diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index c4783a6df..be2828973 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -683,7 +683,7 @@ jobs: env: OPENBLAS_VERSION: 0.3.23 SDE_VERSION: 9.33.0-2024-01-07 - VULKAN_VERSION: 1.4.309.0 + VULKAN_VERSION: 1.4.313.2 strategy: matrix: @@ -736,7 +736,7 @@ jobs: id: get_vulkan if: ${{ matrix.build == 'kompute-x64' || matrix.build == 'vulkan-x64' }} run: | - curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe" + curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe" & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}" Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin" diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 9874736cb..64fff175e 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -302,7 +302,7 @@ jobs: env: OPENBLAS_VERSION: 0.3.23 - VULKAN_VERSION: 1.4.309.0 + VULKAN_VERSION: 1.4.313.2 strategy: matrix: @@ -332,7 +332,7 @@ jobs: id: get_vulkan if: ${{ matrix.backend == 'vulkan' }} run: | - curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe" + curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe" & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}" Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin" diff --git a/ci/run.sh b/ci/run.sh index 940055705..e1b777c30 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -779,7 +779,7 @@ function gg_run_rerank_tiny { model_f16="${path_models}/ggml-model-f16.gguf" # for this model, the SEP token is "" - (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?hi\nwhat is panda?it's a bear\nwhat is panda?The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log + (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log # sample output # rerank score 0: 0.029 diff --git a/common/arg.cpp b/common/arg.cpp index 231de227a..c4ad85c47 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2706,6 +2706,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.embd_sep = value; } ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--cls-separator"}, "STRING", + "separator of classification sequences (default \\t) for example \"<#seq#>\"", + [](common_params & params, const std::string & value) { + params.cls_sep = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); add_opt(common_arg( {"--host"}, "HOST", string_format("ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: %s)", params.hostname.c_str()), @@ -3210,6 +3217,32 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.speculative.model.path = value; } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT")); + add_opt(common_arg( + {"-ctkd", "--cache-type-k-draft"}, "TYPE", + string_format( + "KV cache data type for K for the draft model\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.speculative.cache_type_k) + ), + [](common_params & params, const std::string & value) { + params.speculative.cache_type_k = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_K_DRAFT")); + add_opt(common_arg( + {"-ctvd", "--cache-type-v-draft"}, "TYPE", + string_format( + "KV cache data type for V for the draft model\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.speculative.cache_type_v) + ), + [](common_params & params, const std::string & value) { + params.speculative.cache_type_v = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_V_DRAFT")); add_opt(common_arg( {"-mv", "--model-vocoder"}, "FNAME", diff --git a/common/common.cpp b/common/common.cpp index eb80cee08..e4e71ad13 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -706,11 +706,17 @@ bool fs_validate_filename(const std::string & filename) { // disable C++17 deprecation warning for std::codecvt_utf8 # pragma clang diagnostic push # pragma clang diagnostic ignored "-Wdeprecated-declarations" +#elif defined(__GNUC__) +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Wdeprecated-declarations" #endif + std::wstring_convert, char32_t> converter; #if defined(__clang__) # pragma clang diagnostic pop +#elif defined(__GNUC__) +# pragma GCC diagnostic pop #endif filename_utf32 = converter.from_bytes(filename); @@ -1284,6 +1290,9 @@ std::vector common_tokenize( int n_tokens = text.length() + 2 * add_special; std::vector result(n_tokens); n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); + if (n_tokens == std::numeric_limits::min()) { + throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit"); + } if (n_tokens < 0) { result.resize(-n_tokens); int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); diff --git a/common/common.h b/common/common.h index 00b6ca03a..e08a59eae 100644 --- a/common/common.h +++ b/common/common.h @@ -199,6 +199,9 @@ struct common_params_speculative { float p_split = 0.1f; // speculative decoding split probability float p_min = 0.75f; // minimum speculative decoding probability (greedy) + ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K + ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V + struct cpu_params cpuparams; struct cpu_params cpuparams_batch; @@ -355,6 +358,7 @@ struct common_params { int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix std::string embd_sep = "\n"; // separator of embeddings + std::string cls_sep = "\t"; // separator of classification sequences // server params int32_t port = 8080; // server listens on this network port diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index d38a74f95..637891f50 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -41,49 +41,6 @@ static std::string build_repetition(const std::string & item_rule, int min_items return result; } -/* Minimalistic replacement for std::string_view, which is only available from C++17 onwards */ -class string_view { - const std::string & _str; - const size_t _start; - const size_t _end; -public: - string_view(const std::string & str, size_t start = 0, size_t end = std::string::npos) : _str(str), _start(start), _end(end == std::string::npos ? str.length() : end) {} - - size_t size() const { - return _end - _start; - } - - size_t length() const { - return size(); - } - - operator std::string() const { - return str(); - } - - std::string str() const { - return _str.substr(_start, _end - _start); - } - - string_view substr(size_t pos, size_t len = std::string::npos) const { - return string_view(_str, _start + pos, len == std::string::npos ? _end : _start + pos + len); - } - - char operator[](size_t pos) const { - auto index = _start + pos; - if (index >= _end) { - throw std::out_of_range("string_view index out of range"); - } - return _str[_start + pos]; - } - - bool operator==(const string_view & other) const { - std::string this_str = *this; - std::string other_str = other; - return this_str == other_str; - } -}; - static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) { auto has_min = min_value != std::numeric_limits::min(); auto has_max = max_value != std::numeric_limits::max(); @@ -112,14 +69,14 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream & } out << "}"; }; - std::function uniform_range = - [&](const string_view & from, const string_view & to) { + std::function uniform_range = + [&](const std::string_view & from, const std::string_view & to) { size_t i = 0; while (i < from.length() && i < to.length() && from[i] == to[i]) { i++; } if (i > 0) { - out << "\"" << from.substr(0, i).str() << "\""; + out << "\"" << from.substr(0, i) << "\""; } if (i < from.length() && i < to.length()) { if (i > 0) { diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 798082d3c..32456d5c7 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2145,7 +2145,6 @@ class Llama4Model(LlamaModel): def set_vocab(self): self._set_vocab_gpt2() - self.gguf_writer.add_add_bos_token(True) def set_gguf_parameters(self): super().set_gguf_parameters() @@ -2194,7 +2193,7 @@ class Llama4VisionModel(MmprojModel): name += ".weight" if "multi_modal_projector.linear_1" in name: # despite the name with number postfix, this is a single fully connected layer - return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC], data_torch)] + return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch)] return [(self.map_tensor_name(name), data_torch)] return [] @@ -3918,9 +3917,6 @@ class BertModel(TextModel): special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) - self.gguf_writer.add_add_bos_token(True) - self.gguf_writer.add_add_eos_token(True) - @ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification") class DistilBertModel(BertModel): @@ -3962,8 +3958,6 @@ class RobertaModel(BertModel): 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 @@ -4950,8 +4944,6 @@ class JinaBertV2Model(BertModel): self.gguf_writer.add_token_type_count(2) else: raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel') - self.gguf_writer.add_add_bos_token(True) - self.gguf_writer.add_add_eos_token(True) @ModelBase.register("OpenELMForCausalLM") @@ -5553,9 +5545,6 @@ class T5Model(TextModel): special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) - self.gguf_writer.add_add_bos_token(False) - self.gguf_writer.add_add_eos_token(True) - def set_gguf_parameters(self): if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: logger.warning("Couldn't find context length in config.json, assuming default value of 512") @@ -5693,9 +5682,6 @@ class T5EncoderModel(TextModel): special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) - self.gguf_writer.add_add_bos_token(False) - self.gguf_writer.add_add_eos_token(True) - def set_gguf_parameters(self): if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: logger.warning("Couldn't find context length in config.json, assuming default value of 512") @@ -6491,8 +6477,8 @@ def parse_args() -> argparse.Namespace: help="model is executed on big endian machine", ) parser.add_argument( - "model", type=Path, - help="directory containing model file", + "model", type=str, + help="directory containing model file or huggingface repository ID (if --remote)", nargs="?", ) parser.add_argument( @@ -6603,18 +6589,20 @@ def main() -> None: else: logging.basicConfig(level=logging.INFO) - dir_model = args.model - if args.remote: + hf_repo_id = args.model from huggingface_hub import snapshot_download local_dir = snapshot_download( - repo_id=str(dir_model), + repo_id=hf_repo_id, allow_patterns=["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"]) dir_model = Path(local_dir) logger.info(f"Downloaded config and tokenizer to {local_dir}") + else: + hf_repo_id = None + dir_model = Path(args.model) if not dir_model.is_dir(): - logger.error(f'Error: {args.model} is not a directory') + logger.error(f'Error: {dir_model} is not a directory') sys.exit(1) ftype_map: dict[str, gguf.LlamaFileType] = { @@ -6634,9 +6622,9 @@ def main() -> None: if args.outfile is not None: fname_out = args.outfile - elif args.remote: + elif hf_repo_id: # if remote, use the model ID as the output file name - fname_out = Path("./" + str(args.model).replace("/", "-") + "-{ftype}.gguf") + fname_out = Path("./" + hf_repo_id.replace("/", "-") + "-{ftype}.gguf") else: fname_out = dir_model @@ -6665,7 +6653,7 @@ def main() -> None: split_max_tensors=args.split_max_tensors, split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run, small_first_shard=args.no_tensor_first_split, - remote_hf_model_id=str(args.model) if args.remote else None) + remote_hf_model_id=hf_repo_id) if args.vocab_only: logger.info("Exporting model vocab...") diff --git a/docs/build.md b/docs/build.md index 680b0d839..20a6f606e 100644 --- a/docs/build.md +++ b/docs/build.md @@ -1,6 +1,6 @@ # Build llama.cpp locally -The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h). +The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](../include/llama.h). The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 681929d27..0ec2999a0 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -133,10 +133,36 @@ int main(int argc, char ** argv) { // max batch size const uint64_t n_batch = params.n_batch; + // get added sep and eos token, if any + const std::string added_sep_token = llama_vocab_get_add_sep(vocab) ? llama_vocab_get_text(vocab, llama_vocab_sep(vocab)) : ""; + const std::string added_eos_token = llama_vocab_get_add_eos(vocab) ? llama_vocab_get_text(vocab, llama_vocab_eos(vocab)) : ""; + // tokenize the prompts and trim std::vector> inputs; for (const auto & prompt : prompts) { - auto inp = common_tokenize(ctx, prompt, true, true); + std::vector inp; + + // split classification pairs and insert expected separator tokens + if (pooling_type == LLAMA_POOLING_TYPE_RANK && prompt.find(params.cls_sep) != std::string::npos) { + std::vector pairs = split_lines(prompt, params.cls_sep); + std::string final_prompt; + + for (size_t i = 0; i < pairs.size(); i++) { + final_prompt += pairs[i]; + if (i != pairs.size() - 1) { + if (!added_eos_token.empty()) { + final_prompt += added_eos_token; + } + if (!added_sep_token.empty()) { + final_prompt += added_sep_token; + } + } + } + + inp = common_tokenize(ctx, final_prompt, true, true); + } else { + inp = common_tokenize(ctx, prompt, true, true); + } if (inp.size() > n_batch) { LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n", __func__, (long long int) inp.size(), (long long int) n_batch); @@ -145,11 +171,11 @@ int main(int argc, char ** argv) { inputs.push_back(inp); } - // check if the last token is SEP + // check if the last token is SEP/EOS // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true' for (auto & inp : inputs) { - if (inp.empty() || inp.back() != llama_vocab_sep(vocab)) { - LOG_WRN("%s: last token in the prompt is not SEP\n", __func__); + if (inp.empty() || (inp.back() != llama_vocab_sep(vocab) && inp.back() != llama_vocab_eos(vocab))) { + LOG_WRN("%s: last token in the prompt is not SEP or EOS\n", __func__); LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__); } } diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp index 2aee0a919..cf1178043 100644 --- a/examples/simple-chat/simple-chat.cpp +++ b/examples/simple-chat/simple-chat.cpp @@ -98,7 +98,7 @@ int main(int argc, char ** argv) { auto generate = [&](const std::string & prompt) { std::string response; - const bool is_first = llama_memory_seq_pos_max(llama_get_memory(ctx), 0) == 0; + const bool is_first = llama_memory_seq_pos_max(llama_get_memory(ctx), 0) == -1; // tokenize the prompt const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true); diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 43456bd80..5c4949d71 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -489,6 +489,7 @@ extern "C" { GGML_OP_UPSCALE, // nearest interpolate GGML_OP_PAD, GGML_OP_PAD_REFLECT_1D, + GGML_OP_ROLL, GGML_OP_ARANGE, GGML_OP_TIMESTEP_EMBEDDING, GGML_OP_ARGSORT, @@ -1801,6 +1802,17 @@ extern "C" { int p0, int p1); + // Move tensor elements by an offset given for each dimension. Elements that + // are shifted beyond the last position are wrapped around to the beginning. + GGML_API struct ggml_tensor * ggml_roll( + struct ggml_context * ctx, + struct ggml_tensor * a, + int