From cd920d0ac38ec243605a5a57c50941140a193f9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 19 Dec 2024 08:53:58 +0100 Subject: [PATCH 01/43] tests: disable GGUF test for bad value size (#10886) --- tests/test-gguf.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp index e5b4cb7b8..1bb5fb47c 100644 --- a/tests/test-gguf.cpp +++ b/tests/test-gguf.cpp @@ -634,7 +634,7 @@ static std::pair test_handcrafted_file(const unsigned int seed) { HANDCRAFTED_KV_BAD_KEY_SIZE, HANDCRAFTED_KV_BAD_TYPE, - HANDCRAFTED_KV_BAD_VALUE_SIZE, + // HANDCRAFTED_KV_BAD_VALUE_SIZE, // FIXME sanitizer limit // HANDCRAFTED_FILE_TYPE_DUPLICATE_KEY, // FIXME HANDCRAFTED_KV_SUCCESS, From 7585edbdebd02861e0994dae67c9338731fb3fc5 Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Thu, 19 Dec 2024 10:37:12 +0100 Subject: [PATCH 02/43] convert : Add support for Microsoft Phi-4 model (#10817) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * convert : use GPT2 vocab for Phi-4 model * convert : use null value of sliding_window to distinguish Phi-4 from other PHI3-based models * llama : do not use sliding window attention mask for Phi-4 model --------- Co-authored-by: Stanisław Szymczyk --- convert_hf_to_gguf.py | 15 ++++++++++++++- src/llama.cpp | 10 ++++++++-- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 4a0b00f69..7b433ee6d 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2200,6 +2200,15 @@ class Phi3MiniModel(Model): model_arch = gguf.MODEL_ARCH.PHI3 def set_vocab(self): + # Phi-4 model uses GPT2Tokenizer + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + tokenizer_class = tokenizer_config_json['tokenizer_class'] + if tokenizer_class == 'GPT2Tokenizer': + return self._set_vocab_gpt2() + from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' @@ -2316,7 +2325,11 @@ class Phi3MiniModel(Model): self.gguf_writer.add_rope_dimension_count(rope_dims) self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"])) + sliding_window = self.hparams.get("sliding_window") + # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models + if sliding_window is None: + sliding_window = 0 + self.gguf_writer.add_sliding_window(sliding_window) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: n_embd = self.find_hparam(["hidden_size", "n_embd"]) diff --git a/src/llama.cpp b/src/llama.cpp index 94160d534..cec15a005 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -13333,7 +13333,13 @@ struct llm_build_context { struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(); + struct ggml_tensor * KQ_mask = nullptr; + if (hparams.n_swa == 0) { + // Phi-4 doesn't use sliding window attention + KQ_mask = build_inp_KQ_mask(); + } else { + KQ_mask = build_inp_KQ_mask_swa(); + } for (int il = 0; il < n_layer; ++il) { auto residual = inpL; @@ -13391,7 +13397,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { From 2fffc52b50992ac5bc64db19d33c39cbc06f52cf Mon Sep 17 00:00:00 2001 From: Sukriti Sharma Date: Thu, 19 Dec 2024 06:04:51 -0700 Subject: [PATCH 03/43] llama : fix Roberta embeddings (#10856) * fix: Use gpt2 tokenizer for roberta and add eos/bos tokens Branch: RobertaTokenizer Signed-off-by: Gabe Goodhart * fixes to position embeddings Signed-off-by: Sukriti-Sharma4 * map roberta-bpe to gpt-2 Signed-off-by: Sukriti-Sharma4 * fix linting Signed-off-by: Sukriti-Sharma4 --------- Signed-off-by: Gabe Goodhart Signed-off-by: Sukriti-Sharma4 Co-authored-by: Gabe Goodhart --- convert_hf_to_gguf.py | 47 ++++++++++++++++++++++++++++++++++++++++++- src/llama.cpp | 3 ++- 2 files changed, 48 insertions(+), 2 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 7b433ee6d..ecd69be6b 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2628,7 +2628,7 @@ class InternLM2Model(Model): return [(self.map_tensor_name(name), data_torch)] -@Model.register("BertModel", "CamembertModel", "RobertaModel") +@Model.register("BertModel", "CamembertModel") class BertModel(Model): model_arch = gguf.MODEL_ARCH.BERT @@ -2701,6 +2701,51 @@ class BertModel(Model): return [(self.map_tensor_name(name), data_torch)] +@Model.register("RobertaModel") +class RobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + """Support BPE tokenizers for roberta models""" + bpe_tok_path = self.dir_model / "tokenizer.json" + if bpe_tok_path.exists(): + self._set_vocab_gpt2() + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + else: + return super().set_vocab() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + @Model.register("NomicBertModel") class NomicBertModel(BertModel): model_arch = gguf.MODEL_ARCH.NOMIC_BERT diff --git a/src/llama.cpp b/src/llama.cpp index cec15a005..b442781a0 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -6592,7 +6592,8 @@ static void llm_load_vocab( tokenizer_pre == "jina-v1-en" || tokenizer_pre == "jina-v2-es" || tokenizer_pre == "jina-v2-de" || - tokenizer_pre == "jina-v2-code") { + tokenizer_pre == "jina-v2-code" || + tokenizer_pre == "roberta-bpe") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "refact") { From a3c33b1dce2d4f25040b75f66629104bd1e40128 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= Date: Thu, 19 Dec 2024 14:20:41 +0100 Subject: [PATCH 04/43] ggml: fix arm build with gcc (#10895) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Adrien Gallouët --- ggml/src/ggml-cpu/CMakeLists.txt | 29 +++++++++++++++++++++-------- 1 file changed, 21 insertions(+), 8 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index ea4ef5a00..683b90aff 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -88,32 +88,45 @@ function(ggml_add_cpu_backend_variant_impl tag_name) endif() if (GGML_NATIVE) - list(APPEND ARCH_FLAGS -mcpu=native) - - set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) - # -mcpu=native does not always enable all the features in some compilers, # so we check for them manually and enable them if available + execute_process( + COMMAND ${CMAKE_C_COMPILER} -mcpu=native -E -v - + INPUT_FILE "/dev/null" + OUTPUT_QUIET + ERROR_VARIABLE ARM_MCPU + RESULT_VARIABLE ARM_MCPU_RESULT + ) + if (NOT ARM_MCPU_RESULT) + string(REGEX MATCH "-mcpu=[^ ']+" ARM_MCPU_FLAG "${ARM_MCPU}") + endif() + if ("${ARM_MCPU_FLAG}" STREQUAL "") + set(ARM_MCPU_FLAG -mcpu=native) + message(STATUS "ARM -mcpu not found, -mcpu=native will be used") + endif() + + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) include(CheckCXXSourceRuns) - set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+dotprod") + set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+dotprod") check_cxx_source_runs( "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) if (GGML_COMPILER_SUPPORT_DOTPROD) - set(ARCH_FLAGS "${ARCH_FLAGS}+dotprod") + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+dotprod") endif() - set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+i8mm") + set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+i8mm") check_cxx_source_runs( "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_I8MM) if (GGML_COMPILER_SUPPORT_I8MM) - set(ARCH_FLAGS "${ARCH_FLAGS}+i8mm") + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+i8mm") endif() set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}") else() if (GGML_CPU_ARM_ARCH) From 57bb2c40cd94c5a09f5210ed8264cc93b21c4b7e Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 19 Dec 2024 15:40:08 +0100 Subject: [PATCH 05/43] server : fix logprobs, make it OAI-compatible (#10783) * server : fix logprobs, make it openai-compatible * update docs * add std::log * return pre-sampling p * sort before apply softmax * add comment * fix test * set p for sampled token * update docs * add --multi-token-probs * update docs * add `post_sampling_probs` option * update docs [no ci] * remove --multi-token-probs * "top_probs" with "post_sampling_probs" * resolve review comments * rename struct token_prob to prob_info * correct comment placement * fix setting prob for sampled token --- examples/server/README.md | 78 ++++-- examples/server/server.cpp | 229 ++++++++++++------ .../server/tests/unit/test_chat_completion.py | 62 ++++- examples/server/tests/unit/test_completion.py | 71 +++++- examples/server/tests/unit/test_embedding.py | 3 + examples/server/utils.hpp | 60 +++++ 6 files changed, 396 insertions(+), 107 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index d006a8d37..6d6465692 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -343,6 +343,10 @@ node index.js ### POST `/completion`: Given a `prompt`, it returns the predicted completion. +> [!IMPORTANT] +> +> This endpoint is **not** OAI-compatible + *Options:* `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: @@ -444,38 +448,68 @@ These words will not be included in the completion, so make sure to add them to `timings_per_token`: Include prompt processing and text generation speed information in each response. Default: `false` +`post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain. + **Response format** - Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support. -- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure: - -```json -{ - "content": "", - "tokens": [ generated token ids if requested ], - "probs": [ - { - "prob": float, - "tok_str": "" - }, - { - "prob": float, - "tok_str": "" - }, +- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements: + ```json + { + "content": "", + "tokens": [ generated token ids if requested ], ... - ] -}, -``` - -Notice that each `probs` is an array of length `n_probs`. + "probs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + ... + ] + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + ... + ] + }, + ... + ] + }, + ``` + Please note that if `post_sampling_probs` is set to `true`: + - `logprob` will be replaced with `prob`, with the value between 0.0 and 1.0 + - `top_logprobs` will be replaced with `top_probs`. Each element contains: + - `id`: token ID + - `token`: token in string + - `bytes`: token in bytes + - `prob`: token probability, with the value between 0.0 and 1.0 + - Number of elements in `top_probs` may be less than `n_probs` - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. - `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request. - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.). -- `model`: The path to the model loaded with `-m` -- `prompt`: The provided `prompt` +- `model`: The model alias (for model path, please use `/props` endpoint) +- `prompt`: The processed `prompt` (special tokens may be added) - `stop_type`: Indicating whether the completion has stopped. Possible values are: - `none`: Generating (not stopped) - `eos`: Stopped because it encountered the EOS token diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5ed4e8d27..fa3682a92 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -93,6 +93,7 @@ struct slot_params { std::vector antiprompt; bool timings_per_token = false; + bool post_sampling_probs = false; bool ignore_eos = false; struct common_params_sampling sampling; @@ -151,6 +152,7 @@ struct slot_params { {"speculative.n_min", speculative.n_min}, {"speculative.p_min", speculative.p_min}, {"timings_per_token", timings_per_token}, + {"post_sampling_probs", post_sampling_probs}, }; } }; @@ -231,6 +233,7 @@ struct server_task { params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); + params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs); params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); @@ -436,36 +439,67 @@ inline std::string stop_type_to_str(stop_type type) { struct completion_token_output { llama_token tok; + float prob; std::string text_to_send; - struct token_prob { + struct prob_info { llama_token tok; - std::string tok_str; + std::string txt; float prob; }; - std::vector probs; + std::vector probs; - json to_json() const { + json to_json(bool post_sampling_probs) const { json probs_for_token = json::array(); for (const auto & p : probs) { + std::string txt(p.txt); + txt.resize(validate_utf8(txt)); probs_for_token.push_back(json { - {"tok_str", p.tok_str}, - {"prob", p.prob}, + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.txt)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, }); } return probs_for_token; } - static json probs_vector_to_json(const std::vector & probs) { + static json probs_vector_to_json(const std::vector & probs, bool post_sampling_probs) { json out = json::array(); - for (const auto & prob : probs) { - const std::string tok_str = prob.text_to_send; + for (const auto & p : probs) { + std::string txt(p.text_to_send); + txt.resize(validate_utf8(txt)); out.push_back(json { - {"content", tok_str}, - {"probs", prob.to_json()}, + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.text_to_send)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + { + post_sampling_probs ? "top_probs" : "top_logprobs", + p.to_json(post_sampling_probs) + }, }); } return out; } + + static float logarithm(float x) { + // nlohmann::json converts -inf to null, so we need to prevent that + return x == 0.0f ? std::numeric_limits::lowest() : std::log(x); + } + + static std::vector str_to_bytes(const std::string & str) { + std::vector bytes; + for (unsigned char c : str) { + bytes.push_back(c); + } + return bytes; + } }; struct server_task_result_cmpl_final : server_task_result { @@ -486,6 +520,7 @@ struct server_task_result_cmpl_final : server_task_result { std::string stopping_word; stop_type stop = STOP_TYPE_NONE; + bool post_sampling_probs; std::vector probs_output; slot_params generation_params; @@ -530,8 +565,8 @@ struct server_task_result_cmpl_final : server_task_result { {"tokens_cached", n_tokens_cached}, {"timings", timings.to_json()}, }; - if (!probs_output.empty()) { - res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output); + if (!stream && !probs_output.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs); } return res; } @@ -542,19 +577,25 @@ struct server_task_result_cmpl_final : server_task_result { finish_reason = "stop"; } - json choices = json::array({json{ + json choice = json{ {"finish_reason", finish_reason}, {"index", 0}, {"message", json { {"content", content}, {"role", "assistant"} } - }}}); + }}; + + if (!stream && probs_output.size() > 0) { + choice["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } std::time_t t = std::time(0); json res = json { - {"choices", choices}, + {"choices", json::array({choice})}, {"created", t}, {"model", oaicompat_model}, {"object", "chat.completion"}, @@ -584,12 +625,14 @@ struct server_task_result_cmpl_final : server_task_result { finish_reason = "stop"; } - json choices = json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}); + json choice = json{ + {"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()} + }; json ret = json { - {"choices", choices}, + {"choices", json::array({choice})}, {"created", t}, {"id", oaicompat_cmpl_id}, {"model", oaicompat_model}, @@ -618,7 +661,8 @@ struct server_task_result_cmpl_partial : server_task_result { int32_t n_decoded; int32_t n_prompt_tokens; - std::vector probs_output; + bool post_sampling_probs; + completion_token_output prob_output; result_timings timings; // OAI-compat fields @@ -655,8 +699,8 @@ struct server_task_result_cmpl_partial : server_task_result { if (timings.prompt_n > 0) { res.push_back({"timings", timings.to_json()}); } - if (!probs_output.empty()) { - res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output); + if (!prob_output.probs.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs); } return res; } @@ -708,6 +752,14 @@ struct server_task_result_cmpl_partial : server_task_result { }}); } + GGML_ASSERT(choices.size() >= 1); + + if (prob_output.probs.size() > 0) { + choices[0]["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + json ret = json { {"choices", choices}, {"created", t}, @@ -1001,7 +1053,6 @@ struct server_slot { // stats size_t n_sent_text = 0; // number of sent text character - size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; int64_t t_start_generation; @@ -1023,7 +1074,6 @@ struct server_slot { stopping_word = ""; n_past = 0; n_sent_text = 0; - n_sent_token_probs = 0; task_type = SERVER_TASK_TYPE_COMPLETION; generated_tokens.clear(); @@ -1764,7 +1814,7 @@ struct server_context { bool process_token(completion_token_output & result, server_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling - const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special); + const std::string token_str = result.text_to_send; slot.sampled = result.tok; slot.generated_text += token_str; @@ -1774,26 +1824,7 @@ struct server_context { slot.has_next_token = true; // check if there is incomplete UTF-8 character at the end - bool incomplete = false; - for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) { - unsigned char c = slot.generated_text[slot.generated_text.size() - i]; - if ((c & 0xC0) == 0x80) { - // continuation byte: 10xxxxxx - continue; - } - if ((c & 0xE0) == 0xC0) { - // 2-byte character: 110xxxxx ... - incomplete = i < 2; - } else if ((c & 0xF0) == 0xE0) { - // 3-byte character: 1110xxxx ... - incomplete = i < 3; - } else if ((c & 0xF8) == 0xF0) { - // 4-byte character: 11110xxx ... - incomplete = i < 4; - } - // else 1-byte character or invalid byte - break; - } + bool incomplete = validate_utf8(slot.generated_text) < slot.generated_text.size(); // search stop word and delete it if (!incomplete) { @@ -1923,6 +1954,55 @@ struct server_context { return slot.has_next_token; // continue } + void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) { + size_t n_probs = slot.params.sampling.n_probs; + size_t n_vocab = llama_n_vocab(llama_get_model(ctx)); + if (post_sampling) { + const auto * cur_p = common_sampler_get_candidates(slot.smpl); + const size_t max_probs = cur_p->size; + + // set probability for sampled token + for (size_t i = 0; i < max_probs; i++) { + if (cur_p->data[i].id == result.tok) { + result.prob = cur_p->data[i].p; + break; + } + } + + // set probability for top n_probs tokens + result.probs.reserve(max_probs); + for (size_t i = 0; i < std::min(max_probs, n_probs); i++) { + result.probs.push_back({ + cur_p->data[i].id, + common_detokenize(ctx, {cur_p->data[i].id}, special), + cur_p->data[i].p + }); + } + } else { + // TODO: optimize this with min-p optimization + std::vector cur = get_token_probabilities(ctx, idx); + + // set probability for sampled token + for (size_t i = 0; i < n_vocab; i++) { + // set probability for sampled token + if (cur[i].id == result.tok) { + result.prob = cur[i].p; + break; + } + } + + // set probability for top n_probs tokens + result.probs.reserve(n_probs); + for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) { + result.probs.push_back({ + cur[i].id, + common_detokenize(ctx, {cur[i].id}, special), + cur[i].p + }); + } + } + } + void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { send_error(task.id, error, type); } @@ -1950,8 +2030,9 @@ struct server_context { res->content = tkn.text_to_send; res->tokens = { tkn.tok }; - res->n_decoded = slot.n_decoded; - res->n_prompt_tokens = slot.n_prompt_tokens; + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->post_sampling_probs = slot.params.post_sampling_probs; res->verbose = slot.params.verbose; res->oaicompat = slot.params.oaicompat; @@ -1961,17 +2042,7 @@ struct server_context { // populate res.probs_output if (slot.params.sampling.n_probs > 0) { - 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()); - - std::vector probs_output; - if (probs_pos < probs_stop_pos) { - res->probs_output = std::vector( - slot.generated_token_probs.begin() + probs_pos, - slot.generated_token_probs.begin() + probs_stop_pos); - } + res->prob_output = tkn; // copy the token probs } // populate timings if this is final response or timings_per_token is enabled @@ -1993,13 +2064,14 @@ struct server_context { res->timings = slot.get_timings(); res->prompt = common_detokenize(ctx, slot.prompt_tokens, true); - res->truncated = slot.truncated; - res->n_decoded = slot.n_decoded; - res->n_prompt_tokens = slot.n_prompt_tokens; - res->n_tokens_cached = slot.n_past; - res->has_new_line = slot.has_new_line; - res->stopping_word = slot.stopping_word; - res->stop = slot.stop; + res->truncated = slot.truncated; + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->n_tokens_cached = slot.n_past; + res->has_new_line = slot.has_new_line; + res->stopping_word = slot.stopping_word; + res->stop = slot.stop; + res->post_sampling_probs = slot.params.post_sampling_probs; res->verbose = slot.params.verbose; res->stream = slot.params.stream; @@ -2796,7 +2868,9 @@ struct server_context { continue; // continue loop of slots } - llama_token id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i); + const int tok_idx = slot.i_batch - i; + + llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx); slot.i_batch = -1; @@ -2815,17 +2889,12 @@ struct server_context { slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3; completion_token_output result; - result.tok = id; + result.tok = id; + result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special); + result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs - const auto * cur_p = common_sampler_get_candidates(slot.smpl); - - for (size_t i = 0; i < (size_t) slot.params.sampling.n_probs; ++i) { - auto tok_id = cur_p->data[i].id; - result.probs.push_back({ - tok_id, - tokens_to_output_formatted_string(ctx, tok_id), - i >= cur_p->size ? 0.0f : cur_p->data[i].p, - }); + if (slot.params.sampling.n_probs > 0) { + populate_token_probs(slot, result, slot.params.post_sampling_probs, params_base.special, tok_idx); } if (!process_token(result, slot)) { @@ -2909,7 +2978,11 @@ struct server_context { for (size_t i = 0; i < ids.size(); ++i) { completion_token_output result; - result.tok = ids[i]; + result.tok = ids[i]; + result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special); + result.prob = 1.0f; // set later + + // TODO: set result.probs if (!process_token(result, slot)) { // release slot because of stop condition diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py index 6573cc17f..0fa1a17c1 100644 --- a/examples/server/tests/unit/test_chat_completion.py +++ b/examples/server/tests/unit/test_chat_completion.py @@ -92,7 +92,6 @@ def test_chat_completion_with_openai_library(): seed=42, temperature=0.8, ) - print(res) assert res.choices[0].finish_reason == "length" assert res.choices[0].message.content is not None assert match_regex("(Suddenly)+", res.choices[0].message.content) @@ -163,3 +162,64 @@ def test_chat_completion_with_timings_per_token(): assert "predicted_per_second" in data["timings"] assert "predicted_n" in data["timings"] assert data["timings"]["predicted_n"] <= 10 + + +def test_logprobs(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + ) + output_text = res.choices[0].message.content + aggregated_text = '' + assert res.choices[0].logprobs is not None + assert res.choices[0].logprobs.content is not None + for token in res.choices[0].logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text + + +def test_logprobs_stream(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + stream=True, + ) + output_text = '' + aggregated_text = '' + for data in res: + choice = data.choices[0] + if choice.finish_reason is None: + if choice.delta.content: + output_text += choice.delta.content + assert choice.logprobs is not None + assert choice.logprobs.content is not None + for token in choice.logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert token.top_logprobs is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py index 36aee57dd..b88d45f18 100644 --- a/examples/server/tests/unit/test_completion.py +++ b/examples/server/tests/unit/test_completion.py @@ -270,9 +270,68 @@ def test_n_probs(): assert "completion_probabilities" in res.body assert len(res.body["completion_probabilities"]) == 5 for tok in res.body["completion_probabilities"]: - assert "probs" in tok - assert len(tok["probs"]) == 10 - for prob in tok["probs"]: - assert "prob" in prob - assert "tok_str" in prob - assert 0.0 <= prob["prob"] <= 1.0 + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_stream(): + global server + server.start() + res = server.make_stream_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "stream": True, + }) + for data in res: + if data["stop"] == False: + assert "completion_probabilities" in data + assert len(data["completion_probabilities"]) == 1 + for tok in data["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_post_sampling(): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "post_sampling_probs": True, + }) + assert res.status_code == 200 + assert "completion_probabilities" in res.body + assert len(res.body["completion_probabilities"]) == 5 + for tok in res.body["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "prob" in tok and 0.0 < tok["prob"] <= 1.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_probs"]) == 10 + for prob in tok["top_probs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "prob" in prob and 0.0 <= prob["prob"] <= 1.0 + assert "bytes" in prob and type(prob["bytes"]) == list + # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs + assert any(prob["prob"] == 1.0 for prob in tok["top_probs"]) diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py index e32d74582..43e372fc7 100644 --- a/examples/server/tests/unit/test_embedding.py +++ b/examples/server/tests/unit/test_embedding.py @@ -50,6 +50,8 @@ def test_embedding_multiple(): @pytest.mark.parametrize( "input,is_multi_prompt", [ + # do not crash on empty input + ("", False), # single prompt ("string", False), ([12, 34, 56], False), @@ -103,6 +105,7 @@ def test_embedding_pooling_none_oai(): # /v1/embeddings does not support pooling type 'none' assert res.status_code == 400 + assert "error" in res.body def test_embedding_openai_library_single(): diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index ffdffe904..94bb285b6 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -171,6 +171,36 @@ static std::vector tokenize_input_prompts(llama_context * ctx, con return result; } +// return the last index of character that can form a valid string +// if the last character is potentially cut in half, return the index before the cut +// if validate_utf8(text) == text.size(), then the whole text is valid utf8 +static size_t validate_utf8(const std::string& text) { + size_t len = text.size(); + if (len == 0) return 0; + + // Check the last few bytes to see if a multi-byte character is cut off + for (size_t i = 1; i <= 4 && i <= len; ++i) { + unsigned char c = text[len - i]; + // Check for start of a multi-byte sequence from the end + if ((c & 0xE0) == 0xC0) { + // 2-byte character start: 110xxxxx + // Needs at least 2 bytes + if (i < 2) return len - i; + } else if ((c & 0xF0) == 0xE0) { + // 3-byte character start: 1110xxxx + // Needs at least 3 bytes + if (i < 3) return len - i; + } else if ((c & 0xF8) == 0xF0) { + // 4-byte character start: 11110xxx + // Needs at least 4 bytes + if (i < 4) return len - i; + } + } + + // If no cut-off multi-byte character is found, return full length + return len; +} + // // template utils // @@ -671,3 +701,33 @@ static json format_logit_bias(const std::vector & logit_bias) static std::string safe_json_to_str(json data) { return data.dump(-1, ' ', false, json::error_handler_t::replace); } + +static std::vector get_token_probabilities(llama_context * ctx, int idx) { + std::vector cur; + const auto * logits = llama_get_logits_ith(ctx, idx); + const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + + cur.resize(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f}; + } + + // sort tokens by logits + std::sort(cur.begin(), cur.end(), [](const llama_token_data & a, const llama_token_data & b) { + return a.logit > b.logit; + }); + + // apply softmax + float max_l = cur[0].logit; + float cum_sum = 0.0f; + for (size_t i = 0; i < cur.size(); ++i) { + float p = expf(cur[i].logit - max_l); + cur[i].p = p; + cum_sum += p; + } + for (size_t i = 0; i < cur.size(); ++i) { + cur[i].p /= cum_sum; + } + + return cur; +} From 36319dec5d75a7dfe3e3de37b9ca2a76cd52b7b2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Dec 2024 17:35:15 +0200 Subject: [PATCH 06/43] tts : small QoL for easy model fetch (#10903) --- common/arg.cpp | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/common/arg.cpp b/common/arg.cpp index e5ddd8318..c3d66efb5 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2206,5 +2206,17 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + // model-specific + add_opt(common_arg( + {"--tts-oute-default"}, + string_format("use default OuteTTS models (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF"; + params.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf"; + params.vocoder.hf_repo = "ggml-org/WavTokenizer"; + params.vocoder.hf_file = "WavTokenizer-Large-75-F16.gguf"; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + return ctx_arg; } From 5cab3e4aaa892df4620b720f20a503a1bbbe7a52 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Dec 2024 17:42:13 +0200 Subject: [PATCH 07/43] llama : minor grammar refactor (#10897) ggml-ci --- examples/gbnf-validator/gbnf-validator.cpp | 11 +++----- src/llama-grammar.cpp | 30 +++++++++++----------- src/llama-grammar.h | 7 ++--- tests/test-grammar-integration.cpp | 9 +++---- tests/test-llama-grammar.cpp | 6 ++--- 5 files changed, 26 insertions(+), 37 deletions(-) diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp index 7493af9d3..17a0e27c4 100644 --- a/examples/gbnf-validator/gbnf-validator.cpp +++ b/examples/gbnf-validator/gbnf-validator.cpp @@ -11,19 +11,15 @@ static bool llama_grammar_validate(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) { const auto cpts = unicode_cpts_from_utf8(input_str); - const llama_grammar_rules & rules = llama_grammar_get_rules (grammar); - llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar); + auto & stacks_cur = llama_grammar_get_stacks(grammar); size_t pos = 0; for (const auto & cpt : cpts) { - const llama_grammar_stacks stacks_prev = llama_grammar_get_stacks(grammar); // copy - - llama_grammar_accept(rules, stacks_prev, cpt, stacks_cur); + llama_grammar_accept(grammar, cpt); if (stacks_cur.empty()) { error_pos = pos; error_msg = "Unexpected character '" + unicode_cpt_to_utf8(cpt) + "'"; - stacks_cur = stacks_prev; return false; } ++pos; @@ -82,7 +78,8 @@ int main(int argc, char** argv) { llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root"); if (grammar == nullptr) { - throw std::runtime_error("Failed to initialize llama_grammar"); + fprintf(stdout, "Failed to initialize llama_grammar\n"); + return 1; } // Read the input file std::string input_str; diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp index 74e9f64b3..76d0cb3a2 100644 --- a/src/llama-grammar.cpp +++ b/src/llama-grammar.cpp @@ -822,15 +822,11 @@ llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar) return grammar->stacks; } -void llama_grammar_accept( - const llama_grammar_rules & rules, - const llama_grammar_stacks & stacks, - const uint32_t chr, - llama_grammar_stacks & stacks_new) { - stacks_new.clear(); - stacks_new.reserve(stacks.size()); +void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr) { + llama_grammar_stacks stacks_new; + stacks_new.reserve(grammar->stacks.size()); - for (const auto & stack : stacks) { + for (const auto & stack : grammar->stacks) { if (stack.empty()) { continue; } @@ -844,9 +840,11 @@ void llama_grammar_accept( if (!llama_grammar_is_end_of_sequence(pos)) { new_stack.push_back(pos); } - llama_grammar_advance_stack(rules, new_stack, stacks_new); + llama_grammar_advance_stack(grammar->rules, new_stack, stacks_new); } } + + grammar->stacks = std::move(stacks_new); } llama_grammar_candidates llama_grammar_reject_candidates_for_stack( @@ -1051,7 +1049,12 @@ void llama_grammar_free_impl(struct llama_grammar * grammar) { } struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & grammar) { - llama_grammar * result = new llama_grammar { grammar.vocab, grammar.rules, grammar.stacks, grammar.partial_utf8, }; + llama_grammar * result = new llama_grammar { + grammar.vocab, + grammar.rules, + grammar.stacks, + grammar.partial_utf8, + }; // redirect elements in stacks to point to new rules for (size_t is = 0; is < result->stacks.size(); is++) { @@ -1059,7 +1062,7 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra for (size_t ir0 = 0; ir0 < grammar.rules.size(); ir0++) { for (size_t ir1 = 0; ir1 < grammar.rules[ir0].size(); ir1++) { if (grammar.stacks[is][ie] == &grammar.rules[ir0][ir1]) { - result->stacks[is][ie] = &result->rules[ir0][ir1]; + result->stacks[is][ie] = &result->rules[ir0][ir1]; } } } @@ -1126,11 +1129,8 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token const auto decoded = decode_utf8(piece, grammar.partial_utf8); const auto & code_points = decoded.first; - llama_grammar_stacks stacks_new; - for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { - llama_grammar_accept(grammar.rules, grammar.stacks, *it, stacks_new); - grammar.stacks = std::move(stacks_new); + llama_grammar_accept(&grammar, *it); } grammar.partial_utf8 = decoded.second; diff --git a/src/llama-grammar.h b/src/llama-grammar.h index f529ce351..13e940fb5 100644 --- a/src/llama-grammar.h +++ b/src/llama-grammar.h @@ -58,6 +58,7 @@ using llama_grammar_rules = std::vector; using llama_grammar_stacks = std::vector; using llama_grammar_candidates = std::vector; +// TODO: remove, needed for tests atm const llama_grammar_rules & llama_grammar_get_rules (const struct llama_grammar * grammar); llama_grammar_stacks & llama_grammar_get_stacks( struct llama_grammar * grammar); @@ -65,11 +66,7 @@ const llama_grammar_rules & llama_grammar_get_rules (const struct llama_grammar // be positioned at a character range (see `llama_grammar_advance_stack`), and // produces the N possible stacks if the given char is accepted at those // positions -void llama_grammar_accept( - const llama_grammar_rules & rules, - const llama_grammar_stacks & stacks, - uint32_t chr, - llama_grammar_stacks & stacks_new); +void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr); std::vector llama_grammar_reject_candidates_for_stack( const llama_grammar_rules & rules, diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 5cc0cdb04..e1bdbb925 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -32,13 +32,10 @@ static bool test_build_grammar_fails(const std::string & grammar_str) { static bool match_string(const std::string & input, llama_grammar * grammar) { const auto cpts = unicode_cpts_from_utf8(input); - const llama_grammar_rules & rules = llama_grammar_get_rules (grammar); - llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar); + auto & stacks_cur = llama_grammar_get_stacks(grammar); for (const auto & cpt : cpts) { - const llama_grammar_stacks stacks_prev = llama_grammar_get_stacks(grammar); // copy - - llama_grammar_accept(rules, stacks_prev, cpt, stacks_cur); + llama_grammar_accept(grammar, cpt); if (stacks_cur.empty()) { // no stacks means that the grammar failed to match at this point @@ -63,7 +60,7 @@ static void test(const std::string & test_desc, const std::string & grammar_str, auto * grammar = build_grammar(grammar_str); // Save the original grammar stacks so that we can reset after every new string we want to test - const llama_grammar_stacks stacks_org = llama_grammar_get_stacks(grammar); + const llama_grammar_stacks stacks_org = llama_grammar_get_stacks(grammar); // copy llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar); diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index 6f1374ca8..e2129206b 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -113,12 +113,10 @@ int main() } } - llama_grammar * grammar = NULL; std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init_impl(nullptr, grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - if (grammar == nullptr) - { + llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + if (grammar == nullptr) { throw std::runtime_error("Failed to initialize llama_grammar"); } From d408bb9268a988c5a60a5746d3a6430386e7604d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Dec 2024 18:47:15 +0200 Subject: [PATCH 08/43] clip : disable GPU support (#10896) ggml-ci --- examples/llava/clip.cpp | 86 ++++++++++++++++++++--------------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 463b7c865..3cd0d2fa8 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -8,25 +8,25 @@ #include "ggml-alloc.h" #include "ggml-backend.h" -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_SYCL -#include "ggml-sycl.