mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-12 19:50:17 +00:00
Merge branch 'master' into gg/llama-perf
ggml-ci
This commit is contained in:
Georgi Gerganov
commit
44f0218532
@ -89,6 +89,7 @@ Typically finetunes of the base models below are supported as well.
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- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
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- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
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- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
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- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
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(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))
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@ -173,7 +173,6 @@ static bool gpt_params_parse_ex(int argc, char ** argv, gpt_params_context & ctx
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std::string arg;
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const std::string arg_prefix = "--";
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gpt_params & params = ctx_arg.params;
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gpt_sampler_params & sparams = params.sparams;
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std::unordered_map<std::string, llama_arg *> arg_to_options;
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for (auto & opt : ctx_arg.options) {
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@ -283,10 +282,6 @@ static bool gpt_params_parse_ex(int argc, char ** argv, gpt_params_context & ctx
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params.kv_overrides.back().key[0] = 0;
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}
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if (sparams.seed == LLAMA_DEFAULT_SEED) {
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sparams.seed = time(NULL);
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}
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return true;
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}
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@ -831,7 +826,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
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[](gpt_params & params) {
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params.special = true;
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}
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).set_examples({LLAMA_EXAMPLE_MAIN}));
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).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
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add_opt(llama_arg(
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{"-cnv", "--conversation"},
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format(
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@ -917,7 +912,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
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).set_sparam());
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add_opt(llama_arg(
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{"-s", "--seed"}, "SEED",
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format("RNG seed (default: %d, use random seed for < 0)", params.sparams.seed),
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format("RNG seed (default: %u, use random seed for %u)", params.sparams.seed, LLAMA_DEFAULT_SEED),
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[](gpt_params & params, const std::string & value) {
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params.sparams.seed = std::stoul(value);
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}
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@ -1430,20 +1425,18 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
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params.split_mode = LLAMA_SPLIT_MODE_NONE;
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} else if (arg_next == "layer") {
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params.split_mode = LLAMA_SPLIT_MODE_LAYER;
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}
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else if (arg_next == "row") {
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} else if (arg_next == "row") {
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#ifdef GGML_USE_SYCL
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fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
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exit(1);
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#endif // GGML_USE_SYCL
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params.split_mode = LLAMA_SPLIT_MODE_ROW;
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}
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else {
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} else {
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throw std::invalid_argument("invalid value");
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}
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#ifndef GGML_USE_CUDA_SYCL_VULKAN
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fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
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#endif // GGML_USE_CUDA_SYCL_VULKAN
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if (!llama_supports_gpu_offload()) {
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fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the split mode has no effect.\n");
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}
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}
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));
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add_opt(llama_arg(
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@ -1468,9 +1461,9 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
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params.tensor_split[i] = 0.0f;
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}
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}
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#ifndef GGML_USE_CUDA_SYCL_VULKAN
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fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
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#endif // GGML_USE_CUDA_SYCL_VULKAN
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if (!llama_supports_gpu_offload()) {
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fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting a tensor split has no effect.\n");
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}
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}
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));
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add_opt(llama_arg(
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@ -1478,9 +1471,9 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
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format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu),
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[](gpt_params & params, int value) {
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params.main_gpu = value;
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#ifndef GGML_USE_CUDA_SYCL_VULKAN
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fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
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#endif // GGML_USE_CUDA_SYCL_VULKAN
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if (!llama_supports_gpu_offload()) {
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fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the main GPU has no effect.\n");
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}
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}
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));
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add_opt(llama_arg(
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@ -56,14 +56,6 @@
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
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#define GGML_USE_CUDA_SYCL
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#endif
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#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
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#define GGML_USE_CUDA_SYCL_VULKAN
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#endif
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#if defined(LLAMA_USE_CURL)
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#ifdef __linux__
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#include <linux/limits.h>
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@ -950,11 +942,37 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
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#ifdef LLAMA_USE_CURL
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#define CURL_MAX_RETRY 3
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#define CURL_RETRY_DELAY_SECONDS 2
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static bool starts_with(const std::string & str, const std::string & prefix) {
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// While we wait for C++20's std::string::starts_with...