shift0, + int shift1, + int shift2, + int shift3); + + // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151 // timesteps: [N,] // return: [N, dim] diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 0c453741b..9cb2c228d 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -286,6 +286,10 @@ function(ggml_add_cpu_backend_variant tag_name) foreach (feat ${ARGN}) set(GGML_INTERNAL_${feat} ON) endforeach() + elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC") + foreach (feat ${ARGN}) + set(GGML_INTERNAL_${feat} ON) + endforeach() endif() ggml_add_cpu_backend_variant_impl(${tag_name}) @@ -337,6 +341,19 @@ if (GGML_CPU_ALL_VARIANTS) else() message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}") endif() + elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC") + if (CMAKE_SYSTEM_NAME MATCHES "Linux") + ggml_add_cpu_backend_variant(power0) + ggml_add_cpu_backend_variant(power7_1 POWER7) + ggml_add_cpu_backend_variant(power7_2 POWER7 VSX) + ggml_add_cpu_backend_variant(power8_1 POWER8) + ggml_add_cpu_backend_variant(power8_2 POWER8 VSX) + ggml_add_cpu_backend_variant(power9 POWER9 VSX) + ggml_add_cpu_backend_variant(power10 POWER10 VSX) + ggml_add_cpu_backend_variant(power11 POWER11 VSX) + else() + message(FATAL_ERROR "Unsupported PowerPC target OS: ${CMAKE_SYSTEM_NAME}") + endif() else() message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS not yet supported with ${GGML_SYSTEM_ARCH} on ${CMAKE_SYSTEM_NAME}") endif() diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index 405d8e315..2d93771fd 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -69,6 +69,9 @@ #if defined(__clang__) # pragma clang diagnostic push # pragma clang diagnostic ignored "-Wdeprecated-declarations" +#elif defined(__GNUC__) +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Wdeprecated-declarations" #endif namespace fs = std::filesystem; @@ -91,6 +94,8 @@ static std::string path_str(const fs::path & path) { #if defined(__clang__) # pragma clang diagnostic pop +#elif defined(__GNUC__) +# pragma GCC diagnostic pop #endif #ifdef _WIN32 diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index df0034057..71b1d67b8 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -388,6 +388,27 @@ function(ggml_add_cpu_backend_variant_impl tag_name) else() list(APPEND ARCH_FLAGS -mcpu=native -mtune=native -mpowerpc64) endif() + elseif(GGML_CPU_ALL_VARIANTS) + # Begin with the lowest baseline + set(ARCH_DEFINITIONS "") + + # When a feature is selected, bump the MCPU to the first + # version that supported it + foreach(PVER RANGE 7 11) + if(DEFINED GGML_INTERNAL_POWER${PVER}) + set(POWERPC_MCPU "power${PVER}") + list(APPEND ARCH_DEFINITIONS GGML_USE_POWER${PVER}) + endif() + endforeach() + if (GGML_INTERNAL_VSX) + list(APPEND ARCH_DEFINITIONS GGML_USE_VSX) + list(APPEND ARCH_FLAGS -mvsx) + endif() + + if (DEFINED POWERPC_MCPU) + list(APPEND ARCH_FLAGS -mcpu=${POWERPC_MCPU}) + endif() + ggml_add_cpu_backend_features(${GGML_CPU_NAME} powerpc ${ARCH_DEFINITIONS}) else() if (GGML_CPU_POWERPC_CPUTYPE) list(APPEND ARCH_FLAGS -mcpu=${GGML_CPU_POWERPC_CPUTYPE}) @@ -465,9 +486,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name) # Fetch KleidiAI sources: include(FetchContent) - set(KLEIDIAI_COMMIT_TAG "v1.6.0") + set(KLEIDIAI_COMMIT_TAG "v1.9.0") set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz") - set(KLEIDIAI_ARCHIVE_MD5 "75b4ad68f25ab673dcc01065e5a0b05f") + set(KLEIDIAI_ARCHIVE_MD5 "2a8e1bb55d201557553545536489a017") if (POLICY CMP0135) cmake_policy(SET CMP0135 NEW) diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index 9337e01b6..39a0dd301 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -256,45 +256,43 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo UNUSED(blocklen); #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; - 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); + 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 = 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); + 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); - int32x4_t ret = vdupq_n_s32(0); + int32x4_t ret = vdupq_n_s32(0); - ret = vdotq_laneq_s32(ret, b0 << 4, a0, 0); - ret = vdotq_laneq_s32(ret, b1 << 4, a0, 1); - ret = vdotq_laneq_s32(ret, b2 << 4, a0, 2); - ret = vdotq_laneq_s32(ret, b3 << 4, a0, 3); + ret = vdotq_laneq_s32(ret, b0 << 4, a0, 0); + ret = vdotq_laneq_s32(ret, b1 << 4, a0, 1); + ret = vdotq_laneq_s32(ret, b2 << 4, a0, 2); + ret = vdotq_laneq_s32(ret, b3 << 4, a0, 3); - ret = vdotq_laneq_s32(ret, b0 & 0xf0U, a1, 0); - ret = vdotq_laneq_s32(ret, b1 & 0xf0U, a1, 1); - ret = vdotq_laneq_s32(ret, b2 & 0xf0U, a1, 2); - ret = vdotq_laneq_s32(ret, b3 & 0xf0U, a1, 3); + ret = vdotq_laneq_s32(ret, b0 & 0xf0U, a1, 0); + ret = vdotq_laneq_s32(ret, b1 & 0xf0U, a1, 1); + ret = vdotq_laneq_s32(ret, b2 & 0xf0U, a1, 2); + ret = vdotq_laneq_s32(ret, b3 & 0xf0U, a1, 3); - 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; + acc = vfmaq_f32(acc, vcvtq_n_f32_s32(ret, 4), + vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd))); + a_ptr++; + b_ptr++; } - return; + vst1q_f32(s, acc); + s += ncols_interleaved; } + return; #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) float sumf[4]; int sumi; @@ -341,50 +339,48 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo UNUSED(blocklen); #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; - 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); + 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); + 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); + 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 << 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); + 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); + 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; + acc = vfmaq_f32(acc, vcvtq_n_f32_s32(ret, 4), + vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd))); + a_ptr++; + b_ptr++; } - return; + vst1q_f32(s, acc); + s += ncols_interleaved; } + return; #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) float sumf[4]; int sumi; @@ -432,7 +428,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) #if defined(__ARM_FEATURE_SVE) - if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) { + if (ggml_cpu_get_sve_cnt() == QK8_0) { const void * b_ptr = vx; const void * a_ptr = vy; float * res_ptr = s; @@ -547,54 +543,52 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const UNUSED(blocklen); #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 int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - float * res_ptr = s; + const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + float * res_ptr = s; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); - float32x4_t sumf = vdupq_n_f32(0); - for (int l = 0; l < nb; l++) { - uint8x16_t b_0 = vld1q_u8(b_ptr[l].qs + 0); - uint8x16_t b_1 = vld1q_u8(b_ptr[l].qs + 16); - uint8x16_t b_2 = vld1q_u8(b_ptr[l].qs + 32); - uint8x16_t b_3 = vld1q_u8(b_ptr[l].qs + 48); + float32x4_t sumf = vdupq_n_f32(0); + for (int l = 0; l < nb; l++) { + uint8x16_t b_0 = vld1q_u8(b_ptr[l].qs + 0); + uint8x16_t b_1 = vld1q_u8(b_ptr[l].qs + 16); + uint8x16_t b_2 = vld1q_u8(b_ptr[l].qs + 32); + uint8x16_t b_3 = vld1q_u8(b_ptr[l].qs + 48); - int8x16_t b_0_hi = vqtbl1q_s8(kvalues, b_0 >> 4); - int8x16_t b_0_lo = vqtbl1q_s8(kvalues, b_0 & 0x0F); - int8x16_t b_1_hi = vqtbl1q_s8(kvalues, b_1 >> 4); - int8x16_t b_1_lo = vqtbl1q_s8(kvalues, b_1 & 0x0F); - int8x16_t b_2_hi = vqtbl1q_s8(kvalues, b_2 >> 4); - int8x16_t b_2_lo = vqtbl1q_s8(kvalues, b_2 & 0x0F); - int8x16_t b_3_hi = vqtbl1q_s8(kvalues, b_3 >> 4); - int8x16_t b_3_lo = vqtbl1q_s8(kvalues, b_3 & 0x0F); + int8x16_t b_0_hi = vqtbl1q_s8(kvalues, b_0 >> 4); + int8x16_t b_0_lo = vqtbl1q_s8(kvalues, b_0 & 0x0F); + int8x16_t b_1_hi = vqtbl1q_s8(kvalues, b_1 >> 4); + int8x16_t b_1_lo = vqtbl1q_s8(kvalues, b_1 & 0x0F); + int8x16_t b_2_hi = vqtbl1q_s8(kvalues, b_2 >> 4); + int8x16_t b_2_lo = vqtbl1q_s8(kvalues, b_2 & 0x0F); + int8x16_t b_3_hi = vqtbl1q_s8(kvalues, b_3 >> 4); + int8x16_t b_3_lo = vqtbl1q_s8(kvalues, b_3 & 0x0F); - int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 0); - int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16); + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16); - int32x4_t sumi = vdupq_n_s32(0); - sumi = vdotq_laneq_s32(sumi, b_0_lo, a_0, 0); - sumi = vdotq_laneq_s32(sumi, b_0_hi, a_1, 0); - sumi = vdotq_laneq_s32(sumi, b_1_lo, a_0, 1); - sumi = vdotq_laneq_s32(sumi, b_1_hi, a_1, 1); - sumi = vdotq_laneq_s32(sumi, b_2_lo, a_0, 2); - sumi = vdotq_laneq_s32(sumi, b_2_hi, a_1, 2); - sumi = vdotq_laneq_s32(sumi, b_3_lo, a_0, 3); - sumi = vdotq_laneq_s32(sumi, b_3_hi, a_1, 3); + int32x4_t sumi = vdupq_n_s32(0); + sumi = vdotq_laneq_s32(sumi, b_0_lo, a_0, 0); + sumi = vdotq_laneq_s32(sumi, b_0_hi, a_1, 0); + sumi = vdotq_laneq_s32(sumi, b_1_lo, a_0, 1); + sumi = vdotq_laneq_s32(sumi, b_1_hi, a_1, 1); + sumi = vdotq_laneq_s32(sumi, b_2_lo, a_0, 2); + sumi = vdotq_laneq_s32(sumi, b_2_hi, a_1, 2); + sumi = vdotq_laneq_s32(sumi, b_3_lo, a_0, 3); + sumi = vdotq_laneq_s32(sumi, b_3_hi, a_1, 3); - float32x4_t a_d = vcvt_f32_f16(vld1_dup_f16((const float16_t *)&a_ptr[l].d)); - float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); - float32x4_t d = a_d * b_d; + float32x4_t a_d = vcvt_f32_f16(vld1_dup_f16((const float16_t *)&a_ptr[l].d)); + float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); + float32x4_t d = a_d * b_d; - sumf = vmlaq_f32(sumf, d, vcvtq_f32_s32(sumi)); - } - - vst1q_f32(res_ptr + x * 4, sumf); + sumf = vmlaq_f32(sumf, d, vcvtq_f32_s32(sumi)); } - return; + + vst1q_f32(res_ptr + x * 4, sumf); } + return; #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) { float sumf[4]; @@ -643,465 +637,463 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo UNUSED(ncols_interleaved); UNUSED(blocklen); -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v23.16b, #0x0\n" - "movi v16.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v0.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v21.16b, #0x0\n" - "movi v8.16b, #0x0\n" - "movi v1.16b, #0x0\n" - "3:" // Block loop - "ldr q3, [x28, #0x0]\n" - "ldr q31, [x25, #0x0]\n" - "movi v28.16b, #0x4\n" - "movi v10.4s, #0x0\n" - "ldr q22, [x28, #0x10]\n" - "ldr q6, [x25, #0x10]\n" - "movi v29.4s, #0x0\n" - "movi v9.4s, #0x0\n" - "ldr q27, [x28, #0x20]\n" - "ldr q30, [x28, #0x30]\n" - "movi v20.4s, #0x0\n" - "movi v24.16b, #0xf0\n" - "ldr d2, [x25, #-0x8]\n" - "ldr d26, [x23, #-0x8]\n" - "sshl v12.16b, v3.16b, v28.16b\n" - "sub x20, x28, #0x8\n" - "ldr d17, [x20, #0x0]\n" - "and v3.16b, v3.16b, v24.16b\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" - ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" - ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" - ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" - "sshl v31.16b, v22.16b, v28.16b\n" - "and v22.16b, v22.16b, v24.16b\n" - "fcvtl v17.4s, v17.4h\n" - "fcvtl v2.4s, v2.4h\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" - ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" - ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" - ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" - "sshl v6.16b, v27.16b, v28.16b\n" - "sshl v28.16b, v30.16b, v28.16b\n" - "and v27.16b, v27.16b, v24.16b\n" - "and v30.16b, v30.16b, v24.16b\n" - "ldr q24, [x25, #0x20]\n" - ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x30]\n" - ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" - ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" - ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" - ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x40]\n" - ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x50]\n" - ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" - ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" - ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" - ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x60]\n" - ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" - ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" - ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" - ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" - "fmul v24.4s, v17.4s, v2.s[0]\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v15.4s, v10.4s, v24.4s\n" - "ldr q24, [x23, #0x0]\n" - "fmul v10.4s, v17.4s, v2.s[1]\n" - "fmla v19.4s, v29.4s, v10.4s\n" - "ldr q10, [x23, #0x10]\n" - "fmul v29.4s, v17.4s, v2.s[2]\n" - "fmul v2.4s, v17.4s, v2.s[3]\n" - "fmla v18.4s, v9.4s, v29.4s\n" - "movi v9.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" - "fmla v14.4s, v20.4s, v2.4s\n" - "movi v20.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x20]\n" - ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" - ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" - ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" - ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x30]\n" - ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x40]\n" - ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" - ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" - ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" - ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x50]\n" - ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x60]\n" - ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" - ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" - ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" - ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x0]\n" - ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" - ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" - ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" - ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" - "fmul v10.4s, v17.4s, v26.s[0]\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v11.4s, v9.4s, v10.4s\n" - "ldr q9, [x22, #0x10]\n" - "fmul v10.4s, v17.4s, v26.s[1]\n" - "fmla v13.4s, v29.4s, v10.4s\n" - "ldr d29, [x22, #-0x8]\n" - "fmul v10.4s, v17.4s, v26.s[2]\n" - "fmul v26.4s, v17.4s, v26.s[3]\n" - "fcvtl v29.4s, v29.4h\n" - "fmla v23.4s, v20.4s, v10.4s\n" - "movi v20.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v16.4s, v2.4s, v26.4s\n" - "movi v26.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x20]\n" - ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x30]\n" - ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x40]\n" - ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x50]\n" - ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x60]\n" - ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x21, #0x0]\n" - ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" - ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" - ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" - "fmul v9.4s, v17.4s, v29.