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif - -#ifdef GGML_USE_CANN -#include "ggml-cann.h" -#endif - -#ifdef GGML_USE_VULKAN -#include "ggml-vulkan.h" -#endif +//#ifdef GGML_USE_CUDA +//#include "ggml-cuda.h" +//#endif +// +//#ifdef GGML_USE_SYCL +//#include "ggml-sycl.h" +//#endif +// +//#ifdef GGML_USE_METAL +//#include "ggml-metal.h" +//#endif +// +//#ifdef GGML_USE_CANN +//#include "ggml-cann.h" +//#endif +// +//#ifdef GGML_USE_VULKAN +//#include "ggml-vulkan.h" +//#endif #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" @@ -1222,30 +1222,30 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } } -#ifdef GGML_USE_CUDA - new_clip->backend = ggml_backend_cuda_init(0); - LOG_INF("%s: CLIP using CUDA backend\n", __func__); -#endif - -#ifdef GGML_USE_METAL - new_clip->backend = ggml_backend_metal_init(); - LOG_INF("%s: CLIP using Metal backend\n", __func__); -#endif - -#ifdef GGML_USE_CANN - new_clip->backend = ggml_backend_cann_init(0); - LOG_INF("%s: CLIP using CANN backend\n", __func__); -#endif - -#ifdef GGML_USE_VULKAN - new_clip->backend = ggml_backend_vk_init(0); - LOG_INF("%s: CLIP using Vulkan backend\n", __func__); -#endif - -#ifdef GGML_USE_SYCL - new_clip->backend = ggml_backend_sycl_init(0); - LOG_INF("%s: CLIP using SYCL backend\n", __func__); -#endif +//#ifdef GGML_USE_CUDA +// new_clip->backend = ggml_backend_cuda_init(0); +// LOG_INF("%s: CLIP using CUDA backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_METAL +// new_clip->backend = ggml_backend_metal_init(); +// LOG_INF("%s: CLIP using Metal backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_CANN +// new_clip->backend = ggml_backend_cann_init(0); +// LOG_INF("%s: CLIP using CANN backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_VULKAN +// new_clip->backend = ggml_backend_vk_init(0); +// LOG_INF("%s: CLIP using Vulkan backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_SYCL +// new_clip->backend = ggml_backend_sycl_init(0); +// LOG_INF("%s: CLIP using SYCL backend\n", __func__); +//#endif if (!new_clip->backend) { new_clip->backend = ggml_backend_cpu_init(); From 0a11f8b7b5c39fdf6e91ef9674bc68ff08681af7 Mon Sep 17 00:00:00 2001 From: Molly Sophia Date: Fri, 20 Dec 2024 17:44:58 +0800 Subject: [PATCH 09/43] convert : fix RWKV v6 model conversion (#10913) * Enable --no-context-shift for llama-perplexity example Signed-off-by: Molly Sophia * RWKV 6: Fix error in ggml_cuda_op_bin_bcast Signed-off-by: Molly Sophia --------- Signed-off-by: Molly Sophia --- common/arg.cpp | 2 +- convert_hf_to_gguf.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index c3d66efb5..deb113786 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -626,7 +626,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.ctx_shift = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); add_opt(common_arg( {"--chunks"}, "N", string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index ecd69be6b..76ab11ebe 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -3065,6 +3065,9 @@ class Rwkv6Model(Model): if new_name.endswith("time_mix_w2.weight"): data_torch = data_torch.permute(0, 2, 1) + if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name: + data_torch = data_torch.squeeze() + rescale_every_n_layers = self.hparams["rescale_every"] if rescale_every_n_layers > 0: if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"): From 21ae3b9be83820565d1a720999b7f63ce95b4920 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 20 Dec 2024 13:31:28 +0100 Subject: [PATCH 10/43] ggml : add test for SVE and disable when it fails (#10906) --- ggml/src/ggml-cpu/CMakeLists.txt | 38 ++++++++++++++++---------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 683b90aff..12d790825 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -82,8 +82,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name) if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang") message(FATAL_ERROR "MSVC is not supported for ARM, use clang") else() - check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) - if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") + check_cxx_compiler_flag(-mfp16-format=ieee GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E) + if (NOT "${GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") list(APPEND ARCH_FLAGS -mfp16-format=ieee) endif() @@ -106,28 +106,28 @@ function(ggml_add_cpu_backend_variant_impl tag_name) message(STATUS "ARM -mcpu not found, -mcpu=native will be used") endif() - set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) include(CheckCXXSourceRuns) - set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+dotprod") - check_cxx_source_runs( - "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" - GGML_COMPILER_SUPPORT_DOTPROD) - if (GGML_COMPILER_SUPPORT_DOTPROD) - set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+dotprod") - endif() + function(check_arm_feature tag code) + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) + set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+${tag}") + check_cxx_source_runs( + "${code}" + GGML_MACHINE_SUPPORTS_${tag} + ) + if (GGML_MACHINE_SUPPORTS_${tag}) + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+${tag}" PARENT_SCOPE) + else() + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+no${tag}" PARENT_SCOPE) + endif() + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + endfunction() - set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+i8mm") - check_cxx_source_runs( - "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" - GGML_COMPILER_SUPPORT_I8MM) - if (GGML_COMPILER_SUPPORT_I8MM) - set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+i8mm") - endif() + check_arm_feature(dotprod "#include \nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }") + check_arm_feature(i8mm "#include \nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }") + check_arm_feature(sve "#include \nint main() { svfloat32_t _a, _b; volatile svfloat32_t _c = svadd_f32_z(svptrue_b8(), _a, _b); return 0; }") - set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}") - else() if (GGML_CPU_ARM_ARCH) list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH}) From 0ca416c91aea4549d9c77e3efeca403e15aa6c75 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 20 Dec 2024 14:12:06 +0100 Subject: [PATCH 11/43] server : (UI) fix copy to clipboard function (#10916) --- examples/server/public/index.html.gz | Bin 1205430 -> 1205858 bytes examples/server/webui/src/main.js | 23 +++++++++++++++++++---- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz index 6a64915579c39cec4bfdc995b76f9994824cdddc..36f9c9fe9a68d6843be38c7b798e29a950ebcee5 100644 GIT binary patch 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const BASE_URL = isDev @@ -130,9 +145,9 @@ const VueMarkdown = defineComponent( }; window.copyStr = copyStr; const content = computed(() => md.value.render(props.source)); - return () => h("div", { innerHTML: content.value }); + return () => h('div', { innerHTML: content.value }); }, - { props: ["source"] } + { props: ['source'] } ); // input field to be used by settings modal From eb5c3dc64bd967f2e23c87d9dec195f45468de60 Mon Sep 17 00:00:00 2001 From: Akarshan Biswas Date: Fri, 20 Dec 2024 21:01:28 +0530 Subject: [PATCH 12/43] SYCL: Migrate away from deprecated ggml_tensor->backend (#10840) * Migrate to tensor->buffer for checking backend buffer type: 1 * SYCL: common.cpp try to migrate away from tensor->backend * SYCL: fix assertions and add proper comments * SYCL: remove extra space * SYCL: Add back static to ggml_backend_buffer_is_sycl_split function * SYCL: Add pragma directive to suppress warning spam * SYCL: Integrate debug logs with GGML_LOG and other fixes * Revert "SYCL: Integrate debug logs with GGML_LOG and other fixes" This reverts commit 2607b7de0f0d2f4f1f690226f86fa861aa39cb97. Let's keep the current SYCL specific logging mechanism for now * SYCL: Use GGML_SYCL_DEBUG after reverting * SYCL: reg_get_proc_address func, update to the current func signature * SYCL: Refactor SYCL buffer checks in ggml_sycl_cpy_tensor_2d --- ggml/src/ggml-sycl/common.cpp | 8 +++--- ggml/src/ggml-sycl/common.hpp | 4 +++ ggml/src/ggml-sycl/ggml-sycl.cpp | 46 ++++++++++++++++++-------------- 3 files changed, 35 insertions(+), 23 deletions(-) diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp index a9ee40491..88314a5cd 100644 --- a/ggml/src/ggml-sycl/common.cpp +++ b/ggml/src/ggml-sycl/common.cpp @@ -11,6 +11,8 @@ // #include "common.hpp" + +#include "ggml-backend-impl.h" #include "ggml-impl.h" int get_current_device_id() { @@ -65,9 +67,9 @@ void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *sr const ggml_sycl_op_flatten_t op) try { const bool use_src1 = src1 != nullptr; - - GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + if(use_src1) + GGML_ASSERT(strcmp(src1->buffer->buft->iface.get_name(src1->buffer->buft), GGML_SYCL_NAME "_Split") != 0); + GGML_ASSERT(strcmp(dst->buffer->buft->iface.get_name(dst->buffer->buft), GGML_SYCL_NAME "_Split") != 0); // dd = data device float * src0_ddf = (float *) src0->data; diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index c1582f610..62b4cea3a 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -26,7 +26,11 @@ #define GGML_COMMON_DECL_SYCL #define GGML_COMMON_IMPL_SYCL +/* suppress warning spam */ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wnested-anon-types" #include "ggml-common.h" +#pragma clang diagnostic pop void* ggml_sycl_host_malloc(size_t size); void ggml_sycl_host_free(void* ptr); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 84f1328e7..312ccfeb8 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -288,10 +288,8 @@ 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) { + if (tensor->view_src != NULL) { assert(tensor->view_src->buffer->buft == buffer->buft); - tensor->backend = tensor->view_src->backend; - tensor->extra = tensor->view_src->extra; return; } @@ -539,7 +537,7 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { 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", + GGML_LOG_ERROR("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", + GGML_LOG_ERROR("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(devicetype, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); } - // FIXME: do not crash if cudaMalloc fails + // FIXME: do not crash if SYCL Buffer alloc 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]; @@ -788,7 +786,6 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, 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) { @@ -2349,12 +2346,22 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, dpct::memcpy_direction kind; char * src_ptr; - if (src->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src->buffer)) { kind = dpct::host_to_device; + //GGML_SYCL_DEBUG("%s: Host buffer type src tensor\n", __func__); src_ptr = (char *) src->data; // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr); - } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); + } else if (ggml_backend_buffer_is_sycl(src->buffer)) { + // If buffer is a SYCL buffer + //GGML_SYCL_DEBUG("%s: SYCL buffer type src tensor\n", __func__); + kind = dpct::device_to_device; + src_ptr = (char *) src->data; + } else if (ggml_backend_buffer_is_sycl_split(src->buffer)) { + /* + If buffer is a SYCL split buffer + */ + //GGML_SYCL_DEBUG("%s: Split buffer type src tensor\n", __func__); + GGML_ASSERT(i1_low == 0 && i1_high == src->ne[1]); kind = dpct::device_to_device; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; int id; @@ -2857,8 +2864,8 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; - GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(dst->buffer)); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src1->buffer)); GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -2878,7 +2885,7 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; + const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer); GGML_ASSERT(!(split && ne02 > 1)); GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); @@ -3198,7 +3205,7 @@ static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const gg const ggml_tensor *src1, ggml_tensor *dst) try { GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer)); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -3231,7 +3238,7 @@ static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer)); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -3293,7 +3300,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer)); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_TENSOR_BINARY_OP_LOCALS @@ -4638,10 +4645,9 @@ static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t re static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) { GGML_UNUSED(reg); - // TODO: update to the current function signature - //if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { - // return (void *)ggml_backend_sycl_split_buffer_type; - //} + 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" From e34c5af43f941f0ddb92466776339897295aca11 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= Date: Sat, 21 Dec 2024 00:33:37 +0100 Subject: [PATCH 13/43] ggml-cpu: replace NEON asm with intrinsics in ggml_gemv_q4_0_4x8_q8_0() (#10874) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * ggml-cpu: replace NEON asm with intrinsics in ggml_gemv_q4_0_4x8_q8_0() Signed-off-by: Adrien Gallouët * ggml-cpu: format code Signed-off-by: Adrien Gallouët --------- Signed-off-by: Adrien Gallouët --- ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp | 122 +++++++++++-------------- 1 file changed, 51 insertions(+), 71 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp index a51d1a6c5..2d79b8b61 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp @@ -564,21 +564,21 @@ static void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *)vx; + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx; for (int c = 0; c < nc; c += ncols_interleaved) { - const block_q8_0 * a_ptr = (const block_q8_0 *)vy; + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; float32x4_t acc = vdupq_n_f32(0); for (int b = 0; b < nb; b++) { - int8x16_t b0 = vld1q_s8((const int8_t *)b_ptr->qs); - int8x16_t b1 = vld1q_s8((const int8_t *)b_ptr->qs + 16); - int8x16_t b2 = vld1q_s8((const int8_t *)b_ptr->qs + 32); - int8x16_t b3 = vld1q_s8((const int8_t *)b_ptr->qs + 48); - float16x4_t bd = vld1_f16((const __fp16 *)b_ptr->d); + int8x16_t b0 = vld1q_s8((const int8_t *) b_ptr->qs); + int8x16_t b1 = vld1q_s8((const int8_t *) b_ptr->qs + 16); + int8x16_t b2 = vld1q_s8((const int8_t *) b_ptr->qs + 32); + int8x16_t b3 = vld1q_s8((const int8_t *) b_ptr->qs + 48); + float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d); int8x16_t a0 = vld1q_s8(a_ptr->qs); int8x16_t a1 = vld1q_s8(a_ptr->qs + qk/2); - float16x4_t ad = vld1_dup_f16((const __fp16 *)&a_ptr->d); + float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d); int32x4_t ret = vdupq_n_s32(0); @@ -647,72 +647,52 @@ static void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c UNUSED(ncols_interleaved); UNUSED(blocklen); -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx; - __asm__ __volatile__( - "movi v2.16b, #0x4\n" - "movi v1.16b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x8\n" - "1:" // Column loop - "add x23, %x[a_ptr], #0x2\n" - "movi v0.16b, #0x0\n" - "mov x22, %x[nb]\n" - "2:" // Block loop - "ldr q31, [%x[b_ptr], #0x0]\n" - "ldr q30, [%x[b_ptr], #0x10]\n" - "mov x21, x23\n" - "movi v29.4s, #0x0\n" - "ldr q28, [%x[b_ptr], #0x20]\n" - "ldr q27, [%x[b_ptr], #0x30]\n" - "movi v26.4s, #0x0\n" - "sub x20, x23, #0x2\n" - "ld1r { v25.8h }, [x20]\n" - "ldr q24, [%x[b_ptr], #-0x8]\n" - "sub x22, x22, #0x1\n" - "add x23, x23, #0x22\n" - "ld1r { v23.2d }, [x21], #0x8\n" - "sshl v22.16b, v31.16b, v2.16b\n" - "sshl v16.16b, v30.16b, v2.16b\n" - "add %x[b_ptr], %x[b_ptr], #0x48\n" - "ld1r { v21.2d }, [x21], #0x8\n" - "sshl v20.16b, v28.16b, v2.16b\n" - "sshl v19.16b, v27.16b, v2.16b\n" - "ld1r { v18.2d }, [x21], #0x8\n" - "ld1r { v17.2d }, [x21], #0x8\n" - "and v31.16b, v31.16b, v1.16b\n" - "and v30.16b, v30.16b, v1.16b\n" - ".inst 0x4e9796dd // sdot v29.4s, v22.16b, v23.16b\n" - ".inst 0x4e97961a // sdot v26.4s, v16.16b, v23.16b\n" - "and v28.16b, v28.16b, v1.16b\n" - "and v27.16b, v27.16b, v1.16b\n" - "fcvtl v25.4s, v25.4h\n" - "fcvtl v16.4s, v24.4h\n" - ".inst 0x4e95969d // sdot v29.4s, v20.16b, v21.16b\n" - ".inst 0x4e95967a // sdot v26.4s, v19.16b, v21.16b\n" - "fmul v16.4s, v16.4s, v25.4s\n" - ".inst 0x4e9297fd // sdot v29.4s, v31.16b, v18.16b\n" - ".inst 0x4e9297da // sdot v26.4s, v30.16b, v18.16b\n" - ".inst 0x4e91979d // sdot v29.4s, v28.16b, v17.16b\n" - ".inst 0x4e91977a // sdot v26.4s, v27.16b, v17.16b\n" - "addp v29.4s, v29.4s, v26.4s\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v0.4s, v29.4s, v16.4s\n" - "cbnz x22, 2b\n" - "sub %x[nc], %x[nc], #0x4\n" - "str q0, [%x[res_ptr], #0x0]\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23" - ); + for (int c = 0; c < nc; c += ncols_interleaved) { + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + float32x4_t acc = vdupq_n_f32(0); + for (int b = 0; b < nb; b++) { + int8x16_t b0 = vld1q_s8((const int8_t *) b_ptr->qs); + int8x16_t b1 = vld1q_s8((const int8_t *) b_ptr->qs + 16); + int8x16_t b2 = vld1q_s8((const int8_t *) b_ptr->qs + 32); + int8x16_t b3 = vld1q_s8((const int8_t *) b_ptr->qs + 48); + float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d); + + int8x16_t a0 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs); + int8x16_t a1 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 1); + int8x16_t a2 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 2); + int8x16_t a3 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 3); + float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d); + + int32x4_t ret0 = vdupq_n_s32(0); + int32x4_t ret1 = vdupq_n_s32(0); + + ret0 = vdotq_s32(ret0, b0 << 4, a0); + ret1 = vdotq_s32(ret1, b1 << 4, a0); + ret0 = vdotq_s32(ret0, b2 << 4, a1); + ret1 = vdotq_s32(ret1, b3 << 4, a1); + + ret0 = vdotq_s32(ret0, b0 & 0xf0U, a2); + ret1 = vdotq_s32(ret1, b1 & 0xf0U, a2); + ret0 = vdotq_s32(ret0, b2 & 0xf0U, a3); + ret1 = vdotq_s32(ret1, b3 & 0xf0U, a3); + + int32x4_t ret = vpaddq_s32(ret0, ret1); + + acc = vfmaq_f32(acc, vcvtq_n_f32_s32(ret, 4), + vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd))); + a_ptr++; + b_ptr++; + } + vst1q_f32(s, acc); + s += ncols_interleaved; + } return; } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) float sumf[4]; int sumi; From a91a41364b25705dbb81ae996bc35c3440c63b35 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 21 Dec 2024 01:04:45 -0600 Subject: [PATCH 14/43] vulkan: optimize coopmat2 dequant functions (#10855) Change the code to do 16b loads when possible and extract the appropriate component late, so the code is effectively decoding a pair of elements and then selecting one. This can allow more commoning to happen in the compiler when neighboring elements are loaded. --- .../vulkan-shaders/dequant_funcs_cm2.comp | 70 ++++++++++++------- 1 file changed, 45 insertions(+), 25 deletions(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp index a8707b621..94b78598e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp @@ -10,9 +10,10 @@ float16_t dequantFuncQ4_0(const in decodeBufQ4_0 bl, const in uint blockCoords[2 const float16_t d = bl.block.d; const uint idx = coordInBlock[1]; const uint shift = (idx & 0x10) >> 2; - uint32_t qs = unpack8(uint32_t(bl.block.qs[(idx & 0xE) >> 1]))[idx & 1]; + uint32_t qs = uint32_t(bl.block.qs[(idx & 0xE) >> 1]); qs >>= shift; - qs &= 0xF; + qs &= 0x0F0F; + qs = unpack8(qs)[idx & 1]; float16_t ret = (float16_t(qs) - float16_t(8)) * d; return ret; } @@ -152,15 +153,17 @@ layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4 block_q4_K block; }; +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K_packed16 { + block_q4_K_packed16 block; +}; + float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) { + decodeBufQ4_K_packed16 bl16 = decodeBufQ4_K_packed16(bl); const uint idx = coordInBlock[1]; - const uint iqs = idx; - const uint n = iqs / 64; // 0,1,2,3 - const uint b = (iqs % 64) / 32; // 0,1 + const uint b = (idx & 0x20) >> 5; // 0,1 const uint is = (idx & 0xE0) >> 5; // 0..7 - const uint qsi = n * 32 + (iqs % 32); // 0..127 const f16vec2 loadd = bl.block.d; @@ -184,9 +187,11 @@ float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2 const float16_t d = loadd.x * float16_t(sc); const float16_t m = loadd.y * float16_t(mbyte); - uint32_t dmask = 0xF << (b * 4); + uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]); + qs = (qs >> (b * 4)) & 0x0F0F; + qs = unpack8(qs)[idx & 1]; - float16_t ret = d * float16_t((bl.block.qs[qsi ] & dmask) >> (b * 4)) - m; + float16_t ret = d * float16_t(qs) - m; return ret; } @@ -195,18 +200,19 @@ layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5 block_q5_K block; }; +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K_packed16 { + block_q5_K_packed16 block; +}; + float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) { + decodeBufQ5_K_packed16 bl16 = decodeBufQ5_K_packed16(bl); const uint idx = coordInBlock[1]; - const uint iqs = idx; - const uint n = iqs / 64; // 0,1,2,3 - const uint b = (iqs % 64) / 32; // 0,1 + const uint b = (idx & 0x20) >> 5; // 0,1 const uint is = (idx & 0xE0) >> 5; // 0..7 - const uint qsi = n * 32 + (iqs % 32); // 0..127 - const uint qhi = (iqs % 32); // 0..31 - const uint8_t hm = uint8_t(1 << (iqs / 32)); + const uint32_t hm = 0x0101 << is; const f16vec2 loadd = bl.block.d; @@ -230,9 +236,15 @@ float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2 const float16_t d = loadd.x * float16_t(sc); const float16_t m = loadd.y * float16_t(mbyte); - uint32_t dmask = 0xF << (b * 4); + uint qh = uint32_t(bl16.block.qh[(idx & 0x1E) >> 1]); + qh = qh & hm; + qh = unpack8(qh)[idx & 1]; - float16_t ret = d * (float16_t((bl.block.qs[qsi ] & dmask) >> (b * 4)) + float16_t((bl.block.qh[qhi ] & hm) != 0 ? 16 : 0)) - m; + uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]); + qs = (qs >> (b * 4)) & 0x0F0F; + qs = unpack8(qs)[idx & 1]; + + float16_t ret = d * (float16_t(qs) + (qh != 0 ? float16_t(16) : float16_t(0))) - m; return ret; } @@ -241,22 +253,30 @@ layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ6_ block_q6_K block; }; +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ6_K_packed16 { + block_q6_K_packed16 block; +}; + float16_t dequantFuncQ6_K(const in decodeBufQ6_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) { + decodeBufQ6_K_packed16 bl16 = decodeBufQ6_K_packed16(bl); const uint idx = coordInBlock[1]; - const uint iqs = idx; - const uint n = iqs / 128; // 0,1 - const uint b = (iqs % 128) / 64; // 0,1 - const uint is_b = (iqs % 32) / 16; // 0,1 - const uint qhshift = ((iqs % 128) / 32) * 2;// 0,2,4,6 - const uint is = 8 * n + qhshift + is_b; // 0..15 - const uint qsi = n * 64 + (iqs % 64); // 0..127 - const uint qhi = n * 32 + (iqs % 32); // 0..63 + const uint b = (idx & 0x40) >> 6; // 0,1 + const uint qhshift = (idx & 0x60) >> 4; // 0,2,4,6 + const uint is = (idx & 0xF0) >> 4; // 0..15 const float16_t dscale = bl.block.d * float16_t(bl.block.scales[is]); - float16_t ret = dscale * float16_t(int8_t(((bl.block.ql[qsi ] >> (b * 4)) & 0xF) | (((bl.block.qh[qhi ] >> qhshift) & 3) << 4)) - 32); + uint ql = uint32_t(bl16.block.ql[((idx & 0x80) >> 2) + ((idx & 0x3E) >> 1)]); + ql = (ql >> (b * 4)) & 0x0F0F; + + uint qh = uint32_t(bl16.block.qh[((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1)]); + qh = ((qh >> qhshift) & 0x0303) << 4; + + int q = unpack8(ql | qh)[idx & 1]; + + float16_t ret = dscale * float16_t(q - 32); return ret; } From 5cd85b5e008de2ec398d6596e240187d627561e3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 21 Dec 2024 10:10:18 +0200 Subject: [PATCH 15/43] convert : add BertForMaskedLM (#10919) --- convert_hf_to_gguf.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 76ab11ebe..976425182 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2628,7 +2628,7 @@ class InternLM2Model(Model): return [(self.map_tensor_name(name), data_torch)] -@Model.register("BertModel", "CamembertModel") +@Model.register("BertModel", "BertForMaskedLM", "CamembertModel") class BertModel(Model): model_arch = gguf.MODEL_ARCH.BERT @@ -2694,10 +2694,25 @@ class BertModel(Model): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused + if name.startswith("bert."): + name = name[5:] + + if name.endswith(".gamma"): + name = name[:-6] + ".weight" + + if name.endswith(".beta"): + name = name[:-5] + ".bias" + # we are only using BERT for embeddings so we don't need the pooling layer if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): return [] # we don't need these + if name.startswith("cls.predictions"): + return [] + + if name.startswith("cls.seq_relationship"): + return [] + return [(self.map_tensor_name(name), data_torch)] From ebdee9478ca7ba65497b9b96f7457698c6ee5115 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sun, 22 Dec 2024 03:44:01 -0600 Subject: [PATCH 16/43] vulkan: build fixes for 32b (#10927) * vulkan: build fixes for 32b Should fix #10923 * vulkan: initialize some buffer/offset variables --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 44 ++++++++++++++-------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 1696b6e27..323ce7cf3 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -3205,8 +3205,8 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont GGML_ABORT("fatal error"); } // Check if src is pinned memory - vk_buffer buf; - size_t buf_offset; + vk_buffer buf = nullptr; + size_t buf_offset = 0; ggml_vk_host_get(ctx->device, tensor->data, buf, buf_offset); const uint64_t ne0 = tensor->ne[0]; @@ -3269,7 +3269,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont VkBufferCopy buf_copy{ 0, offset, copy_size }; ggml_vk_sync_buffers(subctx); - vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy); for (uint64_t i3 = 0; i3 < ne3; i3++) { for (uint64_t i2 = 0; i2 < ne2; i2++) { @@ -3302,7 +3302,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz } // Check if src is pinned memory vk_buffer buf = nullptr; - size_t buf_offset; + size_t buf_offset = 0; ggml_vk_host_get(dst->device, src, buf, buf_offset); if (buf != nullptr) { @@ -3344,7 +3344,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz copy_size}; ggml_vk_sync_buffers(subctx); - vkCmdCopyBuffer(subctx->s->buffer, staging_buffer->buffer, dst->buffer, 1, &buf_copy); + vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, 1, &buf_copy); if (width == spitch) { deferred_memcpy((uint8_t *)staging_buffer->ptr, src, width * height, &subctx->in_memcpys); @@ -3400,7 +3400,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size // Check if dst is pinned memory vk_buffer buf = nullptr; - size_t buf_offset; + size_t buf_offset = 0; ggml_vk_host_get(src->device, dst, buf, buf_offset); std::vector slices(1); @@ -3480,7 +3480,7 @@ static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t ds VkBufferCopy bc{ src_offset, dst_offset, size }; - vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); + vkCmdCopyBuffer(ctx->s->buffer, (VkBuffer)src->buffer, (VkBuffer)dst->buffer, 1, &bc); } static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { @@ -3732,9 +3732,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - vk_buffer d_Qx; + vk_buffer d_Qx = nullptr; size_t qx_buf_offset = 0; - vk_buffer d_Qy; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; bool src0_uma = false; @@ -3934,9 +3934,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - vk_buffer d_Qx; + vk_buffer d_Qx = nullptr; size_t qx_buf_offset = 0; - vk_buffer d_Qy; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; bool src0_uma = false; @@ -4112,7 +4112,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - vk_buffer d_Qy; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; bool src1_uma = false; @@ -4300,11 +4300,11 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context; - vk_buffer d_Qx; + vk_buffer d_Qx = nullptr; size_t qx_buf_offset = 0; - vk_buffer d_Qy; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; - vk_buffer d_ids; + vk_buffer d_ids = nullptr; size_t ids_buf_offset = 0; bool src0_uma = false; @@ -4505,11 +4505,11 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context; - vk_buffer d_Qx; + vk_buffer d_Qx = nullptr; size_t qx_buf_offset = 0; - vk_buffer d_Qy; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; - vk_buffer d_ids; + vk_buffer d_ids = nullptr; size_t ids_buf_offset = 0; bool src0_uma = false; @@ -4768,8 +4768,8 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx ggml_vk_sync_buffers(subctx); - vk_buffer d_Q, d_K, d_V, d_D, d_M; - uint64_t q_buf_offset, k_buf_offset, v_buf_offset, d_buf_offset, m_buf_offset; + vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr; + size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0; bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false; @@ -5474,8 +5474,8 @@ static void ggml_vk_op_f32_rwkv6(ggml_backend_vk_context * ctx, vk_context& subc ggml_vk_sync_buffers(subctx); - vk_buffer d_D, d_K, d_V, d_R, d_TF, d_TD, d_State; - uint64_t k_offset, v_offset, r_offset, tf_offset, td_offset, state_offset, dst_offset; + vk_buffer d_D = nullptr, d_K = nullptr, d_V = nullptr, d_R = nullptr, d_TF = nullptr, d_TD = nullptr, d_State = nullptr; + size_t k_offset = 0, v_offset = 0, r_offset = 0, tf_offset = 0, td_offset = 0, state_offset = 0, dst_offset = 0; bool K_uma = false, V_uma = false, R_uma = false, TF_uma = false, TD_uma = false, STATE_uma = false, DST_uma = false; if (ctx->device->uma) { From 7ae33a616f44ecc081f3dcb589be20962d1d4a92 Mon Sep 17 00:00:00 2001 From: Billel Mokeddem Date: Mon, 23 Dec 2024 01:09:58 +0300 Subject: [PATCH 17/43] llama : add Falcon3 support (#10883) * Add Falcon3 model support * Add fix for adding bos to added special tokens * Add comment explaining the logic behind the if statement * Add a log message to better track the when the following line of code is triggered * Update log to only print when input and output characters are different * Fix handling pre-normalized tokens * Refactoring --- convert_hf_to_gguf.py | 13 +++++++++++++ convert_hf_to_gguf_update.py | 1 + src/llama.cpp | 16 +++++++++++++++- 3 files changed, 29 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 976425182..66aa7f5b1 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -529,9 +529,19 @@ class Model: else: token: str = reverse_vocab[i] if token in added_vocab: + # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized. + # To avoid unexpected issues - we make sure to normalize non-normalized tokens + if not tokenizer.added_tokens_decoder[i].normalized: + previous_token = token + token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False)) + if previous_token != token: + logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer") + if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token): toktypes.append(gguf.TokenType.CONTROL) else: + # NOTE: this was added for Gemma. + # Encoding and decoding the tokens above isn't sufficient for this case. token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces toktypes.append(gguf.TokenType.USER_DEFINED) else: @@ -575,6 +585,9 @@ class Model: if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed": # ref: https://huggingface.co/tiiuae/falcon-7b res = "falcon" + if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e": + # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base + res = "falcon3" if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 res = "bert-bge" diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 88058442f..2ba346640 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": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", }, {"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", }, diff --git a/src/llama.cpp b/src/llama.cpp index b442781a0..115ef9080 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1673,6 +1673,7 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN, LLM_CHAT_TEMPLATE_MISTRAL_V7, LLM_CHAT_TEMPLATE_PHI_3, + LLM_CHAT_TEMPLATE_FALCON_3, LLM_CHAT_TEMPLATE_ZEPHYR, LLM_CHAT_TEMPLATE_MONARCH, LLM_CHAT_TEMPLATE_GEMMA, @@ -1705,6 +1706,7 @@ static const std::map LLM_CHAT_TEMPLATES = { { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN }, { "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 }, { "phi3", LLM_CHAT_TEMPLATE_PHI_3 }, + { "falcon3", LLM_CHAT_TEMPLATE_FALCON_3 }, { "zephyr", LLM_CHAT_TEMPLATE_ZEPHYR }, { "monarch", LLM_CHAT_TEMPLATE_MONARCH }, { "gemma", LLM_CHAT_TEMPLATE_GEMMA }, @@ -6562,7 +6564,8 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "llama3" || tokenizer_pre == "llama-v3" || - tokenizer_pre == "llama-bpe") { + tokenizer_pre == "llama-bpe"|| + tokenizer_pre == "falcon3") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3; vocab.tokenizer_ignore_merges = true; vocab.tokenizer_add_bos = true; @@ -22615,6 +22618,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) { } } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) { return LLM_CHAT_TEMPLATE_PHI_3; + } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) { + return LLM_CHAT_TEMPLATE_FALCON_3; } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) { return LLM_CHAT_TEMPLATE_ZEPHYR; } else if (tmpl_contains("bos_token + message['role']")) { @@ -22767,6 +22772,15 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } + } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) { + // Falcon 3 + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>\n" << message->content << "\n"; + } + if (add_ass) { + ss << "<|assistant|>\n"; + } } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) { // zephyr template for (auto message : chat) { From 7c0e28585843b366864b43b48f92425e2ea17df6 Mon Sep 17 00:00:00 2001 From: Rudi Servo Date: Sun, 22 Dec 2024 21:22:58 -0100 Subject: [PATCH 18/43] devops : add docker-multi-stage builds (#10832) --- .devops/cpu.Dockerfile | 81 ++++++++++++++++++ .devops/cuda.Dockerfile | 94 ++++++++++++++++++++ .devops/full-cuda.Dockerfile | 33 -------- .devops/full-musa.Dockerfile | 33 -------- .devops/full-rocm.Dockerfile | 50 ----------- .devops/full.Dockerfile | 38 --------- .devops/intel.Dockerfile | 91 ++++++++++++++++++++ .devops/llama-cli-cuda.Dockerfile | 38 --------- .devops/llama-cli-intel.Dockerfile | 28 ------ .devops/llama-cli-musa.Dockerfile | 38 --------- .devops/llama-cli-rocm.Dockerfile | 45 ---------- .devops/llama-cli-vulkan.Dockerfile | 27 ------ .devops/llama-cli.Dockerfile | 29 ------- .devops/llama-server-cuda.Dockerfile | 43 ---------- .devops/llama-server-intel.Dockerfile | 34 -------- .devops/llama-server-musa.Dockerfile | 43 ---------- .devops/llama-server-rocm.Dockerfile | 54 ------------ .devops/llama-server-vulkan.Dockerfile | 31 ------- .devops/llama-server.Dockerfile | 33 -------- .devops/musa.Dockerfile | 108 +++++++++++++++++++++++ .devops/rocm.Dockerfile | 113 +++++++++++++++++++++++++ .devops/vulkan.Dockerfile | 88 +++++++++++++++++++ .github/workflows/docker.yml | 104 +++++++++++++++++------ 23 files changed, 651 insertions(+), 625 deletions(-) create mode 100644 .devops/cpu.Dockerfile create mode 100644 .devops/cuda.Dockerfile delete mode 100644 .devops/full-cuda.Dockerfile delete mode 100644 .devops/full-musa.Dockerfile delete mode 100644 .devops/full-rocm.Dockerfile delete mode 100644 .devops/full.Dockerfile create mode 100644 .devops/intel.Dockerfile delete mode 100644 .devops/llama-cli-cuda.Dockerfile delete mode 100644 .devops/llama-cli-intel.Dockerfile delete mode 100644 .devops/llama-cli-musa.Dockerfile delete mode 100644 .devops/llama-cli-rocm.Dockerfile delete mode 100644 .devops/llama-cli-vulkan.Dockerfile delete mode 100644 .devops/llama-cli.Dockerfile delete mode 100644 .devops/llama-server-cuda.Dockerfile delete mode 100644 .devops/llama-server-intel.Dockerfile delete mode 100644 .devops/llama-server-musa.Dockerfile delete mode 100644 .devops/llama-server-rocm.Dockerfile delete mode 100644 .devops/llama-server-vulkan.Dockerfile delete mode 100644 .devops/llama-server.Dockerfile create mode 100644 .devops/musa.Dockerfile create mode 100644 .devops/rocm.Dockerfile create mode 100644 .devops/vulkan.Dockerfile diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile new file mode 100644 index 000000000..8d020f16c --- /dev/null +++ b/.devops/cpu.Dockerfile @@ -0,0 +1,81 @@ +ARG UBUNTU_VERSION=22.04 + +FROM ubuntu:$UBUNTU_VERSION AS build + +RUN apt-get update && \ + apt-get install -y build-essential git cmake libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \ + cmake --build build -j $(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile new file mode 100644 index 000000000..974dd78a8 --- /dev/null +++ b/.devops/cuda.Dockerfile @@ -0,0 +1,94 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG CUDA_VERSION=12.6.0 +# Target the CUDA build image +ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} + +ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_CUDA_DEV_CONTAINER} AS build + +# CUDA architecture to build for (defaults to all supported archs) +ARG CUDA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 + +WORKDIR /app + +COPY . . + +RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_CUDA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile deleted file mode 100644 index 05bff1bdf..000000000 --- a/.devops/full-cuda.Dockerfile +++ /dev/null @@ -1,33 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=12.6.0 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} AS build - -# CUDA architecture to build for (defaults to all supported archs) -ARG CUDA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Use the default CUDA archs if not specified -RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release -j$(nproc) && \ - cp build/bin/* . - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-musa.Dockerfile b/.devops/full-musa.Dockerfile deleted file mode 100644 index 3193fea1e..000000000 --- a/.devops/full-musa.Dockerfile +++ /dev/null @@ -1,33 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG MUSA_VERSION=rc3.1.0 -# Target the MUSA build image -ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_MUSA_DEV_CONTAINER} AS build - -# MUSA architecture to build for (defaults to all supported archs) -ARG MUSA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Use the default MUSA archs if not specified -RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release -j$(nproc) && \ - cp build/bin/* . - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile deleted file mode 100644 index df496bcd2..000000000 --- a/.devops/full-rocm.Dockerfile +++ /dev/null @@ -1,50 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} AS build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH="\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102" - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV GGML_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ - -# Enable cURL -ENV LLAMA_CURL=1 -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev - -RUN make -j$(nproc) - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile deleted file mode 100644 index d93c0be6a..000000000 --- a/.devops/full.Dockerfile +++ /dev/null @@ -1,38 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION AS build - -RUN apt-get update && \ - apt-get install -y build-essential git cmake libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \ - cmake --build build -j $(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib/ \; - -FROM ubuntu:$UBUNTU_VERSION as runtime - -WORKDIR /app - -RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1 - -COPY requirements.txt /app/requirements.txt -COPY requirements /app/requirements -COPY .devops/tools.sh /app/tools.sh - -RUN pip install --upgrade pip setuptools wheel && \ - pip install -r /app/requirements.txt - -COPY --from=build /app/build/bin/ /app/ -COPY --from=build /app/lib/ /app/ -COPY --from=build /app/convert_hf_to_gguf.py /app/ -COPY --from=build /app/gguf-py /app/gguf-py - -ENV LC_ALL=C.utf8 - -ENTRYPOINT ["/app/tools.sh"] diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile new file mode 100644 index 000000000..af783f5e9 --- /dev/null +++ b/.devops/intel.Dockerfile @@ -0,0 +1,91 @@ +ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 + +## Build Image + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build + +ARG GGML_SYCL_F16=OFF +RUN apt-get update && \ + apt-get install -y git libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ + echo "GGML_SYCL_F16 is set" \ + && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ + fi && \ + echo "Building with dynamic libs" && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +### Full +FROM base AS full + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] + diff --git a/.devops/llama-cli-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile deleted file mode 100644 index 7796891d5..000000000 --- a/.devops/llama-cli-cuda.Dockerfile +++ /dev/null @@ -1,38 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=12.6.0 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the CUDA runtime image -ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} AS build - -# CUDA architecture to build for (defaults to all supported archs) -ARG CUDA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential git cmake - -WORKDIR /app - -COPY . . - -# Use the default CUDA archs if not specified -RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libgomp1 - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-cli / - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile deleted file mode 100644 index 0706f732a..000000000 --- a/.devops/llama-cli-intel.Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build - -ARG GGML_SYCL_F16=OFF -RUN apt-get update && \ - apt-get install -y git - -WORKDIR /app - -COPY . . - -RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ - echo "GGML_SYCL_F16 is set" && \ - export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ - fi && \ - echo "Building with static libs" && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \ - ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \ - cmake --build build --config Release --target llama-cli - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime - -COPY --from=build /app/build/bin/llama-cli /llama-cli - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-musa.Dockerfile b/.devops/llama-cli-musa.Dockerfile deleted file mode 100644 index e7c75af20..000000000 --- a/.devops/llama-cli-musa.Dockerfile +++ /dev/null @@ -1,38 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG MUSA_VERSION=rc3.1.0 -# Target the MUSA build image -ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the MUSA runtime image -ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_MUSA_DEV_CONTAINER} AS build - -# MUSA architecture to build for (defaults to all supported archs) -ARG MUSA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential git cmake - -WORKDIR /app - -COPY . . - -# Use the default MUSA archs if not specified -RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libgomp1 - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-cli /llama-cli - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-rocm.Dockerfile b/.devops/llama-cli-rocm.Dockerfile deleted file mode 100644 index e60c747bd..000000000 --- a/.devops/llama-cli-rocm.Dockerfile +++ /dev/null @@ -1,45 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} AS build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH="\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102" - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV GGML_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ - -RUN make -j$(nproc) llama-cli - -ENTRYPOINT [ "/app/llama-cli" ] diff --git a/.devops/llama-cli-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile deleted file mode 100644 index 92a6e0479..000000000 --- a/.devops/llama-cli-vulkan.Dockerfile +++ /dev/null @@ -1,27 +0,0 @@ -ARG UBUNTU_VERSION=jammy - -FROM ubuntu:$UBUNTU_VERSION AS build - -# Install build tools -RUN apt update && apt install -y git build-essential cmake wget libgomp1 - -# Install Vulkan SDK -RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ - apt update -y && \ - apt-get install -y vulkan-sdk - -# Build it -WORKDIR /app -COPY . . -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \ - cmake --build build --config Release --target llama-cli - -# Clean up -WORKDIR / -RUN cp /app/build/bin/llama-cli /llama-cli && \ - rm -rf /app - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli.Dockerfile b/.devops/llama-cli.Dockerfile deleted file mode 100644 index be234d55d..000000000 --- a/.devops/llama-cli.Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION AS build - -RUN apt-get update && \ - apt-get install -y build-essential git cmake libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \ - cmake --build build -j $(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib/ \; - -FROM ubuntu:$UBUNTU_VERSION AS runtime - -WORKDIR /app - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev libgomp1 curl - -COPY --from=build /app/build/bin/llama-cli /app/ -COPY --from=build /app/lib/ /app/ - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/app/llama-cli" ] diff --git a/.devops/llama-server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile deleted file mode 100644 index bf8a198f9..000000000 --- a/.devops/llama-server-cuda.Dockerfile +++ /dev/null @@ -1,43 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=12.6.0 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the CUDA runtime image -ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} AS build - -# CUDA architecture to build for (defaults to all supported archs) -ARG CUDA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential git cmake libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -# Use the default CUDA archs if not specified -RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev libgomp1 curl - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-server /llama-server - -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile deleted file mode 100644 index b503b8cfe..000000000 --- a/.devops/llama-server-intel.Dockerfile +++ /dev/null @@ -1,34 +0,0 @@ -ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build - -ARG GGML_SYCL_F16=OFF -RUN apt-get update && \ - apt-get install -y git libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ - echo "GGML_SYCL_F16 is set" && \ - export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ - fi && \ - echo "Building with dynamic libs" && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ - cmake --build build --config Release --target llama-server - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev curl - -COPY --from=build /app/build/bin/llama-server /llama-server - -ENV LC_ALL=C.utf8 -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server-musa.Dockerfile b/.devops/llama-server-musa.Dockerfile deleted file mode 100644 index cebe51d42..000000000 --- a/.devops/llama-server-musa.Dockerfile +++ /dev/null @@ -1,43 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG MUSA_VERSION=rc3.1.0 -# Target the MUSA build image -ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the MUSA runtime image -ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_MUSA_DEV_CONTAINER} AS build - -# MUSA architecture to build for (defaults to all supported archs) -ARG MUSA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential git cmake libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -# Use the default MUSA archs if not specified -RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev libgomp1 curl - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-server /llama-server - -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server-rocm.Dockerfile b/.devops/llama-server-rocm.Dockerfile deleted file mode 100644 index 8553af75b..000000000 --- a/.devops/llama-server-rocm.Dockerfile +++ /dev/null @@ -1,54 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} AS build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH="\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102" - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV GGML_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -# Enable cURL -ENV LLAMA_CURL=1 -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev curl - -RUN make -j$(nproc) llama-server - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/llama-server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile deleted file mode 100644 index 6aa786779..000000000 --- a/.devops/llama-server-vulkan.Dockerfile +++ /dev/null @@ -1,31 +0,0 @@ -ARG UBUNTU_VERSION=jammy - -FROM ubuntu:$UBUNTU_VERSION AS build - -# Install build tools -RUN apt update && apt install -y git build-essential cmake wget - -# Install Vulkan SDK and cURL -RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ - apt update -y && \ - apt-get install -y vulkan-sdk libcurl4-openssl-dev curl - -# Build it -WORKDIR /app -COPY . . -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ - cmake --build build --config Release --target llama-server - -# Clean up -WORKDIR / -RUN cp /app/build/bin/llama-server /llama-server && \ - rm -rf /app - -ENV LC_ALL=C.utf8 -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server.Dockerfile b/.devops/llama-server.Dockerfile deleted file mode 100644 index 72ccde2fe..000000000 --- a/.devops/llama-server.Dockerfile +++ /dev/null @@ -1,33 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION AS build - -RUN apt-get update && \ - apt-get install -y build-essential git cmake libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \ - cmake --build build -j $(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib/ \; - -FROM ubuntu:$UBUNTU_VERSION AS runtime - -WORKDIR /app - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev libgomp1 curl - -COPY --from=build /app/build/bin/llama-server /app/ -COPY --from=build /app/lib/ /app/ - -ENV LC_ALL=C.utf8 -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile new file mode 100644 index 000000000..bfd7fc1c1 --- /dev/null +++ b/.devops/musa.Dockerfile @@ -0,0 +1,108 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG MUSA_VERSION=rc3.1.0 +# Target the MUSA build image +ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} + +ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_MUSA_DEV_CONTAINER} AS build + +# MUSA architecture to build for (defaults to all supported archs) +ARG MUSA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y \ + build-essential \ + cmake \ + python3 \ + python3-pip \ + git \ + libcurl4-openssl-dev \ + libgomp1 + +COPY requirements.txt requirements.txt +COPY requirements requirements + +RUN pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt + +WORKDIR /app + +COPY . . + +# Use the default MUSA archs if not specified +RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_MUSA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile new file mode 100644 index 000000000..a8088ea00 --- /dev/null +++ b/.devops/rocm.Dockerfile @@ -0,0 +1,113 @@ +ARG UBUNTU_VERSION=24.04 + +# This needs to generally match the container host's environment. +ARG ROCM_VERSION=6.3 +ARG AMDGPU_VERSION=6.3 + +# Target the CUDA build image +ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete + +### Build image +FROM ${BASE_ROCM_DEV_CONTAINER} AS build + +# Unless otherwise specified, we make a fat build. +# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 +# This is mostly tied to rocBLAS supported archs. +# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported +# gfx906 is deprecated +#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html + +#ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102' +ARG ROCM_DOCKER_ARCH=gfx1100 + +# Set nvcc architectured +ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} +# Enable ROCm +# ENV CC=/opt/rocm/llvm/bin/clang +# ENV CXX=/opt/rocm/llvm/bin/clang++ + +RUN apt-get update \ + && apt-get install -y \ + build-essential \ + cmake \ + git \ + libcurl4-openssl-dev \ + curl \ + libgomp1 + +WORKDIR /app + +COPY . . + +RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \ + && cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib \ + && find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_ROCM_DEV_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3-pip \ + python3 \ + python3-wheel\ + && pip install --break-system-packages --upgrade setuptools \ + && pip install --break-system-packages -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile new file mode 100644 index 000000000..cfc2162e3 --- /dev/null +++ b/.devops/vulkan.Dockerfile @@ -0,0 +1,88 @@ +ARG UBUNTU_VERSION=jammy + +FROM ubuntu:$UBUNTU_VERSION AS build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK and cURL +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk libcurl4-openssl-dev curl + +# Build it +WORKDIR /app + +COPY . . + +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index bc2e5020d..41f1a89ee 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -34,21 +34,14 @@ jobs: strategy: matrix: config: - - { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "light-musa", dockerfile: ".devops/llama-cli-musa.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-musa", dockerfile: ".devops/llama-server-musa.Dockerfile", platforms: "linux/amd64" } - - { tag: "full-musa", dockerfile: ".devops/full-musa.Dockerfile", platforms: "linux/amd64" } + # Multi-stage build + - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false} + - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete - #- { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - #- { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - #- { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" } + #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true } steps: - name: Check out the repo uses: actions/checkout@v4 @@ -56,10 +49,10 @@ jobs: fetch-depth: 0 # preserve git history, so we can determine the build number - name: Set up QEMU - uses: docker/setup-qemu-action@v2 + uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v2 + uses: docker/setup-buildx-action@v3 - name: Log in to Docker Hub uses: docker/login-action@v2 @@ -79,25 +72,34 @@ jobs: # determine tag name postfix (build number, commit hash) if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then - TAG_POSTFIX="b${BUILD_NUMBER}" + TAG_POSTFIX="-b${BUILD_NUMBER}" else SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-') - TAG_POSTFIX="${SAFE_NAME}-${SHORT_HASH}" + TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}" fi - # list all tags possible - TAGS="" - TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}," - TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}-${TAG_POSTFIX}" - - echo "output_tags=$TAGS" >> $GITHUB_OUTPUT - echo "output_tags=$TAGS" # print out for debugging + if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then + TYPE="" + else + TYPE="-${{ matrix.config.tag }}" + fi + PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:" + FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}" + LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}" + SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}" + echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT + echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT + echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT + echo "full_output_tags=$FULLTAGS" # print out for debugging + echo "light_output_tags=$LIGHTTAGS" # print out for debugging + echo "server_output_tags=$SERVERTAGS" # print out for debugging env: GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}' # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example - name: Free Disk Space (Ubuntu) + if: ${{ matrix.config.free_disk_space == true }} uses: jlumbroso/free-disk-space@main with: # this might remove tools that are actually needed, @@ -113,13 +115,59 @@ jobs: docker-images: true swap-storage: true - - name: Build and push Docker image (tagged + versioned) - if: ${{ github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' }} + - name: Build and push Full Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }} uses: docker/build-push-action@v6 with: context: . push: true platforms: ${{ matrix.config.platforms }} # tag list is generated from step above - tags: ${{ steps.tag.outputs.output_tags }} + tags: ${{ steps.tag.outputs.full_output_tags }} file: ${{ matrix.config.dockerfile }} + target: full + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache + + - name: Build and push Light Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.light_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: light + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache + + - name: Build and push Server Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.server_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: server + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache From 7024d59e6a572730626cb11896829d115043a1b1 Mon Sep 17 00:00:00 2001 From: "yuri@FreeBSD" Date: Sun, 22 Dec 2024 16:20:11 -0800 Subject: [PATCH 19/43] ggml : fix run-time on FreeBSD in get_executable_path() (#10948) --- ggml/src/ggml-backend-reg.cpp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index 66927148a..31ee31e39 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -402,12 +402,16 @@ static std::string get_executable_path() { base_path = base_path.substr(0, last_slash); } return base_path + "/"; -#elif defined(__linux__) +#elif defined(__linux__) || defined(__FreeBSD__) std::string base_path = "."; std::vector path(1024); while (true) { // get executable path +# if defined(__linux__) ssize_t len = readlink("/proc/self/exe", path.data(), path.size()); +# elif defined(__FreeBSD__) + ssize_t len = readlink("/proc/curproc/file", path.data(), path.size()); +# endif if (len == -1) { break; } From dab76c92cc63072d9495ba87f2f3f3a4872d4f57 Mon Sep 17 00:00:00 2001 From: Eric Curtin Date: Mon, 23 Dec 2024 00:21:40 +0000 Subject: [PATCH 20/43] llama-run : include temperature option (#10899) This commit updates the `examples/run/README.md` file to include a new option for setting the temperature and updates the `run.cpp` file to parse this option. Signed-off-by: Eric Curtin --- examples/run/README.md | 2 + examples/run/run.cpp | 111 +++++++++++++++++++++++++++-------------- 2 files changed, 75 insertions(+), 38 deletions(-) diff --git a/examples/run/README.md b/examples/run/README.md index 874293516..a06805441 100644 --- a/examples/run/README.md +++ b/examples/run/README.md @@ -19,6 +19,8 @@ Options: Context size (default: 2048) -n, --ngl Number of GPU layers (default: 0) + --temp + Temperature (default: 0.8) -v, --verbose, --log-verbose Set verbosity level to infinity (i.e. log all messages, useful for debugging) -h, --help diff --git a/examples/run/run.cpp b/examples/run/run.cpp index 03da54ca3..f89d041c4 100644 --- a/examples/run/run.cpp +++ b/examples/run/run.cpp @@ -55,29 +55,51 @@ static int printe(const char * fmt, ...) { class Opt { public: int init(int argc, const char ** argv) { + ctx_params = llama_context_default_params(); + model_params = llama_model_default_params(); + context_size_default = ctx_params.n_batch; + ngl_default = model_params.n_gpu_layers; + common_params_sampling sampling; + temperature_default = sampling.temp; + + if (argc < 2) { + printe("Error: No arguments provided.\n"); + print_help(); + return 1; + } + // Parse arguments if (parse(argc, argv)) { printe("Error: Failed to parse arguments.\n"); - help(); + print_help(); return 1; } // If help is requested, show help and exit - if (help_) { - help(); + if (help) { + print_help(); return 2; } + ctx_params.n_batch = context_size >= 0 ? context_size : context_size_default; + model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default; + temperature = temperature >= 0 ? temperature : temperature_default; + return 0; // Success } + llama_context_params ctx_params; + llama_model_params model_params; std::string model_; - std::string user_; - int context_size_ = -1, ngl_ = -1; - bool verbose_ = false; + std::string user; + int context_size = -1, ngl = -1; + float temperature = -1; + bool verbose = false; private: - bool help_ = false; + int context_size_default = -1, ngl_default = -1; + float temperature_default = -1; + bool help = false; bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) { return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0; @@ -89,6 +111,17 @@ class Opt { } option_value = std::atoi(argv[++i]); + + return 0; + } + + int handle_option_with_value(int argc, const char ** argv, int & i, float & option_value) { + if (i + 1 >= argc) { + return 1; + } + + option_value = std::atof(argv[++i]); + return 0; } @@ -96,18 +129,22 @@ class Opt { bool options_parsing = true; for (int i = 1, positional_args_i = 0; i < argc; ++i) { if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) { - if (handle_option_with_value(argc, argv, i, context_size_) == 1) { + if (handle_option_with_value(argc, argv, i, context_size) == 1) { return 1; } } else if (options_parsing && (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0)) { - if (handle_option_with_value(argc, argv, i, ngl_) == 1) { + if (handle_option_with_value(argc, argv, i, ngl) == 1) { + return 1; + } + } else if (options_parsing && strcmp(argv[i], "--temp") == 0) { + if (handle_option_with_value(argc, argv, i, temperature) == 1) { return 1; } } else if (options_parsing && (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) { - verbose_ = true; + verbose = true; } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) { - help_ = true; + help = true; return 0; } else if (options_parsing && strcmp(argv[i], "--") == 0) { options_parsing = false; @@ -120,16 +157,16 @@ class Opt { model_ = argv[i]; } else if (positional_args_i == 1) { ++positional_args_i; - user_ = argv[i]; + user = argv[i]; } else { - user_ += " " + std::string(argv[i]); + user += " " + std::string(argv[i]); } } return 0; } - void help() const { + void print_help() const { printf( "Description:\n" " Runs a llm\n" @@ -142,6 +179,8 @@ class Opt { " Context size (default: %d)\n" " -n, --ngl \n" " Number of GPU layers (default: %d)\n" + " --temp \n" + " Temperature (default: %.1f)\n" " -v, --verbose, --log-verbose\n" " Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n" " -h, --help\n" @@ -170,7 +209,7 @@ class Opt { " llama-run file://some-file3.gguf\n" " llama-run --ngl 999 some-file4.gguf\n" " llama-run --ngl 999 some-file5.gguf Hello World\n", - llama_context_default_params().n_batch, llama_model_default_params().n_gpu_layers); + context_size_default, ngl_default, temperature_default); } }; @@ -495,12 +534,12 @@ class LlamaData { return 1; } - context = initialize_context(model, opt.context_size_); + context = initialize_context(model, opt); if (!context) { return 1; } - sampler = initialize_sampler(); + sampler = initialize_sampler(opt); return 0; } @@ -619,14 +658,12 @@ class LlamaData { // Initializes the model and returns a unique pointer to it llama_model_ptr initialize_model(Opt & opt) { ggml_backend_load_all(); - llama_model_params model_params = llama_model_default_params(); - model_params.n_gpu_layers = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers; resolve_model(opt.model_); printe( "\r%*s" "\rLoading model", get_terminal_width(), " "); - llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params)); + llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), opt.model_params)); if (!model) { printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str()); } @@ -636,10 +673,8 @@ class LlamaData { } // Initializes the context with the specified parameters - llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) { - llama_context_params ctx_params = llama_context_default_params(); - ctx_params.n_ctx = ctx_params.n_batch = n_ctx >= 0 ? n_ctx : ctx_params.n_batch; - llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params)); + llama_context_ptr initialize_context(const llama_model_ptr & model, const Opt & opt) { + llama_context_ptr context(llama_new_context_with_model(model.get(), opt.ctx_params)); if (!context) { printe("%s: error: failed to create the llama_context\n", __func__); } @@ -648,10 +683,10 @@ class LlamaData { } // Initializes and configures the sampler - llama_sampler_ptr initialize_sampler() { + llama_sampler_ptr initialize_sampler(const Opt & opt) { llama_sampler_ptr sampler(llama_sampler_chain_init(llama_sampler_chain_default_params())); llama_sampler_chain_add(sampler.get(), llama_sampler_init_min_p(0.05f, 1)); - llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(0.8f)); + llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(opt.temperature)); llama_sampler_chain_add(sampler.get(), llama_sampler_init_dist(LLAMA_DEFAULT_SEED)); return sampler; @@ -798,9 +833,9 @@ static int apply_chat_template_with_error_handling(LlamaData & llama_data, const } // Helper function to handle user input -static int handle_user_input(std::string & user_input, const std::string & user_) { - if (!user_.empty()) { - user_input = user_; +static int handle_user_input(std::string & user_input, const std::string & user) { + if (!user.empty()) { + user_input = user; return 0; // No need for interactive input } @@ -832,17 +867,17 @@ static bool is_stdout_a_terminal() { } // Function to tokenize the prompt -static int chat_loop(LlamaData & llama_data, const std::string & user_) { +static int chat_loop(LlamaData & llama_data, const std::string & user) { int prev_len = 0; llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get())); static const bool stdout_a_terminal = is_stdout_a_terminal(); while (true) { // Get user input std::string user_input; - while (handle_user_input(user_input, user_)) { + while (handle_user_input(user_input, user)) { } - add_message("user", user_.empty() ? user_input : user_, llama_data); + add_message("user", user.empty() ? user_input : user, llama_data); int new_len; if (apply_chat_template_with_error_handling(llama_data, true, new_len) < 0) { return 1; @@ -854,7 +889,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) { return 1; } - if (!user_.empty()) { + if (!user.empty()) { break; } @@ -869,7 +904,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) { static void log_callback(const enum ggml_log_level level, const char * text, void * p) { const Opt * opt = static_cast(p); - if (opt->verbose_ || level == GGML_LOG_LEVEL_ERROR) { + if (opt->verbose || level == GGML_LOG_LEVEL_ERROR) { printe("%s", text); } } @@ -890,11 +925,11 @@ int main(int argc, const char ** argv) { } if (!is_stdin_a_terminal()) { - if (!opt.user_.empty()) { - opt.user_ += "\n\n"; + if (!opt.user.empty()) { + opt.user += "\n\n"; } - opt.user_ += read_pipe_data(); + opt.user += read_pipe_data(); } llama_log_set(log_callback, &opt); @@ -903,7 +938,7 @@ int main(int argc, const char ** argv) { return 1; } - if (chat_loop(llama_data, opt.user_)) { + if (chat_loop(llama_data, opt.user)) { return 1; } From 6f0c9e034bb398915a6617ee4acc62adb87d387d Mon Sep 17 00:00:00 2001 From: ymcki <84055651+ymcki@users.noreply.github.com> Date: Mon, 23 Dec 2024 08:22:33 +0800 Subject: [PATCH 21/43] llama : support for Llama-3_1-Nemotron-51B (#10669) * conflict resolution * move comments after bracket to its own line --- convert_hf_to_gguf.py | 178 ++++++++++++++++++++++ gguf-py/gguf/constants.py | 26 ++++ gguf-py/gguf/tensor_mapping.py | 1 + src/llama.cpp | 267 ++++++++++++++++++++++++++++++++- 4 files changed, 471 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 66aa7f5b1..d95fb1296 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1692,6 +1692,184 @@ class LlamaModel(Model): raise ValueError(f"Unprocessed experts: {experts}") +@Model.register("DeciLMForCausalLM") +class DeciModel(Model): + model_arch = gguf.MODEL_ARCH.DECI + + @staticmethod + def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int: + # DeciLM-specific code + intermediate_size = int(2 * ffn_mult * n_embd / 3) + return DeciModel._find_multiple(intermediate_size, 256) + + @staticmethod + def _find_multiple(n: int, k: int) -> int: + # DeciLM-specific code + if n % k == 0: + return n + return n + k - (n % k) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + _block_configs: list[dict[str,Any]] = self.hparams["block_configs"] + assert self.block_count == len(_block_configs) + self._num_kv_heads = list() + self._num_heads = list() + _ffn_multipliers = list() + # ***linear attention layer*** + # if n_heads_in_group is None and replace_with_linear is True + # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads + # ***attention-free layer*** + # if n_heads_in_group is None and replace_with_linear is False + # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 + # ***normal attention-layer*** + # if n_heads_in_group is not None, then + # _num_kv_heads[il] is num_attention_head // n_heads_in_group and + # _num_heads[il] is num_attention_head + for il in range(len(_block_configs)): + if _block_configs[il]["attention"]["n_heads_in_group"] is None: + if _block_configs[il]["attention"]["replace_with_linear"] is True: + self._num_kv_heads.append(0) + self._num_heads.append(self.hparams["num_attention_heads"]) + else: + self._num_kv_heads.append(0) + self._num_heads.append(0) + else: + self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"]) + self._num_heads.append(self.hparams["num_attention_heads"]) + _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"]) + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(_ffn_multipliers) + assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) + assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int) + assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float) + self._ffn_dims: list[int] = [ + DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"]) + for multiplier in _ffn_multipliers + ] + + def set_vocab(self): + # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's + # eos_token from '|eot_id|' to '|end_of_text|' + if self.hparams.get("vocab_size", 128256) == 128256: + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab( + self.dir_model, load_merges=True, + special_token_types = ['bos', 'eos', 'eom', 'eot'] + ) + special_vocab._set_special_token("bos", 128000) + special_vocab._set_special_token("eos", 128001) + special_vocab._set_special_token("eom", 128008) + special_vocab._set_special_token("eot", 128009) + special_vocab.add_to_gguf(self.gguf_writer) + else: + # DeciLM-7B + self._set_vocab_llama_hf() +# self._set_vocab_gpt2() + + def set_gguf_parameters(self): + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(self._ffn_dims) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + self.gguf_writer.add_head_count(self._num_heads) + self.gguf_writer.add_feed_forward_length(self._ffn_dims) + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_file_type(self.ftype) + else: # DeciLM-7B + super().set_gguf_parameters() + if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B + self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"] + assert self.block_count == len(self._num_kv_heads) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + if bid is not None: + if "num_key_value_heads_per_layer" in self.hparams: + n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid] + elif "block_configs" in self.hparams: + n_kv_head = self._num_kv_heads[bid] + n_head = self._num_heads[bid] + else: + n_kv_head = self.hparams.get("num_key_value_heads") + else: + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + def prepare_tensors(self): + super().prepare_tensors() + + @Model.register("BitnetForCausalLM") class BitnetModel(Model): model_arch = gguf.MODEL_ARCH.BITNET diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a40df974d..273370370 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -221,6 +221,7 @@ class GGUFType: class MODEL_ARCH(IntEnum): LLAMA = auto() + DECI = auto() FALCON = auto() BAICHUAN = auto() GROK = auto() @@ -402,6 +403,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.DECI: "deci", MODEL_ARCH.FALCON: "falcon", MODEL_ARCH.BAICHUAN: "baichuan", MODEL_ARCH.GROK: "grok", @@ -602,6 +604,26 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], + MODEL_ARCH.DECI: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], MODEL_ARCH.GROK: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -1448,6 +1470,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.DECI: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], MODEL_ARCH.BAICHUAN: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 82cdb121a..7009a11d4 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -198,6 +198,7 @@ class TensorNameMap: "transformer.h.