op_tensor = ggml_get_rows(ctx, w, b); + } break; + case GGML_OP_MUL_MAT: + { + ggml_tensor * b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512); + op_tensor = ggml_mul_mat(ctx, w, b); + } break; + case GGML_OP_MUL_MAT_ID: + { + int n_expert_used = hparams.n_expert_used; + ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512); + ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512); + op_tensor = ggml_mul_mat_id(ctx, w, b, ids); + } break; + case GGML_OP_ADD: + { + ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512); + op_tensor = ggml_add(ctx, a, w); + } break; + case GGML_OP_MUL: + { + ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512); + op_tensor = ggml_mul(ctx, a, w); + } break; + case GGML_OP_DIV: + { + ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]); + op_tensor = ggml_div(ctx, a, w); + } break; + case GGML_OP_ROPE: + { + int n_embd_head = hparams.n_embd_head_v; + int n_head = hparams.n_head(); + ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512); + ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); + op_tensor = ggml_rope_ext( + ctx, a, b, w, + 0, 0, 0, 0, 0, + 0, 0, 0, 0 + ); + + } break; + case GGML_OP_SSM_CONV: + { + // TODO: ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d); + op_tensor = ggml_ssm_conv(ctx, nullptr, w); + } break; + case GGML_OP_SSM_SCAN: + { + // TODO: ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C); + op_tensor = ggml_ssm_scan(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr); + } break; + case GGML_OP_RWKV_WKV: + { + // TODO: ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state); + op_tensor = ggml_rwkv_wkv(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr); + } break; + default: + GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); + } + + // create a temporary dummy buffer for the weight so that supports_op can check the buffer type + GGML_ASSERT(w->buffer == nullptr); + w->buffer = ggml_backend_buft_alloc_buffer(buft, 0); + bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor); + ggml_backend_buffer_free(w->buffer); + w->buffer = nullptr; + + ggml_free(ctx); + + return op_supported; +} + +// find the first buffer type in the list that can use the tensor +static ggml_backend_buffer_type_t select_weight_buft(const llama_model & model, ggml_tensor * tensor, ggml_op op, const llama_model::buft_list_t & buft_list) { + GGML_ASSERT(!buft_list.empty()); + for (const auto & cur : buft_list) { + ggml_backend_dev_t cur_dev = cur.first; + ggml_backend_buffer_type_t cur_buft = cur.second; + if (weight_buft_supported(model.hparams, tensor, op, cur_buft, cur_dev)) { + return cur_buft; + } + } + return nullptr; +} + +// CPU: ACCEL -> CPU extra -> GPU host -> CPU +static llama_model::buft_list_t make_cpu_buft_list(llama_model & model) { + llama_model::buft_list_t buft_list; + + // add ACCEL buffer types + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) { + auto * buft = ggml_backend_dev_buffer_type(dev); + // skip + if (buft != ggml_backend_cpu_buffer_type()) { + buft_list.emplace_back(dev, buft); + } + } + } + + // add extra buffer types + auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev); + auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t) + ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_cpu_get_extra_bufts"); + if (ggml_backend_dev_get_extra_bufts_fn) { + ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev); + while (extra_bufts && *extra_bufts) { + buft_list.emplace_back(cpu_dev, *extra_bufts); + ++extra_bufts; + } + } + + // add a host buffer type + // storing the tensors in a host buffer is useful when the processing of large batches + // is offloaded to a GPU device, since it reduces the time spent on data transfers + // generally, this will be done using the first device in the list + // a better approach would be to handle this on a weight-by-weight basis using the offload_op + // function of the device to determine if it would benefit from being stored in a host buffer + for (auto * dev : model.devices) { + ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev); + if (buft) { + buft_list.emplace_back(dev, buft); + break; + } + } + + // add the CPU buffer type + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) { + buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev)); + } + } + + return buft_list; +} + +// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU +static llama_model::buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, enum llama_split_mode split_mode, const float * tensor_split) { + llama_model::buft_list_t buft_list; + + // add the device split buffer type if requested and available + if (split_mode == LLAMA_SPLIT_MODE_ROW) { + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev); + auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t) + ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type"); + if (ggml_backend_split_buffer_type_fn) { + size_t dev_index = [&]() { + auto * reg = ggml_backend_dev_backend_reg(dev); + for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) { + if (ggml_backend_reg_dev_get(reg, i) == dev) { + return i; + } + } + throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev))); + }(); + auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split); + if (buft != nullptr) { + buft_list.emplace_back(dev, buft); + } + } + } + + // add the device default buffer type + buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev)); + + return buft_list; +} + // Returns false if cancelled by progress_callback static bool llm_load_tensors( llama_model_loader & ml, @@ -7013,135 +7300,96 @@ static bool llm_load_tensors( void * progress_callback_user_data) { auto & hparams = model.hparams; - // check if the value of main_gpu is valid - if (llama_get_device_count(model) > 0 && - split_mode != LLAMA_SPLIT_MODE_LAYER && - (main_gpu < 0 || main_gpu >= llama_get_device_count(model))) { - throw std::runtime_error(format("invalid value for main_gpu: %d (available devices: %d)", main_gpu, llama_get_device_count(model))); - } - model.split_mode = split_mode; model.main_gpu = main_gpu; model.n_gpu_layers = n_gpu_layers; const int n_layer = hparams.n_layer; - const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0); bool use_mmap_buffer = true; - // there is very little benefit to offloading the input layer, so always keep it on the CPU - model.buft_input = llama_default_buffer_type_cpu(model, true); - //model.buft_input = llama_default_buffer_type_offload(main_gpu); - - model.buft_layer.resize(n_layer); - - // assign cpu layers - for (int i = 0; i < i_gpu_start; ++i) { -#ifdef GGML_USE_AMX - model.buft_layer[i] = { - ggml_backend_amx_buffer_type(), - llama_default_buffer_type_cpu(model, true) - }; -#else - model.buft_layer[i] = llama_default_buffer_type_cpu(model, true); -#endif + // build a list of buffer types for the CPU and GPU devices + model.cpu_buft_list = make_cpu_buft_list(model); + for (auto * dev : model.devices) { + llama_model::buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split); + // add CPU buffer types as a fallback + buft_list.insert(buft_list.end(), model.cpu_buft_list.begin(), model.cpu_buft_list.end()); + model.gpu_buft_list.emplace(dev, std::move(buft_list)); } - if (split_mode == LLAMA_SPLIT_MODE_LAYER) { - // calculate the split points - int device_count = llama_get_device_count(model); - bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); - std::vector splits(device_count); - if (all_zero) { - // default split, by free memory - for (int i = 0; i < device_count; ++i) { - splits[i] = llama_get_device_memory(model, i); - } - } else { - std::copy(tensor_split, tensor_split + device_count, splits.begin()); - } - - // sum and normalize the splits to get the split points - float split_sum = 0.0f; + // calculate the split points + int device_count = llama_get_device_count(model); + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); + std::vector splits(device_count); + if (all_zero) { + // default split, by free memory for (int i = 0; i < device_count; ++i) { - split_sum += splits[i]; - splits[i] = split_sum; - } - for (int i = 0; i < device_count; ++i) { - splits[i] /= split_sum; - } - - // assign the repeating layers to the devices according to the splits - int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); - for (int i = i_gpu_start; i < n_layer; ++i) { - int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); - model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu); - } - // assign the output layer - if (n_gpu_layers > n_layer) { - int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin(); - model.buft_output = llama_default_buffer_type_offload(model, layer_gpu); - } else { - model.buft_output = llama_default_buffer_type_cpu(model, true); + ggml_backend_dev_t dev = model.devices[i]; + size_t total; + size_t free; + ggml_backend_dev_memory(dev, &free, &total); + splits[i] = free; } } else { - ggml_backend_buffer_type_t split_buft; - if (split_mode == LLAMA_SPLIT_MODE_ROW) { - split_buft = llama_default_buffer_type_split(model, main_gpu, tensor_split); - } else { - // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported - split_buft = llama_default_buffer_type_offload(model, main_gpu); - } - // assign the repeating layers - for (int i = i_gpu_start; i < n_layer; ++i) { - model.buft_layer[i] = { - split_buft, - llama_default_buffer_type_offload(model, main_gpu) - }; - } - // assign the output layer - if (n_gpu_layers > n_layer) { - model.buft_output = { - split_buft, - llama_default_buffer_type_offload(model, main_gpu) - }; - } else { - model.buft_output = llama_default_buffer_type_cpu(model, true); - } + std::copy(tensor_split, tensor_split + device_count, splits.begin()); } - // count used buffer types - std::map buft_layer_count; - buft_layer_count[model.buft_input.buft]++; - buft_layer_count[model.buft_input.buft_matrix]++; - buft_layer_count[model.buft_output.buft]++; - buft_layer_count[model.buft_output.buft_matrix]++; - for (int i = 0; i < n_layer; ++i) { - buft_layer_count[model.buft_layer[i].buft]++; - buft_layer_count[model.buft_layer[i].buft_matrix]++; + // sum and normalize the splits to get the split points + float split_sum = 0.0f; + for (int i = 0; i < device_count; ++i) { + split_sum += splits[i]; + splits[i] = split_sum; + } + for (int i = 0; i < device_count; ++i) { + splits[i] /= split_sum; } - // create one context per buffer type - size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output + ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0); + const int act_gpu_layers = model.devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1); + auto get_layer_buft_list = [&](int il) -> llama_model::layer_dev { + if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) { + return {cpu_dev, &model.cpu_buft_list}; + } + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(il - i_gpu_start)/act_gpu_layers) - splits.begin(); + auto * dev = model.devices.at(layer_gpu); + return {dev, &model.gpu_buft_list.at(dev)}; + }; - // for moe merged tensors - ctx_size += ggml_tensor_overhead()*n_layer*3; + // assign the input layer + // there is very little benefit to offloading the input layer, so always keep it on the CPU + model.dev_input = { cpu_dev, &model.cpu_buft_list }; + + // assign the repeating layers to the devices according to the splits + model.dev_layer.resize(n_layer); + for (int il = 0; il < n_layer; ++il) { + model.dev_layer[il] = get_layer_buft_list(il); + } + // assign the output layer + model.dev_output = get_layer_buft_list(n_layer); + + // one ggml context per buffer type + int max_n_tensors = ml.n_tensors; + max_n_tensors += 1; // duplicated output tensor + max_n_tensors += n_layer*2; // duplicated rope freq tensors + const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors; std::map ctx_map; - for (auto & it : buft_layer_count) { - struct ggml_init_params params = { - /*.mem_size =*/ ctx_size, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - ggml_context * ctx = ggml_init(params); - if (!ctx) { - throw std::runtime_error(format("failed to create context")); + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + if (ctx_map.count(buft) == 0) { + ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + throw std::runtime_error(format("failed to create ggml context")); + } + ctx_map[buft] = ctx; + model.ctxs.push_back(ctx); } - ctx_map[it.first] = ctx; - model.ctxs.push_back(ctx); - } - - LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0); + return ctx_map.at(buft); + }; // create tensors for the weights { @@ -7166,15 +7414,107 @@ static bool llm_load_tensors( throw std::runtime_error("model has expert layers but no expert layers are used"); } - ggml_context * ctx_input = ctx_map.at(model.buft_input.buft); - ggml_context * ctx_output = ctx_map.at(model.buft_output.buft); - ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); + int n_moved_tensors = 0; + ggml_tensor * first_moved_tensor = nullptr; + ggml_backend_buffer_type_t first_moved_from_buft = nullptr; + ggml_backend_buffer_type_t first_moved_to_buft = nullptr; - auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); }; - auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); }; + auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list & ne, int flags) -> ggml_tensor * { + ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str()); + + if (!t_meta) { + if (flags & llama_model_loader::TENSOR_NOT_REQUIRED) { + return nullptr; + } + throw std::runtime_error(format("missing tensor %s", tn.str().c_str())); + } + + // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops + // the tensor is duplicated + // to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor + llm_tensor tn_tensor = tn.tensor; + if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & llama_model_loader::TENSOR_DUPLICATED) { + tn_tensor = LLM_TENSOR_OUTPUT; + } + + auto it = llm_tensor_info_mapping.find(tn_tensor); + if (it == llm_tensor_info_mapping.end()) { + throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str())); + } + const auto & info = it->second; + + // tensors with "bias" suffix are always used with GGML_OP_ADD + ggml_op op; + bool bias = strcmp(tn.suffix, "bias") == 0; + if (bias) { + op = GGML_OP_ADD; + } else { + op = info.op; + } + + // sanity checks + if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) { + if (tn.bid != -1) { + GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str()); + } + } else { + if (tn.bid == -1) { + GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str()); + } + } + + // select the buffer type for this tensor + llama_model::buft_list_t * buft_list; + switch (info.layer) { + case LLM_TENSOR_LAYER_INPUT: + buft_list = model.dev_input.buft_list; + break; + case LLM_TENSOR_LAYER_OUTPUT: + buft_list = model.dev_output.buft_list; + break; + case LLM_TENSOR_LAYER_REPEATING: + buft_list = model.dev_layer.at(tn.bid).buft_list; + break; + default: + GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str()); + } + + ggml_backend_buffer_type_t buft = select_weight_buft(model, t_meta, op, *buft_list); + if (!buft) { + throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str())); + } + + // avoid using a host buffer when using mmap + auto * buft_dev = ggml_backend_buft_get_device(buft); + if (ml.use_mmap && buft == ggml_backend_dev_host_buffer_type(buft_dev)) { + auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + buft = ggml_backend_dev_buffer_type(cpu_dev); + } + + if (buft != buft_list->front().second) { + n_moved_tensors++; + if (!first_moved_tensor) { + first_moved_tensor = t_meta; + first_moved_from_buft = buft_list->front().second; + first_moved_to_buft = buft; + } + } + + ggml_context * ctx = ctx_for_buft(buft); + + // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one + if (flags & llama_model_loader::TENSOR_DUPLICATED) { + ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str()); + if (t) { + return t; + } + } + return ml.create_tensor(ctx, tn, ne, flags); + }; model.layers.resize(n_layer); + // TODO: move to a separate function const auto tn = LLM_TN(model.arch); switch (model.arch) { case LLM_ARCH_LLAMA: @@ -7183,82 +7523,51 @@ static bool llm_load_tensors( case LLM_ARCH_GRANITE: case LLM_ARCH_GRANITE_MOE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); if (n_expert == 0) { - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); // optional MLP bias - layer.ffn_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } else { - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); - if (layer.ffn_gate_exps) { - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - } else { - // merge split expert into a single tensor for compatibility with older models - // requires disabling mmap - use_mmap_buffer = false; - - ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type; - ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type; - ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type; - - layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert); - layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert); - layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert); - - ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str()); - - for (uint32_t x = 0; x < n_expert; ++x) { - // the individual experts are loaded into a view of the merged tensor - ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x); - } - } + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); } } } break; @@ -7269,45 +7578,40 @@ static bool llm_load_tensors( const int64_t q_lora_rank = hparams.n_lora_q; const int64_t kv_lora_rank = hparams.n_lora_kv; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); - layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}); + layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); - layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); - layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}); + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0); - layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}); - layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}); + layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0); + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); - layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); - layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } break; case LLM_ARCH_GROK: @@ -7316,904 +7620,782 @@ static bool llm_load_tensors( throw std::runtime_error("Grok model cannot have zero experts"); } - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); - if (layer.ffn_gate_exps) { - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - } else { - // merge split expert into a single tensor for compatibility with older models - // requires disabling mmap - use_mmap_buffer = false; - - ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type; - ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type; - ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type; - - layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert); - layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert); - layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert); - - ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str()); - ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str()); - - for (uint32_t x = 0; x < n_expert; ++x) { - // the individual experts are loaded into a view of the merged tensor - ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x); - ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x); - } - } - - layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); + layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0); } } break; case LLM_ARCH_DBRX: - { - if (n_expert == 0) { - throw std::runtime_error("DBRX model cannot have zero experts"); - } - - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - - // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + if (n_expert == 0) { + throw std::runtime_error("DBRX model cannot have zero experts"); + } - for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); - auto & layer = model.layers[i]; + // output + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + for (int i = 0; i < n_layer; ++i) { + auto & layer = model.layers[i]; - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - } - } break; + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + } + } break; case LLM_ARCH_BAICHUAN: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_FALCON: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_STARCODER: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0); // output { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { // needs to be on GPU - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0); if (model.arch == LLM_ARCH_BERT) { - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0); - model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_out = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_out_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); } - model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); - model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; if (model.arch == LLM_ARCH_BERT) { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); } else { - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); } - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); - layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); + layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); if (model.arch == LLM_ARCH_BERT) { - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); } else { - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); } - layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); - layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); + layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0); + layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0); } } break; case LLM_ARCH_JINA_BERT_V2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings - model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); // token_type_embeddings + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings + model.type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0); // token_type_embeddings - model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm - model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); // LayerNorm + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); //LayerNorm bias - model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; // JinaBertLayer - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); //output_dens - layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm - layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); + layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm + layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); - layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); + layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0); + layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0); } } break; case LLM_ARCH_BLOOM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); - model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_MPT: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + if (!model.output) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); // AWQ ScaleActivation layer - layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_STABLELM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors, present in Stable LM 2 1.6B - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional q and k layernorms, present in StableLM 2 12B - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional FFN norm, not present in StableLM 2 12B which uses parallel residual - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_QWEN: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2}, 0); } } break; case LLM_ARCH_QWEN2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_QWEN2MOE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); - GGML_ASSERT(n_expert > 0); - GGML_ASSERT(n_expert_used > 0); + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0 for QWEN2MOE"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE"); + } // MoE branch const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used; - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); // Shared expert branch const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff; - layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}); - layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}); - layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}); - layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}); + layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0); } } break; case LLM_ARCH_PHI2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - model.output_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.wqkv == nullptr) { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); } - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_PHI3: { const int64_t n_embd_head = n_embd / n_head; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0); - layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); - layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } break; case LLM_ARCH_PLAMO: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_GPT2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_CODESHELL: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_ORION: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } - for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_INTERNLM2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_GEMMA: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); } } break; case LLM_ARCH_GEMMA2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); - layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); } } break; case LLM_ARCH_STARCODER2: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); // optional bias tensors - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}, 0); } } break; case LLM_ARCH_MAMBA: @@ -8224,284 +8406,252 @@ static bool llm_load_tensors( const int64_t dt_rank = hparams.ssm_dt_rank; // only an expansion factor of 2 is supported for now - GGML_ASSERT(2 * n_embd == d_inner); + if (2 * n_embd != d_inner) { + throw std::runtime_error("only an expansion factor of 2 is supported for now"); + } - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed, duplicated to allow offloading - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed, duplicated to allow offloading + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; // norm - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}); + layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0); - layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}); - layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}); + layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0); + layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0); - layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}); + layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0); - layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}); - layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}); + layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0); + layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0); // no "weight" suffix for these - layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}); - layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner}); + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0); + layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0); // out_proj - layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}); + layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0); } } break; case LLM_ARCH_XVERSE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_COMMAND_R: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - // init output from the input tok embed - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + // init output from the input tok embed + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); if (n_layer >= 64){ - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0); } - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_OLMOE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); - GGML_ASSERT(n_expert > 0); - GGML_ASSERT(n_expert_used > 0); + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0"); + } // MoE branch - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); } } break; case LLM_ARCH_OPENELM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - // init output from the input tok embed - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + // init output from the input tok embed + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); for (int i = 0; i < n_layer; ++i) { const int64_t n_head = hparams.n_head(i); const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head; const int64_t n_ff = hparams.n_ff(i); - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_GPTNEOX: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_ARCTIC: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); } } break; case LLM_ARCH_DEEPSEEK2: @@ -8517,349 +8667,313 @@ static bool llm_load_tensors( const int64_t n_ff_exp = hparams.n_ff_exp; const int64_t n_expert_shared = hparams.n_expert_shared; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); if (!is_lite) { - layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}); + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); } - layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}); + layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); if (!is_lite) { - layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); - layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}); + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0); } else { - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); } - layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}); - layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}); + layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0); + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); if (i < (int) hparams.n_layer_dense_lead) { - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } else { - layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); - GGML_ASSERT(n_expert > 0); - GGML_ASSERT(n_expert_used > 0); + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0"); + } // MoE branch - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); - layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); - layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); // Shared expert branch - layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}); - layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}); - layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); } } } break; case LLM_ARCH_BITNET: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wq_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wk_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wv_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.wo_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wq_scale = create_tensor(tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wk_scale = create_tensor(tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv_scale = create_tensor(tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.wo_scale = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_gate_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_gate_scale = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_scale = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_scale = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_T5: { const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}, 0); - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.attn_norm_cross = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}, 0); // this tensor seems to be unused in HF transformers implementation - layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_rel_b_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_T5ENCODER: { const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts; - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0); - layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_JAIS: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); - // Output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + // output + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0); } } break; case LLM_ARCH_CHATGLM: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); } } break; case LLM_ARCH_NEMOTRON: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); // optional MLP bias - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_EXAONE: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; case LLM_ARCH_RWKV6: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // Block 0, LN0 - model.tok_norm = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); - model.tok_norm_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); // output - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); const int time_mix_extra_dim = hparams.time_mix_extra_dim; const int time_decay_extra_dim = hparams.time_decay_extra_dim; @@ -8868,90 +8982,88 @@ static bool llm_load_tensors( const int ffn_size = hparams.n_ff_arr[0]; for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}); + layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0); + layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, 0); - layer.time_mix_w1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}); - layer.time_mix_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}); + layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0); + layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0); - layer.time_mix_lerp_x = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_w = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_v = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}); - layer.time_mix_lerp_g = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}); + layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0); + layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, 0); - layer.time_mix_first = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}); - layer.time_mix_decay = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}); - layer.time_mix_decay_w1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}); - layer.time_mix_decay_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}); - layer.time_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}); - layer.time_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}); - layer.time_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}); - layer.time_mix_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}); + layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0); + layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0); + layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0); + layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0); + layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0); + layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0); + layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0); + layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0); - layer.time_mix_ln = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}); - layer.time_mix_ln_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}); - layer.time_mix_output = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}); + layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0); + layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0); + layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0); - layer.channel_mix_lerp_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}); - layer.channel_mix_lerp_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}); + layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0); + layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0); - layer.channel_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}); - layer.channel_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}); - layer.channel_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}); + layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0); + layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0); + layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0); } } break; case LLM_ARCH_CHAMELEON: { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); - - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); - } + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - auto & layer = model.layers[i]; - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0); + layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); - layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); - layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; default: throw std::runtime_error("unknown architecture"); } + + if (n_moved_tensors > 0) { + LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n", + __func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1, + ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft)); + } } ml.done_getting_tensors(); @@ -8964,27 +9076,29 @@ static bool llm_load_tensors( ctx_bufs.reserve(ctx_map.size()); // Ensure we have enough capacity for the maximum backend buffer we will potentially create - size_t n_max_backend_buffer = ctx_map.size() * ml.files.size(); + const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size(); model.bufs.reserve(n_max_backend_buffer); for (auto & it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; ggml_context * ctx = it.second; + // skip contexts without tensors + if (ggml_get_first_tensor(ctx) == nullptr) { + continue; + } + llama_buf_map bufs; bufs.reserve(n_max_backend_buffer); - // check if this backend device supports buffer_from_host_ptr - // when using a host buffer as the CPU bakcend buffer, use the CPU device to prioritize using buffer_from_host_ptr over the host buffer - ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft == llama_default_buffer_type_cpu(model, true) ? ggml_backend_cpu_buffer_type() : buft); - bool buffer_from_host_ptr_supported = false; - if (dev) { - ggml_backend_dev_props props; - ggml_backend_dev_get_props(dev, &props); - buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr; - } + // check if it is possible to use buffer_from_host_ptr with this buffer type + ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft); + ggml_backend_dev_props props; + ggml_backend_dev_get_props(dev, &props); + bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr; + bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev); - if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported) { + if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) { for (uint32_t idx = 0; idx < ml.files.size(); idx++) { // only the mmap region containing the tensors in the model is mapped to the backend buffer // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers @@ -9027,7 +9141,7 @@ static bool llm_load_tensors( for (auto & buf : bufs) { // indicate that this buffer contains weights - // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight + // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); } @@ -9039,7 +9153,7 @@ static bool llm_load_tensors( LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); if (n_gpu_layers > (int) hparams.n_layer) { - LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__); + LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__); } const int max_backend_supported_layers = hparams.n_layer + 1; @@ -9048,9 +9162,9 @@ static bool llm_load_tensors( LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); } - // print memory requirements + // print memory requirements per buffer type for (ggml_backend_buffer_t buf : model.bufs) { - LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: %10s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); } // populate tensors_by_name @@ -9115,23 +9229,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam return 0; } -#ifdef GGML_USE_KOMPUTE - if (params.n_gpu_layers > 0 && ( - !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) - || !( - model.ftype == LLAMA_FTYPE_ALL_F32 || - model.ftype == LLAMA_FTYPE_MOSTLY_F16 || - model.ftype == LLAMA_FTYPE_MOSTLY_BF16 || - model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || - model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 - ) - )) { - // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file - LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__); - params.n_gpu_layers = 0; - } -#endif - if (!llm_load_tensors( ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock, params.progress_callback, params.progress_callback_user_data @@ -10210,7 +10307,7 @@ struct llm_build_context { cb(tmp, "K_f32", il); for (auto * backend : lctx.backends) { // Figure out which backend KV cache belongs to - if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft)) { + if (ggml_backend_supports_buft(backend, ggml_backend_buffer_get_type(kv_self.k_l[il]->buffer))) { ggml_backend_sched_set_tensor_backend(lctx.sched, tmp, backend); break; } @@ -15184,6 +15281,7 @@ struct llm_build_context { cb(cur, "result_norm", -1); // lm_head + // FIXME: do not use model.tok_embd directly, duplicate as model.output cur = llm_build_lora_mm(lctx, ctx0, model.tok_embd, cur); cb(cur, "result_output", -1); @@ -16334,11 +16432,12 @@ static struct ggml_cgraph * llama_build_graph( const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer; if (ubatch.n_tokens < 32 || full_offload) { if (il != -1 && strcmp(name, "norm") == 0) { + const auto & dev_layer = lctx.model.dev_layer.at(il); for (auto * backend : lctx.backends) { - if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) && - (ggml_backend_supports_op(backend, cur) || ggml_backend_offload_op(backend, cur))) { - ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend); - break; + if (ggml_backend_get_device(backend) == dev_layer.dev) { + if (ggml_backend_supports_op(backend, cur)) { + ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend); + } } } } @@ -17041,7 +17140,22 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { lctx.embd = nullptr; } - lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(lctx.model, true), new_size); + auto * buft = ggml_backend_cpu_buffer_type(); + // try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory + ggml_tensor * output_tensor = lctx.model.output; + if (!output_tensor) { + // bert models don't have an output tensor, use the last layer + output_tensor = lctx.model.layers.back().layer_out_norm; + } + if (output_tensor) { + auto * output_buft = ggml_backend_buffer_get_type(output_tensor->buffer); + auto * output_dev = ggml_backend_buft_get_device(output_buft); + auto * output_dev_host_buft = ggml_backend_dev_host_buffer_type(output_dev); + if (output_dev_host_buft) { + buft = output_dev_host_buft; + } + } + lctx.buf_output = ggml_backend_buft_alloc_buffer(buft, new_size); if (lctx.buf_output == nullptr) { LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0)); return 0; @@ -18832,7 +18946,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c // contexts for each buffer type std::map ctx_map; - auto get_ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { auto it = ctx_map.find(buft); if (it == ctx_map.end()) { // add a new context @@ -18894,7 +19008,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c ggml_free(ctx); throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model"); } - struct ggml_context * dev_ctx = get_ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer)); + struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer)); // validate tensor shape if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) { gguf_free(ctx_gguf); @@ -18953,7 +19067,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c } } - LLAMA_LOG_INFO("%s: loaded %ld tensors from lora file\n", __func__, adapter.ab_map.size()*2); + LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2); // free ctx for reading gguf gguf_free(ctx_gguf); @@ -19092,14 +19206,8 @@ bool llama_supports_mlock(void) { } bool llama_supports_gpu_offload(void) { -#if defined(GGML_USE_KOMPUTE) - // Defined when llama.cpp is compiled with support for offloading model layers to GPU. - return true; -#else return ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU) != nullptr || - ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL) != nullptr || llama_supports_rpc(); -#endif } bool llama_supports_rpc(void) { @@ -19189,8 +19297,7 @@ struct llama_model * llama_load_model_from_file( return nullptr; } - // ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint); - using ggml_backend_rpc_add_device_t = ggml_backend_dev_t (*)(const char *); + typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint); ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device"); if (!ggml_backend_rpc_add_device_fn) { LLAMA_LOG_ERROR("%s: failed to find RPC device add function\n", __func__); @@ -19217,22 +19324,34 @@ struct llama_model * llama_load_model_from_file( ggml_backend_dev_t dev = ggml_backend_dev_get(i); switch (ggml_backend_dev_type(dev)) { case GGML_BACKEND_DEVICE_TYPE_CPU: - case GGML_BACKEND_DEVICE_TYPE_CPU_FULL: - // skip CPU backends since they are `handled separately + case GGML_BACKEND_DEVICE_TYPE_ACCEL: + // skip CPU backends since they are handled separately break; case GGML_BACKEND_DEVICE_TYPE_GPU: - case GGML_BACKEND_DEVICE_TYPE_GPU_FULL: - { - size_t free, total; // NOLINT - ggml_backend_dev_memory(dev, &free, &total); - LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024); model->devices.push_back(dev); break; - } } } + // if using single GPU mode, remove all except the main GPU + if (params.split_mode == LLAMA_SPLIT_MODE_NONE) { + if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) { + LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size()); + llama_free_model(model); + return nullptr; + } + ggml_backend_dev_t main_gpu = model->devices[params.main_gpu]; + model->devices.clear(); + model->devices.push_back(main_gpu); + } + + for (auto * dev : model->devices) { + size_t free, total; // NOLINT + ggml_backend_dev_memory(dev, &free, &total); + LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024); + } + int status = llama_model_load(path_model, *model, params); GGML_ASSERT(status <= 0); if (status < 0) { @@ -19393,53 +19512,21 @@ struct llama_context * llama_new_context_with_model( GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); if (!hparams.vocab_only) { - // initialize backends - int main_gpu = model->main_gpu; - - // with registry - if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) { - if (main_gpu >= 0 && main_gpu < (int)model->devices.size()) { - ggml_backend_dev_t main_dev = model->devices[main_gpu]; - ggml_backend_t backend = ggml_backend_dev_init(main_dev, nullptr); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(main_dev)); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } else { - // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU - for (auto * dev : model->devices) { - ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev)); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } - } - if (main_gpu >= (int)model->devices.size()) { - main_gpu -= (int)model->devices.size(); - } - -#if defined(GGML_USE_KOMPUTE) - if (model->n_gpu_layers > 0) { - auto * backend = ggml_backend_kompute_init(main_gpu); + // GPU backends + for (auto * dev : model->devices) { + ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__); + LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev)); llama_free(ctx); return nullptr; } ctx->backends.push_back(backend); } -#endif - // add other backends (such as BLAS) + // add ACCEL backends (such as BLAS) for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) { + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) { ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); if (backend == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev)); @@ -19450,6 +19537,7 @@ struct llama_context * llama_new_context_with_model( } } + // add CPU backend ctx->backend_cpu = ggml_backend_cpu_init(); if (ctx->backend_cpu == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); @@ -19513,12 +19601,16 @@ struct llama_context * llama_new_context_with_model( // buffer types used for the compute buffer of each backend std::vector backend_buft; for (auto * backend : ctx->backends) { - if (ggml_backend_is_cpu(backend)) { - // use host buffers for the CPU backend compute buffer - backend_buft.push_back(llama_default_buffer_type_cpu(*model, true)); - } else { - backend_buft.push_back(ggml_backend_get_default_buffer_type(backend)); + auto * buft = ggml_backend_get_default_buffer_type(backend); + if (ggml_backend_is_cpu(backend) && !model->devices.empty()) { + // use the host buffer of the first device CPU for faster transfer of the intermediate state + auto * dev = model->devices[0]; + auto * host_buft = ggml_backend_dev_host_buffer_type(dev); + if (host_buft) { + buft = host_buft; + } } + backend_buft.push_back(buft); } const size_t max_nodes = llama_model_max_nodes(*model); @@ -19542,11 +19634,6 @@ struct llama_context * llama_new_context_with_model( continue; } auto * dev = ggml_backend_get_device(backend); - if (!dev) { - // backend is using old interface, not supported - pipeline_parallel = false; - break; - } ggml_backend_dev_props props; ggml_backend_dev_get_props(dev, &props); if (!props.caps.async || !props.caps.events) { @@ -19563,15 +19650,29 @@ struct llama_context * llama_new_context_with_model( LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched)); } - // build worst-case graph + // initialize scheduler with the worst-case graph uint32_t n_seqs = 1; // TODO: worst-case number of sequences uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch); llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph - llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; - ggml_cgraph * gf = llama_build_graph(*ctx, ubatch, true); - // initialize scheduler with the worst-case graph - if (!ggml_backend_sched_reserve(ctx->sched, gf)) { + llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; + ggml_cgraph * gf_pp = llama_build_graph(*ctx, ubatch_pp, true); + + // reserve pp graph first so that buffers are only allocated once + ggml_backend_sched_reserve(ctx->sched, gf_pp); + int n_splits_pp = ggml_backend_sched_get_n_splits(ctx->sched); + int n_nodes_pp = ggml_graph_n_nodes(gf_pp); + + // reserve with tg graph to get the number of splits and nodes + llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; + ggml_cgraph * gf_tg = llama_build_graph(*ctx, ubatch_tg, true); + ggml_backend_sched_reserve(ctx->sched, gf_tg); + int n_splits_tg = ggml_backend_sched_get_n_splits(ctx->sched); + int n_nodes_tg = ggml_graph_n_nodes(gf_tg); + + // reserve again with pp graph to avoid ggml-alloc reallocations during inference + gf_pp = llama_build_graph(*ctx, ubatch_pp, false); + if (!ggml_backend_sched_reserve(ctx->sched, gf_pp)) { LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); llama_free(ctx); return nullptr; @@ -19588,10 +19689,16 @@ struct llama_context * llama_new_context_with_model( } } - // note: the number of splits during measure is higher than during inference due to the kv shift - int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); - LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, ggml_graph_n_nodes(gf)); - LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits); + if (n_nodes_pp == n_nodes_tg) { + LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, n_nodes_pp); + } else { + LLAMA_LOG_INFO("%s: graph nodes = %d (with bs=%d), %d (with bs=1)\n", __func__, n_nodes_pp, n_tokens, n_nodes_tg); + } + if (n_splits_pp == n_splits_tg) { + LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits_pp); + } else { + LLAMA_LOG_INFO("%s: graph splits = %d (with bs=%d), %d (with bs=1)\n", __func__, n_splits_pp, n_tokens, n_splits_tg); + } } } @@ -19851,40 +19958,46 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const GGML_ASSERT(cvec.ctxs.empty()); GGML_ASSERT(cvec.bufs.empty()); - // count layer buffer types - std::map buft_layer_count; - for (int64_t i = 0; i < model.hparams.n_layer; i++) { - buft_layer_count[model.buft_layer[i].buft]++; - } - - // allocate contexts + // create a context for each buffer type std::map ctx_map; - for (auto & it : buft_layer_count) { - int n_layers = it.second; - struct ggml_init_params params = { - /*.mem_size =*/ n_layers * ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - ggml_context * ctx = ggml_init(params); - if (!ctx) { - LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__); - return 1; + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + if (ctx_map.count(buft) == 0) { + struct ggml_init_params params = { + /*.mem_size =*/ model.hparams.n_layer*ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context * ctx = ggml_init(params); + if (!ctx) { + return nullptr; + } + ctx_map[buft] = ctx; + cvec.ctxs.push_back(ctx); } - ctx_map[it.first] = ctx; - } + return ctx_map.at(buft); + }; + // make tensors cvec.tensors.reserve(model.hparams.n_layer); cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0 for (size_t il = 1; il < model.hparams.n_layer; il++) { - struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft); + ggml_backend_buffer_type_t buft = select_buft(*model.dev_layer.at(il).buft_list, + [&](ggml_context * ctx) { + ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd); + ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd); + return ggml_add(ctx, cur, layer_dir); + }); + ggml_context * ctx = ctx_for_buft(buft); + if (!ctx) { + LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__); + return false; + } ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd); cvec.tensors.push_back(tensor); } // allocate tensors / buffers and zero - cvec.ctxs.reserve(ctx_map.size()); cvec.bufs.reserve(ctx_map.size()); for (auto it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; @@ -19895,7 +20008,6 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const return false; } ggml_backend_buffer_clear(buf, 0); - cvec.ctxs.push_back(ctx); cvec.bufs.push_back(buf); } @@ -21218,7 +21330,7 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs)); } } else if ((size_t) i >= ctx->output_ids.size()) { - throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size())); + throw std::runtime_error(format("out of range [0, %zu)", ctx->output_ids.size())); } else { j = ctx->output_ids[i]; } From fc83a9e58479e4dd70054daa7afe5184c1bbe545 Mon Sep 17 00:00:00 2001 From: xctan Date: Wed, 30 Oct 2024 15:00:40 +0800 Subject: [PATCH 79/95] ggml : add Q4_0_8_8 RISC-V GEMV and GEMM kernels (#10029) * ggml : RISC-V vector gemv for q4_0_8x8 * ggml : Added WIP rvv q4_0_8x8 gemm * ggml : Added initial implementation of rvv gemm * ggml : optimize gemm to avoid register spillover * ggml : Fix GCC rvv load alignment issue * ggml : Format gemm rvv code * ggml : Fix a typo in RVV q4_0_8_8 GEMM --- ggml/src/ggml-aarch64.c | 268 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 268 insertions(+) diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c index b27f41147..eb30f8944 100644 --- a/ggml/src/ggml-aarch64.c +++ b/ggml/src/ggml-aarch64.c @@ -991,6 +991,73 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * } } return; +#elif defined(__riscv_v_intrinsic) + if (__riscv_vlenb() >= QK4_0) { + const size_t vl = QK4_0; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + + vfloat32m1_t sumf = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + for (int l = 0; l < nb; l++) { + const int64_t a0 = *(const int64_t *)&a_ptr[l].qs[0]; + const int64_t a1 = *(const int64_t *)&a_ptr[l].qs[8]; + const int64_t a2 = *(const int64_t *)&a_ptr[l].qs[16]; + const int64_t a3 = *(const int64_t *)&a_ptr[l].qs[24]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a0, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a1, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a2, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a3, vl / 4)); + + const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); + const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); + const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); + const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); + const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); + const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); + const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); + + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_hi_m)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + // vector version needs Zvfhmin extension + const float a_scale = GGML_FP16_TO_FP32(a_ptr[l].d); + const float b_scales[8] = { + GGML_FP16_TO_FP32(b_ptr[l].d[0]), + GGML_FP16_TO_FP32(b_ptr[l].d[1]), + GGML_FP16_TO_FP32(b_ptr[l].d[2]), + GGML_FP16_TO_FP32(b_ptr[l].d[3]), + GGML_FP16_TO_FP32(b_ptr[l].d[4]), + GGML_FP16_TO_FP32(b_ptr[l].d[5]), + GGML_FP16_TO_FP32(b_ptr[l].d[6]), + GGML_FP16_TO_FP32(b_ptr[l].d[7]) + }; + const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scale, vl / 4); + sumf = __riscv_vfmacc_vv_f32m1(sumf, tmp1, b_scales_vec, vl / 4); + } + __riscv_vse32_v_f32m1(s + x * ncols_interleaved, sumf, vl / 4); + } + return; + } #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) { float sumf[8]; @@ -3171,6 +3238,207 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * } } } + return; + } +#elif defined(__riscv_v_intrinsic) + if (__riscv_vlenb() >= QK4_0) { + const size_t vl = QK4_0; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + vfloat32m1_t sumf0 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf1 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf2 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf3 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + for (int l = 0; l < nb; l++) { + const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); + const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); + const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); + const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); + const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); + const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); + const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); + + // vector version needs Zvfhmin extension + const float a_scales[4] = { + GGML_FP16_TO_FP32(a_ptr[l].d[0]), + GGML_FP16_TO_FP32(a_ptr[l].d[1]), + GGML_FP16_TO_FP32(a_ptr[l].d[2]), + GGML_FP16_TO_FP32(a_ptr[l].d[3]) + }; + const float b_scales[8] = { + GGML_FP16_TO_FP32(b_ptr[l].d[0]), + GGML_FP16_TO_FP32(b_ptr[l].d[1]), + GGML_FP16_TO_FP32(b_ptr[l].d[2]), + GGML_FP16_TO_FP32(b_ptr[l].d[3]), + GGML_FP16_TO_FP32(b_ptr[l].d[4]), + GGML_FP16_TO_FP32(b_ptr[l].d[5]), + GGML_FP16_TO_FP32(b_ptr[l].d[6]), + GGML_FP16_TO_FP32(b_ptr[l].d[7]) + }; + const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); + + const int64_t A0 = *(const int64_t *)&a_ptr[l].qs[0]; + const int64_t A4 = *(const int64_t *)&a_ptr[l].qs[32]; + const int64_t A8 = *(const int64_t *)&a_ptr[l].qs[64]; + const int64_t Ac = *(const int64_t *)&a_ptr[l].qs[96]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l0; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A0, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A4, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A8, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ac, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l0 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l0)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[0], vl / 4); + sumf0 = __riscv_vfmacc_vv_f32m1(sumf0, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A1 = *(const int64_t *)&a_ptr[l].qs[8]; + const int64_t A5 = *(const int64_t *)&a_ptr[l].qs[40]; + const int64_t A9 = *(const int64_t *)&a_ptr[l].qs[72]; + const int64_t Ad = *(const int64_t *)&a_ptr[l].qs[104]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l1; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A1, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A5, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A9, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ad, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l1 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l1)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[1], vl / 4); + sumf1 = __riscv_vfmacc_vv_f32m1(sumf1, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A2 = *(const int64_t *)&a_ptr[l].qs[16]; + const int64_t A6 = *(const int64_t *)&a_ptr[l].qs[48]; + const int64_t Aa = *(const int64_t *)&a_ptr[l].qs[80]; + const int64_t Ae = *(const int64_t *)&a_ptr[l].qs[112]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l2; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A2, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A6, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Aa, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ae, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l2 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l2)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[2], vl / 4); + sumf2 = __riscv_vfmacc_vv_f32m1(sumf2, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A3 = *(const int64_t *)&a_ptr[l].qs[24]; + const int64_t A7 = *(const int64_t *)&a_ptr[l].qs[56]; + const int64_t Ab = *(const int64_t *)&a_ptr[l].qs[88]; + const int64_t Af = *(const int64_t *)&a_ptr[l].qs[120]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l3; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A3, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A7, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ab, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Af, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l3 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l3)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[3], vl / 4); + sumf3 = __riscv_vfmacc_vv_f32m1(sumf3, tmp1, b_scales_vec, vl / 4); + } + } + __riscv_vse32_v_f32m1(&s[(y * 4 + 0) * bs + x * ncols_interleaved], sumf0, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 1) * bs + x * ncols_interleaved], sumf1, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 2) * bs + x * ncols_interleaved], sumf2, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 3) * bs + x * ncols_interleaved], sumf3, vl / 4); + } + } + return; } #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) From 79a2bc042dcacaad59306865208a8c8c3149e3ea Mon Sep 17 00:00:00 2001 From: Rich Dougherty Date: Thu, 31 Oct 2024 01:22:21 +1300 Subject: [PATCH 80/95] convert : more detailed convert lora usage docs (#10065) --- convert_lora_to_gguf.