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return str.rfind(prefix, 0) == 0;
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}
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static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
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int remaining_attempts = max_attempts;
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while (remaining_attempts > 0) {
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fprintf(stderr, "%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
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CURLcode res = curl_easy_perform(curl);
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if (res == CURLE_OK) {
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return true;
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}
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int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
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fprintf(stderr, "%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
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remaining_attempts--;
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std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
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}
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fprintf(stderr, "%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
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return false;
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}
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static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
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// Initialize libcurl
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@ -1058,9 +1076,8 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
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curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
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CURLcode res = curl_easy_perform(curl.get());
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if (res != CURLE_OK) {
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fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
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bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
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if (!was_perform_successful) {
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return false;
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}
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@ -1135,11 +1152,10 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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};
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// start the download
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fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
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fprintf(stderr, "%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
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llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
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auto res = curl_easy_perform(curl.get());
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if (res != CURLE_OK) {
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fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
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bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
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if (!was_perform_successful) {
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return false;
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}
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@ -310,6 +310,10 @@ llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context
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return cur_p.data[cur_p.selected].id;
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}
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uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl) {
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return llama_sampler_get_seed(gsmpl->chain);
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}
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// helpers
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llama_token_data_array * gpt_sampler_get_candidates(struct gpt_sampler * gsmpl) {
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@ -60,6 +60,8 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
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//
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llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
