vbatts/llama.cpp
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server : more n_past fixes

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Georgi Gerganov 2024-01-30 13:22:33 +02:00
parent 8772d3ee63
commit d0e10bf1b2
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2 changed files with 61 additions and 62 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
}')" }')"

122
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
// multimodal // multimodal
@ -1293,6 +1293,7 @@ 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;
} }
} }
@ -1429,7 +1430,6 @@ struct llama_server_context
// TODO: we always have to take into account the "system_tokens" // TODO: we always have to take into account the "system_tokens"
// this is not great and needs to be improved somehow // this is not great and needs to be improved somehow
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true); llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
slot.n_past += 1; slot.n_past += 1;
} }
@ -1540,25 +1540,6 @@ struct llama_server_context
slot.n_past = common_part(slot.cache_tokens, prompt_tokens); slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
if (slot.ga_n != 1)
{
int ga_i = 0;
int32_t ga_n = slot.ga_n;
int32_t ga_w = slot.ga_w;
int32_t slot_npast = 0;
for (int k = 0; k < slot.n_past; ++k)
{
while (slot_npast >= ga_i + ga_w) {
const int bd = (ga_w/ga_n)*(ga_n - 1);
slot_npast -= bd;
ga_i += ga_w/ga_n;
}
slot_npast++;
}
slot.n_past = slot_npast;
slot.ga_i = ga_i;
}
LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
} }
@ -1573,25 +1554,44 @@ struct llama_server_context
// we have to evaluate at least 1 token to generate logits. // we have to evaluate at least 1 token to generate logits.
LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
slot.n_past--; slot.n_past--;
if (slot.ga_i > 0)
{
slot.n_past--;
}
} }
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())},
}); });
if (slot.ga_n != 1)
{
int ga_i = 0;
int32_t ga_n = slot.ga_n;
int32_t ga_w = slot.ga_w;
int32_t slot_npast = 0;
for (int k = 0; k < slot.n_past; ++k)
{
while (slot_npast >= ga_i + ga_w) {
const int bd = (ga_w/ga_n)*(ga_n - 1);
slot_npast -= bd;
ga_i += ga_w/ga_n;
}
slot_npast++;
}
slot.n_past = slot_npast;
slot.ga_i = ga_i;
LOG_TEE("slot %d : applied self-extend to prompt: %i tokens\n", slot.id, slot.n_past);
}
const bool has_images = process_images(slot); const bool has_images = process_images(slot);
// 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;
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)
@ -1603,7 +1603,6 @@ struct llama_server_context
} }
} }
llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, {slot.id }, false); llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, {slot.id }, false);
slot.n_past += 1;
} }
if (has_images && !ingest_images(slot, n_batch)) if (has_images && !ingest_images(slot, n_batch))
@ -1660,7 +1659,6 @@ struct llama_server_context
LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past + bd, slot.n_past, slot.ga_i); LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past + bd, slot.n_past, slot.ga_i);
} }
slot.n_past += n_tokens;
} }
} }
llama_batch batch_view = llama_batch batch_view =
@ -1779,51 +1777,51 @@ 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");
} }