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v25.4s, v20.4s, v9.4s\n" - "ldr q9, [x21, #0x10]\n" - "fmul v20.4s, v17.4s, v29.s[1]\n" - "fmla v7.4s, v10.4s, v20.4s\n" - "ldr d20, [x21, #-0x8]\n" - "fmul v10.4s, v17.4s, v29.s[2]\n" - "fmul v29.4s, v17.4s, v29.s[3]\n" - "fcvtl v20.4s, v20.4h\n" - "fmla v0.4s, v26.4s, v10.4s\n" - "movi v26.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v4.4s, v2.4s, v29.4s\n" - "movi v2.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" - "ldr q12, [x21, #0x20]\n" - "fmul v24.4s, v17.4s, v20.s[0]\n" - ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x30]\n" - "fmul v31.4s, v17.4s, v20.s[1]\n" - ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" - ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" - ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" - ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x40]\n" - "fmul v6.4s, v17.4s, v20.s[2]\n" - "fmul v20.4s, v17.4s, v20.s[3]\n" - ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x50]\n" - ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" - ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" - ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" - ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x60]\n" - ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" - "ldr q17, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" - ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" - ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" - ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" - ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" - ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" - ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" - ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "fmla v5.4s, v26.4s, v24.4s\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v21.4s, v10.4s, v31.4s\n" - "fmla v8.4s, v2.4s, v6.4s\n" - "fmla v1.4s, v29.4s, v20.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q16, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q0, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q21, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q8, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q1, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q7, [x24, #0x0]\n" - "ldr q5, [x25, #0x0]\n" - "movi v9.16b, #0x4\n" - "movi v4.4s, #0x0\n" - "ldr q3, [x24, #0x10]\n" - "ldr q2, [x25, #0x10]\n" - "movi v1.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q13, [x24, #0x20]\n" - "ldr q31, [x25, #0x20]\n" - "movi v30.4s, #0x0\n" - "movi v29.16b, #0xf0\n" - "ldr q28, [x24, #0x30]\n" - "ldr q27, [x25, #0x30]\n" - "sshl v20.16b, v7.16b, v9.16b\n" - "sub x20, x24, #0x8\n" - "ldr q26, [x25, #0x40]\n" - "ldr q25, [x25, #0x50]\n" - "sshl v17.16b, v3.16b, v9.16b\n" - "and v7.16b, v7.16b, v29.16b\n" - "ldr q24, [x25, #0x60]\n" - "ldr q16, [x25, #0x70]\n" - "sshl v22.16b, v13.16b, v9.16b\n" - "and v3.16b, v3.16b, v29.16b\n" - "ldr d21, [x20, #0x0]\n" - "ldr d12, [x25, #-0x8]\n" - ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" - ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" - ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" - ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" - "sshl v9.16b, v28.16b, v9.16b\n" - "subs x21, x21, #0x1\n" - "and v13.16b, v13.16b, v29.16b\n" - "and v28.16b, v28.16b, v29.16b\n" - "add x25, x25, #0x88\n" - "add x24, x24, #0x48\n" - "fcvtl v21.4s, v21.4h\n" - "fcvtl v12.4s, v12.4h\n" - ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" - ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" - ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" - ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" - "fmul v11.4s, v21.4s, v12.s[0]\n" - "fmul v23.4s, v21.4s, v12.s[1]\n" - "fmul v17.4s, v21.4s, v12.s[2]\n" - ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" - "fmul v6.4s, v21.4s, v12.s[3]\n" - ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" - ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" - ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" - ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" - ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" - ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" - ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" - ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" - ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" - ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" - ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" - ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" - ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" - ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" - ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" - ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" - ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" - ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" - ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" - ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" - ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" - ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" - ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" - "scvtf v4.4s, v4.4s, #0x4\n" - "scvtf v1.4s, v1.4s, #0x4\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "fmla v15.4s, v4.4s, v11.4s\n" - "scvtf v30.4s, v30.4s, #0x4\n" - "fmla v19.4s, v1.4s, v23.4s\n" - "fmla v18.4s, v0.4s, v17.4s\n" - "fmla v14.4s, v30.4s, v6.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q14, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); - return; - } + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v23.16b, #0x0\n" + "movi v16.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v0.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v21.16b, #0x0\n" + "movi v8.16b, #0x0\n" + "movi v1.16b, #0x0\n" + "3:" // Block loop + "ldr q3, [x28, #0x0]\n" + "ldr q31, [x25, #0x0]\n" + "movi v28.16b, #0x4\n" + "movi v10.4s, #0x0\n" + "ldr q22, [x28, #0x10]\n" + "ldr q6, [x25, #0x10]\n" + "movi v29.4s, #0x0\n" + "movi v9.4s, #0x0\n" + "ldr q27, [x28, #0x20]\n" + "ldr q30, [x28, #0x30]\n" + "movi v20.4s, #0x0\n" + "movi v24.16b, #0xf0\n" + "ldr d2, [x25, #-0x8]\n" + "ldr d26, [x23, #-0x8]\n" + "sshl v12.16b, v3.16b, v28.16b\n" + "sub x20, x28, #0x8\n" + "ldr d17, [x20, #0x0]\n" + "and v3.16b, v3.16b, v24.16b\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" + ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" + ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" + ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" + "sshl v31.16b, v22.16b, v28.16b\n" + "and v22.16b, v22.16b, v24.16b\n" + "fcvtl v17.4s, v17.4h\n" + "fcvtl v2.4s, v2.4h\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" + ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" + ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" + ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" + "sshl v6.16b, v27.16b, v28.16b\n" + "sshl v28.16b, v30.16b, v28.16b\n" + "and v27.16b, v27.16b, v24.16b\n" + "and v30.16b, v30.16b, v24.16b\n" + "ldr q24, [x25, #0x20]\n" + ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x30]\n" + ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" + ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" + ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" + ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x40]\n" + ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x50]\n" + ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" + ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" + ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" + ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x60]\n" + ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" + ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" + ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" + ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" + "fmul v24.4s, v17.4s, v2.s[0]\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v15.4s, v10.4s, v24.4s\n" + "ldr q24, [x23, #0x0]\n" + "fmul v10.4s, v17.4s, v2.s[1]\n" + "fmla v19.4s, v29.4s, v10.4s\n" + "ldr q10, [x23, #0x10]\n" + "fmul v29.4s, v17.4s, v2.s[2]\n" + "fmul v2.4s, v17.4s, v2.s[3]\n" + "fmla v18.4s, v9.4s, v29.4s\n" + "movi v9.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" + "fmla v14.4s, v20.4s, v2.4s\n" + "movi v20.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x20]\n" + ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" + ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" + ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" + ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x30]\n" + ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x40]\n" + ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" + ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" + ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" + ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x50]\n" + ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x60]\n" + ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" + ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" + ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" + ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x0]\n" + ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" + ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" + ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" + ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" + "fmul v10.4s, v17.4s, v26.s[0]\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v11.4s, v9.4s, v10.4s\n" + "ldr q9, [x22, #0x10]\n" + "fmul v10.4s, v17.4s, v26.s[1]\n" + "fmla v13.4s, v29.4s, v10.4s\n" + "ldr d29, [x22, #-0x8]\n" + "fmul v10.4s, v17.4s, v26.s[2]\n" + "fmul v26.4s, v17.4s, v26.s[3]\n" + "fcvtl v29.4s, v29.4h\n" + "fmla v23.4s, v20.4s, v10.4s\n" + "movi v20.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v16.4s, v2.4s, v26.4s\n" + "movi v26.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x20]\n" + ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x30]\n" + ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x40]\n" + ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x50]\n" + ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x60]\n" + ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x21, #0x0]\n" + ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" + ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" + ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" + "fmul v9.4s, v17.4s, v29.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v25.4s, v20.4s, v9.4s\n" + "ldr q9, [x21, #0x10]\n" + "fmul v20.4s, v17.4s, v29.s[1]\n" + "fmla v7.4s, v10.4s, v20.4s\n" + "ldr d20, [x21, #-0x8]\n" + "fmul v10.4s, v17.4s, v29.s[2]\n" + "fmul v29.4s, v17.4s, v29.s[3]\n" + "fcvtl v20.4s, v20.4h\n" + "fmla v0.4s, v26.4s, v10.4s\n" + "movi v26.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v4.4s, v2.4s, v29.4s\n" + "movi v2.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" + "ldr q12, [x21, #0x20]\n" + "fmul v24.4s, v17.4s, v20.s[0]\n" + ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x30]\n" + "fmul v31.4s, v17.4s, v20.s[1]\n" + ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" + ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" + ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" + ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x40]\n" + "fmul v6.4s, v17.4s, v20.s[2]\n" + "fmul v20.4s, v17.4s, v20.s[3]\n" + ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x50]\n" + ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" + ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" + ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" + ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x60]\n" + ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" + "ldr q17, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" + ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" + ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" + ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" + ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" + ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" + ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" + ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "fmla v5.4s, v26.4s, v24.4s\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "fmla v21.4s, v10.4s, v31.4s\n" + "fmla v8.4s, v2.4s, v6.4s\n" + "fmla v1.4s, v29.4s, v20.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q16, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q0, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q21, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q8, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q1, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q7, [x24, #0x0]\n" + "ldr q5, [x25, #0x0]\n" + "movi v9.16b, #0x4\n" + "movi v4.4s, #0x0\n" + "ldr q3, [x24, #0x10]\n" + "ldr q2, [x25, #0x10]\n" + "movi v1.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q13, [x24, #0x20]\n" + "ldr q31, [x25, #0x20]\n" + "movi v30.4s, #0x0\n" + "movi v29.16b, #0xf0\n" + "ldr q28, [x24, #0x30]\n" + "ldr q27, [x25, #0x30]\n" + "sshl v20.16b, v7.16b, v9.16b\n" + "sub x20, x24, #0x8\n" + "ldr q26, [x25, #0x40]\n" + "ldr q25, [x25, #0x50]\n" + "sshl v17.16b, v3.16b, v9.16b\n" + "and v7.16b, v7.16b, v29.16b\n" + "ldr q24, [x25, #0x60]\n" + "ldr q16, [x25, #0x70]\n" + "sshl v22.16b, v13.16b, v9.16b\n" + "and v3.16b, v3.16b, v29.16b\n" + "ldr d21, [x20, #0x0]\n" + "ldr d12, [x25, #-0x8]\n" + ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" + ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" + ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" + ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" + "sshl v9.16b, v28.16b, v9.16b\n" + "subs x21, x21, #0x1\n" + "and v13.16b, v13.16b, v29.16b\n" + "and v28.16b, v28.16b, v29.16b\n" + "add x25, x25, #0x88\n" + "add x24, x24, #0x48\n" + "fcvtl v21.4s, v21.4h\n" + "fcvtl v12.4s, v12.4h\n" + ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" + ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" + ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" + ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" + "fmul v11.4s, v21.4s, v12.s[0]\n" + "fmul v23.4s, v21.4s, v12.s[1]\n" + "fmul v17.4s, v21.4s, v12.s[2]\n" + ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" + "fmul v6.4s, v21.4s, v12.s[3]\n" + ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" + ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" + ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" + ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" + ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" + ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" + ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" + ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" + ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" + ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" + ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" + ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" + ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" + ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" + ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" + ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" + ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" + ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" + ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" + ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" + ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" + ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" + ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" + "scvtf v4.4s, v4.4s, #0x4\n" + "scvtf v1.4s, v1.4s, #0x4\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "fmla v15.4s, v4.4s, v11.4s\n" + "scvtf v30.4s, v30.4s, #0x4\n" + "fmla v19.4s, v1.4s, v23.4s\n" + "fmla v18.4s, v0.4s, v17.4s\n" + "fmla v14.4s, v30.4s, v6.