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uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl);
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// helpers
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// access the internal list of current candidate tokens
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@ -31,6 +31,7 @@ import re
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import requests
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import sys
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import json
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import shutil
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from hashlib import sha256
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from enum import IntEnum, auto
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@ -125,6 +126,21 @@ def download_model(model):
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if tokt == TOKENIZER_TYPE.UGM:
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files.append("spiece.model")
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if os.path.isdir(repo):
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# If repo is a path on the file system, copy the directory
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for file in files:
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src_path = os.path.join(repo, file)
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dst_path = f"models/tokenizers/{name}/{file}"
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if os.path.isfile(dst_path):
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logger.info(f"{name}: File {dst_path} already exists - skipping")
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continue
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if os.path.isfile(src_path):
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shutil.copy2(src_path, dst_path)
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logger.info(f"{name}: Copied {src_path} to {dst_path}")
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else:
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logger.warning(f"{name}: Source file {src_path} does not exist")
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else:
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# If repo is a URL, download the files
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for file in files:
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save_path = f"models/tokenizers/{name}/{file}"
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if os.path.isfile(save_path):
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@ -3,32 +3,10 @@
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#include "llama.h"
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#include <algorithm>
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#include <cmath>
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#include <cstdio>
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#include <string>
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#include <vector>
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// mutates the input string
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static std::vector<int> parse_list(char * p) {
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std::vector<int> ret;
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char * q = p;
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while (*p) {
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if (*p == ',') {
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*p = '\0';
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ret.push_back(std::atoi(q));
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q = p + 1;
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}
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++p;
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}
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ret.push_back(std::atoi(q));
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return ret;
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}
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static void print_usage(int, char ** argv) {
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LOG_TEE("\nexample usage:\n");
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LOG_TEE("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]);
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@ -90,8 +90,6 @@ int main(int argc, char ** argv) {
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print_build_info();
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LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
