diff --git a/examples/server/chat.sh b/examples/server/chat.sh index 014360121..da0a6ca68 100755 --- a/examples/server/chat.sh +++ b/examples/server/chat.sh @@ -48,6 +48,7 @@ chat_completion() { top_p: 0.9, n_keep: $n_keep, n_predict: 256, + cache_prompt: true, stop: ["\n### Human:"], stream: true }')" diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 11dd82c33..21bdce8ed 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -185,7 +185,7 @@ struct llama_client_slot llama_sampling_context *ctx_sampling = nullptr; 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 n_past_se = 0; // self-extend @@ -219,7 +219,8 @@ struct llama_client_slot sent_token_probs_index = 0; infill = false; ga_i = 0; - n_past_se = 0; + n_past_se = 0; + generated_token_probs.clear(); for (slot_image & img : images) @@ -1227,7 +1228,7 @@ struct llama_server_context std::vector append_tokens = tokenize(json_prompt, false); // has next image 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; } } @@ -1295,6 +1296,8 @@ struct llama_server_context for (llama_client_slot &slot : slots) { slot.cache_tokens.clear(); + slot.n_past = 0; + slot.n_past_se = 0; } } @@ -1364,26 +1367,26 @@ struct llama_server_context kv_cache_clear(); } 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) { 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 - 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; 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_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++) { @@ -1429,8 +1432,10 @@ struct llama_server_context slot.i_batch = batch.n_tokens; 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; } @@ -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_bos(model)); // always add BOS - 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(), llama_token_suffix(model)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(model)); prompt_tokens = prefix_tokens; } @@ -1582,8 +1587,8 @@ struct llama_server_context } LOG_VERBOSE("prompt ingested", { - {"n_past", slot.n_past}, - {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, + {"n_past", 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())}, }); @@ -1591,10 +1596,13 @@ struct llama_server_context // process the prefix of first image std::vector 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; - int ga_i = slot.ga_i; + + int32_t ga_i = slot.ga_i; int32_t ga_n = slot.ga_n; int32_t ga_w = slot.ga_w; + for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { 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); - slot_npast += 1; + slot_npast++; } if (has_images && !ingest_images(slot, n_batch)) @@ -1666,6 +1674,7 @@ struct llama_server_context slot.n_past_se += n_tokens; } } + llama_batch batch_view = { n_tokens, @@ -1782,51 +1791,51 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); 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()) { - 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 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(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\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(" -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(" 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(" or for intermediate results and KV (with split-mode = row)\n"); #endif 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(" 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-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(" --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(" --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(" -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(" -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(" -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(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); - printf(" --log-disable disables logging to a file.\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-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(" --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(" --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(" -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(" -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(" -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(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); + printf(" --log-disable disables logging to a file.\n"); printf("\n"); printf(" --override-kv KEY=TYPE:VALUE\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(" -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(" 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(" -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("\n"); }