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q14, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) { float sumf[4][4]; @@ -1160,404 +1152,402 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo 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; - size_t res_stride = bs * sizeof(float); + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v22.16b, #0x0\n" - "movi v23.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v6.16b, #0x0\n" - "movi v30.16b, #0x0\n" - "movi v24.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "3:" // Block loop - "ldr q21, [x28, #0x0]\n" - "ldr q16, [x28, #0x10]\n" - "movi v1.16b, #0x4\n" - "movi v19.4s, #0x0\n" - "ldr q27, [x25, #0x0]\n" - "ldr q15, [x25, #0x10]\n" - "movi v26.4s, #0x0\n" - "movi v18.4s, #0x0\n" - "ldr q29, [x28, #0x20]\n" - "ldr q3, [x28, #0x30]\n" - "movi v17.4s, #0x0\n" - "movi v0.16b, #0xf0\n" - "ldr d20, [x25, #-0x8]\n" - "ldr d9, [x23, #-0x8]\n" - "sshl v8.16b, v21.16b, v1.16b\n" - "sshl v31.16b, v16.16b, v1.16b\n" - "and v21.16b, v21.16b, v0.16b\n" - "and v16.16b, v16.16b, v0.16b\n" - "sub x20, x28, #0x8\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" - ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" - "ldr q27, [x25, #0x20]\n" - ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" - ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" - "sshl v15.16b, v29.16b, v1.16b\n" - "sshl v1.16b, v3.16b, v1.16b\n" - "and v29.16b, v29.16b, v0.16b\n" - "and v3.16b, v3.16b, v0.16b\n" - "ldr q0, [x25, #0x30]\n" - "fcvtl v20.4s, v20.4h\n" - ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" - "fcvtl v9.4s, v9.4h\n" - ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" - "ldr q27, [x25, #0x40]\n" - ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" - ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" - "ldr q0, [x25, #0x50]\n" - ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" - ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" - "ldr q27, [x25, #0x60]\n" - ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" - ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" - "ldr q0, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" - ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" - "ldr d27, [x20, #0x0]\n" - ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" - ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" - "fcvtl v27.4s, v27.4h\n" - "uzp1 v0.2d, v19.2d, v26.2d\n" - "uzp2 v26.2d, v19.2d, v26.2d\n" - "fmul v19.4s, v27.4s, v20.s[0]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "fmla v2.4s, v0.4s, v19.4s\n" - "ldr q19, [x23, #0x0]\n" - "uzp1 v0.2d, v18.2d, v17.2d\n" - "uzp2 v18.2d, v18.2d, v17.2d\n" - "fmul v17.4s, v27.4s, v20.s[1]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v10.4s, v26.4s, v17.4s\n" - "ldr q17, [x23, #0x10]\n" - "fmul v26.4s, v27.4s, v20.s[2]\n" - "fmul v20.4s, v27.4s, v20.s[3]\n" - "fmla v12.4s, v0.4s, v26.4s\n" - "ldr d0, [x22, #-0x8]\n" - "ldr d26, [x21, #-0x8]\n" - "fcvtl v0.4s, v0.4h\n" - "fmla v28.4s, v18.4s, v20.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x23, #0x20]\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x23, #0x40]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q19, [x23, #0x60]\n" - ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" - ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" - "uzp1 v19.2d, v20.2d, v18.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp2 v20.2d, v20.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v9.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v11.4s, v19.4s, v18.4s\n" - "ldr q18, [x22, #0x0]\n" - "fmul v19.4s, v27.4s, v9.s[1]\n" - "fmla v13.4s, v20.4s, v19.4s\n" - "movi v19.4s, #0x0\n" - "movi v20.4s, #0x0\n" - ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" - ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" - "ldr q17, [x23, #0x30]\n" - ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" - "ldr q17, [x23, #0x50]\n" - ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" - "ldr q17, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v9.s[2]\n" - "fmul v9.4s, v27.4s, v9.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v22.4s, v17.4s, v19.4s\n" - "ldr q17, [x22, #0x10]\n" - "movi v19.4s, #0x0\n" - ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" - "fmla v23.4s, v20.4s, v9.4s\n" - "movi v20.4s, #0x0\n" - "movi v9.4s, #0x0\n" - ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" - "ldr q18, [x22, #0x20]\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" - ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" - "ldr q18, [x22, #0x40]\n" - ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" - ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" - "ldr q18, [x22, #0x60]\n" - ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" - ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" - "ldr q17, [x22, #0x30]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" - "ldr q17, [x22, #0x50]\n" - ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" - "ldr q17, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v0.s[0]\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v25.4s, v17.4s, v19.4s\n" - "ldr q19, [x21, #0x0]\n" - "fmul v17.4s, v27.4s, v0.s[1]\n" - "fmla v5.4s, v20.4s, v17.4s\n" - "ldr q17, [x21, #0x10]\n" - "uzp1 v20.2d, v9.2d, v18.2d\n" - "uzp2 v9.2d, v9.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v0.s[2]\n" - "fmul v0.4s, v27.4s, v0.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "fmla v7.4s, v20.4s, v18.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x21, #0x20]\n" - "fmla v4.4s, v9.4s, v0.4s\n" - "movi v9.4s, #0x0\n" - "movi v0.4s, #0x0\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - "fmul v8.4s, v27.4s, v26.s[0]\n" - ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" - "ldr q17, [x21, #0x30]\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - "fmul v31.4s, v27.4s, v26.s[1]\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x21, #0x40]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - "fmul v15.4s, v27.4s, v26.s[2]\n" - "fmul v27.4s, v27.4s, v26.s[3]\n" - ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" - "ldr q1, [x21, #0x50]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q26, [x21, #0x60]\n" - ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" - ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" - "ldr q21, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" - ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" - ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" - ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" - "uzp1 v29.2d, v20.2d, v18.2d\n" - "uzp2 v21.2d, v20.2d, v18.2d\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "uzp1 v18.2d, v9.2d, v0.2d\n" - "uzp2 v16.2d, v9.2d, v0.2d\n" - "scvtf v21.4s, v21.4s, #0x4\n" - "fmla v6.4s, v29.4s, v8.4s\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v30.4s, v21.4s, v31.4s\n" - "fmla v24.4s, v18.4s, v15.4s\n" - "fmla v14.4s, v16.4s, v27.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q28, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q22, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q6, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q30, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q24, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q6, [x24, #0x0]\n" - "ldr q5, [x24, #0x10]\n" - "movi v17.16b, #0x4\n" - "movi v8.4s, #0x0\n" - "ldr q4, [x25, #0x0]\n" - "ldr q13, [x25, #0x10]\n" - "movi v27.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q31, [x24, #0x20]\n" - "ldr q14, [x24, #0x30]\n" - "movi v29.4s, #0x0\n" - "movi v22.16b, #0xf0\n" - "ldr q11, [x25, #0x20]\n" - "ldr q23, [x25, #0x30]\n" - "sshl v21.16b, v6.16b, v17.16b\n" - "sshl v16.16b, v5.16b, v17.16b\n" - "ldr q20, [x25, #0x40]\n" - "ldr q26, [x25, #0x50]\n" - "and v6.16b, v6.16b, v22.16b\n" - "and v5.16b, v5.16b, v22.16b\n" - "ldr q25, [x25, #0x60]\n" - "ldr q3, [x25, #0x70]\n" - "sshl v19.16b, v31.16b, v17.16b\n" - "sshl v18.16b, v14.16b, v17.16b\n" - "ldr d17, [x25, #-0x8]\n" - ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" - ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" - "and v31.16b, v31.16b, v22.16b\n" - ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" - ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" - "and v14.16b, v14.16b, v22.16b\n" - "sub x20, x24, #0x8\n" - "ldr d16, [x20, #0x0]\n" - "subs x21, x21, #0x1\n" - "add x25, x25, #0x88\n" - "fcvtl v17.4s, v17.4h\n" - "add x24, x24, #0x48\n" - ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" - ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" - ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" - ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" - "fcvtl v16.4s, v16.4h\n" - ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" - ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" - "fmul v23.4s, v16.4s, v17.s[0]\n" - "fmul v21.4s, v16.4s, v17.s[1]\n" - "fmul v1.4s, v16.4s, v17.s[2]\n" - "fmul v20.4s, v16.4s, v17.s[3]\n" - ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" - ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" - ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" - ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" - ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" - ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" - "uzp1 v19.2d, v8.2d, v27.2d\n" - "uzp2 v18.2d, v8.2d, v27.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp1 v17.2d, v0.2d, v29.2d\n" - "uzp2 v16.2d, v0.2d, v29.2d\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v2.4s, v19.4s, v23.4s\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v10.4s, v18.4s, v21.4s\n" - "fmla v12.4s, v17.4s, v1.4s\n" - "fmla v28.4s, v16.4s, v20.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q28, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); - return; - } + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v22.16b, #0x0\n" + "movi v23.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v6.16b, #0x0\n" + "movi v30.16b, #0x0\n" + "movi v24.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "3:" // Block loop + "ldr q21, [x28, #0x0]\n" + "ldr q16, [x28, #0x10]\n" + "movi v1.16b, #0x4\n" + "movi v19.4s, #0x0\n" + "ldr q27, [x25, #0x0]\n" + "ldr q15, [x25, #0x10]\n" + "movi v26.4s, #0x0\n" + "movi v18.4s, #0x0\n" + "ldr q29, [x28, #0x20]\n" + "ldr q3, [x28, #0x30]\n" + "movi v17.4s, #0x0\n" + "movi v0.16b, #0xf0\n" + "ldr d20, [x25, #-0x8]\n" + "ldr d9, [x23, #-0x8]\n" + "sshl v8.16b, v21.16b, v1.16b\n" + "sshl v31.16b, v16.16b, v1.16b\n" + "and v21.16b, v21.16b, v0.16b\n" + "and v16.16b, v16.16b, v0.16b\n" + "sub x20, x28, #0x8\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" + ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" + "ldr q27, [x25, #0x20]\n" + ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" + ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" + "sshl v15.16b, v29.16b, v1.16b\n" + "sshl v1.16b, v3.16b, v1.16b\n" + "and v29.16b, v29.16b, v0.16b\n" + "and v3.16b, v3.16b, v0.16b\n" + "ldr q0, [x25, #0x30]\n" + "fcvtl v20.4s, v20.4h\n" + ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" + "fcvtl v9.4s, v9.4h\n" + ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" + "ldr q27, [x25, #0x40]\n" + ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" + ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" + "ldr q0, [x25, #0x50]\n" + ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" + ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" + "ldr q27, [x25, #0x60]\n" + ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" + ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" + "ldr q0, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" + ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" + "ldr d27, [x20, #0x0]\n" + ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" + ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" + "fcvtl v27.4s, v27.4h\n" + "uzp1 v0.2d, v19.2d, v26.2d\n" + "uzp2 v26.2d, v19.2d, v26.2d\n" + "fmul v19.4s, v27.4s, v20.s[0]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "fmla v2.4s, v0.4s, v19.4s\n" + "ldr q19, [x23, #0x0]\n" + "uzp1 v0.2d, v18.2d, v17.2d\n" + "uzp2 v18.2d, v18.2d, v17.2d\n" + "fmul v17.4s, v27.4s, v20.s[1]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v10.4s, v26.4s, v17.4s\n" + "ldr q17, [x23, #0x10]\n" + "fmul v26.4s, v27.4s, v20.s[2]\n" + "fmul v20.4s, v27.4s, v20.s[3]\n" + "fmla v12.4s, v0.4s, v26.4s\n" + "ldr d0, [x22, #-0x8]\n" + "ldr d26, [x21, #-0x8]\n" + "fcvtl v0.4s, v0.4h\n" + "fmla v28.4s, v18.4s, v20.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x23, #0x20]\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x23, #0x40]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q19, [x23, #0x60]\n" + ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" + ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" + "uzp1 v19.2d, v20.2d, v18.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp2 v20.2d, v20.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v9.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v11.4s, v19.4s, v18.4s\n" + "ldr q18, [x22, #0x0]\n" + "fmul v19.4s, v27.4s, v9.s[1]\n" + "fmla v13.4s, v20.4s, v19.4s\n" + "movi v19.4s, #0x0\n" + "movi v20.4s, #0x0\n" + ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" + ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" + "ldr q17, [x23, #0x30]\n" + ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" + "ldr q17, [x23, #0x50]\n" + ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" + "ldr q17, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v9.s[2]\n" + "fmul v9.4s, v27.4s, v9.