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llama_backend_init();
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llama_numa_init(params.numa);
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@ -159,8 +159,6 @@ int main(int argc, char ** argv) {
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print_build_info();
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LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
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LOG("%s: llama backend init\n", __func__);
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llama_backend_init();
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llama_numa_init(params.numa);
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@ -301,6 +299,9 @@ int main(int argc, char ** argv) {
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LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
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}
|
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}
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smpl = gpt_sampler_init(model, sparams);
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LOG_TEE("sampling seed: %u\n", gpt_sampler_get_seed(smpl));
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LOG_TEE("sampling: \n%s\n", sparams.print().c_str());
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LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
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LOG_TEE("\n\n");
|
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@ -340,8 +341,6 @@ int main(int argc, char ** argv) {
|
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std::vector<llama_token> embd;
|
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smpl = gpt_sampler_init(model, sparams);
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while (n_remain != 0 || params.interactive) {
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// predict
|
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if (!embd.empty()) {
|
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@ -18,8 +18,8 @@ struct llava_context {
|
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};
|
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static void show_additional_info(int /*argc*/, char ** argv) {
|
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LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
|
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LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
|
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LOG_TEE("\nexample usage:\n\n%s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
|
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LOG_TEE("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n");
|
||||
}
|
||||
|
||||
static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
|
||||
@ -255,7 +255,7 @@ int main(int argc, char ** argv) {
|
||||
|
||||
gpt_params params;
|
||||
|
||||
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, show_additional_info)) {
|
||||
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
||||
@ -191,8 +191,6 @@ int main(int argc, char ** argv) {
|
||||
|
||||
print_build_info();
|
||||
|
||||
LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
|
||||
|
||||
LOG("%s: llama backend init\n", __func__);
|
||||
llama_backend_init();
|
||||
llama_numa_init(params.numa);
|
||||
@ -470,8 +468,10 @@ int main(int argc, char ** argv) {
|
||||
exit(1);
|
||||
}
|
||||
|
||||
LOG_TEE("sampling seed: %u\n", gpt_sampler_get_seed(smpl));
|
||||
LOG_TEE("sampling params: \n%s\n", sparams.print().c_str());
|
||||
LOG_TEE(" sampler constr: \n%s\n", gpt_sampler_print(smpl).c_str());
|
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LOG_TEE("sampler constr: \n%s\n", gpt_sampler_print(smpl).c_str());
|
||||
|
||||
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
|
||||
|
||||
// group-attention state
|
||||
|
||||
@ -2007,8 +2007,6 @@ int main(int argc, char ** argv) {
|
||||
|
||||
print_build_info();
|
||||
|
||||
LOG_TEE("%s: seed = %u\n", __func__, params.sparams.seed);
|
||||
|
||||
llama_backend_init();
|
||||
llama_numa_init(params.numa);
|
||||
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||||
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||||
@ -1266,6 +1266,7 @@ struct server_context {
|
||||
{"n_predict", slot.n_predict}, // Server configured n_predict
|
||||
{"model", params.model_alias},
|
||||
{"seed", slot.sparams.seed},
|
||||
{"seed_cur", slot.smpl ? gpt_sampler_get_seed(slot.smpl) : 0},
|
||||
{"temperature", slot.sparams.temp},
|
||||
{"dynatemp_range", slot.sparams.dynatemp_range},
|
||||
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
|
||||
|
||||
20
flake.lock
20
flake.lock
@ -5,11 +5,11 @@
|
||||