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index bc68f68af..915e21836 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -230,7 +230,7 @@ def get_base_tensor_name(lora_tensor_name: str) -> str: def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( - description="Convert a huggingface PEFT LoRA adapter to a GGML compatible file") + description="Convert a Hugging Face PEFT LoRA adapter to a GGUF file") parser.add_argument( "--outfile", type=Path, help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", @@ -257,11 +257,11 @@ def parse_args() -> argparse.Namespace: ) parser.add_argument( "--base", type=Path, required=True, - help="directory containing base model file", + help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required", ) parser.add_argument( "lora_path", type=Path, - help="directory containing LoRA adapter file", + help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)", ) return parser.parse_args() From 6763f713bb692910e9b2d9d1a82d6959cee2dcf3 Mon Sep 17 00:00:00 2001 From: Rich Dougherty Date: Thu, 31 Oct 2024 01:22:39 +1300 Subject: [PATCH 81/95] readme : more lora detail in main example readme (#10064) --- examples/main/README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/examples/main/README.md b/examples/main/README.md index 5357ac2e2..145216938 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -333,6 +333,15 @@ These options help improve the performance and memory usage of the LLaMA models. For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize). +## LoRA (Low-Rank Adaptation) adapters + +- `--lora FNAME`: Optional path to a LoRA adapter to use with scaling of 1.0. Can be mixed with `--lora-scaled` and can be repeated to use multiple adapters. +- `--lora-scaled FNAME`: Optional path to a LoRA adapter with user-defined scaling. Can be mixed with `--lora` and can repeated to use multiple adapters. + +You can add LoRA adapters using `--lora` or `--lora-scaled`. For example: `--lora my_adapter_1.gguf --lora my_adapter_2.gguf ...` or `--lora-scaled lora_task_A.gguf 0.5 --lora-scaled lora_task_B.gguf 0.5`. + +LoRA adapters should be in GGUF format. To convert from Hugging Face format use the `convert-lora-to-gguf.py` script. LoRA adapters are loaded separately and applied during inference - they are not merged with the main model. This means that mmap model loading is fully supported when using LoRA adapters. The old `--lora-base` flag has been removed now that merging is no longer performed. + ## Additional Options These options provide extra functionality and customization when running the LLaMA models: @@ -341,6 +350,4 @@ These options provide extra functionality and customization when running the LLa - `--verbose-prompt`: Print the prompt before generating text. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. -- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. -- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. - `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache. From b9e02e8184f5e6094a9e87eaf040becd404bfc90 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Wed, 30 Oct 2024 14:51:21 +0100 Subject: [PATCH 82/95] ggml : fix memory leaks when loading invalid gguf files (#10094) * ggml : fix gguf string leak when reading kv pairs fails * ggml : avoid crashing with GGML_ABORT when the KV has an invalid type * ggml : avoid crashing on failed memory allocations when loading a gguf file --- ggml/src/ggml.c | 67 +++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 54 insertions(+), 13 deletions(-) diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index a8da10d79..0d99b0791 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -22136,7 +22136,11 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { return false; } - p->data = GGML_CALLOC(p->n + 1, 1); + p->data = calloc(p->n + 1, 1); + if (!p->data) { + fprintf(stderr, "%s: failed to allocate memory for string of length %" PRIu64 "\n", __func__, p->n); + return false; + } ok = ok && gguf_fread_el(file, p->data, p->n, offset); @@ -22170,7 +22174,11 @@ static void gguf_free_kv(struct gguf_kv * kv) { } struct gguf_context * gguf_init_empty(void) { - struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); + struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context)); + if (!ctx) { + fprintf(stderr, "%s: failed to allocate memory for context\n", __func__); + return NULL; + } memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic)); ctx->header.version = GGUF_VERSION; @@ -22216,7 +22224,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p bool ok = true; - struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); + struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context)); + if (!ctx) { + fprintf(stderr, "%s: failed to allocate memory for context\n", __func__); + fclose(file); + return NULL; + } // read the header { @@ -22255,9 +22268,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p { const uint64_t n_kv = ctx->header.n_kv; - // header.n_kv will hold the actual value of pairs that were successfully read in the loop below - ctx->header.n_kv = 0; - ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv)); + ctx->kv = calloc(n_kv, sizeof(struct gguf_kv)); + if (!ctx->kv) { + fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } for (uint64_t i = 0; i < n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; @@ -22308,7 +22325,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p return NULL; } - kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type)); + kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type)); + if (!kv->value.arr.data) { + fprintf(stderr, "%s: failed to allocate memory for array\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset); } break; @@ -22322,24 +22345,36 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p return NULL; } - kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str)); + kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str)); + if (!kv->value.arr.data) { + fprintf(stderr, "%s: failed to allocate memory for array\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } for (uint64_t j = 0; j < kv->value.arr.n; ++j) { ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset); } } break; case GGUF_TYPE_ARRAY: - default: GGML_ABORT("invalid type"); + default: + { + fprintf(stderr, "%s: invalid array type %d\n", __func__, kv->value.arr.type); + ok = false; + } break; } } break; - default: GGML_ABORT("invalid type"); + default: + { + fprintf(stderr, "%s: invalid type %d\n", __func__, kv->type); + ok = false; + } break; } if (!ok) { break; } - - ctx->header.n_kv++; } if (!ok) { @@ -22352,7 +22387,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the tensor infos if (ctx->header.n_tensors > 0) { - ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); + ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); + if (!ctx->infos) { + fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__); + fclose(file); + gguf_free(ctx); + return NULL; + } for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; From 61408e7fad082dc44a11c8a9f1398da4837aad44 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sergio=20L=C3=B3pez?= Date: Wed, 30 Oct 2024 17:01:52 +0100 Subject: [PATCH 83/95] kompute: add backend registry / device interfaces (#10045) Get in line with the other backends by supporting the newer backend/device registry interfaces. Signed-off-by: Sergio Lopez --- ggml/include/ggml-kompute.h | 4 + ggml/src/ggml-backend.cpp | 9 +- ggml/src/ggml-kompute.cpp | 251 ++++++++++++++++++++++++++++-------- 3 files changed, 206 insertions(+), 58 deletions(-) diff --git a/ggml/include/ggml-kompute.h b/ggml/include/ggml-kompute.h index 171465456..c0c43521b 100644 --- a/ggml/include/ggml-kompute.h +++ b/ggml/include/ggml-kompute.h @@ -11,6 +11,8 @@ extern "C" { #endif +#define GGML_KOMPUTE_MAX_DEVICES 16 + struct ggml_vk_device { int index; int type; // same as VkPhysicalDeviceType @@ -41,6 +43,8 @@ GGML_API bool ggml_backend_is_kompute(ggml_backend_t backend); GGML_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device); +GGML_API ggml_backend_reg_t ggml_backend_kompute_reg(void); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index fd574887f..f397f6252 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -562,6 +562,10 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na #include "ggml-cann.h" #endif +#ifdef GGML_USE_KOMPUTE +#include "ggml-kompute.h" +#endif + struct ggml_backend_registry { std::vector backends; std::vector devices; @@ -591,8 +595,9 @@ struct ggml_backend_registry { #ifdef GGML_USE_AMX register_backend(ggml_backend_amx_reg()); #endif - - // TODO: kompute +#ifdef GGML_USE_KOMPUTE + register_backend(ggml_backend_kompute_reg()); +#endif register_backend(ggml_backend_cpu_reg()); } diff --git a/ggml/src/ggml-kompute.cpp b/ggml/src/ggml-kompute.cpp index 1f2220234..fea69fb04 100644 --- a/ggml/src/ggml-kompute.cpp +++ b/ggml/src/ggml-kompute.cpp @@ -42,6 +42,7 @@ #include #include #include +#include #include #include #include @@ -273,18 +274,9 @@ static std::vector ggml_vk_available_devices_internal(size_t mem return results; } -// public API returns a C-style array -ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count) { - auto devices = ggml_vk_available_devices_internal(memoryRequired); - *count = devices.size(); - if (devices.empty()) { - return nullptr; - } - - size_t nbytes = sizeof (ggml_vk_device) * (devices.size()); - auto * arr = static_cast(malloc(nbytes)); - memcpy(arr, devices.data(), nbytes); - return arr; +static std::vector& ggml_vk_available_devices() { + static std::vector devices = ggml_vk_available_devices_internal(0); + return devices; } static void ggml_vk_filterByVendor(std::vector& devices, const std::string& targetVendor) { @@ -341,7 +333,7 @@ ggml_vk_device ggml_vk_current_device() { if (!komputeManager()->hasDevice()) return ggml_vk_device(); - auto devices = ggml_vk_available_devices_internal(0); + auto devices = ggml_vk_available_devices(); ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName.data()); GGML_ASSERT(!devices.empty()); return devices.front(); @@ -1323,17 +1315,7 @@ static void ggml_vk_cpy_f16_f32(Args&&... args) { ggml_vk_cpy(spirv, 2, 4, std::forward(args)...); } -static bool ggml_vk_supports_op(const struct ggml_tensor * op) { - switch (op->type) { - case GGML_TYPE_F16: - case GGML_TYPE_F32: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - break; - default: - return false; - } - +static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -1410,6 +1392,8 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) { ; } return false; + + GGML_UNUSED(dev); } static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml_cgraph * gf) { @@ -1458,11 +1442,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml any_commands_recorded = true; - if (!ggml_vk_supports_op(dst)) { - fprintf(stderr, "%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); - GGML_ABORT("unsupported op"); - } - const int32_t ne00 = src0 ? src0->ne[0] : 0; const int32_t ne01 = src0 ? src0->ne[1] : 0; const int32_t ne02 = src0 ? src0->ne[2] : 0; @@ -1907,25 +1886,31 @@ static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = { }; ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device) { - static std::vector bufts = []() { - std::vector vec; - auto devices = ggml_vk_available_devices_internal(0); - vec.reserve(devices.size()); + static std::mutex mutex; + std::lock_guard lock(mutex); - for (const auto & dev : devices) { - vec.push_back({ - /* .iface = */ ggml_backend_kompute_buffer_type_interface, - /* .device = */ nullptr, - /* .context = */ new ggml_backend_kompute_buffer_type_context(dev.index, dev.bufferAlignment, dev.maxAlloc) - }); + auto devices = ggml_vk_available_devices(); + int32_t device_count = (int32_t) devices.size(); + GGML_ASSERT(device < device_count); + GGML_ASSERT(devices.size() <= GGML_KOMPUTE_MAX_DEVICES); + + static ggml_backend_buffer_type + ggml_backend_kompute_buffer_types[GGML_KOMPUTE_MAX_DEVICES]; + + static bool ggml_backend_kompute_buffer_type_initialized = false; + + if (!ggml_backend_kompute_buffer_type_initialized) { + for (int32_t i = 0; i < device_count; i++) { + ggml_backend_kompute_buffer_types[i] = { + /* .iface = */ ggml_backend_kompute_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), i), + /* .context = */ new ggml_backend_kompute_buffer_type_context{ i, devices[i].bufferAlignment, devices[i].maxAlloc }, + }; } - return vec; - }(); + ggml_backend_kompute_buffer_type_initialized = true; + } - auto it = std::find_if(bufts.begin(), bufts.end(), [device](const ggml_backend_buffer_type & t) { - return device == static_cast(t.context)->device; - }); - return it < bufts.end() ? &*it : nullptr; + return &ggml_backend_kompute_buffer_types[device]; } // backend @@ -1953,16 +1938,6 @@ static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, st return GGML_STATUS_SUCCESS; } -static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - GGML_UNUSED(backend); - return ggml_vk_supports_op(op); -} - -static bool ggml_backend_kompute_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { - GGML_UNUSED(backend); - return buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name; -} - static struct ggml_backend_i kompute_backend_i = { /* .get_name = */ ggml_backend_kompute_name, /* .free = */ ggml_backend_kompute_free, @@ -1991,7 +1966,7 @@ ggml_backend_t ggml_backend_kompute_init(int device) { ggml_backend_t kompute_backend = new ggml_backend { /* .guid = */ ggml_backend_kompute_guid(), /* .interface = */ kompute_backend_i, - /* .device = */ nullptr, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), device), /* .context = */ s_kompute_context, }; @@ -2001,3 +1976,167 @@ ggml_backend_t ggml_backend_kompute_init(int device) { bool ggml_backend_is_kompute(ggml_backend_t backend) { return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid()); } + +static size_t ggml_backend_kompute_get_device_count() { + auto devices = ggml_vk_available_devices(); + return devices.size(); +} + +static void ggml_backend_kompute_get_device_description(int device, char * description, size_t description_size) { + auto devices = ggml_vk_available_devices(); + GGML_ASSERT((size_t) device < devices.size()); + snprintf(description, description_size, "%s", devices[device].name); +} + +static void ggml_backend_kompute_get_device_memory(int device, size_t * free, size_t * total) { + auto devices = ggml_vk_available_devices(); + GGML_ASSERT((size_t) device < devices.size()); + *total = devices[device].heapSize; + *free = devices[device].heapSize; +} + +////////////////////////// + +struct ggml_backend_kompute_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_kompute_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_kompute_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_kompute_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + ggml_backend_kompute_get_device_memory(ctx->device, free, total); +} + +static ggml_backend_buffer_type_t ggml_backend_kompute_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ggml_backend_kompute_buffer_type(ctx->device); +} + +static bool ggml_backend_kompute_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_kompute_buffer_type_get_name) { + return false; + } + + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + ggml_backend_kompute_buffer_type_context * buft_ctx = (ggml_backend_kompute_buffer_type_context *)buft->context; + + return buft_ctx->device == ctx->device; +} + +static enum ggml_backend_dev_type ggml_backend_kompute_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_kompute_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_kompute_device_get_name(dev); + props->description = ggml_backend_kompute_device_get_description(dev); + props->type = ggml_backend_kompute_device_get_type(dev); + ggml_backend_kompute_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* async = */ false, + /* host_buffer = */ false, + /* .buffer_from_host_ptr = */ false, + /* events = */ false, + }; +} + +static ggml_backend_t ggml_backend_kompute_device_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; + return ggml_backend_kompute_init(ctx->device); +} + +static bool ggml_backend_kompute_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + const int min_batch_size = 32; + + return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || + (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); + + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_kompute_device_i = { + /* .get_name = */ ggml_backend_kompute_device_get_name, + /* .get_description = */ ggml_backend_kompute_device_get_description, + /* .get_memory = */ ggml_backend_kompute_device_get_memory, + /* .get_type = */ ggml_backend_kompute_device_get_type, + /* .get_props = */ ggml_backend_kompute_device_get_props, + /* .init_backend = */ ggml_backend_kompute_device_init, + /* .get_buffer_type = */ ggml_backend_kompute_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_kompute_device_supports_op, + /* .supports_buft = */ ggml_backend_kompute_device_supports_buft, + /* .offload_op = */ ggml_backend_kompute_device_offload_op, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +static const char * ggml_backend_kompute_reg_get_name(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return "Kompute"; +} + +static size_t ggml_backend_kompute_reg_get_device_count(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return ggml_backend_kompute_get_device_count(); +} + +static ggml_backend_dev_t ggml_backend_kompute_reg_get_device(ggml_backend_reg_t reg, size_t device) { + static std::vector devices; + + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + for (size_t i = 0; i < ggml_backend_kompute_get_device_count(); i++) { + ggml_backend_kompute_device_context * ctx = new ggml_backend_kompute_device_context; + char desc[256]; + ggml_backend_kompute_get_device_description(i, desc, sizeof(desc)); + ctx->device = i; + ctx->name = "Kompute" + std::to_string(i); + ctx->description = desc; + devices.push_back(new ggml_backend_device { + /* .iface = */ ggml_backend_kompute_device_i, + /* .reg = */ reg, + /* .context = */ ctx, + }); + } + initialized = true; + } + } + + GGML_ASSERT(device < devices.size()); + return devices[device]; +} + +static const struct ggml_backend_reg_i ggml_backend_kompute_reg_i = { + /* .get_name = */ ggml_backend_kompute_reg_get_name, + /* .get_device_count = */ ggml_backend_kompute_reg_get_device_count, + /* .get_device = */ ggml_backend_kompute_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +ggml_backend_reg_t ggml_backend_kompute_reg() { + static ggml_backend_reg reg = { + /* .iface = */ ggml_backend_kompute_reg_i, + /* .context = */ nullptr, + }; + + return ® +} From 1329c0a75e6a7defc5c380eaf80d8e0f66d7da78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sergio=20L=C3=B3pez?= Date: Thu, 31 Oct 2024 10:09:52 +0100 Subject: [PATCH 84/95] kompute: add mul_mat_q4_k shader (#10097) This is a more or less direct translation from the Metal implementation to GLSL. Signed-off-by: Sergio Lopez --- ggml/src/CMakeLists.txt | 2 + ggml/src/ggml-kompute.cpp | 42 ++++++ ggml/src/kompute-shaders/common.comp | 9 ++ ggml/src/kompute-shaders/op_mul_mat_q4_k.comp | 133 ++++++++++++++++++ 4 files changed, 186 insertions(+) create mode 100644 ggml/src/kompute-shaders/op_mul_mat_q4_k.comp diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index aa405e4d0..915568798 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -800,6 +800,7 @@ if (GGML_KOMPUTE) kompute-shaders/op_mul_mat_q8_0.comp kompute-shaders/op_mul_mat_q4_0.comp kompute-shaders/op_mul_mat_q4_1.comp + kompute-shaders/op_mul_mat_q4_k.comp kompute-shaders/op_mul_mat_q6_k.comp kompute-shaders/op_getrows_f32.comp kompute-shaders/op_getrows_f16.comp @@ -833,6 +834,7 @@ if (GGML_KOMPUTE) shaderop_mul_mat_q8_0.h shaderop_mul_mat_q4_0.h shaderop_mul_mat_q4_1.h + shaderop_mul_mat_q4_k.h shaderop_mul_mat_q6_k.h shaderop_getrows_f32.h shaderop_getrows_f16.h diff --git a/ggml/src/ggml-kompute.cpp b/ggml/src/ggml-kompute.cpp index fea69fb04..2fea9e4cc 100644 --- a/ggml/src/ggml-kompute.cpp +++ b/ggml/src/ggml-kompute.cpp @@ -20,6 +20,7 @@ #include "shaderop_mul_mat_q8_0.h" #include "shaderop_mul_mat_q4_0.h" #include "shaderop_mul_mat_q4_1.h" +#include "shaderop_mul_mat_q4_k.h" #include "shaderop_mul_mat_q6_k.h" #include "shaderop_mul_mat_mat_f32.h" #include "shaderop_getrows_f32.h" @@ -1067,6 +1068,40 @@ static void ggml_vk_mul_mat_q8_0(Args&&... args) { ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward(args)...); } +static void ggml_vk_mul_mat_q4_k( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne10, + int32_t ne11, int32_t ne12, int32_t ne13, int32_t ne0, + int32_t ne1, int32_t r2, int32_t r3 +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_k_comp_spv, + kp::shader_data::op_mul_mat_q4_k_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3; + } pushConsts { + 0, 0, 0, + ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3 + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + static void ggml_vk_mul_mat_q6_k( kp::Sequence& seq, const std::shared_ptr& inA, @@ -1384,6 +1419,7 @@ static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, cons case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_K: return true; default: ; @@ -1635,6 +1671,12 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 ); break; + case GGML_TYPE_Q4_K: + ggml_vk_mul_mat_q4_k( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, ne12/ne02, ne13/ne03 + ); + break; case GGML_TYPE_Q6_K: ggml_vk_mul_mat_q6_k( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, diff --git a/ggml/src/kompute-shaders/common.comp b/ggml/src/kompute-shaders/common.comp index 62d62b025..2aaddf704 100644 --- a/ggml/src/kompute-shaders/common.comp +++ b/ggml/src/kompute-shaders/common.comp @@ -15,6 +15,7 @@ #define TWOPI_F 6.283185307179586f #define QK_K 256 +#define K_SCALE_SIZE 12 #define u8BufToU16(buf, idx) (((uint16_t(buf[idx + 1]) << 8)) | buf[idx]) #define u8BufToFloat16(buf, idx) uint16BitsToHalf u8BufToU16(buf, idx) @@ -64,6 +65,14 @@ mat4 dequantize_q4_1(const block_q4_1 xb, uint il) { return reg; } +#define sizeof_block_q4_k 144 +struct block_q4_k { + float16_t d; + float16_t dmin; + uint8_t scales[K_SCALE_SIZE]; + uint8_t qs[QK_K/2]; +}; + #define sizeof_block_q6_k 210 struct block_q6_k { uint8_t ql[QK_K/2]; // quants, lower 4 bits diff --git a/ggml/src/kompute-shaders/op_mul_mat_q4_k.comp b/ggml/src/kompute-shaders/op_mul_mat_q4_k.comp new file mode 100644 index 000000000..fc8e45aa9 --- /dev/null +++ b/ggml/src/kompute-shaders/op_mul_mat_q4_k.comp @@ -0,0 +1,133 @@ +#version 450 + +#include "common.comp" + +#define N_DST 4 +#define SIZE_OF_BLOCK sizeof_block_q4_k + +layout(local_size_x = 4) in; +layout(local_size_y = 8) in; +layout(local_size_z = 1) in; + +layout (binding = 0) readonly buffer tensorInA { block_q4_k inA[]; }; +layout (binding = 1) readonly buffer tensorInB { float inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne10; + int ne0; + int ne1; + int ne01; + int ne02; + int ne12; + int r2; + int r3; +} pcs; + +void main() { + const uint16_t kmask1 = uint16_t(0x3f3f); + const uint16_t kmask2 = uint16_t(0x0f0f); + const uint16_t kmask3 = uint16_t(0xc0c0); + + const uint ix = gl_SubgroupInvocationID/8; // 0...3 + const uint it = gl_SubgroupInvocationID%8; // 0...7 + const uint iq = it/4; // 0 or 1 + const uint ir = it%4; // 0...3 + + const uint nb = pcs.ne00/QK_K; + + const uint r0 = gl_WorkGroupID.x; + const uint r1 = gl_WorkGroupID.y; + const uint im = gl_WorkGroupID.z; + + const uint first_row = r0 * N_DST; + const uint ib_row = first_row * nb; + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint offset0 = (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); + + const uint xblk = ib_row + offset0 + pcs.inAOff; + const uint y = r1*pcs.ne10 + im*pcs.ne00*pcs.ne1 + pcs.inBOff; + + float yl[16]; + float yh[16]; + float sumf[N_DST] = {0.f, 0.f, 0.f, 0.f}; + float all_sum = 0.f; + + uint y4 = y + ix * QK_K + 64 * iq + 8 * ir; + + for (uint ib = ix; ib < nb; ib += 4) { + const uint blk_idx = ib + xblk; + + float sumy[4] = {0.f, 0.f, 0.f, 0.f}; + for (int i = 0; i < 8; ++i) { + yl[i+0] = inB[y4+i+ 0]; sumy[0] += yl[i+0]; + yl[i+8] = inB[y4+i+ 32]; sumy[1] += yl[i+8]; + yh[i+0] = inB[y4+i+128]; sumy[2] += yh[i+0]; + yh[i+8] = inB[y4+i+160]; sumy[3] += yh[i+8]; + } + + for (int row = 0; row < N_DST; row++) { + uint row_idx = row * nb; + + uint16_t sc_0 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 0); + uint16_t sc_1 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 2); + uint16_t sc_2 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 4); + uint16_t sc_3 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 6); + uint16_t sc_4 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 8); + + uint16_t sc16[4]; + sc16[0] = sc_0 & kmask1; + sc16[1] = sc_2 & kmask1; + sc16[2] = ((sc_4 >> 0) & kmask2) | ((sc_0 & kmask3) >> 2); + sc16[3] = ((sc_4 >> 4) & kmask2) | ((sc_2 & kmask3) >> 2); + + float acc1[4] = {0.f, 0.f, 0.f, 0.f}; + float acc2[4] = {0.f, 0.f, 0.f, 0.f}; + for (int i = 0; i < 8; i += 2) { + uint16_t q1 = u8BufToU16(inA[blk_idx + row_idx].qs, 32 * iq + 8 * ir + i); + uint16_t q2 = u8BufToU16(inA[blk_idx + row_idx].qs, 64 + 32 * iq + 8 * ir + i); + acc1[0] += yl[i+0] * (q1 & 0x000F); + acc1[1] += yl[i+1] * (q1 & 0x0F00); + acc1[2] += yl[i+8] * (q1 & 0x00F0); + acc1[3] += yl[i+9] * (q1 & 0xF000); + acc2[0] += yh[i+0] * (q2 & 0x000F); + acc2[1] += yh[i+1] * (q2 & 0x0F00); + acc2[2] += yh[i+8] * (q2 & 0x00F0); + acc2[3] += yh[i+9] * (q2 & 0xF000); + } + + uint8_t sc8_0 = uint8_t(sc16[0] & 0xFF); + uint8_t sc8_1 = uint8_t(sc16[0] >> 8 ); + uint8_t sc8_2 = uint8_t(sc16[1] & 0xFF); + uint8_t sc8_3 = uint8_t(sc16[1] >> 8 ); + uint8_t sc8_4 = uint8_t(sc16[2] & 0xFF); + uint8_t sc8_5 = uint8_t(sc16[2] >> 8 ); + uint8_t sc8_6 = uint8_t(sc16[3] & 0xFF); + uint8_t sc8_7 = uint8_t(sc16[3] >> 8 ); + + float dall = float(inA[blk_idx + row_idx].d); + float dmin = float(inA[blk_idx + row_idx].dmin); + sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc8_0 + + (acc1[2] + 1.f/256.f * acc1[3]) * sc8_1 * 1.f/16.f + + (acc2[0] + 1.f/256.f * acc2[1]) * sc8_4 + + (acc2[2] + 1.f/256.f * acc2[3]) * sc8_5 * 1.f/16.f) - + dmin * (sumy[0] * sc8_2 + sumy[1] * sc8_3 + sumy[2] * sc8_6 + sumy[3] * sc8_7); + } + + y4 += 4 * QK_K; + } + + for (int row = 0; row < N_DST; ++row) { + all_sum = subgroupAdd(sumf[row]); + if (subgroupElect()) { + out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row + pcs.outOff] = all_sum; + } + } +} From dea5e86051aadcdf42f7db7a8855a78d8f5ff3d6 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Thu, 31 Oct 2024 11:40:59 +0100 Subject: [PATCH 85/95] ggml : check tensor name lengths in gguf files (#10100) --- ggml/src/ggml.c | 45 ++++++++++++++++++++++++++++++++++++--------- src/llama.cpp | 7 +++++-- 2 files changed, 41 insertions(+), 11 deletions(-) diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 0d99b0791..149d8f970 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -22102,18 +22102,46 @@ static size_t gguf_type_size(enum gguf_type type) { return GGUF_TYPE_SIZE[type]; } -static void gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { - GGML_ASSERT(info->n_dims <= GGML_MAX_DIMS); - GGML_ASSERT(0 <= info->type && info->type < GGML_TYPE_COUNT); +static bool gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { + if (info->n_dims > GGML_MAX_DIMS) { + fprintf(stderr, "%s: invalid number of dimensions (%" PRIu32 ")\n", __func__, info->n_dims); + return false; + } + + if (info->type < 0 || info->type >= GGML_TYPE_COUNT) { + fprintf(stderr, "%s: invalid type (%d)\n", __func__, info->type); + return false; + } + + if (strlen(info->name.data) >= GGML_MAX_NAME) { + fprintf(stderr, "%s: tensor '%s' name is too long\n", __func__, info->name.data); + return false; + } for (uint32_t i = 0; i < info->n_dims; ++i) { - GGML_ASSERT(info->ne[i] > 0); + if (info->ne[i] <= 0) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[i]); + return false; + } } // prevent overflow for total number of elements - GGML_ASSERT(INT64_MAX/info->ne[1] > info->ne[0]); - GGML_ASSERT(INT64_MAX/info->ne[2] > info->ne[0]*info->ne[1]); - GGML_ASSERT(INT64_MAX/info->ne[3] > info->ne[0]*info->ne[1]*info->ne[2]); + if (INT64_MAX/info->ne[1] <= info->ne[0]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[1]); + return false; + } + + if (INT64_MAX/info->ne[2] <= info->ne[0]*info->ne[1]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[2]); + return false; + } + + if (INT64_MAX/info->ne[3] <= info->ne[0]*info->ne[1]*info->ne[2]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[3]); + return false; + } + + return true; } static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) { @@ -22414,8 +22442,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); - // TODO: return an error instead of crashing with GGML_ASSERT - gguf_tensor_info_sanitize(info); + ok = ok && gguf_tensor_info_sanitize(info); // make sure there is no duplicated tensor names for (uint64_t j = 0; j < i && ok; ++j) { diff --git a/src/llama.cpp b/src/llama.cpp index ef1b8ee59..60a0db29c 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -4273,8 +4273,11 @@ struct llama_model_loader { llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { const int tensor_idx = gguf_find_tensor(gguf_ctx, name); - offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); + if (tensor_idx < 0) { + throw std::runtime_error(format("tensor '%s' not found in the model", name)); + } + offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) { throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name)); } @@ -7426,7 +7429,7 @@ static bool llm_load_tensors( if (flags & llama_model_loader::TENSOR_NOT_REQUIRED) { return nullptr; } - throw std::runtime_error(format("missing tensor %s", tn.str().c_str())); + throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str())); } // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops From 0a683e8088d849626e7471f9e2ed381f7dbdf2e9 Mon Sep 17 00:00:00 2001 From: Kevin Gibbons Date: Thu, 31 Oct 2024 06:02:35 -0700 Subject: [PATCH 86/95] server : include scheme when printing URL (#10106) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7953b5065..f914ff88c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3259,7 +3259,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.terminate(); }; - LOG_INF("%s: server is listening on %s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); + LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); ctx_server.queue_tasks.start_loop(); From ab3d71f97f5b2915a229099777af00d3eada1d24 Mon Sep 17 00:00:00 2001 From: Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com> Date: Fri, 1 Nov 2024 02:50:39 +0800 Subject: [PATCH 87/95] loader: refactor tensor weights storage (#9935) * loader: refactor tensor weights storage * use sorted map, sort weights by layer --------- Co-authored-by: slaren --- src/llama.cpp | 123 ++++++++++++++++++++++++++------------------------ 1 file changed, 65 insertions(+), 58 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 60a0db29c..bc94d7ff0 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -4271,20 +4271,34 @@ struct llama_model_loader { ggml_tensor * tensor; - llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { - const int tensor_idx = gguf_find_tensor(gguf_ctx, name); + llama_tensor_weight(const llama_file * file, uint16_t idx, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { + const int tensor_idx = gguf_find_tensor(gguf_ctx, ggml_get_name(tensor)); if (tensor_idx < 0) { - throw std::runtime_error(format("tensor '%s' not found in the model", name)); + throw std::runtime_error(format("tensor '%s' not found in the model", ggml_get_name(tensor))); } offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) { - throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name)); + throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", ggml_get_name(tensor))); } } }; - std::vector weights; + // custom comparator to sort weights more nicely by layer + struct weight_name_comparer { + bool operator()(const std::string & a, const std::string & b) const { + int a_layer = -1; + int b_layer = -1; + sscanf(a.c_str(), "blk.