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v22.4s, v17.4s, v19.4s\n" + "ldr q17, [x22, #0x10]\n" + "movi v19.4s, #0x0\n" + ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" + "fmla v23.4s, v20.4s, v9.4s\n" + "movi v20.4s, #0x0\n" + "movi v9.4s, #0x0\n" + ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" + "ldr q18, [x22, #0x20]\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" + ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" + "ldr q18, [x22, #0x40]\n" + ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" + ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" + "ldr q18, [x22, #0x60]\n" + ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" + ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" + "ldr q17, [x22, #0x30]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" + "ldr q17, [x22, #0x50]\n" + ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" + "ldr q17, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v0.s[0]\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v25.4s, v17.4s, v19.4s\n" + "ldr q19, [x21, #0x0]\n" + "fmul v17.4s, v27.4s, v0.s[1]\n" + "fmla v5.4s, v20.4s, v17.4s\n" + "ldr q17, [x21, #0x10]\n" + "uzp1 v20.2d, v9.2d, v18.2d\n" + "uzp2 v9.2d, v9.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v0.s[2]\n" + "fmul v0.4s, v27.4s, v0.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "fmla v7.4s, v20.4s, v18.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x21, #0x20]\n" + "fmla v4.4s, v9.4s, v0.4s\n" + "movi v9.4s, #0x0\n" + "movi v0.4s, #0x0\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + "fmul v8.4s, v27.4s, v26.s[0]\n" + ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" + "ldr q17, [x21, #0x30]\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + "fmul v31.4s, v27.4s, v26.s[1]\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x21, #0x40]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + "fmul v15.4s, v27.4s, v26.s[2]\n" + "fmul v27.4s, v27.4s, v26.s[3]\n" + ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" + "ldr q1, [x21, #0x50]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q26, [x21, #0x60]\n" + ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" + ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" + "ldr q21, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" + ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" + ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" + ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" + "uzp1 v29.2d, v20.2d, v18.2d\n" + "uzp2 v21.2d, v20.2d, v18.2d\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "uzp1 v18.2d, v9.2d, v0.2d\n" + "uzp2 v16.2d, v9.2d, v0.2d\n" + "scvtf v21.4s, v21.4s, #0x4\n" + "fmla v6.4s, v29.4s, v8.4s\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v30.4s, v21.4s, v31.4s\n" + "fmla v24.4s, v18.4s, v15.4s\n" + "fmla v14.4s, v16.4s, v27.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q28, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q22, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q6, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q30, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q24, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q6, [x24, #0x0]\n" + "ldr q5, [x24, #0x10]\n" + "movi v17.16b, #0x4\n" + "movi v8.4s, #0x0\n" + "ldr q4, [x25, #0x0]\n" + "ldr q13, [x25, #0x10]\n" + "movi v27.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q31, [x24, #0x20]\n" + "ldr q14, [x24, #0x30]\n" + "movi v29.4s, #0x0\n" + "movi v22.16b, #0xf0\n" + "ldr q11, [x25, #0x20]\n" + "ldr q23, [x25, #0x30]\n" + "sshl v21.16b, v6.16b, v17.16b\n" + "sshl v16.16b, v5.16b, v17.16b\n" + "ldr q20, [x25, #0x40]\n" + "ldr q26, [x25, #0x50]\n" + "and v6.16b, v6.16b, v22.16b\n" + "and v5.16b, v5.16b, v22.16b\n" + "ldr q25, [x25, #0x60]\n" + "ldr q3, [x25, #0x70]\n" + "sshl v19.16b, v31.16b, v17.16b\n" + "sshl v18.16b, v14.16b, v17.16b\n" + "ldr d17, [x25, #-0x8]\n" + ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" + ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" + "and v31.16b, v31.16b, v22.16b\n" + ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" + ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" + "and v14.16b, v14.16b, v22.16b\n" + "sub x20, x24, #0x8\n" + "ldr d16, [x20, #0x0]\n" + "subs x21, x21, #0x1\n" + "add x25, x25, #0x88\n" + "fcvtl v17.4s, v17.4h\n" + "add x24, x24, #0x48\n" + ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" + ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" + ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" + ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" + "fcvtl v16.4s, v16.4h\n" + ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" + ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" + "fmul v23.4s, v16.4s, v17.s[0]\n" + "fmul v21.4s, v16.4s, v17.s[1]\n" + "fmul v1.4s, v16.4s, v17.s[2]\n" + "fmul v20.4s, v16.4s, v17.s[3]\n" + ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" + ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" + ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" + ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" + ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" + ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" + "uzp1 v19.2d, v8.2d, v27.2d\n" + "uzp2 v18.2d, v8.2d, v27.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp1 v17.2d, v0.2d, v29.2d\n" + "uzp2 v16.2d, v0.2d, v29.2d\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v2.4s, v19.4s, v23.4s\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v10.4s, v18.4s, v21.4s\n" + "fmla v12.4s, v17.4s, v1.4s\n" + "fmla v28.4s, v16.4s, v20.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q28, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) float sumf[4][4]; int sumi; @@ -1615,7 +1605,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) { + if (ggml_cpu_get_sve_cnt() == QK8_0) { const void * b_ptr = vx; const void * a_ptr = vy; float * res_ptr = s; @@ -2083,59 +2073,57 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const UNUSED(blocklen); #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 int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); + const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); - float32x4_t sumf[4]; - for (int m = 0; m < 4; m++) { - sumf[m] = vdupq_n_f32(0); + float32x4_t sumf[4]; + for (int m = 0; m < 4; m++) { + sumf[m] = vdupq_n_f32(0); + } + + for (int l = 0; l < nb; l++) { + float32x4_t a_d = vcvt_f32_f16(vld1_f16((const float16_t *)a_ptr[l].d)); + float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); + + int32x4_t sumi_0 = vdupq_n_s32(0); + int32x4_t sumi_1 = vdupq_n_s32(0); + int32x4_t sumi_2 = vdupq_n_s32(0); + int32x4_t sumi_3 = vdupq_n_s32(0); + + for (int k = 0; k < 4; k++) { + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 16 * k + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16 * k + 64); + + uint8x16_t b = vld1q_u8(b_ptr[l].qs + 16 * k); + int8x16_t b_hi = vqtbl1q_s8(kvalues, b >> 4); + int8x16_t b_lo = vqtbl1q_s8(kvalues, b & 0xF); + + sumi_0 = vdotq_laneq_s32(sumi_0, b_lo, a_0, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_lo, a_0, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_lo, a_0, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_lo, a_0, 3); + sumi_0 = vdotq_laneq_s32(sumi_0, b_hi, a_1, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_hi, a_1, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_hi, a_1, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_hi, a_1, 3); } - for (int l = 0; l < nb; l++) { - float32x4_t a_d = vcvt_f32_f16(vld1_f16((const float16_t *)a_ptr[l].d)); - float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); + sumf[0] = vmlaq_f32(sumf[0], vmulq_laneq_f32(b_d, a_d, 0), vcvtq_f32_s32(sumi_0)); + sumf[1] = vmlaq_f32(sumf[1], vmulq_laneq_f32(b_d, a_d, 1), vcvtq_f32_s32(sumi_1)); + sumf[2] = vmlaq_f32(sumf[2], vmulq_laneq_f32(b_d, a_d, 2), vcvtq_f32_s32(sumi_2)); + sumf[3] = vmlaq_f32(sumf[3], vmulq_laneq_f32(b_d, a_d, 3), vcvtq_f32_s32(sumi_3)); + } - int32x4_t sumi_0 = vdupq_n_s32(0); - int32x4_t sumi_1 = vdupq_n_s32(0); - int32x4_t sumi_2 = vdupq_n_s32(0); - int32x4_t sumi_3 = vdupq_n_s32(0); - - for (int k = 0; k < 4; k++) { - int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 16 * k + 0); - int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16 * k + 64); - - uint8x16_t b = vld1q_u8(b_ptr[l].qs + 16 * k); - int8x16_t b_hi = vqtbl1q_s8(kvalues, b >> 4); - int8x16_t b_lo = vqtbl1q_s8(kvalues, b & 0xF); - - sumi_0 = vdotq_laneq_s32(sumi_0, b_lo, a_0, 0); - sumi_1 = vdotq_laneq_s32(sumi_1, b_lo, a_0, 1); - sumi_2 = vdotq_laneq_s32(sumi_2, b_lo, a_0, 2); - sumi_3 = vdotq_laneq_s32(sumi_3, b_lo, a_0, 3); - sumi_0 = vdotq_laneq_s32(sumi_0, b_hi, a_1, 0); - sumi_1 = vdotq_laneq_s32(sumi_1, b_hi, a_1, 1); - sumi_2 = vdotq_laneq_s32(sumi_2, b_hi, a_1, 2); - sumi_3 = vdotq_laneq_s32(sumi_3, b_hi, a_1, 3); - } - - sumf[0] = vmlaq_f32(sumf[0], vmulq_laneq_f32(b_d, a_d, 0), vcvtq_f32_s32(sumi_0)); - sumf[1] = vmlaq_f32(sumf[1], vmulq_laneq_f32(b_d, a_d, 1), vcvtq_f32_s32(sumi_1)); - sumf[2] = vmlaq_f32(sumf[2], vmulq_laneq_f32(b_d, a_d, 2), vcvtq_f32_s32(sumi_2)); - sumf[3] = vmlaq_f32(sumf[3], vmulq_laneq_f32(b_d, a_d, 3), vcvtq_f32_s32(sumi_3)); - } - - for (int m = 0; m < 4; m++) { - vst1q_f32(s + (y * 4 + m) * bs + x * 4, sumf[m]); - } + for (int m = 0; m < 4; m++) { + vst1q_f32(s + (y * 4 + m) * bs + x * 4, sumf[m]); } } - return; } + return; #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) { float sumf[4][4]; diff --git a/ggml/src/ggml-cpu/arch/powerpc/cpu-feats.cpp b/ggml/src/ggml-cpu/arch/powerpc/cpu-feats.cpp new file mode 100644 index 000000000..fedd64302 --- /dev/null +++ b/ggml/src/ggml-cpu/arch/powerpc/cpu-feats.cpp @@ -0,0 +1,82 @@ +# include "ggml-backend-impl.h" + +#if defined(__powerpc64__) || defined(__ppc64__) || defined(__PPC64__) + +#if defined(__linux__) +#include +#endif + +#include + +struct powerpc_features { + std::string platform = ""; + int power_version = -1; + + bool has_vsx = false; + + powerpc_features() { +#if defined(__linux__) + unsigned long auxval = getauxval(AT_PLATFORM); + if (auxval) { + platform = std::string(reinterpret_cast(auxval)); + // TBD: Do systems exist that return this in uppercase? + if (platform.substr(0, 5) == "power") { + // Extractt a numeric suffix, if one exists + int vpos = -1; + for (int i = platform.length() - 1; i >= 0; i--) { + if (std::isdigit(platform[i])) { + vpos = i; + } else { + break; + } + } + if (vpos > -1) { + power_version = std::stoi(platform.substr(vpos)); + } + } + } +#endif + if (power_version >= 9) { + has_vsx = true; + } + } +}; + +static int ggml_backend_cpu_powerpc_score() { + int score = 1; + powerpc_features pf; + +// Platform scores +#if defined(GGML_USE_POWER7) + if (pf.power_version < 7) { return 0; } + score += 1<<1; +#endif +#if defined(GGML_USE_POWER8) + if (pf.power_version < 8) { return 0; } + score += 1<<2; +#endif +#if defined(GGML_USE_POWER9) + if (pf.power_version < 9) { return 0; } + score += 1<<3; +#endif +#if defined(GGML_USE_POWER10) + if (pf.power_version < 10) { return 0; } + score += 1<<4; +#endif +#if defined(GGML_USE_POWER11) + if (pf.power_version < 11) { return 0; } + score += 1<<5; +#endif + +// Feature scores +#if defined(GGML_USE_VSX) + if (!pf.has_vsx) { return 0; } + score += 1<<6; +#endif + + return score; +} + +GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_powerpc_score) + +#endif // defined(__powerpc64__) || defined(__ppc64__) || defined(__PPC64__) diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 2c12e493b..1d3cd009a 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -74,13 +74,8 @@ #if defined(__ARM_ARCH) struct ggml_arm_arch_features_type { - int has_neon; - int has_dotprod; - int has_i8mm; - int has_sve; int sve_cnt; - int has_sme; -} ggml_arm_arch_features = {-1, -1, -1, -1, 0, -1}; +} ggml_arm_arch_features = { 0 }; #endif @@ -678,87 +673,15 @@ bool ggml_is_numa(void) { #if defined(__linux__) && defined(__aarch64__) #include -#elif defined(__APPLE__) -#include -#endif - -#if !defined(HWCAP2_I8MM) -#define HWCAP2_I8MM (1 << 13) -#endif - -#if !defined(HWCAP2_SME) -#define HWCAP2_SME (1 << 23) #endif static void ggml_init_arm_arch_features(void) { -#if defined(__linux__) && defined(__aarch64__) - uint32_t hwcap = getauxval(AT_HWCAP); - uint32_t hwcap2 = getauxval(AT_HWCAP2); - - ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); - ggml_arm_arch_features.has_dotprod = !!(hwcap & HWCAP_ASIMDDP); - ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); - ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); - ggml_arm_arch_features.has_sme = !!(hwcap2 & HWCAP2_SME); - -#if defined(__ARM_FEATURE_SVE) +#if defined(__linux__) && defined(__aarch64__) && defined(__ARM_FEATURE_SVE) ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); #endif -#elif defined(__APPLE__) - int oldp = 0; - size_t size = sizeof(oldp); - if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_neon = oldp; - - if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_dotprod = oldp; - - if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_i8mm = oldp; - - if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_sme = oldp; - - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#else -// Run-time CPU feature detection not implemented for this platform, fallback to compile time -#if defined(__ARM_NEON) - ggml_arm_arch_features.has_neon = 1; -#else - ggml_arm_arch_features.has_neon = 0; -#endif - -#if defined(__ARM_FEATURE_MATMUL_INT8) - ggml_arm_arch_features.has_i8mm = 1; -#else - ggml_arm_arch_features.has_i8mm = 0; -#endif - -#if defined(__ARM_FEATURE_SVE) - ggml_arm_arch_features.has_sve = 1; - ggml_arm_arch_features.sve_cnt = 16; -#else - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#endif - -#if defined(__ARM_FEATURE_SME) || defined(__ARM_FEATURE_SME2) - ggml_arm_arch_features.has_sme = 1; -#else - ggml_arm_arch_features.has_sme = 0; -#endif -#endif } -#endif + +#endif // __ARM_ARCH struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { GGML_ASSERT(!ggml_get_no_alloc(ctx)); @@ -1967,6 +1890,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_pad_reflect_1d(params, tensor); } break; + case GGML_OP_ROLL: + { + ggml_compute_forward_roll(params, tensor); + } break; case GGML_OP_ARANGE: { ggml_compute_forward_arange(params, tensor); @@ -2291,6 +2218,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_PAD_REFLECT_1D: + case GGML_OP_ROLL: case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: @@ -3443,7 +3371,7 @@ int ggml_cpu_has_vxe(void) { int ggml_cpu_has_neon(void) { #if defined(__ARM_ARCH) && defined(__ARM_NEON) - return ggml_arm_arch_features.has_neon; + return 1; #else return 0; #endif @@ -3451,7 +3379,7 @@ int ggml_cpu_has_neon(void) { int ggml_cpu_has_dotprod(void) { #if defined(__ARM_ARCH) && defined(__ARM_FEATURE_DOTPROD) - return ggml_arm_arch_features.has_dotprod; + return 1; #else return 0; #endif @@ -3459,7 +3387,7 @@ int ggml_cpu_has_dotprod(void) { int ggml_cpu_has_sve(void) { #if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE) - return ggml_arm_arch_features.has_sve; + return 1; #else return 0; #endif @@ -3467,7 +3395,7 @@ int