"nixpkgs-lib": "nixpkgs-lib"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1725024810,
|
||||
"narHash": "sha256-ODYRm8zHfLTH3soTFWE452ydPYz2iTvr9T8ftDMUQ3E=",
|
||||
"lastModified": 1725234343,
|
||||
"narHash": "sha256-+ebgonl3NbiKD2UD0x4BszCZQ6sTfL4xioaM49o5B3Y=",
|
||||
"owner": "hercules-ci",
|
||||
"repo": "flake-parts",
|
||||
"rev": "af510d4a62d071ea13925ce41c95e3dec816c01d",
|
||||
"rev": "567b938d64d4b4112ee253b9274472dc3a346eb6",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@ -20,11 +20,11 @@
|
||||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1724819573,
|
||||
"narHash": "sha256-GnR7/ibgIH1vhoy8cYdmXE6iyZqKqFxQSVkFgosBh6w=",
|
||||
"lastModified": 1725634671,
|
||||
"narHash": "sha256-v3rIhsJBOMLR8e/RNWxr828tB+WywYIoajrZKFM+0Gg=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "71e91c409d1e654808b2621f28a327acfdad8dc2",
|
||||
"rev": "574d1eac1c200690e27b8eb4e24887f8df7ac27c",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@ -36,14 +36,14 @@
|
||||
},
|
||||
"nixpkgs-lib": {
|
||||
"locked": {
|
||||
"lastModified": 1722555339,
|
||||
"narHash": "sha256-uFf2QeW7eAHlYXuDktm9c25OxOyCoUOQmh5SZ9amE5Q=",
|
||||
"lastModified": 1725233747,
|
||||
"narHash": "sha256-Ss8QWLXdr2JCBPcYChJhz4xJm+h/xjl4G0c0XlP6a74=",
|
||||
"type": "tarball",
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
|
||||
},
|
||||
"original": {
|
||||
"type": "tarball",
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
|
||||
}
|
||||
},
|
||||
"root": {
|
||||
|
||||
@ -1942,7 +1942,7 @@ GGML_CALL ggml_backend_t ggml_backend_cann_init(int32_t device) {
|
||||
GGML_CANN_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
ggml_cann_set_device(ctx->device);
|
||||
ggml_backend_t cann_backend =
|
||||
new ggml_backend{/* .guid = */ ggml_backend_cann_guid(),
|
||||
/* .interface = */ ggml_backend_cann_interface,
|
||||
|
||||
@ -26,7 +26,11 @@ void ggml_cuda_op_mul_mat_q(
|
||||
// nrows_dst == nrows of the matrix that the kernel writes into
|
||||
const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
|
||||
|
||||
const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst};
|
||||
// The stream-k decomposition is only faster for recent NVIDIA GPUs.
|
||||
// Also its fixup needs to allocate a temporary buffer in the memory pool.
|
||||
// There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
|
||||
const bool use_stream_k = compute_capability >= CC_VOLTA && compute_capability < CC_OFFSET_AMD && src1_ncols == ne11;
|
||||
const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst, use_stream_k};
|
||||
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
|
||||
@ -2742,6 +2742,7 @@ struct mmq_args {
|
||||
int64_t ne00; int64_t ne01; int64_t stride01;
|
||||
int64_t ne10; int64_t ne11; int64_t stride11;
|
||||
int64_t ne0;
|
||||
bool use_stream_k;
|
||||
};
|
||||
|
||||
template<ggml_type type>
|
||||
@ -2777,8 +2778,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
|
||||
const int ntx = (args.ne11 + mmq_x - 1) / mmq_x;
|
||||
const dim3 block_nums_xy_tiling(nty, ntx, 1);
|
||||
|
||||
const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;
|
||||
if (!use_stream_k) {
|
||||
if (!args.use_stream_k) {
|
||||
if (args.ne01 % mmq_y == 0) {
|
||||
constexpr bool need_check = false;
|
||||
mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>
|
||||
|
||||
39
ggml/src/ggml-cuda/vendors/musa.h
vendored
39
ggml/src/ggml-cuda/vendors/musa.h
vendored
@ -130,42 +130,3 @@
|
||||
#define cudaKernelNodeParams musaKernelNodeParams
|
||||
#define cudaStreamCaptureModeRelaxed musaStreamCaptureModeRelaxed
|
||||
#define cudaStreamEndCapture musaStreamEndCapture
|
||||
|
||||
// XXX: Clang builtins mapping
|
||||
#define __vsub4 __vsub4_musa
|
||||
#define __vcmpeq4 __vcmpeq4_musa
|
||||
#define __vcmpne4 __vcmpne4_musa
|
||||
|
||||
#ifndef __has_builtin
|
||||
#define __has_builtin(x) 0
|
||||
#endif
|
||||
|
||||
typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
|
||||
|
||||
static __device__ __forceinline__ int __vsub4_musa(const int a, const int b) {
|
||||
return __vsubss4(a, b);
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ unsigned int __vcmpeq4_musa(unsigned int a, unsigned int b) {
|
||||
const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
|
||||
const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
|
||||
unsigned int c;
|
||||
uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
|
||||
#pragma unroll
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
vc[i] = va[i] == vb[i] ? 0xff : 0x00;
|
||||
}
|
||||
return c;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ unsigned int __vcmpne4_musa(unsigned int a, unsigned int b) {
|
||||
const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
|
||||
const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