%d.", &a_layer); + sscanf(b.c_str(), "blk.%d.", &b_layer); + if (a_layer != b_layer) { + return a_layer < b_layer; + } + return a < b; + } + }; + + std::map weights_map; std::unordered_map kv_overrides; struct gguf_context * meta = NULL; @@ -4326,7 +4340,14 @@ struct llama_model_loader { // For subsidiary files, `meta` tensor data offset must not be used, // so we build a unified tensors index for weights. for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(files.back().get(), 0, cur->name, meta, cur); + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, meta, cur)); } uint16_t n_split = 0; get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false); @@ -4366,7 +4387,14 @@ struct llama_model_loader { // Save tensors data offset info of the shard. for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur); + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf, cur)); } gguf_free(ctx_gguf); @@ -4376,7 +4404,7 @@ struct llama_model_loader { // sanity check { - const int n_tensors_loaded = (int) weights.size(); + const int n_tensors_loaded = (int) weights_map.size(); if (n_tensors != n_tensors_loaded) { throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded)); } @@ -4386,23 +4414,10 @@ struct llama_model_loader { } n_kv = gguf_get_n_kv(meta); - n_tensors = weights.size(); + n_tensors = weights_map.size(); fver = (enum llama_fver) gguf_get_version(meta); - std::set tensor_names; - for (auto & w : weights) { - n_elements += ggml_nelements(w.tensor); - n_bytes += ggml_nbytes(w.tensor); - // make sure there is no duplicated tensor names - const std::string name(w.tensor->name); - auto found = tensor_names.find(name); - if (found != tensor_names.end()) { - throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name)); - } - tensor_names.insert(name); - } - LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n", __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver)); @@ -4414,8 +4429,10 @@ struct llama_model_loader { uint32_t n_type_max = 0; enum ggml_type type_max = GGML_TYPE_F32; - for (int i = 0; i < n_tensors; i++) { - const ggml_tensor * tensor = weights.at(i).tensor; + for (const auto & it : weights_map) { + const llama_tensor_weight & w = it.second; + const ggml_tensor * tensor = w.tensor; + enum ggml_type type = tensor->type; n_type[type]++; @@ -4426,8 +4443,8 @@ struct llama_model_loader { } if (trace > 0) { - const uint16_t sid = weights.at(i).idx; - LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str()); + const uint16_t sid = w.idx; + LLAMA_LOG_INFO("%s: - tensor split %2d: %32s %-8s [ %s ]\n", __func__, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str()); } } @@ -4691,21 +4708,13 @@ struct llama_model_loader { return llm_kv.arch; } - const char * get_tensor_name(int i) const { - return weights.at(i).tensor->name; - } - const llama_tensor_weight * get_weight(const char * name) const { - for (const auto & weight : weights) { - if (strcmp(name, weight.tensor->name) == 0) { - return &weight; - } + auto pos = weights_map.find(name); + if (pos != weights_map.end()) { + return &pos->second; } - return nullptr; - } - const llama_tensor_weight * get_weight(int i) const { - return get_weight(get_tensor_name(i)); + return nullptr; } const llama_tensor_weight & require_weight(const char * name) const { @@ -4732,10 +4741,6 @@ struct llama_model_loader { return tensor; } - struct ggml_tensor * get_tensor_meta(int i) const { - return get_tensor_meta(get_tensor_name(i)); - } - const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector & ne, bool required) const { const struct ggml_tensor * cur = get_tensor_meta(name.c_str()); @@ -4842,8 +4847,8 @@ struct llama_model_loader { } // compute the total size of all tensors for progress reporting - for (auto & w : weights) { - size_data += ggml_nbytes(w.tensor); + for (const auto & it : weights_map) { + size_data += ggml_nbytes(it.second.tensor); } } @@ -18598,10 +18603,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } } - for (int i = 0; i < ml.n_tensors; ++i) { - const struct ggml_tensor * meta = ml.get_tensor_meta(i); + for (const auto & it : ml.weights_map) { + const struct ggml_tensor * tensor = it.second.tensor; - const std::string name = ggml_get_name(meta); + const std::string name = ggml_get_name(tensor); // TODO: avoid hardcoded tensor names - use the TN_* constants if (name.find("attn_v.weight") != std::string::npos || @@ -18639,20 +18644,22 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector> f32_conv_buf; uint16_t n_split = 1; + const auto & weights_map = ml.weights_map; + // Assume split index is continuous if (params->keep_split) { - for (int i = 0; i < ml.n_tensors; ++i) { - n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split); + for (const auto & it : weights_map) { + n_split = std::max(uint16_t(it.second.idx + 1), n_split); } + } std::vector ctx_outs(n_split, NULL); ctx_outs[0] = ctx_out; // populate the original tensors so we get an initial meta data - for (int i = 0; i < ml.n_tensors; ++i) { - auto weight = ml.get_weight(i); - uint16_t i_split = params->keep_split ? weight->idx : 0; - struct ggml_tensor * tensor = weight->tensor; + for (const auto & it : weights_map) { + uint16_t i_split = params->keep_split ? it.second.idx : 0; + struct ggml_tensor * tensor = it.second.tensor; if (ctx_outs[i_split] == NULL) { ctx_outs[i_split] = gguf_init_empty(); } @@ -18699,12 +18706,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s const auto tn = LLM_TN(model.arch); new_ofstream(0); - for (int i = 0; i < ml.n_tensors; ++i) { - auto weight = ml.get_weight(i); - struct ggml_tensor * tensor = weight->tensor; - if (weight->idx != cur_split && params->keep_split) { + for (const auto & it : weights_map) { + const auto & weight = it.second; + struct ggml_tensor * tensor = weight.tensor; + if (weight.idx != cur_split && params->keep_split) { close_ofstream(); - new_ofstream(weight->idx); + new_ofstream(weight.idx); } const std::string name = ggml_get_name(tensor); From c02e5ab2a675c8bc1abc8b1e4cb6a93b26bdcce7 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Thu, 31 Oct 2024 22:54:23 +0100 Subject: [PATCH 88/95] llama : fix buffer checks for mamba and rwk (#10111) * llama : fix buffer checks for mamba and rwk * llama : fix missing worst case flag during reserve * cuda : fix supports_op for norm * disable sched SET_CAUSE --- ggml/src/ggml-backend.cpp | 2 +- ggml/src/ggml-cuda.cu | 6 ++++-- ggml/src/ggml.c | 1 + src/llama.cpp | 38 +++++++++++++++++++++++++++++--------- 4 files changed, 35 insertions(+), 12 deletions(-) diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index f397f6252..c2afdf391 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -1508,7 +1508,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co return -1; } -#if 1 +#if 0 #define GGML_SCHED_MAX_SPLITS_DEBUG 4096 static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 087091516..b57f1b3b7 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -3107,18 +3107,20 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g } return false; } break; + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0; + break; case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: - case GGML_OP_NORM: case GGML_OP_ADD: case GGML_OP_ADD1: case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: - case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SQRT: diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 149d8f970..6a7154920 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -7272,6 +7272,7 @@ struct ggml_tensor * ggml_ssm_conv( const int64_t n_s = sx->ne[2]; // TODO: maybe support other strides than 1? + // FIXME: this is always true? GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t); GGML_ASSERT(sx->ne[1] == d_inner); GGML_ASSERT(n_t >= 0); diff --git a/src/llama.cpp b/src/llama.cpp index bc94d7ff0..e697c310c 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -7127,7 +7127,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w } break; case GGML_OP_MUL_MAT: { - ggml_tensor * b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512); + ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]); op_tensor = ggml_mul_mat(ctx, w, b); } break; case GGML_OP_MUL_MAT_ID: @@ -7167,18 +7167,38 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w } break; case GGML_OP_SSM_CONV: { - // TODO: ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d); - op_tensor = ggml_ssm_conv(ctx, nullptr, w); + // FIXME + ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789); + op_tensor = ggml_ssm_conv(ctx, conv_x, w); } break; case GGML_OP_SSM_SCAN: { - // TODO: ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C); - op_tensor = ggml_ssm_scan(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr); + // FIXME + const int64_t d_state = w->ne[0]; + const int64_t d_inner = w->ne[1]; + const int64_t n_seq_tokens = 512; + const int64_t n_seqs = 1; + ggml_tensor * s = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs); + ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs); + ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs); + ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs); + ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs); + op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C); } break; case GGML_OP_RWKV_WKV: { - // TODO: ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state); - op_tensor = ggml_rwkv_wkv(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr); + // FIXME + const int64_t S = 123; + const int64_t H = 123; + const int64_t n_tokens = 123; + const int64_t n_seqs = 123; + ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, 1, H, n_tokens); + ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * r = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * tf = w; + ggml_tensor * td = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H); + op_tensor = ggml_rwkv_wkv(ctx, k, v, r, tf, td, state); } break; default: GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); @@ -7453,7 +7473,7 @@ static bool llm_load_tensors( // tensors with "bias" suffix are always used with GGML_OP_ADD ggml_op op; - bool bias = strcmp(tn.suffix, "bias") == 0; + bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0; if (bias) { op = GGML_OP_ADD; } else { @@ -19681,7 +19701,7 @@ struct llama_context * llama_new_context_with_model( int n_nodes_tg = ggml_graph_n_nodes(gf_tg); // reserve again with pp graph to avoid ggml-alloc reallocations during inference - gf_pp = llama_build_graph(*ctx, ubatch_pp, false); + gf_pp = llama_build_graph(*ctx, ubatch_pp, true); if (!ggml_backend_sched_reserve(ctx->sched, gf_pp)) { LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); llama_free(ctx); From 1e9f94994ef908d964cf81069f03d9d3668beb7d Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 1 Nov 2024 00:45:34 +0100 Subject: [PATCH 89/95] quantize : fix --keep-split (#10114) --- src/llama.cpp | 53 +++++++++++++++++++++++++++++---------------------- 1 file changed, 30 insertions(+), 23 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index e697c310c..ed3998a1f 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -4860,19 +4860,12 @@ struct llama_model_loader { *last = 0; *addr = mapping->addr; for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) { - try { - const auto * weight = get_weight(ggml_get_name(tensor)); - if (!weight) { - continue; - } - if (weight->idx != idx) { - continue; - } - *first = std::min(*first, weight->offs); - *last = std::max(*last, weight->offs + ggml_nbytes(tensor)); - } catch(...) { - // the tensor is not in the model + const auto * weight = get_weight(ggml_get_name(tensor)); + if (!weight || weight->idx != idx) { + continue; } + *first = std::min(*first, weight->offs); + *last = std::max(*last, weight->offs + ggml_nbytes(tensor)); } } @@ -5049,7 +5042,6 @@ struct llama_model_loader { ggml_backend_tensor_set(cur, data, 0, n_size); } } else { - GGML_ASSERT(weight->idx < files.size()); const auto & file = files.at(weight->idx); if (ggml_backend_buffer_is_host(cur->buffer)) { file->seek(weight->offs, SEEK_SET); @@ -18623,8 +18615,25 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } } + // make a list of weights + std::vector tensors; + tensors.reserve(ml.weights_map.size()); for (const auto & it : ml.weights_map) { - const struct ggml_tensor * tensor = it.second.tensor; + tensors.push_back(&it.second); + } + + // keep_split requires that the weights are sorted by split index + if (params->keep_split) { + std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) { + if (a->idx == b->idx) { + return a->offs < b->offs; + } + return a->idx < b->idx; + }); + } + + for (const auto * it : tensors) { + const struct ggml_tensor * tensor = it->tensor; const std::string name = ggml_get_name(tensor); @@ -18664,22 +18673,20 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector> f32_conv_buf; uint16_t n_split = 1; - const auto & weights_map = ml.weights_map; // Assume split index is continuous if (params->keep_split) { - for (const auto & it : weights_map) { - n_split = std::max(uint16_t(it.second.idx + 1), n_split); + for (const auto * it : tensors) { + n_split = std::max(uint16_t(it->idx + 1), n_split); } - } std::vector ctx_outs(n_split, NULL); ctx_outs[0] = ctx_out; // populate the original tensors so we get an initial meta data - for (const auto & it : weights_map) { - uint16_t i_split = params->keep_split ? it.second.idx : 0; - struct ggml_tensor * tensor = it.second.tensor; + for (const auto * it : tensors) { + uint16_t i_split = params->keep_split ? it->idx : 0; + struct ggml_tensor * tensor = it->tensor; if (ctx_outs[i_split] == NULL) { ctx_outs[i_split] = gguf_init_empty(); } @@ -18726,8 +18733,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s const auto tn = LLM_TN(model.arch); new_ofstream(0); - for (const auto & it : weights_map) { - const auto & weight = it.second; + for (const auto * it : tensors) { + const auto & weight = *it; struct ggml_tensor * tensor = weight.tensor; if (weight.idx != cur_split && params->keep_split) { close_ofstream(); From 85679d37f34f66783cc04664a06c405b28e8e035 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 1 Nov 2024 00:49:53 +0100 Subject: [PATCH 90/95] llama : improve output buffer type selection (#10098) --- src/llama.cpp | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index ed3998a1f..ca0d259b2 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -17162,18 +17162,10 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { auto * buft = ggml_backend_cpu_buffer_type(); // try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory - ggml_tensor * output_tensor = lctx.model.output; - if (!output_tensor) { - // bert models don't have an output tensor, use the last layer - output_tensor = lctx.model.layers.back().layer_out_norm; - } - if (output_tensor) { - auto * output_buft = ggml_backend_buffer_get_type(output_tensor->buffer); - auto * output_dev = ggml_backend_buft_get_device(output_buft); - auto * output_dev_host_buft = ggml_backend_dev_host_buffer_type(output_dev); - if (output_dev_host_buft) { - buft = output_dev_host_buft; - } + auto * output_dev = lctx.model.dev_output.dev; + auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr; + if (output_dev_host_buft) { + buft = output_dev_host_buft; } lctx.buf_output = ggml_backend_buft_alloc_buffer(buft, new_size); if (lctx.buf_output == nullptr) { From e597e50794f07ec8dc24b9efb18f94ec6386fda0 Mon Sep 17 00:00:00 2001 From: Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com> Date: Fri, 1 Nov 2024 11:09:59 +0800 Subject: [PATCH 91/95] build: fix build error in Windows env with OneAPI setup (#10107) --- ggml/src/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 915568798..7365ac91b 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -1402,7 +1402,7 @@ list(APPEND GGML_EXTRA_LIBS_PRIVATE Threads::Threads) find_library(MATH_LIBRARY m) if (MATH_LIBRARY) - if (NOT WIN32 OR NOT GGML_SYCL) + if (NOT WIN32 OR NOT DEFINED ENV{ONEAPI_ROOT}) list(APPEND GGML_EXTRA_LIBS_PRIVATE m) endif() endif() From f221d56220899f38f0126e683b2432bc79d1e3f6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Nov 2024 10:23:05 +0200 Subject: [PATCH 92/95] ggml : alloc ggml_contexts on the heap (whisper/2525) --- ggml/include/ggml.h | 7 +++-- ggml/src/ggml.c | 67 +++++++++++++-------------------------------- 2 files changed, 23 insertions(+), 51 deletions(-) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index de3c706fc..e5862246c 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -217,7 +217,6 @@ #define GGML_MAX_DIMS 4 #define GGML_MAX_PARAMS 2048 -#define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 10 #define GGML_MAX_N_THREADS 512 #define GGML_MAX_OP_PARAMS 64 @@ -657,6 +656,7 @@ extern "C" { }; // scratch buffer + // TODO: deprecate and remove struct ggml_scratch { size_t offs; size_t size; @@ -760,8 +760,9 @@ extern "C" { // main - GGML_API struct ggml_context * ggml_init(struct ggml_init_params params); - GGML_API void ggml_free(struct ggml_context * ctx); + GGML_API struct ggml_context * ggml_init (struct ggml_init_params params); + GGML_API void ggml_reset(struct ggml_context * ctx); + GGML_API void ggml_free (struct ggml_context * ctx); GGML_API size_t ggml_used_mem(const struct ggml_context * ctx); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 6a7154920..59f2ed043 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -306,6 +306,7 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) { } #define GGML_DEBUG 0 + #define GGML_GELU_FP16 #define GGML_GELU_QUICK_FP16 @@ -2014,7 +2015,7 @@ static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object); struct ggml_context { size_t mem_size; - void* mem_buffer; + void * mem_buffer; bool mem_buffer_owned; bool no_alloc; bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers @@ -3263,7 +3264,6 @@ struct ggml_numa_nodes { // struct ggml_state { - struct ggml_context_container contexts[GGML_MAX_CONTEXTS]; struct ggml_numa_nodes numa; }; @@ -3845,7 +3845,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); g_state = (struct ggml_state) { - /*.contexts =*/ { { 0 } }, /*.numa =*/ { .n_nodes = 0, .total_cpus = 0, @@ -3864,26 +3863,9 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { is_first_call = false; } - // find non-used context in g_state - struct ggml_context * ctx = NULL; + ggml_critical_section_end(); - for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { - if (!g_state.contexts[i].used) { - g_state.contexts[i].used = true; - ctx = &g_state.contexts[i].context; - - GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i); - break; - } - } - - if (ctx == NULL) { - GGML_PRINT_DEBUG("%s: no unused context found\n", __func__); - - ggml_critical_section_end(); - - return NULL; - } + struct ggml_context * ctx = GGML_MALLOC(sizeof(struct ggml_context)); // allow to call ggml_init with 0 size if (params.mem_size == 0) { @@ -3911,42 +3893,31 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: context initialized\n", __func__); - ggml_critical_section_end(); - return ctx; } +void ggml_reset(struct ggml_context * ctx) { + if (ctx == NULL) { + return; + } + + ctx->n_objects = 0; + ctx->objects_begin = NULL; + ctx->objects_end = NULL; + ctx->scratch = (struct ggml_scratch) { 0, 0, NULL, }; + ctx->scratch_save = (struct ggml_scratch) { 0, 0, NULL, }; +} + void ggml_free(struct ggml_context * ctx) { if (ctx == NULL) { return; } - // make this function thread safe - ggml_critical_section_start(); - - bool found = false; - - for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { - if (&g_state.contexts[i].context == ctx) { - g_state.contexts[i].used = false; - - GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n", - __func__, i, ggml_used_mem(ctx)); - - if (ctx->mem_buffer_owned) { - ggml_aligned_free(ctx->mem_buffer, ctx->mem_size); - } - - found = true; - break; - } + if (ctx->mem_buffer_owned) { + ggml_aligned_free(ctx->mem_buffer, ctx->mem_size); } - if (!found) { - GGML_PRINT_DEBUG("%s: context not found\n", __func__); - } - - ggml_critical_section_end(); + GGML_FREE(ctx); } size_t ggml_used_mem(const struct ggml_context * ctx) { From 815fe72adcea5ec79d358db6a4c479191f396b3c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Nov 2024 10:28:24 +0200 Subject: [PATCH 93/95] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index da40927e1..48863847c 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -162e232411ee98ceb0cccfa84886118d917d2123 +bb78a40dc60e04c626bac2b65840b509988e990d From 1804adb0cfee4811eaf633741503d683a46e4c77 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Nov 2024 12:58:45 +0200 Subject: [PATCH 94/95] ggml : remove ggml_scratch (#10121) ggml-ci --- ggml/include/ggml.h | 9 ------ ggml/src/ggml.c | 67 ++------------------------------------------- 2 files changed, 2 insertions(+), 74 deletions(-) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index e5862246c..41df85557 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -655,14 +655,6 @@ extern "C" { void * abort_callback_data; }; - // scratch buffer - // TODO: deprecate and remove - struct ggml_scratch { - size_t offs; - size_t size; - void * data; - }; - struct ggml_init_params { // memory pool size_t mem_size; // bytes @@ -766,7 +758,6 @@ extern "C" { GGML_API size_t ggml_used_mem(const struct ggml_context * ctx); - GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch); GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx); GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 59f2ed043..84f2c766b 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -2018,15 +2018,11 @@ struct ggml_context { void * mem_buffer; bool mem_buffer_owned; bool no_alloc; - bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers int n_objects; struct ggml_object * objects_begin; struct ggml_object * objects_end; - - struct ggml_scratch scratch; - struct ggml_scratch scratch_save; }; struct ggml_context_container { @@ -3879,12 +3875,9 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : ggml_aligned_malloc(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, - /*.no_alloc_save =*/ params.no_alloc, /*.n_objects =*/ 0, /*.objects_begin =*/ NULL, /*.objects_end =*/ NULL, - /*.scratch =*/ { 0, 0, NULL, }, - /*.scratch_save =*/ { 0, 0, NULL, }, }; GGML_ASSERT(ctx->mem_buffer != NULL); @@ -3904,8 +3897,6 @@ void ggml_reset(struct ggml_context * ctx) { ctx->n_objects = 0; ctx->objects_begin = NULL; ctx->objects_end = NULL; - ctx->scratch = (struct ggml_scratch) { 0, 0, NULL, }; - ctx->scratch_save = (struct ggml_scratch) { 0, 0, NULL, }; } void ggml_free(struct ggml_context * ctx) { @@ -3924,14 +3915,6 @@ size_t ggml_used_mem(const struct ggml_context * ctx) { return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size; } -size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) { - const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0; - - ctx->scratch = scratch; - - return result; -} - bool ggml_get_no_alloc(struct ggml_context * ctx) { return ctx->no_alloc; } @@ -3959,27 +3942,6 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { return max_size; } -// IMPORTANT: -// when creating "opt" tensors, always save and load the scratch buffer -// this is an error prone process, but it is necessary to support inplace -// operators when using scratch buffers -// TODO: implement a better way -static void ggml_scratch_save(struct ggml_context * ctx) { - // this is needed to allow opt tensors to store their data - // TODO: again, need to find a better way - ctx->no_alloc_save = ctx->no_alloc; - ctx->no_alloc = false; - - ctx->scratch_save = ctx->scratch; - ctx->scratch.data = NULL; -} - -static void ggml_scratch_load(struct ggml_context * ctx) { - ctx->no_alloc = ctx->no_alloc_save; - - ctx->scratch = ctx->scratch_save; -} - //////////////////////////////////////////////////////////////////////////////// static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) { @@ -4060,29 +4022,13 @@ static struct ggml_tensor * ggml_new_tensor_impl( size_t obj_alloc_size = 0; if (view_src == NULL && !ctx->no_alloc) { - if (ctx->scratch.data != NULL) { - // allocate tensor data in the scratch buffer - if (ctx->scratch.offs + data_size > ctx->scratch.size) { - GGML_LOG_WARN("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", - __func__, ctx->scratch.offs + data_size, ctx->scratch.size); - assert(false); - return NULL; - } - - data = (char * const) ctx->scratch.data + ctx->scratch.offs; - - ctx->scratch.offs += data_size; - } else { - // allocate tensor data in the context's memory pool - obj_alloc_size = data_size; - } + // allocate tensor data in the context's memory pool + obj_alloc_size = data_size; } struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); GGML_ASSERT(obj_new); - // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here - struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); #ifdef __clang__ @@ -4178,24 +4124,16 @@ struct ggml_tensor * ggml_new_tensor_4d( } struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { - ggml_scratch_save(ctx); - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); - ggml_scratch_load(ctx); - ggml_set_i32(result, value); return result; } struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { - ggml_scratch_save(ctx); - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); - ggml_scratch_load(ctx); - ggml_set_f32(result, value); return result; @@ -20263,7 +20201,6 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { uint64_t size_eval = 0; // compute size of intermediate results - // TODO: does not take into account scratch buffers !!!! for (int i = 0; i < cgraph->n_nodes; ++i) { size_eval += ggml_nbytes_pad(cgraph->nodes[i]); } From d865d1478cd4e403f82d793c2afcd0f943412f05 Mon Sep 17 00:00:00 2001 From: sasha0552 Date: Fri, 1 Nov 2024 13:33:14 +0000 Subject: [PATCH 95/95] server : fix smart selection of available slot (#10120) * Fix smart selection of available slot * minor fix * replace vectors of tokens with shorthands --- examples/server/server.cpp | 35 +++++++++---------------- examples/server/utils.hpp | 52 ++++++++++++++++++++++++++++++++++---- 2 files changed, 59 insertions(+), 28 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f914ff88c..54cdb4b72 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -725,12 +725,12 @@ struct server_context { return nullptr; } - server_slot * get_available_slot(const std::string & prompt) { + server_slot * get_available_slot(const server_task & task) { server_slot * ret = nullptr; // find the slot that has at least n% prompt similarity - if (ret == nullptr && slot_prompt_similarity != 0.0f && !prompt.empty()) { - int max_lcp_len = 0; + if (ret == nullptr && slot_prompt_similarity != 0.0f) { + int max_lcs_len = 0; float similarity = 0; for (server_slot & slot : slots) { @@ -740,25 +740,25 @@ struct server_context { } // skip the slot if it does not contains cached tokens - if (slot.prompt_tokens.empty()) { + if (slot.cache_tokens.empty()) { continue; } - // length of the Longest Common Prefix between the current slot's prompt and the input prompt - int lcp_len = longest_common_prefix(slot.cache_tokens, slot.prompt_tokens); + // length of the Longest Common Subsequence between the current slot's prompt and the input prompt + int lcs_len = longest_common_subsequence(slot.cache_tokens, task.prompt_tokens); - // fraction of the common substring length compared to the current slot's prompt length - similarity = static_cast(lcp_len) / static_cast(slot.prompt_tokens.size()); + // fraction of the common subsequence length compared to the current slot's prompt length + similarity = static_cast(lcs_len) / static_cast(slot.cache_tokens.size()); // select the current slot if the criteria match - if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) { - max_lcp_len = lcp_len; + if (lcs_len > max_lcs_len && similarity > slot_prompt_similarity) { + max_lcs_len = lcs_len; ret = &slot; } } if (ret != nullptr) { - SLT_DBG(*ret, "selected slot by lcp similarity, max_lcp_len = %d, similarity = %f\n", max_lcp_len, similarity); + SLT_DBG(*ret, "selected slot by lcs similarity, max_lcs_len = %d, similarity = %f\n", max_lcs_len, similarity); } } @@ -1514,18 +1514,7 @@ struct server_context { { const int id_slot = json_value(task.data, "id_slot", -1); - server_slot * slot; - - if (id_slot != -1) { - slot = get_slot_by_id(id_slot); - } else { - std::string prompt; - if (task.data.contains("prompt") && task.data.at("prompt").is_string()) { - prompt = json_value(task.data, "prompt", std::string()); - } - - slot = get_available_slot(prompt); - } + server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task); if (slot == nullptr) { // if no slot is available, we defer this task for processing later diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 58f5a5684..871a17a4f 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -439,18 +439,60 @@ static std::string gen_chatcmplid() { // other common utils // -static size_t longest_common_prefix(const std::vector & a, const std::vector & b) { +static size_t longest_common_prefix(const llama_tokens & a, const llama_tokens & b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} return i; } -static size_t longest_common_prefix(const std::string & a, const std::string & b) { - size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} +static size_t longest_common_subsequence(const llama_tokens & a, const llama_tokens & b) { + // check for empty sequences + if (a.empty() || b.empty()) { + return 0; + } - return i; + // get the lengths of the input sequences + int a_len = a.size(); + int b_len = b.size(); + + // initialize the maximum length of the longest common subsequence (LCS) + int max_length = 0; + + // use two rows instead of a 2D matrix to optimize space + std::vector prev_row(b_len + 1, 0); + std::vector curr_row(b_len + 1, 0); + + // iterate through the elements of a + for (int i = 1; i <= a_len; i++) { + // iterate through the elements of b + for (int j = 1; j <= b_len; j++) { + // if elements at the current positions match + if (a[i - 1] == b[j - 1]) { + // if it's the first element of either sequences, set LCS length to 1 + if (i == 1 || j == 1) { + curr_row[j] = 1; + } else { + // increment LCS length by 1 compared to the previous element + curr_row[j] = prev_row[j - 1] + 1; + } + + // update max_length if necessary + if (curr_row[j] > max_length) { + max_length = curr_row[j]; + } + } else { + // reset LCS length if elements don't match + curr_row[j] = 0; + } + } + + // update the previous row for the next iteration + prev_row = curr_row; + } + + // return the maximum length of the LCS + return max_length; } static bool ends_with(const std::string & str, const std::string & suffix) {