ggml_cpu_has_sve(void) { int ggml_cpu_has_matmul_int8(void) { #if defined(__ARM_ARCH) && defined(__ARM_FEATURE_MATMUL_INT8) - return ggml_arm_arch_features.has_i8mm; + return 1; #else return 0; #endif @@ -3483,7 +3411,7 @@ int ggml_cpu_get_sve_cnt(void) { int ggml_cpu_has_sme(void) { #if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SME) - return ggml_arm_arch_features.has_sme; + return 1; #else return 0; #endif diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index 1c545f803..7ed3874af 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -62,7 +62,7 @@ #define NOINLINE __attribute__((__noinline__)) #endif -#if defined(__ARM_NEON) || defined(__AVX512F__) +#if defined(__ARM_NEON) || defined(__AVX512F__) || defined(__VXE__) || defined(__VXE2__) #define VECTOR_REGISTERS 32 #else #define VECTOR_REGISTERS 16 @@ -109,6 +109,12 @@ inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); } inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); } #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +#if defined(__VXE__) || defined(__VXE2__) +inline float32x4_t add(float32x4_t x, float32x4_t y) { return vec_add(x, y); } +inline float32x4_t sub(float32x4_t x, float32x4_t y) { return vec_sub(x, y); } +inline float32x4_t mul(float32x4_t x, float32x4_t y) { return vec_mul(x, y); } +#endif + #if defined(__MMA__) typedef vector unsigned char vec_t; typedef __vector_quad acc_t; @@ -162,6 +168,13 @@ inline float16x8_t madd(float16x8_t a, float16x8_t b, float16x8_t c) { #endif #endif +#if defined(__VXE__) || defined(__VXE2__) +template <> +inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) { + return vec_madd(a, b, c); +} +#endif + //////////////////////////////////////////////////////////////////////////////////////////////////// // VECTORIZED HORIZONTAL SUM @@ -178,6 +191,13 @@ inline float hsum(float16x8_t x) { } #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +#if defined(__VXE__) || defined(__VXE2__) +inline float hsum(float32x4_t x) { + float32x4_t tmp = x + vec_reve(x); + return tmp[0] + tmp[1]; +} +#endif + #if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) inline float hsum(__m128 x) { #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) @@ -227,6 +247,21 @@ template <> inline float32x4_t load(const ggml_fp16_t *p) { #endif // _MSC_VER #endif // __ARM_NEON +#if defined(__VXE__) || defined(__VXE2__) +template <> inline float32x4_t load(const ggml_fp16_t * p) { + float tmp[4]; + + for (int i = 0; i < 4; i++) { + tmp[i] = GGML_FP16_TO_FP32(p[i]); + } + + return vec_xl(0, (const float *)(tmp)); +} +template <> inline float32x4_t load(const float * p) { + return vec_xl(0, p); +} +#endif + #if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) template <> inline __m128 load(const float *p) { return _mm_loadu_ps(p); @@ -3319,6 +3354,14 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 (const float *)B, ldb, (float *)C, ldc}; return tb.matmul(m, n); +#elif defined(__VXE__) || defined(__VXE2__) + if (n < 4) + return false; + tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ params, + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc}; + return tb.matmul(m, n); #elif defined(__MMA__) if (k % 8) return false; @@ -3410,6 +3453,16 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 (float *)C, ldc}; return tb.matmul(m, n); } +#elif defined(__VXE__) || defined(__VXE2__) + if (n < 4) + return false; + if (Btype == GGML_TYPE_F16) { + tinyBLAS<4, float32x4_t, float32x4_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); + } #endif return false; } diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.h b/ggml/src/ggml-cpu/llamafile/sgemm.h index 3d2909515..729e8853d 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.h +++ b/ggml/src/ggml-cpu/llamafile/sgemm.h @@ -1,6 +1,11 @@ #pragma once #include #include + +#if defined(__VXE__) || defined(__VXE2__) +#include +#endif + #ifdef __cplusplus extern "C" { #endif diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 711d8abcc..43e804a02 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -6793,6 +6793,73 @@ void ggml_compute_forward_pad_reflect_1d( } } +// ggml_compute_forward_roll + +static int64_t ggml_wrap_index(int64_t i, int64_t ne) { + if (i < 0) { + return i + ne; + } else if (i >= ne) { + return i - ne; + } + return i; +} + +static void ggml_compute_forward_roll_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const float * src_data = (const float *) src0->data; + float * dst_data = (float *) dst->data; + + GGML_TENSOR_UNARY_OP_LOCALS + + const int s0 = ggml_get_op_params_i32(dst, 0); + const int s1 = ggml_get_op_params_i32(dst, 1); + const int s2 = ggml_get_op_params_i32(dst, 2); + const int s3 = ggml_get_op_params_i32(dst, 3); + + const int64_t total = ne1 * ne2 * ne3; + const int64_t per_thread = (total + params->nth) / params->nth; + const int64_t start = params->ith * per_thread; + const int64_t end = std::min(start + per_thread, total); + + for (int64_t i = start; i < end; ++i) { + const int64_t i1 = i % ne1; + const int64_t i2 = (i / ne1) % ne2; + const int64_t i3 = i / (ne2 * ne1); + float * dst_row = dst_data + (i3*nb3 + i2*nb2 + i1*nb1) / sizeof(float); + + const int64_t i01 = ggml_wrap_index(i1 - s1, ne01); + const int64_t i02 = ggml_wrap_index(i2 - s2, ne02); + const int64_t i03 = ggml_wrap_index(i3 - s3, ne03); + const float * src_row = src_data + (i03*nb03 + i02*nb02 + i01*nb01) / sizeof(float); + + const int64_t s = ggml_wrap_index(-s0, ne00); + const int64_t n = ne00 - s; + ggml_vec_cpy_f32(n, dst_row, src_row + s); + ggml_vec_cpy_f32(s, dst_row + n, src_row); + } +} + +void ggml_compute_forward_roll( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_roll_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + // ggml_compute_forward_arange static void ggml_compute_forward_arange_f32( diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h index dc081b9e6..2d8544d7d 100644 --- a/ggml/src/ggml-cpu/ops.h +++ b/ggml/src/ggml-cpu/ops.h @@ -72,6 +72,7 @@ void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params void ggml_compute_forward_upscale(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_pad(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_pad_reflect_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_roll(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_arange(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst); diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp index 5c6715d5c..692c53e01 100644 --- a/ggml/src/ggml-cpu/repack.cpp +++ b/ggml/src/ggml-cpu/repack.cpp @@ -1163,13 +1163,24 @@ template op) { case GGML_OP_MUL_MAT: - size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1])); - return true; + { + size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1])); + return true; + } case GGML_OP_MUL_MAT_ID: - size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1])); - size = GGML_PAD(size, sizeof(int64_t)); // + padding for next bloc. - size += sizeof(int64_t) * (1+op->src[0]->ne[2]) * op->src[1]->ne[2]; - return true; + { + size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1])); + size = GGML_PAD(size, sizeof(int64_t)); // + padding for next bloc. + + const int64_t ne02 = op->src[0]->ne[2]; // n_as, n_expert + const int64_t ne12 = op->src[1]->ne[2]; // n_tokens + + const size_t sizeof_mmid_row_mapping = sizeof(int64_t); + + size += sizeof_mmid_row_mapping*ne02*(ne12 + 1); + + return true; + } default: // GGML_ABORT("fatal error"); break; @@ -1305,14 +1316,17 @@ template wsize >= (GGML_PAD(nbw3, sizeof(int64_t)) + n_as * sizeof(int64_t) + - n_as * ne12 * sizeof(mmid_row_mapping))); + GGML_ASSERT(params->wsize >= + (GGML_PAD(nbw3, sizeof(int64_t)) + + n_as*(ne12 + 1)*sizeof(mmid_row_mapping)) + ); - auto * wdata = (char *) params->wdata; - auto * wdata_src1_end = (char *) wdata + GGML_PAD(nbw3, sizeof(int64_t)); - auto * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] + auto * wdata = (char *)params->wdata; + auto * wdata_src1_end = (char *)wdata + GGML_PAD(nbw3, sizeof(int64_t)); - struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) (matrix_row_counts + n_as); // [n_as][ne12] + // total of [n_as][ne12 + 1] elemets of type mmid_row_mapping (2*int32_t = int64_t) + auto * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] + struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) (matrix_row_counts + n_as); // [n_as][ne12] // src1: float32 => param type for (int64_t i12 = 0; i12 < ne12; ++i12) { @@ -1397,44 +1411,45 @@ template q4_0_4x4_q8_0; -static const tensor_traits q4_0_4x8_q8_0; -static const tensor_traits q4_0_8x8_q8_0; -static const tensor_traits q4_K_8x8_q8_K; - -// instance for IQ4 -static const tensor_traits iq4_nl_4x4_q8_0; - } // namespace ggml::cpu::repack static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(const struct ggml_tensor * cur) { + + // instance for Q4 + static const ggml::cpu::repack::tensor_traits q4_0_4x4_q8_0; + static const ggml::cpu::repack::tensor_traits q4_0_4x8_q8_0; + static const ggml::cpu::repack::tensor_traits q4_0_8x8_q8_0; + static const ggml::cpu::repack::tensor_traits q4_K_8x8_q8_K; + + // instance for IQ4 + static const ggml::cpu::repack::tensor_traits iq4_nl_4x4_q8_0; + if (cur->type == GGML_TYPE_Q4_0) { if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) { if (cur->ne[1] % 8 == 0) { - return &ggml::cpu::repack::q4_0_8x8_q8_0; + return &q4_0_8x8_q8_0; } } if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { if (cur->ne[1] % 4 == 0) { - return &ggml::cpu::repack::q4_0_4x8_q8_0; + return &q4_0_4x8_q8_0; } } if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { if (cur->ne[1] % 4 == 0) { - return &ggml::cpu::repack::q4_0_4x4_q8_0; + return &q4_0_4x4_q8_0; } } } else if (cur->type == GGML_TYPE_Q4_K) { if (ggml_cpu_has_avx2()) { if (cur->ne[1] % 8 == 0) { - return &ggml::cpu::repack::q4_K_8x8_q8_K; + return &q4_K_8x8_q8_K; } } } else if (cur->type == GGML_TYPE_IQ4_NL) { if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { if (cur->ne[1] % 4 == 0) { - return &ggml::cpu::repack::iq4_nl_4x4_q8_0; + return &iq4_nl_4x4_q8_0; } } } diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index c14a12f54..1369bc2d9 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -19,10 +19,10 @@ #endif #include "ggml-common.h" -#include #include #include #include +#include #include #include @@ -241,8 +241,18 @@ static bool fp16_mma_available(const int cc) { #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN) return false; #else - return (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) || - GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc); + if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) || + GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc)) { + return true; + } else if (GGML_CUDA_CC_IS_RDNA4(cc)) { +#if defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12) + return true; +#else + return false; +#endif // defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12) + } else { + return false; + } #endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN) } @@ -252,6 +262,10 @@ static bool fp16_mma_hardware_available(const int cc) { GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc); } +static bool bf16_mma_hardware_available(const int cc) { + return GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_AMPERE; +} + // Volta technically had FP16 tensor cores but they work very differently compared to Turing and later. static bool new_mma_available(const int cc) { return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING; @@ -362,6 +376,26 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { #endif // FP16_AVAILABLE } +// Row reduction kernel template - compute sum (norm=false) or mean (norm=true) +template +static __global__ void reduce_rows_f32(const float * x, float * dst, const int ncols) { + const int row = blockIdx.x; + const int col = threadIdx.x; + + float sum = 0.0f; + for (int i = col; i < ncols; i += blockDim.x) { + sum += x[row * ncols + i]; + } + + sum = warp_reduce_sum(sum); + + if (col != 0) { + return; + } + + dst[row] = norm ? sum / ncols : sum; +} + template static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll @@ -767,21 +801,7 @@ struct ggml_backend_cuda_context { name(GGML_CUDA_NAME + std::to_string(device)) { } - ~ggml_backend_cuda_context() { - if (copy_event != nullptr) { - CUDA_CHECK(cudaEventDestroy(copy_event)); - } - for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) { - for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) { - if (streams[i][j] != nullptr) { - CUDA_CHECK(cudaStreamDestroy(streams[i][j])); - } - } - if (cublas_handles[i] != nullptr) { - CUBLAS_CHECK(cublasDestroy(cublas_handles[i])); - } - } - } + ~ggml_backend_cuda_context(); cudaStream_t stream(int device, int stream) { if (streams[device][stream] == nullptr) { diff --git a/ggml/src/ggml-cuda/conv2d-dw.cu b/ggml/src/ggml-cuda/conv2d-dw.cu new file mode 100644 index 000000000..7583233b1 --- /dev/null +++ b/ggml/src/ggml-cuda/conv2d-dw.cu @@ -0,0 +1,161 @@ +#include "conv2d-dw.cuh" + +struct conv_params { + int in_w, in_h; + int out_w, out_h; + int kernel_w, kernel_h; + int stride_x, stride_y; + int padding_x, padding_y; + int dilation_x, dilation_y; + int channels, batches; +}; + +struct kernel_bounds { + int y_min, y_max; + int x_min, x_max; +}; + +__device__ __forceinline__ kernel_bounds calculate_kernel_bounds(int out_x, int out_y, const conv_params & params) { + kernel_bounds bounds; + bounds.y_min = max(0, (params.padding_y - out_y * params.stride_y + params.dilation_y - 1) / params.dilation_y); + bounds.y_max = + min(params.kernel_h, + (params.in_h + params.padding_y - out_y * params.stride_y + params.dilation_y - 1) / params.dilation_y); + bounds.x_min = max(0, (params.padding_x - out_x * params.stride_x + params.dilation_x - 1) / params.dilation_x); + bounds.x_max = + min(params.kernel_w, + (params.in_w + params.padding_x - out_x * params.stride_x + params.dilation_x - 1) / params.dilation_x); + return bounds; +} + +__device__ __forceinline__ int calculate_input_coord(int out_coord, int kern_coord, int stride, int dilation, int padding) { + return out_coord * stride + kern_coord * dilation - padding; +} + +struct whcn_layout { + __device__ static int input_index(int n, int c, int y, int x, const conv_params & params) { + return n * (params.channels * params.in_w * params.in_h) + c * params.in_w * params.in_h + y * params.in_w + x; + } + + __device__ static int kernel_index(int c, int ky, int kx, const conv_params & params) { + return c * params.kernel_h * params.kernel_w + ky * params.kernel_w + kx; + } + + __device__ static int output_index(int n, int c, int y, int x, const conv_params & params) { + return n * (params.channels * params.out_w * params.out_h) + c * params.out_w * params.out_h + + y * params.out_w + x; + } + + __device__ static void unpack_indices(int global_idx, const conv_params & params, int & n, int & c, int & out_y, + int & out_x) { + out_x = global_idx % params.out_w; + out_y = (global_idx / params.out_w) % params.out_h; + c = (global_idx / (params.out_w * params.out_h)) % params.channels; + n = global_idx / (params.out_w * params.out_h * params.channels); + } +}; + +struct cwhn_layout { + __device__ static int input_index(int n, int c, int y, int x, const conv_params & params) { + return n * (params.channels * params.in_w * params.in_h) + (y * params.in_w + x) * params.channels + c; + } + + __device__ static int kernel_index(int c, int ky, int kx, const conv_params & params) { + return (ky * params.kernel_w + kx) * params.channels + c; + } + + __device__ static int output_index(int n, int c, int y, int x, const conv_params & params) { + return n * (params.channels * params.out_w * params.out_h) + y * (params.out_w * params.channels) + + x * params.channels + c; + } + + __device__ static void unpack_indices(int global_idx, const conv_params & params, int & n, int & c, int & out_y, + int & out_x) { + c = global_idx % params.channels; + out_x = (global_idx / params.channels) % params.out_w; + out_y = (global_idx / (params.channels * params.out_w)) % params.out_h; + n = global_idx / (params.channels * params.out_w * params.out_h); + } +}; + +template +__global__ void conv2d_dw_kernel(const T * __restrict__ input, const T * __restrict__ kernel, T * __restrict__ output, + const int in_w, const int in_h, const int out_w, const int out_h, + const int kernel_w, const int kernel_h, const int stride_x, const int stride_y, + const int padding_x, const int padding_y, const int dilation_x, const int dilation_y, + const int channels, const int batches) { + const int global_idx = blockIdx.x * blockDim.x + threadIdx.x; + const int total_elements = batches * channels * out_h * out_w; + + if (global_idx >= total_elements) { + return; + } + + conv_params params = { in_w, in_h, out_w, out_h, kernel_w, kernel_h, stride_x, + stride_y, padding_x, padding_y, dilation_x, dilation_y, channels, batches }; + + int batch_idx, channel_idx, out_y_idx, out_x_idx; + Layout::unpack_indices(global_idx, params, batch_idx, channel_idx, out_y_idx, out_x_idx); + + T accumulator = 0; + kernel_bounds bounds = calculate_kernel_bounds(out_x_idx, out_y_idx, params); + + for (int kern_y = bounds.y_min; kern_y < bounds.y_max; ++kern_y) { + int in_y_idx = calculate_input_coord(out_y_idx, kern_y, params.stride_y, params.dilation_y, params.padding_y); + + for (int kern_x = bounds.x_min; kern_x < bounds.x_max; ++kern_x) { + int in_x_idx = calculate_input_coord(out_x_idx, kern_x, params.stride_x, params.dilation_x, params.padding_x); + + const T input_val = input[Layout::input_index(batch_idx, channel_idx, in_y_idx, in_x_idx, params)]; + const T kernel_val = kernel[Layout::kernel_index(channel_idx, kern_y, kern_x, params)]; + + accumulator += input_val * kernel_val; + } + } + + output[Layout::output_index(batch_idx, channel_idx, out_y_idx, out_x_idx, params)] = accumulator; +} + +void ggml_cuda_op_conv2d_dw(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * kernel = dst->src[0]; + const ggml_tensor * input = dst->src[1]; + + GGML_ASSERT(kernel->type == GGML_TYPE_F32 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); + const float * w_d = (const float *) kernel->data; + const float * x_d = (const float *) input->data; + float * y_d = (float *) dst->data; + + const int32_t * p = (const int32_t *) dst->op_params; + const int stride_x = p[0]; + const int stride_y = p[1]; + const int padding_x = p[2]; + const int padding_y = p[3]; + const int dilation_x = p[4]; + const int dilation_y = p[5]; + + const int in_w = input->ne[0]; + const int in_h = input->ne[1]; + const int kernel_w = kernel->ne[0]; + const int kernel_h = kernel->ne[1]; + const int out_w = dst->ne[0]; + const int out_h = dst->ne[1]; + const int channels = dst->ne[2]; + const int batches = dst->ne[3]; + + cudaStream_t st = ctx.stream(); + + const int total = batches * channels * out_h * out_w; + const int blocks = (total + CUDA_CONV2D_DW_BLOCK_SIZE - 1) / CUDA_CONV2D_DW_BLOCK_SIZE; + + if (ggml_is_contiguous(input)) { + conv2d_dw_kernel<<>>( + x_d, w_d, y_d, in_w, in_h, out_w, out_h, kernel_w, kernel_h, stride_x, stride_y, padding_x, padding_y, + dilation_x, dilation_y, channels, batches); + } else if (ggml_is_contiguous_channels(input)) { + conv2d_dw_kernel<<>>( + x_d, w_d, y_d, in_w, in_h, out_w, out_h, kernel_w, kernel_h, stride_x, stride_y, padding_x, padding_y, + dilation_x, dilation_y, channels, batches); + } else { + GGML_ABORT("Unsupported memory layout for conv_2d_dw"); + } +} diff --git a/ggml/src/ggml-cuda/conv2d-dw.cuh b/ggml/src/ggml-cuda/conv2d-dw.cuh new file mode 100644 index 000000000..b5d5a69d3 --- /dev/null +++ b/ggml/src/ggml-cuda/conv2d-dw.cuh @@ -0,0 +1,5 @@ +#pragma once +#include "common.cuh" + +#define CUDA_CONV2D_DW_BLOCK_SIZE 256 +void ggml_cuda_op_conv2d_dw(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/conv2d-transpose.cu b/ggml/src/ggml-cuda/conv2d-transpose.cu new file mode 100644 index 000000000..03224e404 --- /dev/null +++ b/ggml/src/ggml-cuda/conv2d-transpose.cu @@ -0,0 +1,91 @@ +#include + +#include "conv2d-transpose.cuh" +#include "ggml.h" + +__global__ void conv2d_transpose_kernel(const float * __restrict__ input, const half * __restrict__ kernel, + float * __restrict__ output, const int in_w, const int in_h, const int out_w, + const int out_h, const int kernel_w, const int kernel_h, const int stride, + const int c_in, const int c_out, const int batches) { + const int global_idx = blockIdx.x * blockDim.x + threadIdx.x; + + const int total_elements = out_w * out_h * c_out * batches; + + if (global_idx >= total_elements) { + return; + } + + const int out_x_idx = global_idx % out_w; + const int out_y_idx = (global_idx / out_w) % out_h; + const int c_idx = (global_idx / (out_w * out_h)) % c_out; + const int n_idx = global_idx / (out_w * out_h * c_out); + + float accumulator = 0; + // For each output idx, find the inputs that contribute to it by checking stride alignment and bounds + + for (int c_in_idx = 0; c_in_idx < c_in; c_in_idx++) { + for (int kh = 0; kh < kernel_h; ++kh) { + int in_y = out_y_idx - kh; + if (in_y < 0 || in_y % stride) continue; + in_y /= stride; + if (in_y >= in_h) continue; + + for (int kw = 0; kw < kernel_w; ++kw) { + int in_x = out_x_idx - kw; + if (in_x < 0 || in_x % stride) continue; + in_x /= stride; + if (in_x >= in_w) continue; + + const int input_idx = (in_w * in_h * c_in) * n_idx + (in_w * in_h) * c_in_idx + (in_w) *in_y + in_x; + const int kernel_idx = + (kernel_h * kernel_w * c_out) * c_in_idx + (kernel_h * kernel_w) * c_idx + (kernel_w) *kh + kw; + + float input_val = input[input_idx]; + half kern_val = kernel[kernel_idx]; + + accumulator += input_val * (float) kern_val; + } + } + } + + output[(out_w * out_h * c_out) * n_idx + (out_w * out_h) * c_idx + (out_w) *out_y_idx + out_x_idx] = accumulator; +} + +//input is (W, H, C_in, N), Kernel is (W, H, C_out, C_in) +void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * kernel = dst->src[0]; + const ggml_tensor * input = dst->src[1]; + + GGML_ASSERT(kernel->type == GGML_TYPE_F16 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); + + const float * input_data = (const float *) input->data; + float * output_data = (float *) dst->data; + const half * kernel_data = (const half *) kernel->data; + + const int input_w = input->ne[0]; + const int input_h = input->ne[1]; + const int output_w = dst->ne[0]; + const int output_h = dst->ne[1]; + const int channels_in = input->ne[2]; + const int channels_out = kernel->ne[2]; + const int kernel_w = kernel->ne[0]; + const int kernel_h = kernel->ne[1]; + const int stride = dst->op_params[0]; + const int batches = input->ne[3]; + + GGML_ASSERT(channels_in == kernel->ne[3]); + GGML_ASSERT(stride > 0); + + cudaStream_t st = ctx.stream(); + + GGML_ASSERT(ggml_is_contiguous(input)); + GGML_ASSERT(ggml_is_contiguous(kernel)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + const int total = (output_w * output_h * channels_out * batches); + const int blocks = (total + CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE - 1) / CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE; + + conv2d_transpose_kernel<<>>( + input_data, kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w, kernel_h, stride, + channels_in, channels_out, batches); +} diff --git a/ggml/src/ggml-cuda/conv2d-transpose.cuh b/ggml/src/ggml-cuda/conv2d-transpose.cuh new file mode 100644 index 000000000..c9430b248 --- /dev/null +++ b/ggml/src/ggml-cuda/conv2d-transpose.cuh @@ -0,0 +1,4 @@ +#include "common.cuh" + +#define CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE 256 +void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index dc6736046..7a6377ee6 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -11,6 +11,8 @@ #include "ggml-cuda/clamp.cuh" #include "ggml-cuda/concat.cuh" #include "ggml-cuda/conv-transpose-1d.cuh" +#include "ggml-cuda/conv2d-dw.cuh" +#include "ggml-cuda/conv2d-transpose.cuh" #include "ggml-cuda/convert.cuh" #include "ggml-cuda/count-equal.cuh" #include "ggml-cuda/cpy.cuh" @@ -35,6 +37,7 @@ #include "ggml-cuda/ssm-scan.cuh" #include "ggml-cuda/sum.cuh" #include "ggml-cuda/sumrows.cuh" +#include "ggml-cuda/mean.cuh" #include "ggml-cuda/tsembd.cuh" #include "ggml-cuda/unary.cuh" #include "ggml-cuda/upscale.cuh" @@ -47,6 +50,7 @@ #include #include #include +#include #include #include #include @@ -54,9 +58,8 @@ #include #include #include -#include -#include #include +#include #include #include #include @@ -97,8 +100,7 @@ int ggml_cuda_get_device() { static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) { ggml_cuda_set_device(device); cudaError_t err; - if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr) - { + if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr) { err = cudaMallocManaged(ptr, size); #if defined(GGML_USE_HIP) if (err == hipSuccess) { @@ -116,9 +118,7 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) err = cudaMalloc(ptr, size); } #endif // defined(GGML_USE_HIP) - } - else - { + } else { err = cudaMalloc(ptr, size); } return err; @@ -514,6 +514,33 @@ std::unique_ptr ggml_backend_cuda_context::new_pool_for_device(i return std::unique_ptr(new ggml_cuda_pool_leg(device)); } +// destroying a cuBLAS handle while a graph is being captured in a different thread can result in a CUDA error +// this lock is used to ensure that no cuBLAS handle is destroyed while a graph is being captured + +static std::mutex ggml_cuda_lock; +static std::condition_variable ggml_cuda_lock_cv; +static std::atomic ggml_cuda_lock_counter; + +ggml_backend_cuda_context::~ggml_backend_cuda_context() { + std::unique_lock lock(ggml_cuda_lock); + ggml_cuda_lock_cv.wait(lock, []{ return ggml_cuda_lock_counter.load(std::memory_order_relaxed) == 0; }); + + if (copy_event != nullptr) { + CUDA_CHECK(cudaEventDestroy(copy_event)); + } + for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) { + for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) { + if (streams[i][j] != nullptr) { + CUDA_CHECK(cudaStreamDestroy(streams[i][j])); + } + } + if (cublas_handles[i] != nullptr) { + CUBLAS_CHECK(cublasDestroy(cublas_handles[i])); + } + } +} + + // cuda buffer struct ggml_backend_cuda_buffer_context { @@ -1916,16 +1943,14 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor && ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) && src0->view_src; bool use_mul_mat_vec = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src0->ne[0] % 2 == 0 && src1->ne[1] == 1; + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; bool use_mul_mat_q = ggml_is_quantized(src0->type) && !bad_padding_clear && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; - bool any_gpus_with_slow_fp16 = false; - bool any_gpus_without_fp16_mma = false; + bool any_gpus_with_slow_fp16 = false; if (split) { ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; @@ -1936,16 +1961,16 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor continue; } - const int cc = ggml_cuda_info().devices[id].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc); - any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_hardware_available(cc); + const int cc = ggml_cuda_info().devices[id].cc; + use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); + use_mul_mat_vec = use_mul_mat_vec && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]); + any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc); } } else { - const int cc = ggml_cuda_info().devices[ctx.device].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc); - any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_hardware_available(cc); + const int cc = ggml_cuda_info().devices[ctx.device].cc; + use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); + use_mul_mat_vec = use_mul_mat_vec && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]); + any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc); } // debug helpers @@ -1956,7 +1981,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - if (!split && use_mul_mat_vec && (src0->ne[1] <= MMV_MAX_ROWS || any_gpus_without_fp16_mma)) { + if (!split && use_mul_mat_vec) { // the custom F16 vector kernel can be used over batched cuBLAS GEMM // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention) ggml_cuda_mul_mat_vec(ctx, src0, src1, nullptr, dst); @@ -2310,6 +2335,12 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_IM2COL: ggml_cuda_op_im2col(ctx, dst); break; + case GGML_OP_CONV_2D_DW: + ggml_cuda_op_conv2d_dw(ctx, dst); + break; + case GGML_OP_CONV_TRANSPOSE_2D: + ggml_cuda_conv_2d_transpose_p0(ctx, dst); + break; case GGML_OP_CONV_TRANSPOSE_1D: ggml_cuda_op_conv_transpose_1d(ctx,dst); break; @@ -2322,6 +2353,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_SUM_ROWS: ggml_cuda_op_sum_rows(ctx, dst); break; + case GGML_OP_MEAN: + ggml_cuda_op_mean(ctx, dst); + break; case GGML_OP_SSM_CONV: ggml_cuda_op_ssm_conv(ctx, dst); break; @@ -2685,6 +2719,11 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph)); graph_evaluated_or_captured = true; // CUDA graph has been captured + + std::lock_guard lock(ggml_cuda_lock); + if (ggml_cuda_lock_counter.fetch_sub(1, std::memory_order_relaxed) == 1) { + ggml_cuda_lock_cv.notify_all(); + } } else { graph_evaluated_or_captured = true; // ggml graph has been directly evaluated } @@ -2760,7 +2799,13 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, } } - if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture + if (use_cuda_graph && cuda_graph_update_required) { + // Start CUDA graph capture + { + std::lock_guard lock(ggml_cuda_lock); + ggml_cuda_lock_counter.fetch_add(1, std::memory_order_relaxed); + } + CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed)); } @@ -3220,9 +3265,12 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g return op->src[0]->nb[0] == ggml_type_size(op->src[0]->type) && ggml_is_contiguous_2(op->src[0]); } case GGML_OP_IM2COL: + case GGML_OP_CONV_2D_DW: + case GGML_OP_CONV_TRANSPOSE_2D: case GGML_OP_POOL_2D: case GGML_OP_SUM: case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: case GGML_OP_ARGSORT: case GGML_OP_ACC: return true; diff --git a/ggml/src/ggml-cuda/mean.cu b/ggml/src/ggml-cuda/mean.cu new file mode 100644 index 000000000..4b238a399 --- /dev/null +++ b/ggml/src/ggml-cuda/mean.cu @@ -0,0 +1,19 @@ +#include "mean.cuh" + +void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *) src0->data; + float * dst_d = (float *) dst->data; + cudaStream_t stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(src0)); + + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + const dim3 block_dims(WARP_SIZE, 1, 1); + const dim3 block_nums(nrows, 1, 1); + reduce_rows_f32<<>>(src0_d, dst_d, ncols); +} diff --git a/ggml/src/ggml-cuda/mean.cuh b/ggml/src/ggml-cuda/mean.cuh new file mode 100644 index 000000000..2b9b10433 --- /dev/null +++ b/ggml/src/ggml-cuda/mean.