|
||||
unsigned int c;
|
||||
uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
|
||||
#pragma unroll
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
vc[i] = va[i] == vb[i] ? 0x00 : 0xff;
|
||||
}
|
||||
return c;
|
||||
}
|
||||
|
||||
@ -5137,13 +5137,17 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
|
||||
case GGML_OP_SCALE:
|
||||
case GGML_OP_SQR:
|
||||
case GGML_OP_CLAMP:
|
||||
return true;
|
||||
case GGML_OP_CONT:
|
||||
return op->src[0]->type != GGML_TYPE_BF16;
|
||||
case GGML_OP_DIAG_MASK_INF:
|
||||
case GGML_OP_SOFT_MAX:
|
||||
return true;
|
||||
case GGML_OP_ROPE:
|
||||
return ggml_is_contiguous(op->src[0]);
|
||||
case GGML_OP_IM2COL:
|
||||
// TODO: add support for the new F32 operations
|
||||
return op->src[0]->type == GGML_TYPE_F16;
|
||||
case GGML_OP_POOL_2D:
|
||||
case GGML_OP_SUM_ROWS:
|
||||
case GGML_OP_ARGSORT:
|
||||
|
||||
@ -1127,6 +1127,10 @@ extern "C" {
|
||||
int32_t n_logit_bias,
|
||||
const llama_logit_bias * logit_bias);
|
||||
|
||||
|
||||
// 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);
|
||||
|
||||
/// @details Sample and accept a token from the idx-th output of the last evaluation
|
||||
//
|
||||
// Shorthand for:
|
||||
|
||||
@ -8,6 +8,7 @@
|
||||
#include <cstring>
|
||||
#include <ctime>
|
||||
#include <cfloat>
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
#include <random>
|
||||
@ -162,6 +163,19 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
|
||||
cur_p->size = k;
|
||||
}
|
||||
|
||||
static uint32_t get_rng_seed(uint32_t seed) {
|
||||
if (seed == LLAMA_DEFAULT_SEED) {
|
||||
// use system clock if std::random_device is not a true RNG
|
||||
static bool is_rd_prng = std::random_device().entropy() == 0;
|
||||
if (is_rd_prng) {
|
||||
return (uint32_t) std::chrono::system_clock::now().time_since_epoch().count();
|
||||
}
|
||||
std::random_device rd;
|
||||
return rd();
|
||||
}
|
||||
return seed;
|
||||
}
|
||||
|
||||
// llama_sampler API
|
||||
|
||||
const char * llama_sampler_name(const struct llama_sampler * smpl) {
|
||||
@ -387,6 +401,7 @@ struct llama_sampler * llama_sampler_init_greedy() {
|
||||
|
||||
struct llama_sampler_dist {
|
||||
const uint32_t seed;
|
||||
uint32_t seed_cur;
|
||||
|
||||
std::mt19937 rng;
|
||||
};
|
||||
@ -416,7 +431,8 @@ static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sample
|
||||
|
||||
static void llama_sampler_dist_reset(struct llama_sampler * smpl) {
|
||||
auto * ctx = (llama_sampler_dist *) smpl->ctx;
|
||||
ctx->rng = std::mt19937(ctx->seed);
|
||||
ctx->seed_cur = get_rng_seed(ctx->seed);
|
||||
ctx->rng.seed(ctx->seed_cur);
|
||||
}
|
||||
|
||||
static void llama_sampler_dist_free(struct llama_sampler * smpl) {
|
||||
@ -433,11 +449,13 @@ static struct llama_sampler_i llama_sampler_dist_i = {
|
||||
};
|
||||
|
||||
struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
|
||||
auto seed_cur = get_rng_seed(seed);
|
||||
return new llama_sampler {
|
||||
/* .iface = */ &llama_sampler_dist_i,
|
||||
/* .ctx = */ new llama_sampler_dist {
|
||||
/* .seed = */ seed,
|
||||
/* .rng = */ std::mt19937(seed),
|
||||
/* .seed_cur = */ seed_cur,
|
||||
/* .rng = */ std::mt19937(seed_cur),
|
||||
},
|
||||
};
|
||||
}
|
||||
@ -1032,6 +1050,7 @@ struct llama_sampler_mirostat {
|
||||
const int32_t n_vocab;
|
||||
|
||||
const uint32_t seed;
|
||||
uint32_t seed_cur;
|
||||
|
||||
const float tau;
|
||||
const float eta;
|
||||
@ -1100,7 +1119,8 @@ static struct llama_sampler * llama_sampler_mirostat_clone(const struct llama_sa
|
||||
static void llama_sampler_mirostat_reset(struct llama_sampler * smpl) {
|
||||
auto * ctx = (llama_sampler_mirostat *) smpl->ctx;
|
||||
ctx->mu = 2.0f*ctx->tau;
|
||||
ctx->rng = std::mt19937(ctx->seed);
|
||||
ctx->seed_cur = get_rng_seed(ctx->seed);
|
||||
ctx->rng.seed(ctx->seed_cur);
|
||||
}
|
||||
|
||||
static void llama_sampler_mirostat_free(struct llama_sampler * smpl) {
|
||||
@ -1117,16 +1137,18 @@ static struct llama_sampler_i llama_sampler_mirostat_i = {
|
||||
};
|
||||
|
||||
struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
|
||||
auto seed_cur = get_rng_seed(seed);
|
||||
return new llama_sampler {
|
||||
/* .iface = */ &llama_sampler_mirostat_i,
|
||||
/* .ctx = */ new llama_sampler_mirostat {
|
||||
/* .n_vocab = */ n_vocab,
|
||||
/* .seed = */ seed,
|
||||
/* .seed_cur = */ seed_cur,
|
||||
/* .tau = */ tau,
|
||||
/* .eta = */ eta,
|
||||
/* .m = */ m,
|
||||
/* .mu = */ 2.0f*tau,
|
||||
/* .rng = */ std::mt19937(seed),
|
||||
/* .rng = */ std::mt19937(seed_cur),
|
||||
},
|
||||
};
|
||||
}
|
||||
@ -1135,6 +1157,7 @@ struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t see
|
||||
|
||||
struct llama_sampler_mirostat_v2 {
|
||||
const uint32_t seed;
|
||||
