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server : fix context shift (#5195)

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* server : fix context shift + simplify self-extend

* server : take system_tokens into account

* server : more n_past fixes

* server : rever n_past_se changes
This commit is contained in:
Georgi Gerganov 2024-01-30 20:17:30 +02:00 committed by GitHub
parent 4003be0e5f
commit e6f291d158
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2 changed files with 60 additions and 50 deletions
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1
examples/server/chat.sh
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@ -48,6 +48,7 @@ chat_completion() {
top_p: 0.9, top_p: 0.9,
n_keep: $n_keep, n_keep: $n_keep,
n_predict: 256, n_predict: 256,
cache_prompt: true,
stop: ["\n### Human:"], stop: ["\n### Human:"],
stream: true stream: true
}')" }')"

109
examples/server/server.cpp
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@ -185,7 +185,7 @@ struct llama_client_slot
llama_sampling_context *ctx_sampling = nullptr; llama_sampling_context *ctx_sampling = nullptr;
int32_t ga_i = 0; // group-attention state int32_t ga_i = 0; // group-attention state
int32_t ga_n = 1;// group-attention factor int32_t ga_n = 1; // group-attention factor
int32_t ga_w = 512; // group-attention width int32_t ga_w = 512; // group-attention width
int32_t n_past_se = 0; // self-extend int32_t n_past_se = 0; // self-extend
@ -219,7 +219,8 @@ struct llama_client_slot
sent_token_probs_index = 0; sent_token_probs_index = 0;
infill = false; infill = false;
ga_i = 0; ga_i = 0;
n_past_se = 0; n_past_se = 0;
generated_token_probs.clear(); generated_token_probs.clear();
for (slot_image & img : images) for (slot_image & img : images)
@ -1227,7 +1228,7 @@ struct llama_server_context
std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
for (int i = 0; i < (int) append_tokens.size(); ++i) for (int i = 0; i < (int) append_tokens.size(); ++i)
{ {
llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true);
slot.n_past += 1; slot.n_past += 1;
} }
} }
@ -1295,6 +1296,8 @@ struct llama_server_context
for (llama_client_slot &slot : slots) for (llama_client_slot &slot : slots)
{ {
slot.cache_tokens.clear(); slot.cache_tokens.clear();
slot.n_past = 0;
slot.n_past_se = 0;
} }
} }
@ -1364,26 +1367,26 @@ struct llama_server_context
kv_cache_clear(); kv_cache_clear();
} }
return true; return true;
} else {
task_server task;
task.type = TASK_TYPE_NEXT_RESPONSE;
task.target_id = -1;
queue_tasks.post(task);
} }
task_server task;
task.type = TASK_TYPE_NEXT_RESPONSE;
task.target_id = -1;
queue_tasks.post(task);
for (llama_client_slot &slot : slots) for (llama_client_slot &slot : slots)
{ {
if (slot.ga_n == 1) if (slot.ga_n == 1)
{ {
if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx)
{ {
// Shift context // Shift context
const int n_left = slot.n_past - slot.params.n_keep - 1; const int n_left = system_tokens.size() + slot.n_past - slot.params.n_keep - 1;
const int n_discard = n_left / 2; const int n_discard = n_left / 2;
LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, system_tokens.size() + slot.n_past, -n_discard);
for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
{ {
@ -1429,8 +1432,10 @@ struct llama_server_context
slot.i_batch = batch.n_tokens; slot.i_batch = batch.n_tokens;
const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true);
// TODO: we always have to take into account the "system_tokens"
// this is not great and needs to be improved somehow
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true);
slot.n_past += 1; slot.n_past += 1;
} }
@ -1481,8 +1486,8 @@ struct llama_server_context
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model));
prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
prefix_tokens.push_back(llama_token_middle(model)); prefix_tokens.push_back(llama_token_middle(model));
prompt_tokens = prefix_tokens; prompt_tokens = prefix_tokens;
} }
@ -1582,8 +1587,8 @@ struct llama_server_context
} }
LOG_VERBOSE("prompt ingested", { LOG_VERBOSE("prompt ingested", {
{"n_past", slot.n_past}, {"n_past", slot.n_past},
{"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
{"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())},
}); });
@ -1591,10 +1596,13 @@ struct llama_server_context
// process the prefix of first image // process the prefix of first image
std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
int ga_i = slot.ga_i;
int32_t ga_i = slot.ga_i;
int32_t ga_n = slot.ga_n; int32_t ga_n = slot.ga_n;
int32_t ga_w = slot.ga_w; int32_t ga_w = slot.ga_w;
for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
{ {
if (slot.ga_n != 1) if (slot.ga_n != 1)
@ -1606,7 +1614,7 @@ struct llama_server_context
} }
} }
llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false);
slot_npast += 1; slot_npast++;
} }
if (has_images && !ingest_images(slot, n_batch)) if (has_images && !ingest_images(slot, n_batch))
@ -1666,6 +1674,7 @@ struct llama_server_context
slot.n_past_se += n_tokens; slot.n_past_se += n_tokens;
} }
} }
llama_batch batch_view = llama_batch batch_view =
{ {
n_tokens, n_tokens,
@ -1782,51 +1791,51 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_mlock_supported()) if (llama_mlock_supported())
{ {
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
} }
if (llama_mmap_supported()) if (llama_mmap_supported())
{ {
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
} }
printf(" --numa attempt optimizations that help on some NUMA systems\n"); printf(" --numa attempt optimizations that help on some NUMA systems\n");
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
printf(" -ngl N, --n-gpu-layers N\n"); printf(" -ngl N, --n-gpu-layers N\n");
printf(" number of layers to store in VRAM\n"); printf(" number of layers to store in VRAM\n");
printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
printf(" how to split the model across multiple GPUs, one of:\n"); printf(" how to split the model across multiple GPUs, one of:\n");
printf(" - none: use one GPU only\n"); printf(" - none: use one GPU only\n");
printf(" - layer (default): split layers and KV across GPUs\n"); printf(" - layer (default): split layers and KV across GPUs\n");
printf(" - row: split rows across GPUs\n"); printf(" - row: split rows across GPUs\n");
printf(" -ts SPLIT --tensor-split SPLIT\n"); printf(" -ts SPLIT --tensor-split SPLIT\n");
printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n");
printf(" or for intermediate results and KV (with split-mode = row)\n"); printf(" or for intermediate results and KV (with split-mode = row)\n");
#endif #endif
printf(" -m FNAME, --model FNAME\n"); printf(" -m FNAME, --model FNAME\n");
printf(" model path (default: %s)\n", params.model.c_str()); printf(" model path (default: %s)\n", params.model.c_str());
printf(" -a ALIAS, --alias ALIAS\n"); printf(" -a ALIAS, --alias ALIAS\n");
printf(" set an alias for the model, will be added as `model` field in completion response\n"); printf(" set an alias for the model, will be added as `model` field in completion response\n");
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n");
printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
printf(" -spf FNAME, --system-prompt-file FNAME\n"); printf(" -spf FNAME, --system-prompt-file FNAME\n");
printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n");
printf(" --log-disable disables logging to a file.\n"); printf(" --log-disable disables logging to a file.\n");
printf("\n"); printf("\n");
printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf(" -gan N, --grp-attn-n N Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`");
printf(" -gaw N, --grp-attn-w N Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`");
printf("\n"); printf("\n");
} }