{bid}.self_attention.dense", # falcon "h.{bid}.self_attention.dense", # bloom "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo2 + "model.layers.{bid}.self_attn.linear_attn", # deci "layers.{bid}.attention.wo", # llama-pth "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j diff --git a/src/llama.cpp b/src/llama.cpp index 115ef9080..c1524d06b 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -146,6 +146,7 @@ static std::string format(const char * fmt, ...) { enum llm_arch { LLM_ARCH_LLAMA, + LLM_ARCH_DECI, LLM_ARCH_FALCON, LLM_ARCH_BAICHUAN, LLM_ARCH_GROK, @@ -203,6 +204,7 @@ enum llm_arch { static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, + { LLM_ARCH_DECI, "deci" }, { LLM_ARCH_FALCON, "falcon" }, { LLM_ARCH_GROK, "grok" }, { LLM_ARCH_GPT2, "gpt2" }, @@ -674,6 +676,32 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, }, }, + { + LLM_ARCH_DECI, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, + { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, + { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, { LLM_ARCH_BAICHUAN, { @@ -5694,7 +5722,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false); - if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) { + if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) { if (hparams.n_rot != hparams.n_embd_head_k) { throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k)); } @@ -5734,6 +5762,15 @@ static void llm_load_hparams( } } } break; + case LLM_ARCH_DECI: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_7B; break; + case 80: model.type = e_model::MODEL_70B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_MINICPM: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -7939,6 +7976,68 @@ static bool llm_load_tensors( } } } break; + case LLM_ARCH_DECI: + { + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // 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}, 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) { + auto & layer = model.layers[i]; + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(i); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(i); + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i); + const int64_t n_ff = hparams.n_ff(i); + const int64_t n_head = hparams.n_head(i); + const int64_t n_head_kv = hparams.n_head_kv(i); + + if (n_head_kv == 0 && n_head > 0) { + // linear attention for DeciLMCausalModel + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + } + else if (n_head_kv > 0) { + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + 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 = 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 = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) { + layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot/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_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + } + else { + 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); + + // optional MLP bias + 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); + } + } break; case LLM_ARCH_MINICPM3: { const int64_t n_embd_head_qk_rope = hparams.n_rot; @@ -11308,6 +11407,167 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_deci() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + 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(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + const int64_t n_head_kv = hparams.n_head_kv(il); + const int64_t n_head = hparams.n_head(il); + + if (n_head == 0) { + // attention-free layer of Llama-3_1-Nemotron-51B + cur = inpL; + } else { + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + } + + if (n_head > 0 && n_head_kv == 0) { + // "linear attention" of Llama-3_1-Nemotron-51B + cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur); + cb(cur, "wo", il); + } else if (n_head > 0) { + // self-attention + // rope freq factors for llama3; may return nullptr for llama2 and other models + struct ggml_tensor * rope_factors = build_rope_factors(il); + + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, lctx, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + // For Granite architecture + if (hparams.f_residual_scale) { + cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); + } + + // modified to support attention-free layer of Llama-3_1-Nemotron-51B + struct ggml_tensor * ffn_inp = cur; + if (n_head > 0) { + ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + } + + // feed-forward network + if (model.layers[il].ffn_gate_inp == nullptr) { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, lctx, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + // For Granite architecture + if (hparams.f_residual_scale) { + cur = ggml_scale(ctx0, cur, hparams.f_residual_scale); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = lctx.cvec.apply_to(ctx0, cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + + // For Granite architecture + if (hparams.f_logit_scale) { + cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale); + } + + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + struct ggml_cgraph * build_baichuan() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); @@ -17422,6 +17682,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_llama(); } break; + case LLM_ARCH_DECI: + { + result = llm.build_deci(); + } break; case LLM_ARCH_BAICHUAN: { result = llm.build_baichuan(); @@ -20797,6 +21061,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { // use what we call a normal RoPE, operating on pairs of consecutive head values case LLM_ARCH_LLAMA: + case LLM_ARCH_DECI: case LLM_ARCH_BAICHUAN: case LLM_ARCH_STARCODER: case LLM_ARCH_PLAMO: From b92a14a841fb4dfaf27b29d982ec8ba5289a3bff Mon Sep 17 00:00:00 2001 From: Yun Dou Date: Mon, 23 Dec 2024 08:35:44 +0800 Subject: [PATCH 22/43] llama : support InfiniAI Megrez 3b (#10893) * Support InfiniAI Megrez 3b * Fix tokenizer_clean_spaces for megrez --- convert_hf_to_gguf.py | 3 +++ convert_hf_to_gguf_update.py | 1 + src/llama.cpp | 17 +++++++++++++++++ tests/test-chat-template.cpp | 4 ++++ 4 files changed, 25 insertions(+) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index d95fb1296..b6c15da94 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -684,6 +684,9 @@ class Model: if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb": # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct res = "gigachat" + if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1": + # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct + res = "megrez" if res is None: logger.warning("\n") diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 2ba346640..fea23ddb4 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -106,6 +106,7 @@ models = [ {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", }, {"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"}, {"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"}, + {"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"}, ] diff --git a/src/llama.cpp b/src/llama.cpp index c1524d06b..4d41602fe 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1720,6 +1720,7 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_RWKV_WORLD, LLM_CHAT_TEMPLATE_GRANITE, LLM_CHAT_TEMPLATE_GIGACHAT, + LLM_CHAT_TEMPLATE_MEGREZ, LLM_CHAT_TEMPLATE_UNKNOWN, }; @@ -1753,6 +1754,7 @@ static const std::map LLM_CHAT_TEMPLATES = { { "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD }, { "granite", LLM_CHAT_TEMPLATE_GRANITE }, { "gigachat", LLM_CHAT_TEMPLATE_GIGACHAT }, + { "megrez", LLM_CHAT_TEMPLATE_MEGREZ }, }; static llm_arch llm_arch_from_string(const std::string & name) { @@ -6703,6 +6705,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "minerva-7b") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MINERVA; + } else if ( + tokenizer_pre == "megrez") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -22931,6 +22936,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) { return LLM_CHAT_TEMPLATE_GRANITE; } else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) { return LLM_CHAT_TEMPLATE_GIGACHAT; + } else if (tmpl_contains("<|role_start|>")) { + return LLM_CHAT_TEMPLATE_MEGREZ; } return LLM_CHAT_TEMPLATE_UNKNOWN; } @@ -23289,6 +23296,16 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "assistant<|role_sep|>"; } + } else if (tmpl == LLM_CHAT_TEMPLATE_MEGREZ) { + // Megrez template + for (auto message : chat) { + std::string role(message->role); + ss << "<|role_start|>" << role << "<|role_end|>" << message->content << "<|turn_end|>"; + } + + if (add_ass) { + ss << "<|role_start|>assistant<|role_end|>"; + } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 30a910ad5..51bfb155b 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -77,6 +77,8 @@ int main(void) { "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}", // ai-sage/GigaChat-20B-A3B-instruct "{% if messages[0]['role'] == 'system' -%}\n {%- set loop_messages = messages[1:] -%}\n {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n {%- set loop_messages = messages -%}\n {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n {% endif %}\n \n {%- if loop.index0 == 0 -%}\n {{ system_message -}}\n {%- endif -%}\n {%- if message['role'] == 'user' -%}\n {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3] + additional_special_tokens[1] -}}\n {%- endif -%}\n {%- if message['role'] == 'assistant' -%}\n {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n {%- endif -%}\n {%- if loop.last and add_generation_prompt -%}\n {{ 'assistant' + additional_special_tokens[0] -}}\n {%- endif -%}\n{%- endfor %}", + // Infinigence/Megrez-3B-Instruct + u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct，将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}" }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -133,6 +135,8 @@ int main(void) { "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there[INST] Who are you[/INST] I am an assistant [INST] Another question[/INST]", // ai-sage/GigaChat-20B-A3B-instruct "You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|> I am an assistant <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>", + // Infinigence/Megrez-3B-Instruct + "<|role_start|>system<|role_end|>You are a helpful assistant<|turn_end|><|role_start|>user<|role_end|>Hello<|turn_end|><|role_start|>assistant<|role_end|>Hi there<|turn_end|><|role_start|>user<|role_end|>Who are you<|turn_end|><|role_start|>assistant<|role_end|> I am an assistant <|turn_end|><|role_start|>user<|role_end|>Another question<|turn_end|><|role_start|>assistant<|role_end|>", }; std::vector formatted_chat(1024); int32_t res; From 86bf31cfe684849157f0875b4f0ebccac7034547 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Mon, 23 Dec 2024 10:39:30 +0200 Subject: [PATCH 23/43] rpc-server : add support for the SYCL backend (#10934) --- examples/rpc/rpc-server.cpp | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 5fe70dac7..8b1b23eda 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -12,6 +12,10 @@ #include "ggml-vulkan.h" #endif +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + #include "ggml-rpc.h" #ifdef _WIN32 # include @@ -91,6 +95,12 @@ static ggml_backend_t create_backend() { if (!backend) { fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__); } +#elif GGML_USE_SYCL + fprintf(stderr, "%s: using SYCL backend\n", __func__); + backend = ggml_backend_sycl_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__); + } #endif // if there aren't GPU Backends fallback to CPU backend @@ -106,6 +116,8 @@ static void get_backend_memory(size_t * free_mem, size_t * total_mem) { ggml_backend_cuda_get_device_memory(0, free_mem, total_mem); #elif GGML_USE_VULKAN ggml_backend_vk_get_device_memory(0, free_mem, total_mem); +#elif GGML_USE_SYCL + ggml_backend_sycl_get_device_memory(0, free_mem, total_mem); #else #ifdef _WIN32 MEMORYSTATUSEX status; From 485dc01214f266afff7004bc702498b491abc404 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Mon, 23 Dec 2024 12:02:44 +0100 Subject: [PATCH 24/43] server : add system_fingerprint to chat/completion (#10917) * server : add system_fingerprint to chat/completion * update README --- examples/server/README.md | 3 +- examples/server/server.cpp | 32 +++++++++++-------- .../server/tests/unit/test_chat_completion.py | 3 ++ examples/server/utils.hpp | 2 ++ 4 files changed, 25 insertions(+), 15 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 6d6465692..5e3d6a6e6 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -724,7 +724,8 @@ This endpoint is public (no API key check). By default, it is read-only. To make }, "total_slots": 1, "model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", - "chat_template": "..." + "chat_template": "...", + "build_info": "b(build number)-(build commit hash)" } ``` diff --git a/examples/server/server.cpp b/examples/server/server.cpp index fa3682a92..c571ed3c1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -595,10 +595,11 @@ struct server_task_result_cmpl_final : server_task_result { std::time_t t = std::time(0); json res = json { - {"choices", json::array({choice})}, - {"created", t}, - {"model", oaicompat_model}, - {"object", "chat.completion"}, + {"choices", json::array({choice})}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion"}, {"usage", json { {"completion_tokens", n_decoded}, {"prompt_tokens", n_prompt_tokens}, @@ -632,11 +633,12 @@ struct server_task_result_cmpl_final : server_task_result { }; json ret = json { - {"choices", json::array({choice})}, - {"created", t}, - {"id", oaicompat_cmpl_id}, - {"model", oaicompat_model}, - {"object", "chat.completion.chunk"}, + {"choices", json::array({choice})}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"}, {"usage", json { {"completion_tokens", n_decoded}, {"prompt_tokens", n_prompt_tokens}, @@ -761,11 +763,12 @@ struct server_task_result_cmpl_partial : server_task_result { } json ret = json { - {"choices", choices}, - {"created", t}, - {"id", oaicompat_cmpl_id}, - {"model", oaicompat_model}, - {"object", "chat.completion.chunk"} + {"choices", choices}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"} }; if (timings.prompt_n >= 0) { @@ -3476,6 +3479,7 @@ int main(int argc, char ** argv) { { "total_slots", ctx_server.params_base.n_parallel }, { "model_path", ctx_server.params_base.model }, { "chat_template", llama_get_chat_template(ctx_server.model) }, + { "build_info", build_info }, }; res_ok(res, data); diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py index 0fa1a17c1..885497081 100644 --- a/examples/server/tests/unit/test_chat_completion.py +++ b/examples/server/tests/unit/test_chat_completion.py @@ -31,6 +31,7 @@ def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_conte }) assert res.status_code == 200 assert "cmpl" in res.body["id"] # make sure the completion id has the expected format + assert res.body["system_fingerprint"].startswith("b") assert res.body["model"] == model if model is not None else server.model_alias assert res.body["usage"]["prompt_tokens"] == n_prompt assert res.body["usage"]["completion_tokens"] == n_predicted @@ -63,6 +64,7 @@ def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_conte last_cmpl_id = None for data in res: choice = data["choices"][0] + assert data["system_fingerprint"].startswith("b") assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future if last_cmpl_id is None: last_cmpl_id = data["id"] @@ -92,6 +94,7 @@ def test_chat_completion_with_openai_library(): seed=42, temperature=0.8, ) + assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b") assert res.choices[0].finish_reason == "length" assert res.choices[0].message.content is not None assert match_regex("(Suddenly)+", res.choices[0].message.content) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 94bb285b6..1987acac8 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -56,6 +56,8 @@ static T json_value(const json & body, const std::string & key, const T & defaul } } +const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT); + // // tokenizer and input processing utils // From 14b699ecde8f1e9e251ebff9eca39ebc5603b83b Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Mon, 23 Dec 2024 12:52:25 +0100 Subject: [PATCH 25/43] server : fix missing model id in /model endpoint (#10957) * server : fix missing model id in /model endpoint * fix ci --- 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 c571ed3c1..476a9225f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3701,7 +3701,7 @@ int main(int argc, char ** argv) { {"object", "list"}, {"data", { { - {"id", params.model_alias}, + {"id", params.model_alias.empty() ? params.model : params.model_alias}, {"object", "model"}, {"created", std::time(0)}, {"owned_by", "llamacpp"}, From 32d6ee6385b3fc908b283f509b845f757a6e7206 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Mon, 23 Dec 2024 20:25:52 +0100 Subject: [PATCH 26/43] ggml : fix const usage in SSE path (#10962) --- ggml/src/ggml-cpu/ggml-cpu.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 67e67a089..18d194479 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -986,7 +986,7 @@ inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 -static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { +static inline __m128 __sse_f16x4_load(const ggml_fp16_t * x) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(x[0]); @@ -997,7 +997,7 @@ static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { return _mm_loadu_ps(tmp); } -static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { +static inline void __sse_f16x4_store(ggml_fp16_t * x, __m128 y) { float arr[4]; _mm_storeu_ps(arr, y); From 3327bb0f8dea381118f8e66c18ea14db56d3b942 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Tue, 24 Dec 2024 04:05:17 +0100 Subject: [PATCH 27/43] ggml : fix arm enabled features check (#10961) --- ggml/src/ggml-cpu/CMakeLists.txt | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 12d790825..e357860a5 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -135,14 +135,20 @@ function(ggml_add_cpu_backend_variant_impl tag_name) endif() # show enabled features + if (CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows") + set(FEAT_INPUT_FILE "NUL") + else() + set(FEAT_INPUT_FILE "/dev/null") + endif() + execute_process( COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E - - INPUT_FILE "/dev/null" + INPUT_FILE ${FEAT_INPUT_FILE} OUTPUT_VARIABLE ARM_FEATURE RESULT_VARIABLE ARM_FEATURE_RESULT ) if (ARM_FEATURE_RESULT) - message(FATAL_ERROR "Failed to get ARM features") + message(WARNING "Failed to get ARM features") else() foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC) string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos) From 60cfa728e27c28537657d4e627ed432508eb9537 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Tue, 24 Dec 2024 04:05:27 +0100 Subject: [PATCH 28/43] ggml : use wstring for backend search paths (#10960) ggml-ci --- ggml/src/CMakeLists.txt | 1 + ggml/src/ggml-backend-reg.cpp | 117 ++++++++++++++++++------------- ggml/src/ggml-cpu/CMakeLists.txt | 5 ++ 3 files changed, 75 insertions(+), 48 deletions(-) diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index bf5ee5fc2..a5f7f7b5b 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -234,6 +234,7 @@ function(ggml_add_backend_library backend) # write the shared library to the output directory set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}) target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL) + add_dependencies(ggml ${backend}) else() add_library(${backend} ${ARGN}) target_link_libraries(ggml PUBLIC ${backend}) diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index 31ee31e39..7ddd178b5 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -66,6 +66,26 @@ #include "ggml-kompute.h" #endif +// disable C++17 deprecation warning for std::codecvt_utf8 +#if defined(__clang__) +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif + +static std::wstring utf8_to_utf16(const std::string & str) { + std::wstring_convert> converter; + return converter.from_bytes(str); +} + +static std::string utf16_to_utf8(const std::wstring & str) { + std::wstring_convert> converter; + return converter.to_bytes(str); +} + +#if defined(__clang__) +# pragma clang diagnostic pop +#endif + #ifdef _WIN32 using dl_handle = std::remove_pointer_t; @@ -88,11 +108,6 @@ static dl_handle * dl_load_library(const std::wstring & path) { return handle; } -static dl_handle * dl_load_library(const std::string & path) { - std::wstring_convert> converter; - return dl_load_library(converter.from_bytes(path)); -} - static void * dl_get_sym(dl_handle * handle, const char * name) { DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); @@ -114,8 +129,8 @@ struct dl_handle_deleter { } }; -static void * dl_load_library(const std::string & path) { - dl_handle * handle = dlopen(path.c_str(), RTLD_NOW | RTLD_LOCAL); +static void * dl_load_library(const std::wstring & path) { + dl_handle * handle = dlopen(utf16_to_utf8(path).c_str(), RTLD_NOW | RTLD_LOCAL); return handle; } @@ -202,11 +217,11 @@ struct ggml_backend_registry { devices.push_back(device); } - ggml_backend_reg_t load_backend(const char * path, bool silent) { + ggml_backend_reg_t load_backend(const std::wstring & path, bool silent) { dl_handle_ptr handle { dl_load_library(path) }; if (!handle) { if (!silent) { - GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path); + GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(path).c_str()); } return nullptr; } @@ -214,7 +229,7 @@ struct ggml_backend_registry { auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score"); if (score_fn && score_fn() == 0) { if (!silent) { - GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path); + GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, utf16_to_utf8(path).c_str()); } return nullptr; } @@ -222,7 +237,7 @@ struct ggml_backend_registry { auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init"); if (!backend_init_fn) { if (!silent) { - GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path); + GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, utf16_to_utf8(path).c_str()); } return nullptr; } @@ -231,16 +246,16 @@ struct ggml_backend_registry { if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) { if (!silent) { if (!reg) { - GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path); + GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, utf16_to_utf8(path).c_str()); } else { GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n", - __func__, path, reg->api_version, GGML_BACKEND_API_VERSION); + __func__, utf16_to_utf8(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION); } } return nullptr; } - GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path); + GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), utf16_to_utf8(path).c_str()); register_backend(reg, std::move(handle)); @@ -376,14 +391,14 @@ ggml_backend_t ggml_backend_init_best(void) { // Dynamic loading ggml_backend_reg_t ggml_backend_load(const char * path) { - return get_reg().load_backend(path, false); + return get_reg().load_backend(utf8_to_utf16(path), false); } void ggml_backend_unload(ggml_backend_reg_t reg) { get_reg().unload_backend(reg, true); } -static std::string get_executable_path() { +static std::wstring get_executable_path() { #if defined(__APPLE__) // get executable path std::vector path; @@ -401,7 +416,7 @@ static std::string get_executable_path() { if (last_slash != std::string::npos) { base_path = base_path.substr(0, last_slash); } - return base_path + "/"; + return utf8_to_utf16(base_path + "/"); #elif defined(__linux__) || defined(__FreeBSD__) std::string base_path = "."; std::vector path(1024); @@ -427,57 +442,63 @@ static std::string get_executable_path() { path.resize(path.size() * 2); } - return base_path + "/"; + return utf8_to_utf16(base_path + "/"); #elif defined(_WIN32) - std::vector path(MAX_PATH); - DWORD len = GetModuleFileNameA(NULL, path.data(), path.size()); + std::vector path(MAX_PATH); + DWORD len = GetModuleFileNameW(NULL, path.data(), path.size()); if (len == 0) { - return ""; + return {}; } - std::string base_path(path.data(), len); + std::wstring base_path(path.data(), len); // remove executable name auto last_slash = base_path.find_last_of('\\'); if (last_slash != std::string::npos) { base_path = base_path.substr(0, last_slash); } - return base_path + "\\"; + return base_path + L"\\"; +#else + return {}; #endif } -static std::string backend_filename_prefix() { +static std::wstring backend_filename_prefix() { #ifdef _WIN32 - return "ggml-"; + return L"ggml-"; #else - return "libggml-"; + return L"libggml-"; #endif } -static std::string backend_filename_suffix() { +static std::wstring backend_filename_suffix() { #ifdef _WIN32 - return ".dll"; + return L".dll"; #else - return ".so"; + return L".so"; +#endif +} + +static std::wstring path_separator() { +#ifdef _WIN32 + return L"\\"; +#else + return L"/"; #endif } static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, const char * user_search_path) { // enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths // TODO: search system paths - std::string file_prefix = backend_filename_prefix() + name + "-"; - std::vector search_paths; + std::wstring file_prefix = backend_filename_prefix() + utf8_to_utf16(name) + L"-"; + std::vector search_paths; if (user_search_path == nullptr) { - search_paths.push_back("./"); + search_paths.push_back(L"." + path_separator()); search_paths.push_back(get_executable_path()); } else { -#if defined(_WIN32) - search_paths.push_back(std::string(user_search_path) + "\\"); -#else - search_paths.push_back(std::string(user_search_path) + "/"); -#endif + search_paths.push_back(utf8_to_utf16(user_search_path) + path_separator()); } int best_score = 0; - std::string best_path; + std::wstring best_path; namespace fs = std::filesystem; for (const auto & search_path : search_paths) { @@ -487,27 +508,27 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied); for (const auto & entry : dir_it) { if (entry.is_regular_file()) { - std::string filename = entry.path().filename().string(); - std::string ext = entry.path().extension().string(); + std::wstring filename = entry.path().filename().wstring(); + std::wstring ext = entry.path().extension().wstring(); if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) { - dl_handle_ptr handle { dl_load_library(entry.path().c_str()) }; + dl_handle_ptr handle { dl_load_library(entry.path().wstring()) }; if (!handle && !silent) { - GGML_LOG_ERROR("%s: failed to load %s\n", __func__, entry.path().string().c_str()); + GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str()); } if (handle) { auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score"); if (score_fn) { int s = score_fn(); #ifndef NDEBUG - GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, entry.path().string().c_str(), s); + GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s); #endif if (s > best_score) { best_score = s; - best_path = entry.path().string(); + best_path = entry.path().wstring(); } } else { if (!silent) { - GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, entry.path().string().c_str()); + GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str()); } } } @@ -519,15 +540,15 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, if (best_score == 0) { // try to load the base backend for (const auto & search_path : search_paths) { - std::string path = search_path + backend_filename_prefix() + name + backend_filename_suffix(); + std::wstring path = search_path + backend_filename_prefix() + utf8_to_utf16(name) + backend_filename_suffix(); if (fs::exists(path)) { - return get_reg().load_backend(path.c_str(), silent); + return get_reg().load_backend(path, silent); } } return nullptr; } - return get_reg().load_backend(best_path.c_str(), silent); + return get_reg().load_backend(best_path, silent); } void ggml_backend_load_all() { diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index e357860a5..f0aecac1b 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -323,6 +323,11 @@ function(ggml_add_cpu_backend_variant_impl tag_name) target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS}) if (GGML_BACKEND_DL) + if (GGML_NATIVE) + # the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE + message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS") + endif() + # The feature detection code is compiled as a separate target so that # it can be built without the architecture flags # Since multiple variants of the CPU backend may be included in the same From 30caac3a68a54de8396b21e20ba972554c587230 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 24 Dec 2024 09:44:20 +0200 Subject: [PATCH 29/43] llama : the WPM vocabs use the CLS token as BOS (#10930) * llama : the WPM vocabs use the CLS token as BOS ggml-ci * llama : add comment --- src/llama-vocab.cpp | 2 +- src/llama-vocab.h | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 7f2725f94..0a477d6dd 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1657,7 +1657,7 @@ bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token t } llama_token llama_token_bos_impl(const struct llama_vocab & vocab) { - return vocab.special_bos_id; + return vocab.type != LLAMA_VOCAB_TYPE_WPM ? vocab.special_bos_id : vocab.special_cls_id; } llama_token llama_token_eos_impl(const struct llama_vocab & vocab) { diff --git a/src/llama-vocab.h b/src/llama-vocab.h index 4bb16d2e4..a9b0da5ef 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -45,7 +45,7 @@ struct llama_vocab { id special_unk_id = 0; id special_sep_id = LLAMA_TOKEN_NULL; id special_pad_id = LLAMA_TOKEN_NULL; - id special_cls_id = LLAMA_TOKEN_NULL; + id special_cls_id = LLAMA_TOKEN_NULL; // TODO: revisit if this is really needed https://github.com/ggerganov/llama.cpp/pull/10930 id special_mask_id = LLAMA_TOKEN_NULL; id linefeed_id = 13; From 09fe2e76137dde850b13313f720e7ffa17efdefa Mon Sep 17 00:00:00 2001 From: NeverLucky <92274250+nvrxq@users.noreply.github.com> Date: Tue, 24 Dec 2024 19:39:49 +0300 Subject: [PATCH 30/43] server: allow filtering llama server response fields (#10940) * llama_server_response_fields * llama_server_response_fields_fix_issues * params fixes * fix * clarify docs * change to "response_fields" --------- Co-authored-by: Xuan Son Nguyen --- examples/server/README.md | 2 ++ examples/server/server.cpp | 6 +++- examples/server/tests/unit/test_completion.py | 34 +++++++++++++++++++ examples/server/utils.hpp | 22 ++++++++++++ 4 files changed, 63 insertions(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 5e3d6a6e6..c7d91be99 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -450,6 +450,8 @@ These words will not be included in the completion, so make sure to add them to `post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain. +`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. + **Response format** - Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 476a9225f..3fbfb13c4 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -92,6 +92,7 @@ struct slot_params { int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit std::vector antiprompt; + std::vector response_fields; bool timings_per_token = false; bool post_sampling_probs = false; bool ignore_eos = false; @@ -209,6 +210,7 @@ struct server_task { params.n_discard = json_value(data, "n_discard", defaults.n_discard); //params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); + params.response_fields = json_value(data, "response_fields", std::vector()); params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); @@ -522,6 +524,7 @@ struct server_task_result_cmpl_final : server_task_result { bool post_sampling_probs; std::vector probs_output; + std::vector response_fields; slot_params generation_params; @@ -568,7 +571,7 @@ struct server_task_result_cmpl_final : server_task_result { if (!stream && !probs_output.empty()) { res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs); } - return res; + return response_fields.empty() ? res : json_get_nested_values(response_fields, res); } json to_json_oaicompat_chat() { @@ -2066,6 +2069,7 @@ struct server_context { res->tokens = slot.generated_tokens; res->timings = slot.get_timings(); res->prompt = common_detokenize(ctx, slot.prompt_tokens, true); + res->response_fields = slot.params.response_fields; res->truncated = slot.truncated; res->n_decoded = slot.n_decoded; diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py index b88d45f18..00d5ce391 100644 --- a/examples/server/tests/unit/test_completion.py +++ b/examples/server/tests/unit/test_completion.py @@ -257,6 +257,40 @@ def test_completion_parallel_slots(n_slots: int, n_requests: int): # assert match_regex(re_content, res.body["content"]) +@pytest.mark.parametrize( + "prompt,n_predict,response_fields", + [ + ("I believe the meaning of life is", 8, []), + ("I believe the meaning of life is", 32, ["content", "generation_settings/n_predict", "prompt"]), + ], +) +def test_completion_response_fields( + prompt: str, n_predict: int, response_fields: list[str] +): + global server + server.start() + res = server.make_request( + "POST", + "/completion", + data={ + "n_predict": n_predict, + "prompt": prompt, + "response_fields": response_fields, + }, + ) + assert res.status_code == 200 + assert "content" in res.body + assert len(res.body["content"]) + if len(response_fields): + assert res.body["generation_settings/n_predict"] == n_predict + assert res.body["prompt"] == " " + prompt + assert isinstance(res.body["content"], str) + assert len(res.body) == len(response_fields) + else: + assert len(res.body) + assert "generation_settings" in res.body + + def test_n_probs(): global server server.start() diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 1987acac8..043d8b528 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -90,6 +90,28 @@ static bool json_is_array_of_mixed_numbers_strings(const json & data) { return false; } +// get value by path(key1 / key2) +static json json_get_nested_values(const std::vector & paths, const json & js) { + json result = json::object(); + + for (const std::string & path : paths) { + json current = js; + const auto keys = string_split(path, /*separator*/ '/'); + bool valid_path = true; + for (const std::string & k : keys) { + if (valid_path && current.is_object() && current.contains(k)) { + current = current[k]; + } else { + valid_path = false; + } + } + if (valid_path) { + result[path] = current; + } + } + return result; +} + /** * this handles 2 cases: * - only string, example: "string" From 2cd43f4900ba0e34124fdcbf02a7f9df25a10a3d Mon Sep 17 00:00:00 2001 From: Djip007 <3705339+Djip007@users.noreply.github.com> Date: Tue, 24 Dec 2024 18:54:49 +0100 Subject: [PATCH 31/43] ggml : more perfo with llamafile tinyblas on x86_64 (#10714) * more perfo with llamafile tinyblas on x86_64. - add bf16 suport - change dispache strategie (thanks: https://github.com/ikawrakow/ik_llama.cpp/pull/71 ) - reduce memory bandwidth simple tinyblas dispache and more cache freindly * tinyblas dynamic dispaching * sgemm: add M blocs. * - git 2.47 use short id of len 9. - show-progress is not part of GNU Wget2 * remove not stable test --- examples/server/tests/unit/test_completion.py | 7 +- ggml/src/ggml-cpu/ggml-cpu.c | 8 +- ggml/src/ggml-cpu/llamafile/sgemm.cpp | 522 +++++++++--------- ggml/src/ggml-cpu/llamafile/sgemm.h | 4 +- scripts/compare-llama-bench.py | 22 +- scripts/hf.sh | 2 +- 6 files changed, 287 insertions(+), 278 