uint32_t seed_cur;
|
||||
|
||||
const float tau;
|
||||
const float eta;
|
||||
@ -1179,7 +1202,8 @@ static void llama_sampler_mirostat_v2_apply(struct llama_sampler * smpl, llama_t
|
||||
static void llama_sampler_mirostat_v2_reset(struct llama_sampler * smpl) {
|
||||
auto * ctx = (llama_sampler_mirostat_v2 *) smpl->ctx;
|
||||
ctx->mu = 2.0f*ctx->tau;
|
||||
ctx->rng = std::mt19937(ctx->seed);
|
||||
ctx->seed_cur = get_rng_seed(ctx->seed);
|
||||
ctx->rng.seed(ctx->seed_cur);
|
||||
}
|
||||
|
||||
static struct llama_sampler * llama_sampler_mirostat_v2_clone(const struct llama_sampler * smpl) {
|
||||
@ -1212,14 +1236,16 @@ static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
|
||||
};
|
||||
|
||||
struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
|
||||
auto seed_cur = get_rng_seed(seed);
|
||||
return new llama_sampler {
|
||||
/* .iface = */ &llama_sampler_mirostat_v2_i,
|
||||
/* .ctx = */ new llama_sampler_mirostat_v2 {
|
||||
/* .seed = */ seed,
|
||||
/* .seed_cur = */ seed_cur,
|
||||
/* .tau = */ tau,
|
||||
/* .eta = */ eta,
|
||||
/* .mu = */ 2.0f*tau,
|
||||
/* .rng = */ std::mt19937(seed),
|
||||
/* .rng = */ std::mt19937(seed_cur),
|
||||
},
|
||||
};
|
||||
}
|
||||
@ -1505,6 +1531,8 @@ struct llama_sampler * llama_sampler_init_penalties(
|
||||
ignore_eos = false;
|
||||
}
|
||||
|
||||
penalty_last_n = std::max(penalty_last_n, 0);
|
||||
|
||||
return new llama_sampler {
|
||||
/* .iface = */ &llama_sampler_penalties_i,
|
||||
/* .ctx = */ new llama_sampler_penalties {
|
||||
@ -1568,6 +1596,7 @@ static void llama_sampler_logit_bias_apply(struct llama_sampler * smpl, llama_to
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static struct llama_sampler * llama_sampler_logit_bias_clone(const struct llama_sampler * smpl) {
|
||||
const auto * ctx = (const llama_sampler_logit_bias *) smpl->ctx;
|
||||
return llama_sampler_init_logit_bias(ctx->n_vocab, ctx->logit_bias.size(), ctx->logit_bias.data());
|
||||
@ -1600,6 +1629,34 @@ struct llama_sampler * llama_sampler_init_logit_bias(
|
||||
};
|
||||
}
|
||||
|
||||
// utils
|
||||
|
||||
uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) {
|
||||
if (smpl->iface == &llama_sampler_dist_i) {
|
||||
return ((const llama_sampler_dist *) smpl->ctx)->seed_cur;
|
||||
}
|
||||
|
||||
if (smpl->iface == &llama_sampler_mirostat_i) {
|
||||
return ((const llama_sampler_mirostat *) smpl->ctx)->seed_cur;
|
||||
}
|
||||
|
||||
if (smpl->iface == &llama_sampler_mirostat_v2_i) {
|
||||
return ((const llama_sampler_mirostat_v2 *) smpl->ctx)->seed_cur;
|
||||
}
|
||||
|
||||
if (smpl->iface == &llama_sampler_chain_i) {
|
||||
const auto * ctx = (const llama_sampler_chain *) smpl->ctx;
|
||||
for (auto it = ctx->samplers.rbegin(); it != ctx->samplers.rend(); ++it) {
|
||||
const uint32_t seed = llama_sampler_get_seed(*it);
|
||||
if (seed != LLAMA_DEFAULT_SEED) {
|
||||
return seed;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return LLAMA_DEFAULT_SEED;
|
||||
}
|
||||
|
||||
// perf
|
||||
|
||||
struct llama_perf_sampler_data llama_perf_sampler(const struct llama_sampler * chain) {
|
||||
|
||||
@ -16078,19 +16078,21 @@ static int llama_decode_internal(
|
||||
return -1;
|
||||
}
|
||||
|
||||
for (uint32_t i = 0; i < n_tokens_all; ++i) {
|
||||
if (batch_all.token[i] < 0 || (uint32_t)batch_all.token[i] >= lctx.model.vocab.n_vocab) {
|
||||
LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch_all.token[i]);
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
const auto & model = lctx.model;
|
||||
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
|
||||
|
||||
if (batch_all.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", __func__, i, batch_all.token[i]);
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
GGML_ASSERT(n_tokens_all <= cparams.n_batch);
|
||||
|
||||
GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
|
||||
@ -16377,19 +16379,21 @@ static int llama_encode_internal(
|
||||
return -1;
|
||||
}
|
||||
|
||||
for (uint32_t i = 0; i < n_tokens; ++i) {
|
||||
if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= lctx.model.vocab.n_vocab) {
|
||||
LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch.token[i]);
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
const auto & model = lctx.model;
|
||||
const auto & hparams = model.hparams;
|
||||
const auto & cparams = lctx.cparams;
|
||||
|
||||
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
|
||||
|
||||
if (batch.token) {
|
||||
for (uint32_t i = 0; i < n_tokens; ++i) {
|
||||
if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) {
|
||||
LLAMA_LOG_ERROR("%s: invalid token[%d] = %d", __func__, i, batch.token[i]);
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
|
||||
GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user