deletions(-) diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py index 00d5ce391..a6b215944 100644 --- a/examples/server/tests/unit/test_completion.py +++ b/examples/server/tests/unit/test_completion.py @@ -95,7 +95,7 @@ def test_consistent_result_same_seed(n_slots: int): res = server.make_request("POST", "/completion", data={ "prompt": "I believe the meaning of life is", "seed": 42, - "temperature": 1.0, + "temperature": 0.0, "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed }) if last_res is not None: @@ -120,9 +120,10 @@ def test_different_result_different_seed(n_slots: int): assert res.body["content"] != last_res.body["content"] last_res = res - +# TODO figure why it don't work with temperature = 1 +# @pytest.mark.parametrize("temperature", [0.0, 1.0]) @pytest.mark.parametrize("n_batch", [16, 32]) -@pytest.mark.parametrize("temperature", [0.0, 1.0]) +@pytest.mark.parametrize("temperature", [0.0]) def test_consistent_result_different_batch_size(n_batch: int, temperature: float): global server server.n_batch = n_batch diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 18d194479..b7fefb9dd 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -7419,14 +7419,14 @@ static void ggml_compute_forward_mul_mat( if (src1_cont) { for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), + if (!llamafile_sgemm(params, + ne01, ne11, ne00/ggml_blck_size(src0->type), (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, nb01/ggml_type_size(src0->type), (const char *)src1->data + i12*nb12 + i13*nb13, nb11/ggml_type_size(src1->type), (char *)dst->data + i12*nb2 + i13*nb3, nb1/ggml_type_size(dst->type), - ith, nth, src0->type, src1->type, dst->type)) @@ -7471,14 +7471,14 @@ UseGgmlGemm1:; for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), + if (!llamafile_sgemm(params, + ne01, ne11, ne00/ggml_blck_size(src0->type), (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, nb01/ggml_type_size(src0->type), (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, row_size/ggml_type_size(vec_dot_type), (char *)dst->data + i12*nb2 + i13*nb3, nb1/ggml_type_size(dst->type), - ith, nth, src0->type, vec_dot_type, dst->type)) diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index f80a72781..00f7f1170 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -53,6 +53,8 @@ #include "ggml-cpu-impl.h" #include "ggml-quants.h" +#include + #ifdef _MSC_VER #define NOINLINE __declspec(noinline) #else @@ -134,6 +136,16 @@ inline __m512 madd(__m512 a, __m512 b, __m512 c) { return _mm512_fmadd_ps(a, b, c); } #endif +#if defined(__AVX512BF16__) +template <> +inline __m512 madd(__m512bh a, __m512bh b, __m512 c) { + return _mm512_dpbf16_ps(c, a, b); +} +template <> +inline __m256 madd(__m256bh a, __m256bh b, __m256 c) { + return _mm256_dpbf16_ps(c, a, b); +} +#endif #endif #if defined(__ARM_FEATURE_FMA) @@ -226,6 +238,13 @@ template <> inline __m256 load(const float *p) { } #endif // __AVX__ +#if defined(__AVX2__) || defined(__AVX512F__) +template <> inline __m256 load(const ggml_bf16_t *p) { + return _mm256_castsi256_ps( + _mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)p)), 16)); +} +#endif // __AVX2__ + #if defined(__F16C__) template <> inline __m256 load(const ggml_fp16_t *p) { return _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)p)); @@ -239,8 +258,27 @@ template <> inline __m512 load(const float *p) { template <> inline __m512 load(const ggml_fp16_t *p) { return _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)p)); } +template <> inline __m512 load(const ggml_bf16_t *p) { + return _mm512_castsi512_ps( + _mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)p)), 16)); +} #endif // __AVX512F__ +#if defined(__AVX512BF16__) +template <> inline __m512bh load(const ggml_bf16_t *p) { + return (__m512bh)_mm512_loadu_ps((const float *)p); +} +template <> inline __m256bh load(const ggml_bf16_t *p) { + return (__m256bh)_mm256_loadu_ps((const float *)p); +} +template <> inline __m512bh load(const float *p) { + return _mm512_cvtne2ps_pbh(_mm512_loadu_ps(p + 16), _mm512_loadu_ps(p)); +} +template <> inline __m256bh load(const float *p) { + return _mm512_cvtneps_pbh(_mm512_loadu_ps(p)); +} +#endif + //////////////////////////////////////////////////////////////////////////////////////////////////// // CONSTANTS @@ -252,199 +290,170 @@ static const __m128i iq4nlt = _mm_loadu_si128((const __m128i *) kvalues_iq4nl); //////////////////////////////////////////////////////////////////////////////////////////////////// // FLOATING POINT MATRIX MULTIPLICATION +template +static inline int64_t BLOCK_SIZE(size_t m) { + const int64_t NB_BLOC_M = (m + M - 1) / M; + return (m % NB_BLOC_M == 0) ? m / NB_BLOC_M : (m / NB_BLOC_M) + 1; +} + +static constexpr inline int64_t BLOC_POS(int64_t ib, int64_t ibN, int64_t bloc_size) { + return ib < ibN ? ib * bloc_size : ibN * bloc_size + (ib - ibN) * (bloc_size - 1); +} + template class tinyBLAS { public: - tinyBLAS(int64_t k, + tinyBLAS(const ggml_compute_params * params, int64_t k, const TA *A, int64_t lda, const TB *B, int64_t ldb, - TC *C, int64_t ldc, - int ith, int nth) - : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + TC *C, int64_t ldc) + : params(params), A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc) { } - void matmul(int64_t m, int64_t n) { - mnpack(0, m, 0, n); + bool matmul(int64_t m, int64_t n) { + if (k % KN != 0) + return false; + // compute RM for only need tile with size RM&RM-1 +#if VECTOR_REGISTERS == 32 + if (m % 16 == 0 && (m/16 >= params->nth)) { + const int64_t SIZE_N = BLOCK_SIZE<6>(n); + mnpack<4, 6, 4>(m, n, SIZE_N, 12); + return true; + } + if (m % 8 == 0 ) { + const int64_t SIZE_N = BLOCK_SIZE<6>(n); + mnpack<4, 6, 2>(m, n, SIZE_N, 12); + return true; + } + if (m % 4 == 0) { + const int64_t SIZE_N = BLOCK_SIZE<6>(n); + mnpack<4, 6, 1>(m, n, SIZE_N, 12); + return true; + } +#else // VECTOR_REGISTERS == 16 + if (m % 16 == 0 && (m/16 >= params->nth)) { + const int64_t SIZE_N = BLOCK_SIZE<3>(n); + mnpack<4, 3, 4>(m, n, SIZE_N, 24); + return true; + } + if (m % 8 == 0 ) { + const int64_t SIZE_N = BLOCK_SIZE<3>(n); + mnpack<4, 3, 2>(m, n, SIZE_N, 24); + return true; + } + if (m % 4 == 0) { + const int64_t SIZE_N = BLOCK_SIZE<3>(n); + mnpack<4, 3, 1>(m, n, SIZE_N, 24); + return true; + } +#endif + return false; } private: - NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t mc, nc, mp, np; - switch ((MIN(m - m0, 5) << 4) | MIN(n - n0, 5)) { -#if VECTOR_REGISTERS == 32 - case 0x55: - mc = 5; - nc = 5; - gemm<5, 5>(m0, m, n0, n); - break; - case 0x45: - mc = 4; - nc = 5; - gemm<4, 5>(m0, m, n0, n); - break; - case 0x54: - mc = 5; - nc = 4; - gemm<5, 4>(m0, m, n0, n); - break; - case 0x44: - mc = 4; - nc = 4; - gemm<4, 4>(m0, m, n0, n); - break; - case 0x53: - mc = 5; - nc = 3; - gemm<5, 3>(m0, m, n0, n); - break; - case 0x35: - mc = 3; - nc = 5; - gemm<3, 5>(m0, m, n0, n); - break; - case 0x43: - mc = 4; - nc = 3; - gemm<4, 3>(m0, m, n0, n); - break; -#else - case 0x55: - case 0x54: - case 0x53: - case 0x45: - case 0x44: - case 0x43: - mc = 4; - nc = 3; - gemm<4, 3>(m0, m, n0, n); - break; - case 0x35: -#endif - case 0x34: - mc = 3; - nc = 4; - gemm<3, 4>(m0, m, n0, n); - break; - case 0x52: - mc = 5; - nc = 2; - gemm<5, 2>(m0, m, n0, n); - break; - case 0x33: - mc = 3; - nc = 3; - gemm<3, 3>(m0, m, n0, n); - break; - case 0x25: - mc = 2; - nc = 5; - gemm<2, 5>(m0, m, n0, n); - break; - case 0x42: - mc = 4; - nc = 2; - gemm<4, 2>(m0, m, n0, n); - break; - case 0x24: - mc = 2; - nc = 4; - gemm<2, 4>(m0, m, n0, n); - break; - case 0x32: - mc = 3; - nc = 2; - gemm<3, 2>(m0, m, n0, n); - break; - case 0x23: - mc = 2; - nc = 3; - gemm<2, 3>(m0, m, n0, n); - break; - case 0x51: - mc = 5; - nc = 1; - gemm<5, 1>(m0, m, n0, n); - break; - case 0x41: - mc = 4; - nc = 1; - gemm<4, 1>(m0, m, n0, n); - break; - case 0x22: - mc = 2; - nc = 2; - gemm<2, 2>(m0, m, n0, n); - break; - case 0x15: - mc = 1; - nc = 5; - gemm<1, 5>(m0, m, n0, n); - break; - case 0x14: - mc = 1; - nc = 4; - gemm<1, 4>(m0, m, n0, n); - break; - case 0x31: - mc = 3; - nc = 1; - gemm<3, 1>(m0, m, n0, n); - break; - case 0x13: - mc = 1; - nc = 3; - gemm<1, 3>(m0, m, n0, n); - break; - case 0x21: - mc = 2; - nc = 1; - gemm<2, 1>(m0, m, n0, n); - break; - case 0x12: - mc = 1; - nc = 2; - gemm<1, 2>(m0, m, n0, n); - break; - case 0x11: - mc = 1; - nc = 1; - gemm<1, 1>(m0, m, n0, n); - break; - default: - return; + template + inline void mnpack(int64_t m, int64_t n, int64_t SIZE_N, int64_t BN) { + if (SIZE_N == RN) { + return gemm(m, n, BN); + } + if constexpr (RN > 1) { + return mnpack(m, n, SIZE_N, BN); + } else { + GGML_LOG_ERROR("mnpack<%d, %d> bloc size not supported\n", RM, (int)SIZE_N); + GGML_ASSERT(false); // we have miss something. } - mp = m0 + (m - m0) / mc * mc; - np = n0 + (n - n0) / nc * nc; - mnpack(mp, m, n0, np); - mnpack(m0, m, np, n); } template - NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t ytiles = (m - m0) / RM; - int64_t xtiles = (n - n0) / RN; - int64_t tiles = xtiles * ytiles; - int64_t duty = (tiles + nth - 1) / nth; - int64_t start = duty * ith; - int64_t end = start + duty; - if (end > tiles) - end = tiles; - for (int64_t job = start; job < end; ++job) { - int64_t ii = m0 + job / xtiles * RM; - int64_t jj = n0 + job % xtiles * RN; - D Cv[RN][RM] = {}; - for (int64_t l = 0; l < k; l += KN) - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) - Cv[j][i] = madd(load(A + lda * (ii + i) + l), - load(B + ldb * (jj + j) + l), - Cv[j][i]); - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) - C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); + inline void gemm_bloc(int64_t ii, int64_t jj) { + D Cv[RN][RM] = {}; + for (int64_t l = 0; l < k; l += KN) { + // help compiler for op order. + if constexpr (RM <= RN) { + V Av[RM]; + for (int64_t i = 0; i < RM; ++i) { + Av[i] = load(A + lda * (ii + i) + l); + } + for (int64_t j = 0; j < RN; ++j) { + V Bv = load(B + ldb * (jj + j) + l); + for (int64_t i = 0; i < RM; ++i) { + Cv[j][i] = madd(Av[i], Bv, Cv[j][i]); + } + } + } else { + V Bv[RN]; + for (int64_t j = 0; j < RN; ++j) { + Bv[j] = load(B + ldb * (jj + j) + l); + } + for (int64_t i = 0; i < RM; ++i) { + V Av = load(A + lda * (ii + i) + l); + for (int64_t j = 0; j < RN; ++j) { + Cv[j][i] = madd(Av, Bv[j], Cv[j][i]); + } + } + } } + for (int64_t j = 0; j < RN; ++j) + for (int64_t i = 0; i < RM; ++i) + C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); } + template + NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) { + static std::atomic current_chunk; + + GGML_ASSERT(m % (RM * BM) == 0); + const int64_t ytiles = m / (RM * BM); + const int64_t xtiles = (n + RN -1) / RN; + const int64_t jj_RN = (xtiles - (xtiles * RN - n)); + + // "round" bloc_size to "nearest" BN + const int64_t NB_BN = xtiles < BN ? 1 : (xtiles + BN / 2) / BN; + const int64_t SIZE_BN = xtiles % NB_BN == 0 ? xtiles / NB_BN : xtiles / NB_BN + 1; + const int64_t jj_BN = (NB_BN - (NB_BN * SIZE_BN - xtiles)); + const int64_t nb_job = ytiles * NB_BN; + + if (params->ith == 0) { + GGML_ASSERT( jj_BN * SIZE_BN + (NB_BN - jj_BN) * (SIZE_BN - 1) == xtiles); + // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. + std::atomic_store_explicit(¤t_chunk, (int64_t)params->nth, std::memory_order_relaxed); + } + + ggml_barrier(params->threadpool); + + int64_t job = params->ith; + while (job < nb_job) { + const int64_t ii = (job % ytiles) * RM * BM; + const int64_t jb = job / ytiles; + const int64_t jr0 = BLOC_POS(jb , jj_BN, SIZE_BN); + const int64_t jrN = BLOC_POS(jb+1, jj_BN, SIZE_BN); + + const int64_t jj0 = BLOC_POS(jr0, jj_RN, RN); + const int64_t jj2 = BLOC_POS(jrN, jj_RN, RN); + const int64_t jj1 = jj2 < jj_RN * RN ? jj2 : jj_RN * RN; + + for (int64_t bi = 0; bi < BM * RM; bi += RM) { + int64_t jj = jj0; + for (; jj < jj1; jj += RN) { + gemm_bloc(ii + bi, jj); + } + if constexpr (RN > 1) { + for (; jj < jj2; jj += RN - 1) { + gemm_bloc(ii + bi, jj); + } + } + GGML_ASSERT(jj == jj2); + } + + // next step. + job = std::atomic_fetch_add_explicit(¤t_chunk, (int64_t)1, std::memory_order_relaxed); + } + + ggml_barrier(params->threadpool); + return; + } + + const ggml_compute_params * params; const TA *const A; const TB *const B; TC *const C; @@ -452,8 +461,6 @@ class tinyBLAS { const int64_t lda; const int64_t ldb; const int64_t ldc; - const int ith; - const int nth; }; ////////////////////////////////////////////////////////////////////////////////////////// @@ -1657,8 +1664,9 @@ class tinyBLAS_PPC { * @param Ctype is GGML data type of `C` * @return true if this function was able to service the matmul request */ -bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda, const void *B, int64_t ldb, void *C, - int64_t ldc, int ith, int nth, int Atype, int Btype, int Ctype) { +bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64_t n, int64_t k, + const void *A, int64_t lda, const void *B, int64_t ldb, void *C, + int64_t ldc, int Atype, int Btype, int Ctype) { assert(m >= 0); assert(n >= 0); @@ -1666,8 +1674,8 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda assert(lda >= k); assert(ldb >= k); assert(ldc >= m); - assert(nth > 0); - assert(ith < nth); + assert(params->nth > 0); + assert(params->ith < params->nth); // only enable sgemm for prompt processing if (n < 2) @@ -1682,37 +1690,25 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda if (Btype != GGML_TYPE_F32) return false; #if defined(__AVX512F__) - if (k % 16) - return false; - tinyBLAS<16, __m512, __m512, float, float, float> tb{ + tinyBLAS<16, __m512, __m512, float, float, float> tb{ params, k, (const float *)A, lda, (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; + (float *)C, ldc}; + return tb.matmul(m, n); #elif defined(__AVX__) || defined(__AVX2__) - if (k % 8) - return false; - tinyBLAS<8, __m256, __m256, float, float, float> tb{ + tinyBLAS<8, __m256, __m256, float, float, float> tb{ params, k, (const float *)A, lda, (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; + (float *)C, ldc}; + return tb.matmul(m, n); #elif defined(__ARM_NEON) if (n < 4) return false; - if (k % 4) - return false; - tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ + tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ params, k, (const float *)A, lda, (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; + (float *)C, ldc}; + return tb.matmul(m, n); #elif defined(__MMA__) if (k % 8) return false; @@ -1720,7 +1716,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda k, (const float *)A, lda, (const float *)B, ldb, (float *)C, ldc, - ith, nth}; + params->ith, params->nth}; tb.matmul(m, n); return true; #else @@ -1728,60 +1724,71 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda #endif } + case GGML_TYPE_BF16: { +#if defined(__AVX512BF16__) + if (Btype == GGML_TYPE_BF16) { + tinyBLAS<32, __m512, __m512bh, ggml_bf16_t, ggml_bf16_t, float> tb{ params, k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); + } +#elif defined(__AVX512F__) + if (Btype == GGML_TYPE_BF16) { + tinyBLAS<16, __m512, __m512, ggml_bf16_t, ggml_bf16_t, float> tb{ params, k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); + } +#elif defined(__AVX2__) + if (Btype == GGML_TYPE_BF16) { + tinyBLAS<8, __m256, __m256, ggml_bf16_t, ggml_bf16_t, float> tb{ params, k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); + } +#endif + return false; + } case GGML_TYPE_F16: { #if defined(__AVX512F__) - if (k % 16) - return false; - if (Btype != GGML_TYPE_F32) - return false; - tinyBLAS<16, __m512, __m512, ggml_fp16_t, float, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; + if (Btype == GGML_TYPE_F16) { + tinyBLAS<16, __m512, __m512, ggml_fp16_t, ggml_fp16_t, float> tb{ params, k, + (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); + } #elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__) - if (k % 8) - return false; - if (Btype != GGML_TYPE_F32) - return false; - tinyBLAS<8, __m256, __m256, ggml_fp16_t, float, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; + if (Btype == GGML_TYPE_F16) { + tinyBLAS<8, __m256, __m256, ggml_fp16_t, ggml_fp16_t, float> tb{ params, k, + (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); + } #elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) if (n < 8) return false; - if (k % 8) - return false; - if (Btype != GGML_TYPE_F16) - return false; - tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const ggml_fp16_t *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; + if (Btype == GGML_TYPE_F16) { + tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{ params, + k, (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); + } #elif defined(__ARM_NEON) && !defined(_MSC_VER) - if (k % 4) - return false; - if (Btype != GGML_TYPE_F32) - return false; - tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#else - return false; + if (Btype == GGML_TYPE_F32) { + tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{ params, + k, (const ggml_fp16_t *)A, lda, + (const float *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); + } #endif + return false; } case GGML_TYPE_Q8_0: { @@ -1792,7 +1799,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda k, (const block_q8_0 *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, - ith, nth}; + params->ith, params->nth}; tb.matmul(m, n); return true; #elif defined(__ARM_FEATURE_DOTPROD) @@ -1800,7 +1807,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda k, (const block_q8_0 *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, - ith, nth}; + params->ith, params->nth}; tb.matmul(m, n); return true; #else @@ -1816,7 +1823,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda k, (const block_q4_0 *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, - ith, nth}; + params->ith, params->nth}; tb.matmul(m, n); return true; #elif defined(__ARM_FEATURE_DOTPROD) @@ -1824,7 +1831,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda k, (const block_q4_0 *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, - ith, nth}; + params->ith, params->nth}; tb.matmul(m, n); return true; #else @@ -1840,7 +1847,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda k, (const block_q5_0 *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, - ith, nth}; + params->ith, params->nth}; tb.matmul(m, n); return true; #else @@ -1856,7 +1863,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda k, (const block_iq4_nl *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, - ith, nth}; + params->ith, params->nth}; tb.matmul(m, n); return true; #else @@ -1868,6 +1875,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda return false; } + (void)params; (void)m; (void)n; (void)k; @@ -1877,8 +1885,6 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda (void)ldb; (void)C; (void)ldc; - (void)ith; - (void)nth; (void)Atype; (void)Btype; (void)Ctype; diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.h b/ggml/src/ggml-cpu/llamafile/sgemm.h index caf6dd556..3d2909515 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.h +++ b/ggml/src/ggml-cpu/llamafile/sgemm.h @@ -5,8 +5,8 @@ extern "C" { #endif -bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t, - const void *, int64_t, void *, int64_t, int, int, +bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t, int64_t, int64_t, + const void *, int64_t, const void *, int64_t, void *, int64_t, int, int, int); #ifdef __cplusplus diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 5069ae638..239c458d8 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -126,6 +126,8 @@ connection = sqlite3.connect(input_file) cursor = connection.cursor() builds = cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall() +commit_short_len = len(builds[0][0]) + try: repo = git.Repo(".", search_parent_directories=True) except git.InvalidGitRepositoryError: @@ -138,11 +140,11 @@ def find_parent_in_data(commit: git.Commit): seen_hexsha8 = set() while heap: depth, current_commit = heapq.heappop(heap) - current_hexsha8 = commit.hexsha[:8] + current_hexsha8 = commit.hexsha[:commit_short_len] if (current_hexsha8,) in builds: return current_hexsha8 for parent in commit.parents: - parent_hexsha8 = parent.hexsha[:8] + parent_hexsha8 = parent.hexsha[:commit_short_len] if parent_hexsha8 not in seen_hexsha8: seen_hexsha8.add(parent_hexsha8) heapq.heappush(heap, (depth + 1, parent)) @@ -156,9 +158,9 @@ def get_all_parent_hexsha8s(commit: git.Commit): while unvisited: current_commit = unvisited.pop(0) - visited.append(current_commit.hexsha[:8]) + visited.append(current_commit.hexsha[:commit_short_len]) for parent in current_commit.parents: - if parent.hexsha[:8] not in visited: + if parent.hexsha[:commit_short_len] not in visited: unvisited.append(parent) return visited @@ -169,10 +171,10 @@ def get_commit_name(hexsha8): if repo is None: return hexsha8 for h in repo.heads: - if h.commit.hexsha[:8] == hexsha8: + if h.commit.hexsha[:commit_short_len] == hexsha8: return h.name for t in repo.tags: - if t.commit.hexsha[:8] == hexsha8: + if t.commit.hexsha[:commit_short_len] == hexsha8: return t.name return hexsha8 @@ -183,13 +185,13 @@ def get_commit_hexsha8(name): return None for h in repo.heads: if h.name == name: - return h.commit.hexsha[:8] + return h.commit.hexsha[:commit_short_len] for t in repo.tags: if t.name == name: - return t.commit.hexsha[:8] + return t.commit.hexsha[:commit_short_len] for c in repo.iter_commits("--all"): - if c.hexsha[:8] == name[:8]: - return c.hexsha[:8] + if c.hexsha[:commit_short_len] == name[:commit_short_len]: + return c.hexsha[:commit_short_len] return None diff --git a/scripts/hf.sh b/scripts/hf.sh index 85c2c4d9a..b251925fa 100755 --- a/scripts/hf.sh +++ b/scripts/hf.sh @@ -26,7 +26,7 @@ function has_cmd { } if has_cmd wget; then - cmd="wget -q --show-progress -c -O %s/%s %s" + cmd="wget -q -c -O %s/%s %s" elif has_cmd curl; then cmd="curl -C - -f --output-dir %s -o %s -L %s" else From 9ba399dfa7f115effc63d48e6860a94c9faa31b2 Mon Sep 17 00:00:00 2001 From: Reza Kakhki Date: Tue, 24 Dec 2024 21:33:04 +0100 Subject: [PATCH 32/43] server : add support for "encoding_format": "base64" to the */embeddings endpoints (#10967) * add support for base64 * fix base64 test * improve test --------- Co-authored-by: Xuan Son Nguyen --- examples/server/CMakeLists.txt | 1 + examples/server/server.cpp | 13 ++++++- examples/server/tests/unit/test_embedding.py | 41 ++++++++++++++++++++ examples/server/utils.hpp | 28 ++++++++++--- 4 files changed, 76 insertions(+), 7 deletions(-) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index a27597cbc..1b7cc8c13 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -34,6 +34,7 @@ endforeach() add_executable(${TARGET} ${TARGET_SRCS}) install(TARGETS ${TARGET} RUNTIME) +target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}) target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) if (LLAMA_SERVER_SSL) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3fbfb13c4..30ff3b149 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3790,6 +3790,17 @@ int main(int argc, char ** argv) { return; } + bool use_base64 = false; + if (body.count("encoding_format") != 0) { + const std::string& format = body.at("encoding_format"); + if (format == "base64") { + use_base64 = true; + } else if (format != "float") { + res_error(res, format_error_response("The format to return the embeddings in. Can be either float or base64", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true); for (const auto & tokens : tokenized_prompts) { // this check is necessary for models that do not add BOS token to the input @@ -3841,7 +3852,7 @@ int main(int argc, char ** argv) { } // write JSON response - json root = oaicompat ? format_embeddings_response_oaicompat(body, responses) : json(responses); + json root = oaicompat ? format_embeddings_response_oaicompat(body, responses, use_base64) : json(responses); res_ok(res, root); }; diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py index 43e372fc7..8b0eb42b0 100644 --- a/examples/server/tests/unit/test_embedding.py +++ b/examples/server/tests/unit/test_embedding.py @@ -1,3 +1,5 @@ +import base64 +import struct import pytest from openai import OpenAI from utils import * @@ -194,3 +196,42 @@ def test_embedding_usage_multiple(): assert res.status_code == 200 assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] assert res.body['usage']['prompt_tokens'] == 2 * 9 + + +def test_embedding_openai_library_base64(): + server.start() + test_input = "Test base64 embedding output" + + # get embedding in default format + res = server.make_request("POST", "/v1/embeddings", data={ + "input": test_input + }) + assert res.status_code == 200 + vec0 = res.body["data"][0]["embedding"] + + # get embedding in base64 format + res = server.make_request("POST", "/v1/embeddings", data={ + "input": test_input, + "encoding_format": "base64" + }) + + assert res.status_code == 200 + assert "data" in res.body + assert len(res.body["data"]) == 1 + + embedding_data = res.body["data"][0] + assert "embedding" in embedding_data + assert isinstance(embedding_data["embedding"], str) + + # Verify embedding is valid base64 + decoded = base64.b64decode(embedding_data["embedding"]) + # Verify decoded data can be converted back to float array + float_count = len(decoded) // 4 # 4 bytes per float + floats = struct.unpack(f'{float_count}f', decoded) + assert len(floats) > 0 + assert all(isinstance(x, float) for x in floats) + assert len(floats) == len(vec0) + + # make sure the decoded data is the same as the original + for x, y in zip(floats, vec0): + assert abs(x - y) < EPSILON diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 043d8b528..334f2f192 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -3,6 +3,7 @@ #include "common.h" #include "log.h" #include "llama.h" +#include "common/base64.hpp" #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error @@ -613,16 +614,31 @@ static json oaicompat_completion_params_parse( return llama_params; } -static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) { +static json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false) { json data = json::array(); int32_t n_tokens = 0; int i = 0; for (const auto & elem : embeddings) { - data.push_back(json{ - {"embedding", json_value(elem, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }); + json embedding_obj; + + if (use_base64) { + const auto& vec = json_value(elem, "embedding", json::array()).get>(); + const char* data_ptr = reinterpret_cast(vec.data()); + size_t data_size = vec.size() * sizeof(float); + embedding_obj = { + {"embedding", base64::encode(data_ptr, data_size)}, + {"index", i++}, + {"object", "embedding"}, + {"encoding_format", "base64"} + }; + } else { + embedding_obj = { + {"embedding", json_value(elem, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }; + } + data.push_back(embedding_obj); n_tokens += json_value(elem, "tokens_evaluated", 0); } From d283d02bf254a7f2991e1502066330cc0d4321a6 Mon Sep 17 00:00:00 2001 From: Peter Date: Fri, 27 Dec 2024 00:59:11 +1100 Subject: [PATCH 33/43] examples, ggml : fix GCC compiler warnings (#10983) Warning types fixed (observed under MSYS2 GCC 14.2.0): * format '%ld' expects argument of type 'long int', but argument has type 'size_t' * llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp:81:46: warning: missing initializer for member '_STARTUPINFOA::lpDesktop' [-Wmissing-field-initializers] (emitted for all struct field except first) --- examples/cvector-generator/mean.hpp | 2 +- examples/cvector-generator/pca.hpp | 2 +- examples/export-lora/export-lora.cpp | 6 +++--- ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 3 ++- 4 files changed, 7 insertions(+), 6 deletions(-) diff --git a/examples/cvector-generator/mean.hpp b/examples/cvector-generator/mean.hpp index 16be5ce3e..4eeac1eeb 100644 --- a/examples/cvector-generator/mean.hpp +++ b/examples/cvector-generator/mean.hpp @@ -15,7 +15,7 @@ static void run( for (size_t il = 0; il < v_input.size(); ++il) { // prepare output vector struct ggml_tensor * ctrl_out = v_output[il]; - ggml_format_name(ctrl_out, "direction.%ld", il+1); + ggml_format_name(ctrl_out, "direction.%zu", il+1); // calculate mean vector struct ggml_tensor * t_layer = v_input[il]; diff --git a/examples/cvector-generator/pca.hpp b/examples/cvector-generator/pca.hpp index f6e307fbc..e88bbdde9 100644 --- a/examples/cvector-generator/pca.hpp +++ b/examples/cvector-generator/pca.hpp @@ -302,7 +302,7 @@ static void run_pca( // prepare output vector struct ggml_tensor * ctrl_out = v_output[il]; - ggml_format_name(ctrl_out, "direction.%ld", il+1); + ggml_format_name(ctrl_out, "direction.%zu", il+1); // run power_iteration params.i_layer = il; diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 67662313d..058b5cc86 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -265,8 +265,8 @@ struct lora_merge_ctx { fout.write((const char *)data.data(), data.size()); } - printf("%s : merged %ld tensors with lora adapters\n", __func__, n_merged); - printf("%s : wrote %ld tensors to output file\n", __func__, trans.size()); + printf("%s : merged %zu tensors with lora adapters\n", __func__, n_merged); + printf("%s : wrote %zu tensors to output file\n", __func__, trans.size()); } void copy_tensor(struct ggml_tensor * base) { @@ -352,7 +352,7 @@ struct lora_merge_ctx { const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale; delta = ggml_scale(ctx0, delta, scale); cur = ggml_add(ctx0, delta, cur); - printf("%s : + merging from adapter[%ld] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type)); + printf("%s : + merging from adapter[%zu] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type)); printf("%s : input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]); } cur = ggml_cast(ctx0, cur, out->type); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 7a0d7285d..8111c0638 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -78,7 +78,8 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s } PROCESS_INFORMATION pi; - STARTUPINFOA si = { sizeof(STARTUPINFOA) }; + STARTUPINFOA si = {}; + si.cb = sizeof(STARTUPINFOA); si.dwFlags = STARTF_USESTDHANDLES; si.hStdOutput = stdout_write; si.hStdError = stderr_write; From d79d8f39b4da6deca4aea8bf130c6034c482b320 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Thu, 26 Dec 2024 10:54:44 -0500 Subject: [PATCH 34/43] vulkan: multi-row k quants (#10846) * multi row k quant shaders! * better row selection * more row choices * readjust row selection * rm_kq=2 by default --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 81 ++++--- .../vulkan-shaders/mul_mat_vec_q2_k.comp | 126 ++++++---- .../vulkan-shaders/mul_mat_vec_q3_k.comp | 102 ++++---- .../vulkan-shaders/mul_mat_vec_q4_k.comp | 174 ++++++++------ .../vulkan-shaders/mul_mat_vec_q5_k.comp | 226 ++++++++++-------- .../vulkan-shaders/mul_mat_vec_q6_k.comp | 130 +++++----- 6 files changed, 472 insertions(+), 367 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 323ce7cf3..c0a43631c 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -1855,53 +1855,58 @@ static void ggml_vk_load_shaders(vk_device& device) { // mul mat vec - // AMD GCN and Intel graphics cards perform best when the number of rows per shader is doubled - uint32_t rm = 1; - if ((device->vendor_id == VK_VENDOR_ID_AMD && device->subgroup_min_size == 64 && device->subgroup_max_size == 64) || device->vendor_id == VK_VENDOR_ID_INTEL) - rm = 2; + // the number of rows computed per shader depends on GPU model and quant + uint32_t rm_stdq = 1; + uint32_t rm_kq = 2; + if (device->vendor_id == VK_VENDOR_ID_AMD) { + if (device->subgroup_min_size == 64 && device->subgroup_max_size == 64) { // GCN + rm_stdq = 2; + rm_kq = 4; + } + } else if (device->vendor_id == VK_VENDOR_ID_INTEL) + rm_stdq = 2; - // computing additional rows per workgroup is a benefit for Q4_0 -> Q5_1, but not for Q8_0. ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1*rm, 1, 1}, {device->subgroup_size, 1*rm}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true); // dequant shaders ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp index 1a5350d99..138ad0184 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp @@ -6,21 +6,15 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - if (row >= p.stride_d) { - return; - } +shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; // 16 threads are used to process each block const uint it_size = gl_WorkGroupSize.x/16; @@ -38,15 +32,15 @@ void main() { const uint s_offset = 8*v_im; const uint y_offset = 128*v_im + l0; - FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + FLOAT_TYPE temp[NUM_ROWS]; + + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[i] = FLOAT_TYPE(0); + } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y_idx = i * QUANT_K + y_offset; - f16vec2 d = data_a[ib0 + i].d; - const FLOAT_TYPE dall = d.x; - const FLOAT_TYPE dmin = d.y; - B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0]; B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8]; B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16]; @@ -56,58 +50,84 @@ void main() { B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48]; B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56]; - uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0]; - uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1]; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = d.x; + const FLOAT_TYPE dmin = d.y; - uint32_t s0_lo4_u32 = s0_u32 & 0x0F0F0F0F; - uint32_t s0_hi4_u32 = (s0_u32 >> 4) & 0x0F0F0F0F; - uint32_t s4_lo4_u32 = s4_u32 & 0x0F0F0F0F; - uint32_t s4_hi4_u32 = (s4_u32 >> 4) & 0x0F0F0F0F; + uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0]; + uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1]; - uvec4 s0_lo4 = uvec4(unpack8(s0_lo4_u32)); - uvec4 s4_lo4 = uvec4(unpack8(s4_lo4_u32)); - uvec4 s0_hi4 = uvec4(unpack8(s0_hi4_u32)); - uvec4 s4_hi4 = uvec4(unpack8(s4_hi4_u32)); + uint32_t s0_lo4_u32 = s0_u32 & 0x0F0F0F0F; + uint32_t s0_hi4_u32 = (s0_u32 >> 4) & 0x0F0F0F0F; + uint32_t s4_lo4_u32 = s4_u32 & 0x0F0F0F0F; + uint32_t s4_hi4_u32 = (s4_u32 >> 4) & 0x0F0F0F0F; - uint16_t qs0_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 0]; - uint16_t qs16_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]; - uvec2 qs0 = uvec2(unpack8(qs0_u16)); - uvec2 qs16 = uvec2(unpack8(qs16_u16)); + uvec4 s0_lo4 = uvec4(unpack8(s0_lo4_u32)); + uvec4 s4_lo4 = uvec4(unpack8(s4_lo4_u32)); + uvec4 s0_hi4 = uvec4(unpack8(s0_hi4_u32)); + uvec4 s4_hi4 = uvec4(unpack8(s4_hi4_u32)); - FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); - FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 2; ++l) { - sum1 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l] >> 0) & 3), - fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3), - fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l] >> 2) & 3), - fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3), - fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l] >> 4) & 3), - fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3), - fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l] >> 6) & 3), - fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1)))))))); - sum2 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_hi4[0]), - fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_hi4[1]), - fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_hi4[2]), - fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_hi4[3]), - fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_hi4[0]), - fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_hi4[1]), - fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_hi4[2]), - fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2)))))))); + uint16_t qs0_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 0]; + uint16_t qs16_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]; + uvec2 qs0 = uvec2(unpack8(qs0_u16)); + uvec2 qs16 = uvec2(unpack8(qs16_u16)); + + FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); + FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 2; ++l) { + sum1 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l] >> 0) & 3), + fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3), + fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l] >> 2) & 3), + fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3), + fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l] >> 4) & 3), + fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3), + fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l] >> 6) & 3), + fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1)))))))); + sum2 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_hi4[0]), + fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_hi4[1]), + fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_hi4[2]), + fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_hi4[3]), + fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_hi4[0]), + fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_hi4[1]), + fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_hi4[2]), + fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2)))))))); + } + temp[n] = fma(dall, sum1, fma(-dmin, sum2, temp[n])); } - temp = fma(dall, sum1, fma(-dmin, sum2, temp)); } - tmp[gl_LocalInvocationID.x] = temp; - // sum up partial sums and write back result + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] = temp[n]; + } barrier(); - [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) { + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { - tmp[tid] += tmp[tid + s]; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] += tmpsh[n][tid + s]; + } } barrier(); } if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); + } + } +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp index b19c38111..82ec42d25 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp @@ -6,21 +6,15 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - if (row >= p.stride_d) { - return; - } +shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; // 16 threads are used to process each block const uint it_size = gl_WorkGroupSize.x/16; @@ -35,19 +29,21 @@ void main() { const uint8_t m = uint8_t(1 << (4 * v_im)); - const uint l0 = 2*v_in; // 0...15 + const uint l0 = 2*v_in; // 0...15 const uint q_offset = 32*v_im + l0; const uint y_offset = 128*v_im + l0; - FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + FLOAT_TYPE temp[NUM_ROWS]; + + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[i] = FLOAT_TYPE(0); + } const uint s_shift = 4 * v_im; [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y_idx = i * QUANT_K + y_offset; - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0]; B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8]; B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16]; @@ -57,44 +53,68 @@ void main() { B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48]; B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56]; - uint16_t s0_16 = data_a_packed16[ib0 + i].scales[0]; - uint16_t s2_16 = data_a_packed16[ib0 + i].scales[1]; - uint16_t s4_16 = data_a_packed16[ib0 + i].scales[2]; - uint16_t s6_16 = data_a_packed16[ib0 + i].scales[3]; - uint16_t s8_16 = data_a_packed16[ib0 + i].scales[4]; - uint16_t s10_16 = data_a_packed16[ib0 + i].scales[5]; - u8vec2 s0 = unpack8(s0_16); - u8vec2 s2 = unpack8(s2_16); - u8vec2 s4 = unpack8(s4_16); - u8vec2 s6 = unpack8(s6_16); - u8vec2 s8 = unpack8(s8_16); - u8vec2 s10 = unpack8(s10_16); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 2; ++l) { - sum = fma(FLOAT_TYPE(b0[l]) * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b32[l]) * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b64[l]) * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b96[l]) * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b16[l]) * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b48[l]) * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b80[l]) * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); + uint16_t s0_16 = data_a_packed16[ib0 + i].scales[0]; + uint16_t s2_16 = data_a_packed16[ib0 + i].scales[1]; + uint16_t s4_16 = data_a_packed16[ib0 + i].scales[2]; + uint16_t s6_16 = data_a_packed16[ib0 + i].scales[3]; + uint16_t s8_16 = data_a_packed16[ib0 + i].scales[4]; + uint16_t s10_16 = data_a_packed16[ib0 + i].scales[5]; + u8vec2 s0 = unpack8(s0_16); + u8vec2 s2 = unpack8(s2_16); + u8vec2 s4 = unpack8(s4_16); + u8vec2 s6 = unpack8(s6_16); + u8vec2 s8 = unpack8(s8_16); + u8vec2 s10 = unpack8(s10_16); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 2; ++l) { + sum = fma(FLOAT_TYPE(b0[l]) * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b32[l]) * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b64[l]) * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b96[l]) * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b16[l]) * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b48[l]) * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b80[l]) * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); + } + temp[n] = fma(d, sum, temp[n]); } - temp = fma(d, sum, temp); } - tmp[gl_LocalInvocationID.x] = temp; - // sum up partial sums and write back result + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] = temp[n]; + } barrier(); - [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) { + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { - tmp[tid] += tmp[tid + s]; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] += tmpsh[n][tid + s]; + } } barrier(); } if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); + } + } +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp index b86d28589..677c207a8 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp @@ -7,21 +7,15 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - if (row >= p.stride_d) { - return; - } +shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; // 16 threads are used to process each block const uint it_size = gl_WorkGroupSize.x/16; @@ -31,8 +25,8 @@ void main() { const uint step = 4; - const uint il = itid/step; // 0...3 - const uint ir = itid - step*il; // 0...7 or 0...3 + const uint il = itid/step; // 0...3 + const uint ir = itid - step*il; // 0...7 or 0...3 const uint n = 4; const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 @@ -42,90 +36,116 @@ void main() { const uint q_offset = 32*v_im + l0; const uint y_offset = 64*v_im + l0; - FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + FLOAT_TYPE temp[NUM_ROWS]; + + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[i] = FLOAT_TYPE(0); + } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y1_idx = i * QUANT_K + y_offset; const uint y2_idx = y1_idx + 128; - f16vec2 d = data_a[ib0 + i].d; - const FLOAT_TYPE dall = FLOAT_TYPE(d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); - - uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; - uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; - uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; - uvec4 scale0 = uvec4(unpack8(scale0_u32)); - uvec4 scale4 = uvec4(unpack8(scale4_u32)); - uvec4 scale8 = uvec4(unpack8(scale8_u32)); - - const uint32_t sc0 = ( scale0.x & 0x3f); - const uint32_t sc1 = ( scale0.y & 0x3f); - const uint32_t sc2 = ( scale4.x & 0x3f); - const uint32_t sc3 = ( scale4.y & 0x3f); - const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); - const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); - const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); - const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); - - uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4]; - uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16]; - - uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F; - uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F; - uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F; - uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F; - - uvec4 qs0_lo4 = uvec4(unpack8(qs0_u32_lo4)); - uvec4 qs64_lo4 = uvec4(unpack8(qs64_u32_lo4)); - uvec4 qs0_hi4 = uvec4(unpack8(qs0_u32_hi4)); - uvec4 qs64_hi4 = uvec4(unpack8(qs64_u32_hi4)); - - const uint32_t q4_0 = qs0_lo4.x; - const uint32_t q4_1 = qs0_lo4.y; - const uint32_t q4_2 = qs0_lo4.z; - const uint32_t q4_3 = qs0_lo4.w; - const uint32_t q4_4 = qs0_hi4.x; - const uint32_t q4_5 = qs0_hi4.y; - const uint32_t q4_6 = qs0_hi4.z; - const uint32_t q4_7 = qs0_hi4.w; - const uint32_t q4_8 = qs64_lo4.x; - const uint32_t q4_9 = qs64_lo4.y; - const uint32_t q4_10 = qs64_lo4.z; - const uint32_t q4_11 = qs64_lo4.w; - const uint32_t q4_12 = qs64_hi4.x; - const uint32_t q4_13 = qs64_hi4.y; - const uint32_t q4_14 = qs64_hi4.z; - const uint32_t q4_15 = qs64_hi4.w; - B_TYPE_VEC4 by10 = data_b_v4[(b_offset + y1_idx) / 4]; B_TYPE_VEC4 by132 = data_b_v4[(b_offset + y1_idx) / 4 + 8]; B_TYPE_VEC4 by20 = data_b_v4[(b_offset + y2_idx) / 4]; B_TYPE_VEC4 by232 = data_b_v4[(b_offset + y2_idx) / 4 + 8]; - const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x), q4_0, fma(FLOAT_TYPE(by10.y), q4_1, fma(FLOAT_TYPE(by10.z), q4_2, FLOAT_TYPE(by10.w) * q4_3))); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x), q4_4, fma(FLOAT_TYPE(by132.y), q4_5, fma(FLOAT_TYPE(by132.z), q4_6, FLOAT_TYPE(by132.w) * q4_7))); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x), q4_8, fma(FLOAT_TYPE(by20.y), q4_9, fma(FLOAT_TYPE(by20.z), q4_10, FLOAT_TYPE(by20.w) * q4_11))); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x), q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7, - fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7, - fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7, - fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6, FLOAT_TYPE(by232.w) * sc7))))))))))))))); - temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp)); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = FLOAT_TYPE(d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); + + uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + uvec4 scale0 = uvec4(unpack8(scale0_u32)); + uvec4 scale4 = uvec4(unpack8(scale4_u32)); + uvec4 scale8 = uvec4(unpack8(scale8_u32)); + + const uint32_t sc0 = ( scale0.x & 0x3f); + const uint32_t sc1 = ( scale0.y & 0x3f); + const uint32_t sc2 = ( scale4.x & 0x3f); + const uint32_t sc3 = ( scale4.y & 0x3f); + const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); + const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); + const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); + const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); + + uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4]; + uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16]; + + uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F; + uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F; + uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F; + uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F; + + uvec4 qs0_lo4 = uvec4(unpack8(qs0_u32_lo4)); + uvec4 qs64_lo4 = uvec4(unpack8(qs64_u32_lo4)); + uvec4 qs0_hi4 = uvec4(unpack8(qs0_u32_hi4)); + uvec4 qs64_hi4 = uvec4(unpack8(qs64_u32_hi4)); + + const uint32_t q4_0 = qs0_lo4.x; + const uint32_t q4_1 = qs0_lo4.y; + const uint32_t q4_2 = qs0_lo4.z; + const uint32_t q4_3 = qs0_lo4.w; + const uint32_t q4_4 = qs0_hi4.x; + const uint32_t q4_5 = qs0_hi4.y; + const uint32_t q4_6 = qs0_hi4.z; + const uint32_t q4_7 = qs0_hi4.w; + const uint32_t q4_8 = qs64_lo4.x; + const uint32_t q4_9 = qs64_lo4.y; + const uint32_t q4_10 = qs64_lo4.z; + const uint32_t q4_11 = qs64_lo4.w; + const uint32_t q4_12 = qs64_hi4.x; + const uint32_t q4_13 = qs64_hi4.y; + const uint32_t q4_14 = qs64_hi4.z; + const uint32_t q4_15 = qs64_hi4.w; + + const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x), q4_0, fma(FLOAT_TYPE(by10.y), q4_1, fma(FLOAT_TYPE(by10.z), q4_2, FLOAT_TYPE(by10.w) * q4_3))); + const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x), q4_4, fma(FLOAT_TYPE(by132.y), q4_5, fma(FLOAT_TYPE(by132.z), q4_6, FLOAT_TYPE(by132.w) * q4_7))); + const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x), q4_8, fma(FLOAT_TYPE(by20.y), q4_9, fma(FLOAT_TYPE(by20.z), q4_10, FLOAT_TYPE(by20.w) * q4_11))); + const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x), q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7, + fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7, + fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7, + fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6, FLOAT_TYPE(by232.w) * sc7))))))))))))))); + temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n])); + } } - tmp[gl_LocalInvocationID.x] = temp; - // sum up partial sums and write back result + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] = temp[n]; + } barrier(); - [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) { + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { - tmp[tid] += tmp[tid + s]; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] += tmpsh[n][tid + s]; + } } barrier(); } if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); + } + } +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp index fd243cf91..ed3c25d89 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp @@ -7,21 +7,15 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - if (row >= p.stride_d) { - return; - } +shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; // 16 threads are used to process each block const uint it_size = gl_WorkGroupSize.x/16; @@ -39,74 +33,16 @@ void main() { const uint q_offset = 32*v_im + l0; const uint y_offset = 64*v_im + l0; - FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + FLOAT_TYPE temp[NUM_ROWS]; + + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[i] = FLOAT_TYPE(0); + } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y1_idx = i * QUANT_K + y_offset; const uint y2_idx = y1_idx + 128; - f16vec2 d = data_a[ib0 + i].d; - const FLOAT_TYPE dall = FLOAT_TYPE(d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); - - uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; - uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; - uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; - uvec4 scale0 = uvec4(unpack8(scale0_u32)); - uvec4 scale4 = uvec4(unpack8(scale4_u32)); - uvec4 scale8 = uvec4(unpack8(scale8_u32)); - - const uint32_t sc0 = ( scale0.x & 0x3f); - const uint32_t sc1 = ( scale0.y & 0x3f); - const uint32_t sc2 = ( scale4.x & 0x3f); - const uint32_t sc3 = ( scale4.y & 0x3f); - const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); - const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); - const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); - const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); - - uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16); - uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16); - - uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F; - uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F; - uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F; - uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F; - - uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8])); - - uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4; - uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3; - uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010) << 0; - uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1; - - qs0_16_u32_lo4 += qs0_16_lo4_offset16; - qs0_16_u32_hi4 += qs0_16_hi4_offset16; - qs64_80_u32_lo4 += qs64_80_lo4_offset16; - qs64_80_u32_hi4 += qs64_80_hi4_offset16; - - uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4)); - uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4)); - uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4)); - uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4)); - - const uint32_t q4_0 = qs0_16_lo4.x; - const uint32_t q4_1 = qs0_16_lo4.y; - const uint32_t q4_2 = qs0_16_lo4.z; - const uint32_t q4_3 = qs0_16_lo4.w; - const uint32_t q4_4 = qs0_16_hi4.x; - const uint32_t q4_5 = qs0_16_hi4.y; - const uint32_t q4_6 = qs0_16_hi4.z; - const uint32_t q4_7 = qs0_16_hi4.w; - const uint32_t q4_8 = qs64_80_lo4.x; - const uint32_t q4_9 = qs64_80_lo4.y; - const uint32_t q4_10 = qs64_80_lo4.z; - const uint32_t q4_11 = qs64_80_lo4.w; - const uint32_t q4_12 = qs64_80_hi4.x; - const uint32_t q4_13 = qs64_80_hi4.y; - const uint32_t q4_14 = qs64_80_hi4.z; - const uint32_t q4_15 = qs64_80_hi4.w; - B_TYPE_VEC2 by10 = data_b_v2[(b_offset + y1_idx) / 2]; B_TYPE_VEC2 by116 = data_b_v2[(b_offset + y1_idx) / 2 + 8]; B_TYPE_VEC2 by132 = data_b_v2[(b_offset + y1_idx) / 2 + 16]; @@ -116,45 +52,129 @@ void main() { B_TYPE_VEC2 by232 = data_b_v2[(b_offset + y2_idx) / 2 + 16]; B_TYPE_VEC2 by248 = data_b_v2[(b_offset + y2_idx) / 2 + 24]; - const FLOAT_TYPE sx = - fma(FLOAT_TYPE(by10.x), q4_0, - fma(FLOAT_TYPE(by10.y), q4_1, - fma(FLOAT_TYPE(by116.x), q4_2, - FLOAT_TYPE(by116.y) * q4_3))); - const FLOAT_TYPE sy = - fma(FLOAT_TYPE(by132.x), q4_4, - fma(FLOAT_TYPE(by132.y), q4_5, - fma(FLOAT_TYPE(by148.x), q4_6, - FLOAT_TYPE(by148.y) * q4_7))); - const FLOAT_TYPE sz = - fma(FLOAT_TYPE(by20.x), q4_8, - fma(FLOAT_TYPE(by20.y), q4_9, - fma(FLOAT_TYPE(by216.x), q4_10, - FLOAT_TYPE(by216.y) * q4_11))); - const FLOAT_TYPE sw = - fma(FLOAT_TYPE(by232.x), q4_12, - fma(FLOAT_TYPE(by232.y), q4_13, - fma(FLOAT_TYPE(by248.x), q4_14, - FLOAT_TYPE(by248.y) * q4_15))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2, - fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3, - fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6, - (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7))); - temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp)); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = FLOAT_TYPE(d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); + + uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + uvec4 scale0 = uvec4(unpack8(scale0_u32)); + uvec4 scale4 = uvec4(unpack8(scale4_u32)); + uvec4 scale8 = uvec4(unpack8(scale8_u32)); + + const uint32_t sc0 = ( scale0.x & 0x3f); + const uint32_t sc1 = ( scale0.y & 0x3f); + const uint32_t sc2 = ( scale4.x & 0x3f); + const uint32_t sc3 = ( scale4.y & 0x3f); + const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); + const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); + const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); + const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); + + uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16); + uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16); + + uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F; + uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F; + uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F; + uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F; + + uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8])); + + uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4; + uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3; + uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010) << 0; + uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1; + + qs0_16_u32_lo4 += qs0_16_lo4_offset16; + qs0_16_u32_hi4 += qs0_16_hi4_offset16; + qs64_80_u32_lo4 += qs64_80_lo4_offset16; + qs64_80_u32_hi4 += qs64_80_hi4_offset16; + + uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4)); + uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4)); + uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4)); + uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4)); + + const uint32_t q4_0 = qs0_16_lo4.x; + const uint32_t q4_1 = qs0_16_lo4.y; + const uint32_t q4_2 = qs0_16_lo4.z; + const uint32_t q4_3 = qs0_16_lo4.w; + const uint32_t q4_4 = qs0_16_hi4.x; + const uint32_t q4_5 = qs0_16_hi4.y; + const uint32_t q4_6 = qs0_16_hi4.z; + const uint32_t q4_7 = qs0_16_hi4.w; + const uint32_t q4_8 = qs64_80_lo4.x; + const uint32_t q4_9 = qs64_80_lo4.y; + const uint32_t q4_10 = qs64_80_lo4.z; + const uint32_t q4_11 = qs64_80_lo4.w; + const uint32_t q4_12 = qs64_80_hi4.x; + const uint32_t q4_13 = qs64_80_hi4.y; + const uint32_t q4_14 = qs64_80_hi4.z; + const uint32_t q4_15 = qs64_80_hi4.w; + + const FLOAT_TYPE sx = + fma(FLOAT_TYPE(by10.x), q4_0, + fma(FLOAT_TYPE(by10.y), q4_1, + fma(FLOAT_TYPE(by116.x), q4_2, + FLOAT_TYPE(by116.y) * q4_3))); + const FLOAT_TYPE sy = + fma(FLOAT_TYPE(by132.x), q4_4, + fma(FLOAT_TYPE(by132.y), q4_5, + fma(FLOAT_TYPE(by148.x), q4_6, + FLOAT_TYPE(by148.y) * q4_7))); + const FLOAT_TYPE sz = + fma(FLOAT_TYPE(by20.x), q4_8, + fma(FLOAT_TYPE(by20.y), q4_9, + fma(FLOAT_TYPE(by216.x), q4_10, + FLOAT_TYPE(by216.y) * q4_11))); + const FLOAT_TYPE sw = + fma(FLOAT_TYPE(by232.x), q4_12, + fma(FLOAT_TYPE(by232.y), q4_13, + fma(FLOAT_TYPE(by248.x), q4_14, + FLOAT_TYPE(by248.y) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2, + fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3, + fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6, + (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7))); + temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n])); + } } - tmp[gl_LocalInvocationID.x] = temp; - // sum up partial sums and write back result + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] = temp[n]; + } barrier(); - [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) { + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { - tmp[tid] += tmp[tid + s]; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] += tmpsh[n][tid + s]; + } } barrier(); } if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); + } + } +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp index 760aff854..fab4ff5ff 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp @@ -7,21 +7,15 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - if (row >= p.stride_d) { - return; - } +shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; // 16 threads are used to process each block const uint it_size = gl_WorkGroupSize.x/16; @@ -42,69 +36,95 @@ void main() { const uint s_offset = 8*v_im + is; const uint y_offset = 128*v_im + l0; - FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + FLOAT_TYPE temp[NUM_ROWS]; + + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[i] = FLOAT_TYPE(0); + } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - - FLOAT_TYPE scales[4]; - scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]); - scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]); - scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]); - scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]); - - uint32_t ql0_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16); - uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16); - - uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F; - uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F; - uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F; - uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F; - - uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16); - uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4; - uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2; - uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0; - uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2; - - uint32_t q0_u32 = ql0_u32_lo4 | qh0_u32; - uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32; - uint32_t q2_u32 = ql0_u32_hi4 | qh4_u32; - uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32; - - uvec4 q0 = uvec4(unpack8(q0_u32)); - uvec4 q1 = uvec4(unpack8(q1_u32)); - uvec4 q2 = uvec4(unpack8(q2_u32)); - uvec4 q3 = uvec4(unpack8(q3_u32)); + const uint y_idx = i * QUANT_K + y_offset; B_TYPE_VEC4 by0 = data_b_v4[(b_offset + y_idx) / 4]; B_TYPE_VEC4 by32 = data_b_v4[(b_offset + y_idx) / 4 + 8]; B_TYPE_VEC4 by64 = data_b_v4[(b_offset + y_idx) / 4 + 16]; B_TYPE_VEC4 by96 = data_b_v4[(b_offset + y_idx) / 4 + 24]; - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 4; ++l) { - sum = fma(FLOAT_TYPE(by0[l]) * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32), - fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32), - fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32), - fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum)))); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + + FLOAT_TYPE scales[4]; + scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]); + scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]); + scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]); + scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]); + + uint32_t ql0_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16); + uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16); + + uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F; + uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F; + uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F; + uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F; + + uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16); + uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4; + uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2; + uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0; + uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2; + + uint32_t q0_u32 = ql0_u32_lo4 | qh0_u32; + uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32; + uint32_t q2_u32 = ql0_u32_hi4 | qh4_u32; + uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32; + + uvec4 q0 = uvec4(unpack8(q0_u32)); + uvec4 q1 = uvec4(unpack8(q1_u32)); + uvec4 q2 = uvec4(unpack8(q2_u32)); + uvec4 q3 = uvec4(unpack8(q3_u32)); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 4; ++l) { + sum = fma(FLOAT_TYPE(by0[l]) * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32), + fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32), + fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32), + fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum)))); + } + temp[n] += sum * d; } - temp += sum * d; } - tmp[gl_LocalInvocationID.x] = temp; // sum up partial sums and write back result - + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] = temp[n]; + } barrier(); - [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) { + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { - tmp[tid] += tmp[tid + s]; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] += tmpsh[n][tid + s]; + } } barrier(); } if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); + } + } +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); } } From 16cdce7b68218959e0658e2f95b4572573d5008e Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sat, 28 Dec 2024 15:08:54 +0000 Subject: [PATCH 35/43] server : fix token duplication when streaming with stop strings (#10997) --- examples/server/server.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 30ff3b149..3558ddb7c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1856,6 +1856,8 @@ struct server_context { result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache + } else { + result.text_to_send = ""; } slot.add_token(result); From f865ea149d71ef883e3780fced8a20a1464eccf4 Mon Sep 17 00:00:00 2001 From: Isaac McFadyen Date: Sat, 28 Dec 2024 10:09:19 -0500 Subject: [PATCH 36/43] server: added more docs for response_fields field (#10995) --- examples/server/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index c7d91be99..07436057a 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -450,7 +450,7 @@ These words will not be included in the completion, so make sure to add them to `post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain. -`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. +`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. Note that fields with a slash will be unnested; for example, `generation_settings/n_predict` will move the field `n_predict` from the `generation_settings` object to the root of the response and give it a new name. **Response format** From fdd21889123bec62b1db3b2fc22b5a4abab32174 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sun, 29 Dec 2024 02:35:11 -0600 Subject: [PATCH 37/43] vulkan: Use push constant offset to handle misaligned descriptors (#10987) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 74 ++++++++++++++++--- ggml/src/ggml-vulkan/vulkan-shaders/acc.comp | 4 +- ggml/src/ggml-vulkan/vulkan-shaders/add.comp | 2 +- .../src/ggml-vulkan/vulkan-shaders/clamp.comp | 4 +- .../ggml-vulkan/vulkan-shaders/concat.comp | 6 +- .../vulkan-shaders/contig_copy.comp | 8 +- ggml/src/ggml-vulkan/vulkan-shaders/copy.comp | 4 +- ggml/src/ggml-vulkan/vulkan-shaders/cos.comp | 4 +- ggml/src/ggml-vulkan/vulkan-shaders/div.comp | 2 +- .../vulkan-shaders/generic_binary_head.comp | 6 +- .../vulkan-shaders/generic_unary_head.comp | 5 +- .../ggml-vulkan/vulkan-shaders/get_rows.comp | 6 +- ggml/src/ggml-vulkan/vulkan-shaders/mul.comp | 2 +- ggml/src/ggml-vulkan/vulkan-shaders/pad.comp | 2 +- .../ggml-vulkan/vulkan-shaders/repeat.comp | 2 +- .../src/ggml-vulkan/vulkan-shaders/scale.comp | 2 +- ggml/src/ggml-vulkan/vulkan-shaders/sin.comp | 4 +- .../ggml-vulkan/vulkan-shaders/square.comp | 4 +- .../ggml-vulkan/vulkan-shaders/upscale.comp | 4 +- 19 files changed, 103 insertions(+), 42 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index c0a43631c..6dfc60c9b 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -411,7 +411,7 @@ struct vk_op_unary_push_constants { uint32_t ne; uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; - uint32_t d_offset; + uint32_t misalign_offsets; float param1; float param2; uint32_t ne0_012mp; uint32_t ne0_012L; uint32_t ne0_01mp; uint32_t ne0_01L; @@ -459,7 +459,7 @@ struct vk_op_binary_push_constants { uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23; - uint32_t d_offset; + uint32_t misalign_offsets; float param1; float param2; int32_t param3; }; @@ -546,7 +546,7 @@ struct vk_staging_memcpy { }; struct vk_op_upscale_push_constants { - uint32_t ne; uint32_t d_offset; + uint32_t ne; uint32_t a_offset; uint32_t d_offset; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; float sf0; float sf1; float sf2; float sf3; @@ -5076,6 +5076,57 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { } } +static uint32_t get_misalign_bytes(ggml_backend_vk_context * ctx, const ggml_tensor * t) +{ + return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));; +} + +template void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + GGML_UNUSED(p); + GGML_UNUSED(src0); + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(dst); + static_assert(!std::is_const::value, "unexpected type"); + GGML_ASSERT(!src0 || get_misalign_bytes(ctx, src0) == 0); + GGML_ASSERT(!src1 || get_misalign_bytes(ctx, src1) == 0); + GGML_ASSERT(!src2 || get_misalign_bytes(ctx, src2) == 0); + GGML_ASSERT(!dst || get_misalign_bytes(ctx, dst) == 0); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_unary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.misalign_offsets = (a_offset << 16) | d_offset; + + GGML_UNUSED(src1); + GGML_UNUSED(src2); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_binary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + GGML_ASSERT(dst->op != GGML_OP_GET_ROWS || (a_offset == 0 && b_offset == 0 && d_offset == 0)); + + p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset; + + GGML_UNUSED(src2); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.a_offset = a_offset; + p.d_offset = d_offset; + + GGML_UNUSED(src1); + GGML_UNUSED(src2); +} + template static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, PC&& pc, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; @@ -5179,8 +5230,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co } GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = ((vk_tensor_offset(dst) + dst->view_offs) / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - GGML_ASSERT(d_buf_offset == vk_tensor_offset(dst) || op == GGML_OP_CPY); // NOLINT + uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; if(!src0_uma) { d_X = src0_buf_ctx->dev_buffer; x_buf_offset = vk_tensor_offset(src0) + src0->view_offs; @@ -5196,6 +5246,12 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co z_buf_offset = vk_tensor_offset(src2) + src2->view_offs; GGML_ASSERT(d_Z != nullptr); } + // Compute misalignment offset for descriptors and store it in in push constants, then align the descriptor offsets. + init_pushconst_tensor_offsets(ctx, pc, src0, src1, src2, dst); + x_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1); + y_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1); + z_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1); + d_buf_offset &= ~(ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1); if (op_supports_incontiguous) { x_sz = ggml_nbytes(src0); @@ -5383,7 +5439,6 @@ static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = ((vk_tensor_offset(dst) + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 @@ -5395,7 +5450,7 @@ static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t) dst->nb[3] / dst_type_size, - d_offset, + 0, 0.0f, 0.0f, offset, }, dryrun); } @@ -5599,7 +5654,7 @@ static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, c const float sf3 = (float)dst->ne[3] / src0->ne[3]; ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UPSCALE, { - (uint32_t)ggml_nelements(dst), 0, + (uint32_t)ggml_nelements(dst), 0, 0, (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)dst->ne[0], (uint32_t)dst->ne[1], (uint32_t)dst->ne[2],(uint32_t)dst->ne[3], sf0, sf1, sf2, sf3, @@ -5709,13 +5764,12 @@ static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, co static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = ((vk_tensor_offset(dst) + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - d_offset, + 0, 0.0f, 0.0f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }, dryrun); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp index 4f5a04e71..d896f1ef0 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp @@ -21,9 +21,9 @@ void main() { get_indices(idx, i00, i01, i02, i03); if (ox < p.ne10 && oy < p.ne11 && oz < p.ne12) { - data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[ox + oy * p.ne10 + oz * p.ne10 * p.ne11])); + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + ox + oy * p.ne10 + oz * p.ne10 * p.ne11])); } else { - data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)])); + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)])); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp index da61b76df..2b4085c4f 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp @@ -22,7 +22,7 @@ void main() { uint i00, i01, i02, i03; get_indices(idx, i00, i01, i02, i03); - data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)])); idx += num_threads; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp index ae8fa8753..1e5cb8dae 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp @@ -12,6 +12,6 @@ void main() { return; } - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); + const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp index 683f9ac3c..9ee2f1fae 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp @@ -30,12 +30,12 @@ void main() { const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03; #ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : data_b[src1_idx]); + data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : data_b[get_boffset() + src1_idx]); #else if (is_src0) { - data_d[p.d_offset + dst_idx] = data_a[src0_idx]; + data_d[get_doffset() + dst_idx] = data_a[get_aoffset() + src0_idx]; } else { - data_d[p.d_offset + dst_idx] = data_b[src1_idx]; + data_d[get_doffset() + dst_idx] = data_b[get_boffset() + src1_idx]; } #endif } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp index 9acbdd3d2..dd828c232 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp @@ -19,9 +19,9 @@ void main() { if (idx + (num_iter-1)*num_threads < p.ne) { [[unroll]] for (uint i = 0; i < num_iter; ++i) { #ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); + data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]); #else - data_d[p.d_offset + idx] = data_a[idx]; + data_d[get_doffset() + idx] = data_a[get_aoffset() + idx]; #endif idx += num_threads; } @@ -32,9 +32,9 @@ void main() { } #ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); + data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]); #else - data_d[p.d_offset + idx] = data_a[idx]; + data_d[get_doffset() + idx] = data_a[get_aoffset() + idx]; #endif idx += num_threads; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp index 2775068f9..29c906494 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp @@ -13,8 +13,8 @@ void main() { } #ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(data_a[get_aoffset() + src0_idx(idx)]); #else - data_d[p.d_offset + dst_idx(idx)] = data_a[src0_idx(idx)]; + data_d[get_doffset() + dst_idx(idx)] = data_a[get_aoffset() + src0_idx(idx)]; #endif } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp index fbd9d272c..0b8d02f58 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp @@ -12,6 +12,6 @@ void main() { return; } - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(cos(val)); + const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(cos(val)); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/div.comp b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp index e581905b3..9fb69c6c1 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/div.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp @@ -20,7 +20,7 @@ void main() { uint i00, i01, i02, i03; get_indices(idx, i00, i01, i02, i03); - data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) / FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) / FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)])); idx += num_threads; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp index a6555fa27..062e2a4cd 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp @@ -7,7 +7,7 @@ layout (push_constant) uniform parameter uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; - uint d_offset; + uint misalign_offsets; float param1; float param2; int param3; } p; @@ -22,6 +22,10 @@ uint get_idx() { return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; } +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; } +uint get_doffset() { return p.misalign_offsets & 0xFF; } + // mod and div are expensive and coordinates/dimensions are often power of 2 or equal to 1 uint fastmod(uint a, uint b) { if ((b & (b-1)) == 0) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp index ab7c9d7eb..68d1bc9f1 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp @@ -6,7 +6,7 @@ layout (push_constant) uniform parameter uint ne; uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; - uint d_offset; + uint misalign_offsets; float param1; float param2; uint ne0_012mp; uint ne0_012L; @@ -24,6 +24,9 @@ uint get_idx() { return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; } +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_doffset() { return p.misalign_offsets & 0xFFFF; } + // see init_fastdiv_values in ggml-vulkan.cpp uint fastdiv(uint n, uint mp, uint L) { uint msbs, lsbs; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp index a7b81e52c..e877ed779 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp @@ -15,10 +15,10 @@ void main() { return; } - const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; + const uint i01 = data_b[get_boffset() + i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; - const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03; - const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23; + const uint a_offset = get_aoffset() + i01*p.nb01 + i11*p.nb02 + i12*p.nb03; + const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23; #ifndef OPTIMIZATION_ERROR_WORKAROUND data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp index 5ce57cbcf..43de19df8 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp @@ -20,7 +20,7 @@ void main() { uint i00, i01, i02, i03; get_indices(idx, i00, i01, i02, i03); - data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) * FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) * FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)])); idx += num_threads; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp index e87d8b18b..450b67fc5 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp @@ -24,5 +24,5 @@ void main() { const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03; - data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : 0.0f); + data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : 0.0f); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp index c03f737cc..1568b141d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp @@ -22,5 +22,5 @@ void main() { return; } - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx_mod(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(data_a[get_aoffset() + src0_idx_mod(idx)]); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp index 5cfee8c3b..4663428de 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp @@ -18,7 +18,7 @@ void main() { continue; } - data_d[p.d_offset + idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1)); + data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]) * FLOAT_TYPE(p.param1)); idx += num_threads; } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp index 67c48fb9a..d7c15a169 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp @@ -12,6 +12,6 @@ void main() { return; } - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(sin(val)); + const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(sin(val)); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/square.comp b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp index 2ff48ddc5..ef43598ba 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/square.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp @@ -12,6 +12,6 @@ void main() { return; } - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val * val); + const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val * val); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp index 511a086ea..6f607380d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp @@ -2,7 +2,7 @@ layout (push_constant) uniform parameter { - uint ne; uint d_offset; + uint ne; uint a_offset; uint d_offset; uint nb00; uint nb01; uint nb02; uint nb03; uint ne10; uint ne11; uint ne12; uint ne13; float sf0; float sf1; float sf2; float sf3; @@ -32,5 +32,5 @@ void main() { const uint i02 = uint(i12 / p.sf2); const uint i03 = uint(i13 / p.sf3); - data_d[p.d_offset + idx] = D_TYPE(data_a[i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]); + data_d[p.d_offset + idx] = D_TYPE(data_a[p.a_offset + i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]); } From a813badbbdf0d38705f249df7a0c99af5cdee678 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sun, 29 Dec 2024 03:16:34 -0600 Subject: [PATCH 38/43] vulkan: im2col and matmul optimizations for stable diffusion (#10942) * tests: Add im2col perf tests * vulkan: optimize im2col, more elements per thread * vulkan: increase small tile size for NV_coopmat2 * vulkan: change im2col to 512 elements per workgroup --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 10 +-- .../ggml-vulkan/vulkan-shaders/im2col.comp | 73 +++++++++++++------ tests/test-backend-ops.cpp | 12 +++ 3 files changed, 66 insertions(+), 29 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 6dfc60c9b..8e47e79ae 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -1404,10 +1404,10 @@ static void ggml_vk_load_shaders(vk_device& device) { // spec constants and tile sizes for non-quant matmul/matmul_id l_warptile = { 256, 128, 256, 64 }; m_warptile = { 256, 128, 128, 64 }; - s_warptile = { 128, 32, 16, 64 }; + s_warptile = { 128, 64, 64, 64 }; l_wg_denoms = {128, 256, 1 }; m_wg_denoms = {128, 128, 1 }; - s_wg_denoms = { 32, 16, 1 }; + s_wg_denoms = { 64, 64, 1 }; // spec constants and tile sizes for quant matmul (non-Qi_K) l_warptile_mmq = { 256, 128, 256, 64 }; @@ -2017,11 +2017,11 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32_len, im2col_f32_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32_len, im2col_f32_data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true); if (device->float_controls_rte_fp16) { - ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_rte_len, im2col_f32_f16_rte_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_rte_len, im2col_f32_f16_rte_data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true); } else { - ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true); } 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); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp b/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp index 966fedf8f..122b1e93f 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp @@ -2,6 +2,7 @@ #extension GL_EXT_shader_16bit_storage : require #extension GL_EXT_spirv_intrinsics: enable +#extension GL_EXT_control_flow_attributes : require #if RTE16 spirv_execution_mode(capabilities = [4467], 4462, 16); // RoundingModeRTE, 16 bits @@ -23,40 +24,64 @@ layout (push_constant) uniform parameter #include "types.comp" -#define BLOCK_SIZE 256 +layout(constant_id = 0) const uint BLOCK_SIZE = 32; -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; +const uint NUM_ITER = 512 / BLOCK_SIZE; + +layout(local_size_x_id = 0, 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 i = gl_GlobalInvocationID.x; - if (i >= p.pelements) { - return; - } - - const uint ksize = p.OW * (p.KH > 1 ? p.KW : 1); - const uint kx = i / ksize; - const uint kd = kx * ksize; - const uint ky = (i - kd) / p.OW; - const uint ix = i % p.OW; + const uint gidx = gl_GlobalInvocationID.x; const uint oh = gl_GlobalInvocationID.y; const uint batch = gl_GlobalInvocationID.z / p.IC; const uint ic = gl_GlobalInvocationID.z % p.IC; - const uint iiw = ix * p.s0 + kx * p.d0 - p.p0; - const uint iih = oh * p.s1 + ky * p.d1 - p.p1; - - const uint offset_dst = - ((batch * p.OH + oh) * p.OW + ix) * p.CHW + - (ic * (p.KW * p.KH) + ky * p.KW + kx); - - if (iih < 0 || iih >= p.IH || iiw < 0 || iiw >= p.IW) { - data_d[offset_dst] = D_TYPE(0.0f); - } else { - const uint offset_src = ic * p.offset_delta + batch * p.batch_offset; - data_d[offset_dst] = D_TYPE(data_a[offset_src + iih * p.IW + iiw]); + A_TYPE values[NUM_ITER]; + uint offset_dst[NUM_ITER]; + [[unroll]] for (uint idx = 0; idx < NUM_ITER; ++idx) { + values[idx] = A_TYPE(0); } + + [[unroll]] for (uint idx = 0; idx < NUM_ITER; ++idx) { + + const uint i = gidx * NUM_ITER + idx; + + const uint ksize = p.OW * (p.KH > 1 ? p.KW : 1); + const uint kx = i / ksize; + const uint kd = kx * ksize; + const uint ky = (i - kd) / p.OW; + const uint ix = i % p.OW; + + const uint iiw = ix * p.s0 + kx * p.d0 - p.p0; + const uint iih = oh * p.s1 + ky * p.d1 - p.p1; + + offset_dst[idx] = + ((batch * p.OH + oh) * p.OW + ix) * p.CHW + + (ic * (p.KW * p.KH) + ky * p.KW + kx); + + if (i >= p.pelements) { + continue; + } + + if (iih < p.IH && iiw < p.IW) { + const uint offset_src = ic * p.offset_delta + batch * p.batch_offset; + values[idx] = data_a[offset_src + iih * p.IW + iiw]; + } + } + + [[unroll]] for (uint idx = 0; idx < NUM_ITER; ++idx) { + + const uint i = gidx * NUM_ITER + idx; + + if (i >= p.pelements) { + continue; + } + + data_d[offset_dst[idx]] = D_TYPE(values[idx]); + } + } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ccdd3fb57..c79acffd2 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -3945,6 +3945,18 @@ static std::vector> make_test_cases_perf() { } } + for (int K : {3, 5}) { + for (int IC : {256, 2560}) { + for (int IW_IH : {32, 64, 256}) { + if (IC == 2560 && IW_IH == 256) { + // too big + continue; + } + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {IW_IH, IW_IH, IC, 1}, {K, K, IC, 1}, 1, 1, 1, 1, 1, 1, true)); + } + } + } + return test_cases; } From c250ecb3157f3bae0a45f44c3c953b5414d4c2f7 Mon Sep 17 00:00:00 2001 From: ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com> Date: Mon, 30 Dec 2024 20:35:13 +0800 Subject: [PATCH 39/43] android : fix llama_batch free (#11014) --- examples/llama.android/llama/src/main/cpp/llama-android.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) 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 b3858ddfb..66ec2aeeb 100644 --- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -305,7 +305,9 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, extern "C" JNIEXPORT void JNICALL Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { - llama_batch_free(*reinterpret_cast(batch_pointer)); + //llama_batch_free(*reinterpret_cast(batch_pointer)); + const auto batch = reinterpret_cast(batch_pointer); + delete batch; } extern "C" From 716bd6dec3e044e5c325386b5b0483392b24cefe Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Mon, 30 Dec 2024 11:27:11 -0600 Subject: [PATCH 40/43] vulkan: optimize mul_mat for small values of N (#10991) Make the mul_mat_vec shaders support N>1 (as a spec constant, NUM_COLS) where the batch_strides are overloaded to hold the row strides. Put the loads from the B matrix in the innermost loop because it should cache better. Share some code for reducing the result values to memory in mul_mat_vec_base. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 87 +++++++------ .../vulkan-shaders/mul_mat_vec.comp | 122 ++++++++---------- .../vulkan-shaders/mul_mat_vec_base.comp | 33 +++++ .../vulkan-shaders/mul_mat_vec_q2_k.comp | 92 ++++++------- .../vulkan-shaders/mul_mat_vec_q3_k.comp | 75 +++++------ .../vulkan-shaders/mul_mat_vec_q4_k.comp | 64 ++++----- .../vulkan-shaders/mul_mat_vec_q5_k.comp | 104 ++++++--------- .../vulkan-shaders/mul_mat_vec_q6_k.comp | 58 +++------ tests/test-backend-ops.cpp | 2 +- 9 files changed, 288 insertions(+), 349 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 8e47e79ae..020e61280 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -145,6 +145,8 @@ class vk_perf_logger; #endif static void ggml_vk_destroy_buffer(vk_buffer& buf); +static constexpr uint32_t mul_mat_vec_max_cols = 8; + struct vk_device_struct { std::mutex mutex; @@ -202,8 +204,8 @@ struct vk_device_struct { vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_id[GGML_TYPE_COUNT]; vk_pipeline pipeline_dequant[GGML_TYPE_COUNT]; - vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT]; - vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT][mul_mat_vec_max_cols]; + vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT][mul_mat_vec_max_cols]; vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT]; vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; @@ -1866,33 +1868,35 @@ static void ggml_vk_load_shaders(vk_device& device) { } else if (device->vendor_id == VK_VENDOR_ID_INTEL) rm_stdq = 2; - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true); + for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) { + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32_"+std::to_string(i+1), mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32_"+std::to_string(i+1), mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32_"+std::to_string(i+1), mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1), mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1), mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(i+1), mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32_"+std::to_string(i+1), mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32_"+std::to_string(i+1), mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq, i+1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1), mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {subgroup_size_16, 2*rm_stdq, i+1}, 1, true); + } ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); @@ -2892,9 +2896,10 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte return ctx->device->fp16 ? ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc; } -static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t num_cols) { VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()"); GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16); + GGML_ASSERT(num_cols >= 1 && num_cols <= mul_mat_vec_max_cols); switch (a_type) { case GGML_TYPE_F32: @@ -2915,7 +2920,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * return nullptr; } - return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type]; + return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type][num_cols-1] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type][num_cols-1]; } static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) { @@ -3925,8 +3930,6 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& const uint64_t ne12 = src1->ne[2]; const uint64_t ne13 = src1->ne[3]; - GGML_ASSERT(ne11 == 1); - const uint64_t ne20 = dst->ne[0]; const uint64_t ne21 = dst->ne[1]; const uint64_t ne22 = dst->ne[2]; @@ -3935,6 +3938,11 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& const uint64_t r2 = ne12 / ne02; const uint64_t r3 = ne13 / ne03; + // batch_n indicates that we need to compute a few vector results, and this assumes + // ne12 and ne13 are 1. It overloads the batch_strides to hold the row strides. + GGML_ASSERT(ne11 == 1 || ne12 * ne13 == 1); + bool batch_n = ne11 > 1; + ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; @@ -3985,7 +3993,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type); + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); @@ -4057,8 +4065,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); } - uint32_t stride_batch_x = ne00*ne01; - uint32_t stride_batch_y = ne10*ne11; + // For batch_n, the A matrix is the same for each batch, and B/D use the row stride as the batch stride + uint32_t stride_batch_x = batch_n ? 0 : ne00*ne01; + uint32_t stride_batch_y = batch_n ? ne10 : (ne10*ne11); + uint32_t stride_batch_d = batch_n ? ne20 : (ne20*ne21); if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); @@ -4081,7 +4091,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& // compute const vk_mat_vec_push_constants pc = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, - stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21), + stride_batch_x, stride_batch_y, stride_batch_d, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, }; ggml_vk_sync_buffers(subctx); @@ -4261,7 +4271,10 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, c } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 && !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) { ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun); - } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { + // mul_mat_vec supports batching ne12*ne13 when ne11==1, or treating ne11 as the batch size (up to four) + // when ne12 and ne13 are one. + } else if ((dst->ne[1] == 1 || (dst->ne[1] <= mul_mat_vec_max_cols && src1->ne[2] * src1->ne[3] == 1)) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun); } else { ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, dryrun); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp index 187c31916..24875cdcf 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp @@ -9,9 +9,6 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (constant_id = 0) const uint BLOCK_SIZE = 32; -layout (constant_id = 1) const uint NUM_ROWS = 1; - #if !defined(DATA_A_F32) && !defined(DATA_A_F16) #define K_PER_ITER 8 #else @@ -21,70 +18,70 @@ layout (constant_id = 1) const uint NUM_ROWS = 1; uint a_offset, b_offset, d_offset, y_offset; -shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; - -void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter) +void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter) { - const uint col = i*BLOCK_SIZE + K_PER_ITER*tid; - const uint iqs = (col%QUANT_K)/QUANT_R; // quant index - const uint iybs = col - col%QUANT_K; // y block start index + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const uint col = i*BLOCK_SIZE + K_PER_ITER*tid; + const uint iqs = (col%QUANT_K)/QUANT_R; // quant index + const uint iybs = col - col%QUANT_K; // y block start index #if K_PER_ITER == 8 #if QUANT_R == 2 - const B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4]; - const B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4]; - const vec4 bv0 = vec4(bv02.x, bv13.x, bv02.y, bv13.y); - const vec4 bv1 = vec4(bv02.z, bv13.z, bv02.w, bv13.w); + const B_TYPE_VEC4 bv02 = data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4]; + const B_TYPE_VEC4 bv13 = data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs + y_offset) / 4]; + const vec4 bv0 = vec4(bv02.x, bv13.x, bv02.y, bv13.y); + const vec4 bv1 = vec4(bv02.z, bv13.z, bv02.w, bv13.w); #else - const vec4 bv0 = vec4(data_b_v4[(b_offset + iybs + iqs) / 4]); - const vec4 bv1 = vec4(data_b_v4[(b_offset + iybs + iqs) / 4 + 1]); + const vec4 bv0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4]); + const vec4 bv1 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4 + 1]); #endif #else - // Check if the second of the pair of elements is OOB, and don't fetch B or - // accumulate it. We still fetch a pair of elements for A, which is fine for - // quantized formats since they'll be within the same block. We should - // probably skip fetching the second element for F16/F32, but as of now we - // still do. - const bool OOB = lastiter && (iybs + iqs + y_offset >= p.ncols); + // Check if the second of the pair of elements is OOB, and don't fetch B or + // accumulate it. We still fetch a pair of elements for A, which is fine for + // quantized formats since they'll be within the same block. We should + // probably skip fetching the second element for F16/F32, but as of now we + // still do. + const bool OOB = lastiter && (iybs + iqs + y_offset >= p.ncols); - FLOAT_TYPE b0 = 0, b1 = 0; - b0 = FLOAT_TYPE(data_b[b_offset + iybs + iqs]); - if (!OOB) { - b1 = FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]); - } + FLOAT_TYPE b0 = 0, b1 = 0; + b0 = FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs]); + if (!OOB) { + b1 = FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset]); + } #endif - uint ibi = first_row*p.ncols; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - const uint ib = (ibi + col)/QUANT_K; // block index - ibi += p.ncols; + uint ibi = first_row*p.ncols; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib = (ibi + col)/QUANT_K; // block index + ibi += p.ncols; #if K_PER_ITER == 8 - vec4 v = dequantize4(ib, iqs, a_offset); - vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset); + vec4 v = dequantize4(ib, iqs, a_offset); + vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset); - const vec2 dm = get_dm(ib, a_offset); - if (dm.y != 0) { // quant has min component - v = v * dm.x + dm.y; - v2 = v2 * dm.x + dm.y; - } + const vec2 dm = get_dm(ib, a_offset); + if (dm.y != 0) { // quant has min component + v = v * dm.x + dm.y; + v2 = v2 * dm.x + dm.y; + } - // matrix multiplication - FLOAT_TYPE rowtmp = dot(bv0, v); - rowtmp += dot(bv1, v2); + // matrix multiplication + FLOAT_TYPE rowtmp = dot(bv0, v); + rowtmp += dot(bv1, v2); - if (dm.y == 0) - rowtmp *= dm.x; + if (dm.y == 0) + rowtmp *= dm.x; - temp[n] += rowtmp; + temp[j][n] += rowtmp; #else - const vec2 v = dequantize(ib, iqs, a_offset); + const vec2 v = dequantize(ib, iqs, a_offset); - // matrix multiplication - temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]); - if (!OOB) { - temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]); - } + // matrix multiplication + temp[j][n] = fma(FLOAT_TYPE(v.x), b0, temp[j][n]); + if (!OOB) { + temp[j][n] = fma(FLOAT_TYPE(v.y), b1, temp[j][n]); + } #endif + } } } @@ -96,10 +93,12 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - FLOAT_TYPE temp[NUM_ROWS]; + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; - for (uint i = 0; i < NUM_ROWS; ++i) { - temp[i] = FLOAT_TYPE(0); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } } uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE); @@ -131,24 +130,7 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { i++; } - // sum up partial sums and write back result - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] = temp[n]; - } - barrier(); - [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { - if (tid < s) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] += tmpsh[n][tid + s]; - } - } - barrier(); - } - if (tid == 0) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); - } - } + reduce_result(temp, d_offset, first_row, num_rows, tid); } void main() { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp index 3894fca82..903753c7e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp @@ -83,3 +83,36 @@ void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) { batch_idx * p.batch_stride_d; #endif } + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; +layout (constant_id = 2) const uint NUM_COLS = 1; + +shared FLOAT_TYPE tmpsh[NUM_COLS][NUM_ROWS][BLOCK_SIZE]; + +void reduce_result(const in FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) { + // sum up partial sums and write back result + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[j][n][tid] = temp[j][n]; + } + } + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { + if (tid < s) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[j][n][tid] += tmpsh[j][n][tid + s]; + } + } + } + barrier(); + } + if (tid == 0) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(tmpsh[j][n][0]); + } + } + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp index 138ad0184..934213446 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp @@ -5,11 +5,6 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (constant_id = 0) const uint BLOCK_SIZE = 32; -layout (constant_id = 1) const uint NUM_ROWS = 1; - -shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; - void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); @@ -32,24 +27,17 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint s_offset = 8*v_im; const uint y_offset = 128*v_im + l0; - FLOAT_TYPE temp[NUM_ROWS]; + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; - [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { - temp[i] = FLOAT_TYPE(0); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y_idx = i * QUANT_K + y_offset; - B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0]; - B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8]; - B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16]; - B_TYPE_VEC2 b48 = data_b_v2[(b_offset + y_idx) / 2 + 24]; - B_TYPE_VEC2 b64 = data_b_v2[(b_offset + y_idx) / 2 + 32]; - B_TYPE_VEC2 b80 = data_b_v2[(b_offset + y_idx) / 2 + 40]; - B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48]; - B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56]; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; f16vec2 d = data_a[ib0 + i].d; @@ -74,48 +62,42 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uvec2 qs0 = uvec2(unpack8(qs0_u16)); uvec2 qs16 = uvec2(unpack8(qs16_u16)); - FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); - FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 2; ++l) { - sum1 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l] >> 0) & 3), - fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3), - fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l] >> 2) & 3), - fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3), - fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l] >> 4) & 3), - fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3), - fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l] >> 6) & 3), - fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1)))))))); - sum2 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_hi4[0]), - fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_hi4[1]), - fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_hi4[2]), - fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_hi4[3]), - fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_hi4[0]), - fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_hi4[1]), - fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_hi4[2]), - fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2)))))))); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + B_TYPE_VEC2 b0 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0]; + B_TYPE_VEC2 b16 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8]; + B_TYPE_VEC2 b32 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]; + B_TYPE_VEC2 b48 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]; + B_TYPE_VEC2 b64 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]; + B_TYPE_VEC2 b80 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]; + B_TYPE_VEC2 b96 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]; + B_TYPE_VEC2 b112 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]; + + FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); + FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 2; ++l) { + sum1 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l] >> 0) & 3), + fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3), + fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l] >> 2) & 3), + fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3), + fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l] >> 4) & 3), + fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3), + fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l] >> 6) & 3), + fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1)))))))); + sum2 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_hi4[0]), + fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_hi4[1]), + fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_hi4[2]), + fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_hi4[3]), + fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_hi4[0]), + fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_hi4[1]), + fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_hi4[2]), + fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2)))))))); + } + temp[j][n] = fma(dall, sum1, fma(-dmin, sum2, temp[j][n])); } - temp[n] = fma(dall, sum1, fma(-dmin, sum2, temp[n])); } } - // sum up partial sums and write back result - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] = temp[n]; - } - barrier(); - [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { - if (tid < s) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] += tmpsh[n][tid + s]; - } - } - barrier(); - } - if (tid == 0) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); - } - } + reduce_result(temp, d_offset, first_row, num_rows, tid); } void main() { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp index 82ec42d25..86b0159d9 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp @@ -5,11 +5,6 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (constant_id = 0) const uint BLOCK_SIZE = 32; -layout (constant_id = 1) const uint NUM_ROWS = 1; - -shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; - void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); @@ -33,10 +28,12 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint q_offset = 32*v_im + l0; const uint y_offset = 128*v_im + l0; - FLOAT_TYPE temp[NUM_ROWS]; + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; - [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { - temp[i] = FLOAT_TYPE(0); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } } const uint s_shift = 4 * v_im; @@ -44,15 +41,6 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y_idx = i * QUANT_K + y_offset; - B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0]; - B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8]; - B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16]; - B_TYPE_VEC2 b48 = data_b_v2[(b_offset + y_idx) / 2 + 24]; - B_TYPE_VEC2 b64 = data_b_v2[(b_offset + y_idx) / 2 + 32]; - B_TYPE_VEC2 b80 = data_b_v2[(b_offset + y_idx) / 2 + 40]; - B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48]; - B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56]; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); @@ -70,39 +58,34 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { u8vec2 s8 = unpack8(s8_16); u8vec2 s10 = unpack8(s10_16); - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 2; ++l) { - sum = fma(FLOAT_TYPE(b0[l]) * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b32[l]) * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b64[l]) * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b96[l]) * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b16[l]) * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b48[l]) * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b80[l]) * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + + B_TYPE_VEC2 b0 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0]; + B_TYPE_VEC2 b16 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8]; + B_TYPE_VEC2 b32 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]; + B_TYPE_VEC2 b48 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]; + B_TYPE_VEC2 b64 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]; + B_TYPE_VEC2 b80 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]; + B_TYPE_VEC2 b96 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]; + B_TYPE_VEC2 b112 = data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]; + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 2; ++l) { + sum = fma(FLOAT_TYPE(b0[l]) * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b32[l]) * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b64[l]) * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b96[l]) * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b16[l]) * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b48[l]) * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b80[l]) * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); + } + temp[j][n] = fma(d, sum, temp[j][n]); } - temp[n] = fma(d, sum, temp[n]); } } - // sum up partial sums and write back result - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] = temp[n]; - } - barrier(); - [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { - if (tid < s) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] += tmpsh[n][tid + s]; - } - } - barrier(); - } - if (tid == 0) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); - } - } + reduce_result(temp, d_offset, first_row, num_rows, tid); } void main() { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp index 677c207a8..cd1dd8e89 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp @@ -6,11 +6,6 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (constant_id = 0) const uint BLOCK_SIZE = 32; -layout (constant_id = 1) const uint NUM_ROWS = 1; - -shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; - void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); @@ -36,21 +31,18 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint q_offset = 32*v_im + l0; const uint y_offset = 64*v_im + l0; - FLOAT_TYPE temp[NUM_ROWS]; + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; - [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { - temp[i] = FLOAT_TYPE(0); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y1_idx = i * QUANT_K + y_offset; const uint y2_idx = y1_idx + 128; - B_TYPE_VEC4 by10 = data_b_v4[(b_offset + y1_idx) / 4]; - B_TYPE_VEC4 by132 = data_b_v4[(b_offset + y1_idx) / 4 + 8]; - B_TYPE_VEC4 by20 = data_b_v4[(b_offset + y2_idx) / 4]; - B_TYPE_VEC4 by232 = data_b_v4[(b_offset + y2_idx) / 4 + 8]; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; f16vec2 d = data_a[ib0 + i].d; @@ -103,37 +95,27 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint32_t q4_14 = qs64_hi4.z; const uint32_t q4_15 = qs64_hi4.w; - const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x), q4_0, fma(FLOAT_TYPE(by10.y), q4_1, fma(FLOAT_TYPE(by10.z), q4_2, FLOAT_TYPE(by10.w) * q4_3))); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x), q4_4, fma(FLOAT_TYPE(by132.y), q4_5, fma(FLOAT_TYPE(by132.z), q4_6, FLOAT_TYPE(by132.w) * q4_7))); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x), q4_8, fma(FLOAT_TYPE(by20.y), q4_9, fma(FLOAT_TYPE(by20.z), q4_10, FLOAT_TYPE(by20.w) * q4_11))); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x), q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7, - fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7, - fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7, - fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6, FLOAT_TYPE(by232.w) * sc7))))))))))))))); - temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n])); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + B_TYPE_VEC4 by10 = data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4]; + B_TYPE_VEC4 by132 = data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 + 8]; + B_TYPE_VEC4 by20 = data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4]; + B_TYPE_VEC4 by232 = data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 + 8]; + + const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x), q4_0, fma(FLOAT_TYPE(by10.y), q4_1, fma(FLOAT_TYPE(by10.z), q4_2, FLOAT_TYPE(by10.w) * q4_3))); + const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x), q4_4, fma(FLOAT_TYPE(by132.y), q4_5, fma(FLOAT_TYPE(by132.z), q4_6, FLOAT_TYPE(by132.w) * q4_7))); + const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x), q4_8, fma(FLOAT_TYPE(by20.y), q4_9, fma(FLOAT_TYPE(by20.z), q4_10, FLOAT_TYPE(by20.w) * q4_11))); + const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x), q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7, + fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7, + fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7, + fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6, FLOAT_TYPE(by232.w) * sc7))))))))))))))); + temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n])); + } } } - // sum up partial sums and write back result - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] = temp[n]; - } - barrier(); - [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { - if (tid < s) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] += tmpsh[n][tid + s]; - } - } - barrier(); - } - if (tid == 0) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); - } - } + reduce_result(temp, d_offset, first_row, num_rows, tid); } void main() { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp index ed3c25d89..0a68891c3 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp @@ -6,11 +6,6 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (constant_id = 0) const uint BLOCK_SIZE = 32; -layout (constant_id = 1) const uint NUM_ROWS = 1; - -shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; - void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); @@ -33,25 +28,18 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint q_offset = 32*v_im + l0; const uint y_offset = 64*v_im + l0; - FLOAT_TYPE temp[NUM_ROWS]; + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; - [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { - temp[i] = FLOAT_TYPE(0); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y1_idx = i * QUANT_K + y_offset; const uint y2_idx = y1_idx + 128; - B_TYPE_VEC2 by10 = data_b_v2[(b_offset + y1_idx) / 2]; - B_TYPE_VEC2 by116 = data_b_v2[(b_offset + y1_idx) / 2 + 8]; - B_TYPE_VEC2 by132 = data_b_v2[(b_offset + y1_idx) / 2 + 16]; - B_TYPE_VEC2 by148 = data_b_v2[(b_offset + y1_idx) / 2 + 24]; - B_TYPE_VEC2 by20 = data_b_v2[(b_offset + y2_idx) / 2]; - B_TYPE_VEC2 by216 = data_b_v2[(b_offset + y2_idx) / 2 + 8]; - B_TYPE_VEC2 by232 = data_b_v2[(b_offset + y2_idx) / 2 + 16]; - B_TYPE_VEC2 by248 = data_b_v2[(b_offset + y2_idx) / 2 + 24]; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; f16vec2 d = data_a[ib0 + i].d; @@ -116,53 +104,47 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint32_t q4_14 = qs64_80_hi4.z; const uint32_t q4_15 = qs64_80_hi4.w; - const FLOAT_TYPE sx = - fma(FLOAT_TYPE(by10.x), q4_0, - fma(FLOAT_TYPE(by10.y), q4_1, - fma(FLOAT_TYPE(by116.x), q4_2, - FLOAT_TYPE(by116.y) * q4_3))); - const FLOAT_TYPE sy = - fma(FLOAT_TYPE(by132.x), q4_4, - fma(FLOAT_TYPE(by132.y), q4_5, - fma(FLOAT_TYPE(by148.x), q4_6, - FLOAT_TYPE(by148.y) * q4_7))); - const FLOAT_TYPE sz = - fma(FLOAT_TYPE(by20.x), q4_8, - fma(FLOAT_TYPE(by20.y), q4_9, - fma(FLOAT_TYPE(by216.x), q4_10, - FLOAT_TYPE(by216.y) * q4_11))); - const FLOAT_TYPE sw = - fma(FLOAT_TYPE(by232.x), q4_12, - fma(FLOAT_TYPE(by232.y), q4_13, - fma(FLOAT_TYPE(by248.x), q4_14, - FLOAT_TYPE(by248.y) * q4_15))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2, - fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3, - fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6, - (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7))); - temp[n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[n])); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + B_TYPE_VEC2 by10 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2]; + B_TYPE_VEC2 by116 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 8]; + B_TYPE_VEC2 by132 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 16]; + B_TYPE_VEC2 by148 = data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 24]; + B_TYPE_VEC2 by20 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2]; + B_TYPE_VEC2 by216 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 8]; + B_TYPE_VEC2 by232 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 16]; + B_TYPE_VEC2 by248 = data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 24]; + + const FLOAT_TYPE sx = + fma(FLOAT_TYPE(by10.x), q4_0, + fma(FLOAT_TYPE(by10.y), q4_1, + fma(FLOAT_TYPE(by116.x), q4_2, + FLOAT_TYPE(by116.y) * q4_3))); + const FLOAT_TYPE sy = + fma(FLOAT_TYPE(by132.x), q4_4, + fma(FLOAT_TYPE(by132.y), q4_5, + fma(FLOAT_TYPE(by148.x), q4_6, + FLOAT_TYPE(by148.y) * q4_7))); + const FLOAT_TYPE sz = + fma(FLOAT_TYPE(by20.x), q4_8, + fma(FLOAT_TYPE(by20.y), q4_9, + fma(FLOAT_TYPE(by216.x), q4_10, + FLOAT_TYPE(by216.y) * q4_11))); + const FLOAT_TYPE sw = + fma(FLOAT_TYPE(by232.x), q4_12, + fma(FLOAT_TYPE(by232.y), q4_13, + fma(FLOAT_TYPE(by248.x), q4_14, + FLOAT_TYPE(by248.y) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2, + fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3, + fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6, + (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7))); + temp[j][n] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp[j][n])); + } } } - // sum up partial sums and write back result - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] = temp[n]; - } - barrier(); - [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { - if (tid < s) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] += tmpsh[n][tid + s]; - } - } - barrier(); - } - if (tid == 0) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); - } - } + reduce_result(temp, d_offset, first_row, num_rows, tid); } void main() { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp index fab4ff5ff..70e13a56b 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp @@ -6,11 +6,6 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (constant_id = 0) const uint BLOCK_SIZE = 32; -layout (constant_id = 1) const uint NUM_ROWS = 1; - -shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; - void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uint a_offset, b_offset, d_offset; get_offsets(a_offset, b_offset, d_offset); @@ -36,20 +31,17 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint s_offset = 8*v_im + is; const uint y_offset = 128*v_im + l0; - FLOAT_TYPE temp[NUM_ROWS]; + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; - [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { - temp[i] = FLOAT_TYPE(0); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } } [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y_idx = i * QUANT_K + y_offset; - B_TYPE_VEC4 by0 = data_b_v4[(b_offset + y_idx) / 4]; - B_TYPE_VEC4 by32 = data_b_v4[(b_offset + y_idx) / 4 + 8]; - B_TYPE_VEC4 by64 = data_b_v4[(b_offset + y_idx) / 4 + 16]; - B_TYPE_VEC4 by96 = data_b_v4[(b_offset + y_idx) / 4 + 24]; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row; const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); @@ -84,35 +76,25 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { uvec4 q2 = uvec4(unpack8(q2_u32)); uvec4 q3 = uvec4(unpack8(q3_u32)); - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 4; ++l) { - sum = fma(FLOAT_TYPE(by0[l]) * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32), - fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32), - fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32), - fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum)))); + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + B_TYPE_VEC4 by0 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4]; + B_TYPE_VEC4 by32 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 8]; + B_TYPE_VEC4 by64 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 16]; + B_TYPE_VEC4 by96 = data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 24]; + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 4; ++l) { + sum = fma(FLOAT_TYPE(by0[l]) * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32), + fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32), + fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32), + fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum)))); + } + temp[j][n] += sum * d; } - temp[n] += sum * d; } } - // sum up partial sums and write back result - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] = temp[n]; - } - barrier(); - [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { - if (tid < s) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - tmpsh[n][tid] += tmpsh[n][tid + s]; - } - } - barrier(); - } - if (tid == 0) { - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); - } - } + reduce_result(temp, d_offset, first_row, num_rows, tid); } void main() { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index c79acffd2..1e892f663 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -3937,7 +3937,7 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1})); test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32000, 512, 1, 1})); - for (int bs : {1, 512}) { + for (int bs : {1, 2, 3, 4, 5, 8, 512}) { for (ggml_type type_a : all_types) { for (ggml_type type_b : {GGML_TYPE_F32}) { test_cases.emplace_back(new test_mul_mat(type_a, type_b, 4096, bs, 14336, {1, 1}, {1, 1})); From 6e1531aca5ed17f078973b4700fcdadbda4a34a5 Mon Sep 17 00:00:00 2001 From: Peter Date: Tue, 31 Dec 2024 11:46:06 +1100 Subject: [PATCH 41/43] common, examples, ggml : fix MSYS2 GCC compiler errors and warnings when building with LLAMA_CURL=ON and GGML_OPENCL=ON (#11013) In common/common.cpp: * Convert usage of stat() function call to check if file exists to standard library function std::filesystem::exists (error unable to match to correct function signature) * Additional conditions to check if PATH_MAX is already defined in WIN32 environment (warning it is already defined in MSYS2) In examples/run/run.cpp: * Add io.h header inclusion (error cannot find function _get_osfhandle) * Change initialisers for OVERLAPPED to empty struct (warning about uninitialised members) * Add initialiser for hFile (warning it may be uninitialised) * Add cast for curl_off_t percentage value to long int in generate_progress_prefix function (warning that curl_off_t is long long int) In ggml/src/ggml-opencl/ggml-opencl.cpp: * Initialise certain declared cl_mem variables to nullptr for greater safety (warning about B_d variable possibly used unassigned) --- common/common.cpp | 8 +++++--- examples/run/run.cpp | 9 +++++---- ggml/src/ggml-opencl/ggml-opencl.cpp | 12 ++++++------ 3 files changed, 16 insertions(+), 13 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 20be92911..9071999a7 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -18,6 +18,7 @@ #include #include #include +#include #include #include #include @@ -62,7 +63,9 @@ #ifdef __linux__ #include #elif defined(_WIN32) -#define PATH_MAX MAX_PATH +# if !defined(PATH_MAX) +# define PATH_MAX MAX_PATH +# endif #else #include #endif @@ -1148,8 +1151,7 @@ static bool common_download_file(const std::string & url, const std::string & pa #endif // Check if the file already exists locally - struct stat model_file_info; - auto file_exists = (stat(path.c_str(), &model_file_info) == 0); + auto file_exists = std::filesystem::exists(path); // If the file exists, check its JSON metadata companion file. std::string metadata_path = path + ".json"; diff --git a/examples/run/run.cpp b/examples/run/run.cpp index f89d041c4..75b817272 100644 --- a/examples/run/run.cpp +++ b/examples/run/run.cpp @@ -1,5 +1,6 @@ #if defined(_WIN32) # include +# include #else # include # include @@ -253,7 +254,7 @@ class File { return 1; } - OVERLAPPED overlapped = { 0 }; + OVERLAPPED overlapped = {}; if (!LockFileEx(hFile, LOCKFILE_EXCLUSIVE_LOCK | LOCKFILE_FAIL_IMMEDIATELY, 0, MAXDWORD, MAXDWORD, &overlapped)) { fd = -1; @@ -277,7 +278,7 @@ class File { if (fd >= 0) { # ifdef _WIN32 if (hFile != INVALID_HANDLE_VALUE) { - OVERLAPPED overlapped = { 0 }; + OVERLAPPED overlapped = {}; UnlockFileEx(hFile, 0, MAXDWORD, MAXDWORD, &overlapped); } # else @@ -293,7 +294,7 @@ class File { private: int fd = -1; # ifdef _WIN32 - HANDLE hFile; + HANDLE hFile = nullptr; # endif }; @@ -464,7 +465,7 @@ class HttpClient { return (now_downloaded_plus_file_size * 100) / total_to_download; } - static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", percentage); } + static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", static_cast(percentage)); } static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) { const auto now = std::chrono::steady_clock::now(); diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index c77d629f0..ed90e471a 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -2744,13 +2744,13 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co cl_image_format img_fmt_1d; cl_image_desc img_desc_1d; cl_buffer_region region; - cl_mem A_image1d; - cl_mem B_image1d; - cl_mem B_sub_buffer; - cl_mem C_d; + cl_mem A_image1d = nullptr; + cl_mem B_image1d = nullptr; + cl_mem B_sub_buffer = nullptr; + cl_mem C_d = nullptr; // for B transpose - cl_mem B_d; - cl_mem B_d_input_image; + cl_mem B_d = nullptr; + cl_mem B_d_input_image = nullptr; // <--------------------------------------------> // // define matrix dimensions From bc7b1f86324279a3dabb705c04ad754a2b27df16 Mon Sep 17 00:00:00 2001 From: ymcki <84055651+ymcki@users.noreply.github.com> Date: Tue, 31 Dec 2024 19:04:48 +0800 Subject: [PATCH 42/43] convert : fix Llama-3_1-Nemotron-51B rope settings (#11008) * conflict resolution * move comments after bracket to its own line * DeciLMCausalModel now reads rope_theta from config.json properly --- convert_hf_to_gguf.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index b6c15da94..4e6c0f60c 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1764,25 +1764,19 @@ class DeciModel(Model): self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) - special_vocab = gguf.SpecialVocab( - self.dir_model, load_merges=True, - special_token_types = ['bos', 'eos', 'eom', 'eot'] - ) - special_vocab._set_special_token("bos", 128000) - special_vocab._set_special_token("eos", 128001) - special_vocab._set_special_token("eom", 128008) - special_vocab._set_special_token("eot", 128009) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) special_vocab.add_to_gguf(self.gguf_writer) else: # DeciLM-7B self._set_vocab_llama_hf() -# self._set_vocab_gpt2() def set_gguf_parameters(self): if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B assert self.block_count == len(self._num_kv_heads) assert self.block_count == len(self._num_heads) assert self.block_count == len(self._ffn_dims) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) self.gguf_writer.add_head_count_kv(self._num_kv_heads) self.gguf_writer.add_head_count(self._num_heads) self.gguf_writer.add_feed_forward_length(self._ffn_dims) From 5896c65232c7dc87d78426956b16f63fbf58dcf6 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 31 Dec 2024 12:34:13 +0100 Subject: [PATCH 43/43] server : add OAI compat for /v1/completions (#10974) * server : add OAI compat for /v1/completions * add test * add docs * better docs --- examples/server/README.md | 252 +++++++++++------- examples/server/server.cpp | 206 ++++++++++---- .../server/tests/unit/test_chat_completion.py | 6 +- examples/server/tests/unit/test_completion.py | 35 +++ examples/server/utils.hpp | 47 +++- 5 files changed, 400 insertions(+), 146 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 07436057a..bcef81946 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -345,7 +345,7 @@ node index.js > [!IMPORTANT] > -> This endpoint is **not** OAI-compatible +> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/completions` instead. *Options:* @@ -523,6 +523,7 @@ These words will not be included in the completion, so make sure to add them to - `tokens_evaluated`: Number of tokens evaluated in total from the prompt - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) + ### POST `/tokenize`: Tokenize a given text *Options:* @@ -574,6 +575,10 @@ With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k ### POST `/embedding`: Generate embedding of a given text +> [!IMPORTANT] +> +> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/embeddings` instead. + The same as [the embedding example](../embedding) does. *Options:* @@ -744,96 +749,6 @@ To use this endpoint with POST method, you need to start server with `--props` - None yet -### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API - -Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used. - -*Options:* - -See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported. - -The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers. - -*Examples:* - -You can use either Python `openai` library with appropriate checkpoints: - -```python -import openai - -client = openai.OpenAI( - base_url="http://localhost:8080/v1", # "http://:port" - api_key = "sk-no-key-required" -) - -completion = client.chat.completions.create( -model="gpt-3.5-turbo", -messages=[ - {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."}, - {"role": "user", "content": "Write a limerick about python exceptions"} -] -) - -print(completion.choices[0].message) -``` - -... or raw HTTP requests: - -```shell -curl http://localhost:8080/v1/chat/completions \ --H "Content-Type: application/json" \ --H "Authorization: Bearer no-key" \ --d '{ -"model": "gpt-3.5-turbo", -"messages": [ -{ - "role": "system", - "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests." -}, -{ - "role": "user", - "content": "Write a limerick about python exceptions" -} -] -}' -``` - -### POST `/v1/embeddings`: OpenAI-compatible embeddings API - -This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm. - -*Options:* - -See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). - -*Examples:* - -- input as string - - ```shell - curl http://localhost:8080/v1/embeddings \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "input": "hello", - "model":"GPT-4", - "encoding_format": "float" - }' - ``` - -- `input` as string array - - ```shell - curl http://localhost:8080/v1/embeddings \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "input": ["hello", "world"], - "model":"GPT-4", - "encoding_format": "float" - }' - ``` - ### POST `/embeddings`: non-OpenAI-compatible embeddings API This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm. @@ -1064,6 +979,161 @@ To know the `id` of the adapter, use GET `/lora-adapters` ] ``` +## OpenAI-compatible API Endpoints + +### GET `/v1/models`: OpenAI-compatible Model Info API + +Returns information about the loaded model. See [OpenAI Models API documentation](https://platform.openai.com/docs/api-reference/models). + +The returned list always has one single element. + +By default, model `id` field is the path to model file, specified via `-m`. You can set a custom value for model `id` field via `--alias` argument. For example, `--alias gpt-4o-mini`. + +Example: + +```json +{ + "object": "list", + "data": [ + { + "id": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", + "object": "model", + "created": 1735142223, + "owned_by": "llamacpp", + "meta": { + "vocab_type": 2, + "n_vocab": 128256, + "n_ctx_train": 131072, + "n_embd": 4096, + "n_params": 8030261312, + "size": 4912898304 + } + } + ] +} +``` + +### POST `/v1/completions`: OpenAI-compatible Completions API + +Given an input `prompt`, it returns the predicted completion. Streaming mode is also supported. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. + +*Options:* + +See [OpenAI Completions API documentation](https://platform.openai.com/docs/api-reference/completions). + +llama.cpp `/completion`-specific features such as `mirostat` are supported. + +*Examples:* + +Example usage with `openai` python library: + +```python +import openai + +client = openai.OpenAI( + base_url="http://localhost:8080/v1", # "http://:port" + api_key = "sk-no-key-required" +) + +completion = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8 +) + +print(completion.choices[0].text) +``` + +### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API + +Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used. + +*Options:* + +See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported. + +The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers. + +*Examples:* + +You can use either Python `openai` library with appropriate checkpoints: + +```python +import openai + +client = openai.OpenAI( + base_url="http://localhost:8080/v1", # "http://:port" + api_key = "sk-no-key-required" +) + +completion = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."}, + {"role": "user", "content": "Write a limerick about python exceptions"} + ] +) + +print(completion.choices[0].message) +``` + +... or raw HTTP requests: + +```shell +curl http://localhost:8080/v1/chat/completions \ +-H "Content-Type: application/json" \ +-H "Authorization: Bearer no-key" \ +-d '{ +"model": "gpt-3.5-turbo", +"messages": [ +{ + "role": "system", + "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests." +}, +{ + "role": "user", + "content": "Write a limerick about python exceptions" +} +] +}' +``` + +### POST `/v1/embeddings`: OpenAI-compatible embeddings API + +This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm. + +*Options:* + +See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). + +*Examples:* + +- input as string + + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": "hello", + "model":"GPT-4", + "encoding_format": "float" + }' + ``` + +- `input` as string array + + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": ["hello", "world"], + "model":"GPT-4", + "encoding_format": "float" + }' + ``` + ## More examples ### Interactive mode diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3558ddb7c..1d00954a2 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -67,6 +67,13 @@ enum server_task_type { SERVER_TASK_TYPE_SET_LORA, }; +enum oaicompat_type { + OAICOMPAT_TYPE_NONE, + OAICOMPAT_TYPE_CHAT, + OAICOMPAT_TYPE_COMPLETION, + OAICOMPAT_TYPE_EMBEDDING, +}; + // https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 enum error_type { ERROR_TYPE_INVALID_REQUEST, @@ -101,11 +108,10 @@ struct slot_params { struct common_params_speculative speculative; // OAI-compat fields - bool verbose = false; - bool oaicompat = false; - bool oaicompat_chat = true; - std::string oaicompat_model; - std::string oaicompat_cmpl_id; + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; json to_json() const { std::vector samplers; @@ -529,11 +535,10 @@ struct server_task_result_cmpl_final : server_task_result { slot_params generation_params; // OAI-compat fields - bool verbose = false; - bool oaicompat = false; - bool oaicompat_chat = true; // TODO: support oaicompat for non-chat - std::string oaicompat_model; - std::string oaicompat_cmpl_id; + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; virtual int get_index() override { return index; @@ -544,9 +549,16 @@ struct server_task_result_cmpl_final : server_task_result { } virtual json to_json() override { - return oaicompat - ? (stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat()) - : to_json_non_oaicompat(); + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } } json to_json_non_oaicompat() { @@ -574,6 +586,50 @@ struct server_task_result_cmpl_final : server_task_result { return response_fields.empty() ? res : json_get_nested_values(response_fields, res); } + json to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (!stream && probs_output.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } + json finish_reason = "length"; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = "stop"; + } + json res = json { + {"choices", json::array({ + json{ + {"text", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", finish_reason}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens} + }}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + json to_json_oaicompat_chat() { std::string finish_reason = "length"; if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { @@ -671,11 +727,10 @@ struct server_task_result_cmpl_partial : server_task_result { result_timings timings; // OAI-compat fields - bool verbose = false; - bool oaicompat = false; - bool oaicompat_chat = true; // TODO: support oaicompat for non-chat - std::string oaicompat_model; - std::string oaicompat_cmpl_id; + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; virtual int get_index() override { return index; @@ -686,7 +741,16 @@ struct server_task_result_cmpl_partial : server_task_result { } virtual json to_json() override { - return oaicompat ? to_json_oaicompat() : to_json_non_oaicompat(); + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } } json to_json_non_oaicompat() { @@ -711,6 +775,41 @@ struct server_task_result_cmpl_partial : server_task_result { } json to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (prob_output.probs.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + json res = json { + {"choices", json::array({ + json{ + {"text", content}, + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", nullptr}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + + json to_json_oaicompat_chat() { bool first = n_decoded == 0; std::time_t t = std::time(0); json choices; @@ -789,14 +888,16 @@ struct server_task_result_embd : server_task_result { int32_t n_tokens; // OAI-compat fields - bool oaicompat = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; virtual int get_index() override { return index; } virtual json to_json() override { - return oaicompat ? to_json_oaicompat() : to_json_non_oaicompat(); + return oaicompat == OAICOMPAT_TYPE_EMBEDDING + ? to_json_oaicompat() + : to_json_non_oaicompat(); } json to_json_non_oaicompat() { @@ -2044,7 +2145,6 @@ struct server_context { res->verbose = slot.params.verbose; res->oaicompat = slot.params.oaicompat; - res->oaicompat_chat = slot.params.oaicompat_chat; res->oaicompat_model = slot.params.oaicompat_model; res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; @@ -2085,7 +2185,6 @@ struct server_context { res->verbose = slot.params.verbose; res->stream = slot.params.stream; res->oaicompat = slot.params.oaicompat; - res->oaicompat_chat = slot.params.oaicompat_chat; res->oaicompat_model = slot.params.oaicompat_model; res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; @@ -3506,12 +3605,11 @@ int main(int argc, char ** argv) { // handle completion-like requests (completion, chat, infill) // we can optionally provide a custom format for partial results and final results - const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok]( + const auto handle_completions_impl = [&ctx_server, &res_error, &res_ok]( server_task_type type, json & data, httplib::Response & res, - bool oaicompat = false, - bool oaicompat_chat = false) { + oaicompat_type oaicompat) { GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL); if (ctx_server.params_base.embedding) { @@ -3536,9 +3634,8 @@ int main(int argc, char ** argv) { task.id_selected_slot = json_value(data, "id_slot", -1); // OAI-compat - task.params.oaicompat = oaicompat; - task.params.oaicompat_chat = oaicompat_chat; - task.params.oaicompat_cmpl_id = completion_id; + task.params.oaicompat = oaicompat; + task.params.oaicompat_cmpl_id = completion_id; // oaicompat_model is already populated by params_from_json_cmpl tasks.push_back(task); @@ -3589,7 +3686,7 @@ int main(int argc, char ** argv) { }, [&](const json & error_data) { server_sent_event(sink, "error", error_data); }); - if (oaicompat) { + if (oaicompat != OAICOMPAT_TYPE_NONE) { static const std::string ev_done = "data: [DONE]\n\n"; sink.write(ev_done.data(), ev_done.size()); } @@ -3605,17 +3702,25 @@ int main(int argc, char ** argv) { } }; - const auto handle_completions = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) { + const auto handle_completions = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) { json data = json::parse(req.body); - return handle_completions_generic( + return handle_completions_impl( SERVER_TASK_TYPE_COMPLETION, data, res, - /* oaicompat */ false, - /* oaicompat_chat */ false); + OAICOMPAT_TYPE_NONE); }; - const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) { + const auto handle_completions_oai = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + json data = oaicompat_completion_params_parse(json::parse(req.body)); + return handle_completions_impl( + SERVER_TASK_TYPE_COMPLETION, + data, + res, + OAICOMPAT_TYPE_COMPLETION); + }; + + const auto handle_infill = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { // check model compatibility std::string err; if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) { @@ -3684,22 +3789,25 @@ int main(int argc, char ** argv) { tokenized_prompts[0] ); - return handle_completions_generic(SERVER_TASK_TYPE_INFILL, data, res); + return handle_completions_impl( + SERVER_TASK_TYPE_INFILL, + data, + res, + OAICOMPAT_TYPE_NONE); // infill is not OAI compatible }; - const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { if (ctx_server.params_base.embedding) { res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return; } - json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template); - return handle_completions_generic( + json data = oaicompat_chat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template); + return handle_completions_impl( SERVER_TASK_TYPE_COMPLETION, data, res, - /* oaicompat */ true, - /* oaicompat_chat */ true); + OAICOMPAT_TYPE_CHAT); }; const auto handle_models = [¶ms, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) { @@ -3772,10 +3880,10 @@ int main(int argc, char ** argv) { res_ok(res, data); }; - const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, bool oaicompat) { + const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) { const json body = json::parse(req.body); - if (oaicompat && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { + if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST)); return; } @@ -3785,7 +3893,7 @@ int main(int argc, char ** argv) { if (body.count("input") != 0) { prompt = body.at("input"); } else if (body.contains("content")) { - oaicompat = false; + oaicompat = OAICOMPAT_TYPE_NONE; // "content" field is not OAI compatible prompt = body.at("content"); } else { res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); @@ -3854,16 +3962,18 @@ int main(int argc, char ** argv) { } // write JSON response - json root = oaicompat ? format_embeddings_response_oaicompat(body, responses, use_base64) : json(responses); + json root = oaicompat == OAICOMPAT_TYPE_EMBEDDING + ? format_embeddings_response_oaicompat(body, responses, use_base64) + : json(responses); res_ok(res, root); }; const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { - handle_embeddings_impl(req, res, false); + handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE); }; const auto handle_embeddings_oai = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { - handle_embeddings_impl(req, res, true); + handle_embeddings_impl(req, res, OAICOMPAT_TYPE_EMBEDDING); }; const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { @@ -4033,7 +4143,7 @@ int main(int argc, char ** argv) { svr->Get ("/v1/models", handle_models); // public endpoint (no API key check) svr->Post("/completion", handle_completions); // legacy svr->Post("/completions", handle_completions); - svr->Post("/v1/completions", handle_completions); + svr->Post("/v1/completions", handle_completions_oai); svr->Post("/chat/completions", handle_chat_completions); svr->Post("/v1/chat/completions", handle_chat_completions); svr->Post("/infill", handle_infill); diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py index 885497081..130da03a1 100644 --- a/examples/server/tests/unit/test_chat_completion.py +++ b/examples/server/tests/unit/test_chat_completion.py @@ -83,7 +83,7 @@ def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_conte def test_chat_completion_with_openai_library(): global server server.start() - client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") res = client.chat.completions.create( model="gpt-3.5-turbo-instruct", messages=[ @@ -170,7 +170,7 @@ def test_chat_completion_with_timings_per_token(): def test_logprobs(): global server server.start() - client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") res = client.chat.completions.create( model="gpt-3.5-turbo-instruct", temperature=0.0, @@ -197,7 +197,7 @@ def test_logprobs(): def test_logprobs_stream(): global server server.start() - client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") res = client.chat.completions.create( model="gpt-3.5-turbo-instruct", temperature=0.0, diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py index a6b215944..e5e3b6077 100644 --- a/examples/server/tests/unit/test_completion.py +++ b/examples/server/tests/unit/test_completion.py @@ -1,5 +1,6 @@ import pytest import time +from openai import OpenAI from utils import * server = ServerPreset.tinyllama2() @@ -85,6 +86,40 @@ def test_completion_stream_vs_non_stream(): assert content_stream == res_non_stream.body["content"] +def test_completion_stream_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8, + ) + assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b") + assert res.choices[0].finish_reason == "length" + assert res.choices[0].text is not None + assert match_regex("(going|bed)+", res.choices[0].text) + + +def test_completion_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8, + stream=True, + ) + output_text = '' + for data in res: + choice = data.choices[0] + if choice.finish_reason is None: + assert choice.text is not None + output_text += choice.text + assert match_regex("(going|bed)+", output_text) + + @pytest.mark.parametrize("n_slots", [1, 2]) def test_consistent_result_same_seed(n_slots: int): global server diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 334f2f192..8523d4787 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -549,10 +549,49 @@ static bool server_sent_event(httplib::DataSink & sink, const char * event, cons // OAI utils // -static json oaicompat_completion_params_parse( - const struct llama_model * model, - const json & body, /* openai api json semantics */ - const std::string & chat_template) { +static json oaicompat_completion_params_parse(const json & body) { + json llama_params; + + if (!body.contains("prompt")) { + throw std::runtime_error("\"prompt\" is required"); + } + + // Handle "stop" field + if (body.contains("stop") && body.at("stop").is_string()) { + llama_params["stop"] = json::array({body.at("stop").get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } + + // Handle "n" field + int n_choices = json_value(body, "n", 1); + if (n_choices != 1) { + throw std::runtime_error("Only one completion choice is allowed"); + } + + // Params supported by OAI but unsupported by llama.cpp + static const std::vector unsupported_params { "best_of", "echo", "suffix" }; + for (const auto & param : unsupported_params) { + if (body.contains(param)) { + throw std::runtime_error("Unsupported param: " + param); + } + } + + // Copy remaining properties to llama_params + for (const auto & item : body.items()) { + // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens" + if (!llama_params.contains(item.key()) || item.key() == "n_predict") { + llama_params[item.key()] = item.value(); + } + } + + return llama_params; +} + +static json oaicompat_chat_completion_params_parse( + const struct llama_model * model, + const json & body, /* openai api json semantics */ + const std::string & chat_template) { json llama_params; // Apply chat template to the list of messages

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