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmv.cu index d8c385e23..1502e9d94 100644 --- a/ggml/src/ggml-cuda/mmv.cu +++ b/ggml/src/ggml-cuda/mmv.cu @@ -2,25 +2,26 @@ #include "common.cuh" #include "mmv.cuh" -template +template static __global__ void mul_mat_vec( const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst, - const int64_t ncols2, const int64_t nchannels_y, const int64_t stride_row, - const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, - const int64_t sample_ratio, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst) { - const int64_t row = blockIdx.x; - const int64_t channel_dst = blockIdx.y; - const int64_t channel_x = ids ? ids[channel_dst] : channel_dst / channel_ratio; - const int64_t channel_y = ids ? channel_dst % nchannels_y : channel_dst; - const int64_t sample_dst = blockIdx.z; - const int64_t sample_x = sample_dst / sample_ratio; - const int64_t sample_y = sample_dst; - const int tid = threadIdx.x; + const int ncols2, const int nchannels_y, const int stride_row, const int stride_col_y2, const int stride_col_dst, + const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, + const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) { + const int row = blockIdx.x; + const int channel_dst = blockIdx.y; + const int channel_x = ids ? ids[channel_dst] : channel_dst / channel_ratio; + const int channel_y = ids ? channel_dst % nchannels_y : channel_dst; + const int sample_dst = blockIdx.z; + const int sample_x = sample_dst / sample_ratio; + const int sample_y = sample_dst; + const int tid = threadIdx.x; + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); - x += sample_x *stride_sample_x + channel_x *stride_channel_x + row*stride_row; - y += sample_y *stride_sample_y + channel_y *stride_channel_y; - dst += sample_dst*stride_sample_dst + channel_dst*stride_channel_dst; + x += int64_t(sample_x) *stride_sample_x + channel_x *stride_channel_x + row*stride_row; + y += int64_t(sample_y) *stride_sample_y + channel_y *stride_channel_y; + dst += int64_t(sample_dst)*stride_sample_dst + channel_dst*stride_channel_dst; const float2 * y2 = (const float2 *) y; @@ -34,81 +35,108 @@ static __global__ void mul_mat_vec( __syncthreads(); } - float sumf = 0.0f; + float sumf[ncols_dst] = {0.0f}; if constexpr (std::is_same::value) { const float2 * x2 = (const float2 *) x; - for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { + for (int col2 = tid; col2 < ncols2; col2 += block_size) { const float2 tmpx = x2[col2]; - const float2 tmpy = y2[col2]; - sumf += tmpx.x*tmpy.x; - sumf += tmpx.y*tmpy.y; + +#pragma unroll + for (int j = 0; j < ncols_dst; ++j) { + const float2 tmpy = y2[j*stride_col_y2 + col2]; + sumf[j] += tmpx.x*tmpy.x; + sumf[j] += tmpx.y*tmpy.y; + } } } else if constexpr (std::is_same::value) { const half2 * x2 = (const half2 *) x; if (std::is_same::value) { - for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { + for (int col2 = tid; col2 < ncols2; col2 += block_size) { const float2 tmpx = __half22float2(x2[col2]); - const float2 tmpy = y2[col2]; - sumf += tmpx.x * tmpy.x; - sumf += tmpx.y * tmpy.y; + +#pragma unroll + for (int j = 0; j < ncols_dst; ++j) { + const float2 tmpy = y2[j*stride_col_y2 + col2]; + sumf[j] += tmpx.x * tmpy.x; + sumf[j] += tmpx.y * tmpy.y; + } } } else { #ifdef FP16_AVAILABLE - half2 sumh2 = make_half2(0.0f, 0.0f); + half2 sumh2[ncols_dst] = {{0.0f, 0.0f}}; - for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { - const float2 tmp = y2[col2]; - sumh2 += x2[col2] * make_half2(tmp.x, tmp.y); + for (int col2 = tid; col2 < ncols2; col2 += block_size) { + const half2 tmpx = x2[col2]; + +#pragma unroll + for (int j = 0; j < ncols_dst; ++j) { + const float2 tmpy = y2[j*stride_col_y2 + col2]; + sumh2[j] += tmpx * make_half2(tmpy.x, tmpy.y); + } } - sumf = __low2float(sumh2) + __high2float(sumh2); +#pragma unroll + for (int j = 0; j < ncols_dst; ++j) { + sumf[j] = __low2float(sumh2[j]) + __high2float(sumh2[j]); + } #else NO_DEVICE_CODE; #endif // FP16_AVAILABLE } } else if constexpr (std::is_same::value) { const int * x2 = (const int *) x; - for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { - const int tmpx = x2[col2]; - const float2 tmpy = y2[col2]; - sumf += float(reinterpret_cast(&tmpx)[0]) * tmpy.x; - sumf += float(reinterpret_cast(&tmpx)[1]) * tmpy.y; + for (int col2 = tid; col2 < ncols2; col2 += block_size) { + const int tmpx = x2[col2]; +#pragma unroll + for (int j = 0; j < ncols_dst; ++j) { + const float2 tmpy = y2[j*stride_col_y2 + col2]; + sumf[j] += float(reinterpret_cast(&tmpx)[0]) * tmpy.x; + sumf[j] += float(reinterpret_cast(&tmpx)[1]) * tmpy.y; + } } } else { static_assert(std::is_same::value, "unsupported type"); } - sumf = warp_reduce_sum(sumf); +#pragma unroll + for (int j = 0; j < ncols_dst; ++j) { + sumf[j] = warp_reduce_sum(sumf[j]); - if (block_size > warp_size) { - buf_iw[tid/warp_size] = sumf; - __syncthreads(); - if (tid >= warp_size) { - return; + if (block_size > warp_size) { + buf_iw[tid/warp_size] = sumf[j]; + __syncthreads(); + if (tid < warp_size) { + sumf[j] = buf_iw[tid]; + sumf[j] = warp_reduce_sum(sumf[j]); + } + if (j < ncols_dst) { + __syncthreads(); + } } - sumf = buf_iw[tid]; - sumf = warp_reduce_sum(sumf); } - if (tid != 0) { + if (tid >= ncols_dst) { return; } - dst[row] = sumf; + dst[tid*stride_col_dst + row] = sumf[tid]; } -template +template static void launch_mul_mat_vec_cuda( const T * x, const float * y, const int32_t * ids, float * dst, - const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst, + const int64_t ncols, const int64_t nrows, + const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst, + const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x, const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst, cudaStream_t stream) { - GGML_ASSERT(ncols % 2 == 0); - GGML_ASSERT(stride_row % 2 == 0); + GGML_ASSERT(ncols % 2 == 0); + GGML_ASSERT(stride_row % 2 == 0); + GGML_ASSERT(stride_col_y % 2 == 0); GGML_ASSERT(ids || nchannels_dst % nchannels_x == 0); GGML_ASSERT( nsamples_dst % nsamples_x == 0); const int64_t channel_ratio = nchannels_dst / nchannels_x; @@ -138,44 +166,52 @@ static void launch_mul_mat_vec_cuda( const dim3 block_dims(block_size_best, 1, 1); switch (block_size_best) { case 32: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 64: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 96: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 128: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 160: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 192: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 224: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 256: { - mul_mat_vec<<>> - (x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y, - stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); + mul_mat_vec<<>> + (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst, + channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } break; default: { GGML_ABORT("fatal error"); @@ -183,23 +219,91 @@ static void launch_mul_mat_vec_cuda( } } +template +static void mul_mat_vec_cuda_switch_ncols_dst( + const T * x, const float * y, const int32_t * ids, float * dst, + const int64_t ncols, const int64_t nrows, const int64_t ncols_dst, + const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst, + const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst, + const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x, + const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst, + cudaStream_t stream) { + switch (ncols_dst) { + case 1: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + case 2: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + case 3: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + case 4: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + case 5: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + case 6: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + case 7: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + case 8: + launch_mul_mat_vec_cuda + (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); + break; + default: + GGML_ABORT("fatal error"); + break; + } +} + template static void mul_mat_vec_cuda( const T * x, const float * y, const int32_t * ids, float * dst, - const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst, + const int64_t ncols, const int64_t nrows, const int64_t ncols_dst, + const int64_t stride_row, const int64_t stride_col_y, const int stride_col_dst, + const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x, const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst, enum ggml_prec prec, cudaStream_t stream) { if constexpr(std::is_same::value) { if (prec == GGML_PREC_DEFAULT) { - launch_mul_mat_vec_cuda - (x, y, ids, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + mul_mat_vec_cuda_switch_ncols_dst + (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); return; } } - launch_mul_mat_vec_cuda - (x, y, ids, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, + mul_mat_vec_cuda_switch_ncols_dst + (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream); } @@ -246,24 +350,24 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * const int64_t stride_channel_dst = ids ? s1 : s2; const int64_t stride_channel_y = ids ? s11 : s12; - GGML_ASSERT(ncols_dst == 1); + GGML_ASSERT(!ids || ncols_dst == 1); switch (src0->type) { case GGML_TYPE_F32: { const float * src0_d = (const float *) src0->data; - mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, s01, + mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1, ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst, ne03, ne3, s03, s13, s3, prec, ctx.stream()); } break; case GGML_TYPE_F16: { const half * src0_d = (const half *) src0->data; - mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, s01, + mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1, ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst, ne03, ne3, s03, s13, s3, prec, ctx.stream()); } break; case GGML_TYPE_BF16: { const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data; - mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, s01, + mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1, ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst, ne03, ne3, s03, s13, s3, prec, ctx.stream()); } break; @@ -282,16 +386,19 @@ void ggml_cuda_op_mul_mat_vec( GGML_ASSERT(dst->type == GGML_TYPE_F32); const int64_t ne00 = src0->ne[0]; + const int64_t ne10 = src1->ne[0]; + const int64_t ne0 = dst->ne[0]; const int64_t row_diff = row_high - row_low; - GGML_ASSERT(src1_ncols == 1); - - const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const int id = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[id].cc; const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32; // ggml_cuda_op provides single, contiguous matrices const int64_t stride_row = ne00; + const int64_t stride_col_y = ne10; + const int64_t stride_col_dst = id == ctx.device ? ne0 : row_diff; // main device has larger memory buffer const int64_t nchannels_x = 1; const int64_t nchannels_y = 1; const int64_t nchannels_dst = 1; @@ -307,19 +414,19 @@ void ggml_cuda_op_mul_mat_vec( switch (src0->type) { case GGML_TYPE_F32: { const float * src0_d = (const float *) src0_dd_i; - mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, stride_row, + mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst, nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream); } break; case GGML_TYPE_F16: { const half * src0_d = (const half *) src0_dd_i; - mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, stride_row, + mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst, nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream); } break; case GGML_TYPE_BF16: { const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i; - mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, stride_row, + mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst, nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream); } break; @@ -334,3 +441,48 @@ void ggml_cuda_op_mul_mat_vec( GGML_UNUSED(src1_ncols); GGML_UNUSED(src1_padded_row_size); } + +bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) { + if (src0_ne[0] % 2 != 0) { + return false; + } + switch (type) { + case GGML_TYPE_F32: + if (GGML_CUDA_CC_IS_NVIDIA(cc)) { + if (cc >= GGML_CUDA_CC_ADA_LOVELACE) { + return ne11 <= 8; + } + if (cc >= GGML_CUDA_CC_TURING) { + return ne11 <= 4; + } + return ne11 <= 3; + } + return ne11 <= 8; + case GGML_TYPE_F16: + if (GGML_CUDA_CC_IS_NVIDIA(cc)) { + const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1); + if (cc >= GGML_CUDA_CC_ADA_LOVELACE) { + return src0_small && ne11 <= 4; + } + if (fp16_mma_hardware_available(cc)) { + return src0_small && ne11 <= 3; + } + return ne11 <= 8; + } + return ne11 <= 8; + case GGML_TYPE_BF16: + if (GGML_CUDA_CC_IS_NVIDIA(cc)) { + const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1); + if (cc >= GGML_CUDA_CC_ADA_LOVELACE) { + return src0_small && ne11 <= 4; + } + if (bf16_mma_hardware_available(cc)) { + return src0_small && ne11 <= 3; + } + return ne11 <= 8; + } + return ne11 <= 8; + default: + return false; + } +} diff --git a/ggml/src/ggml-cuda/mmv.cuh b/ggml/src/ggml-cuda/mmv.cuh index 756e7e1cc..1330bcb6a 100644 --- a/ggml/src/ggml-cuda/mmv.cuh +++ b/ggml/src/ggml-cuda/mmv.cuh @@ -1,8 +1,5 @@ #include "common.cuh" -// maximum number of src0 rows with which to use mul_mat_vec over cuBLAS if FP16 tensor cores are available -#define MMV_MAX_ROWS 512 - void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst); void ggml_cuda_op_mul_mat_vec( @@ -10,3 +7,5 @@ void ggml_cuda_op_mul_mat_vec( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, cudaStream_t stream); + +bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11); diff --git a/ggml/src/ggml-cuda/sumrows.cu b/ggml/src/ggml-cuda/sumrows.cu index 38dbf1b5e..2eee08fa0 100644 --- a/ggml/src/ggml-cuda/sumrows.cu +++ b/ggml/src/ggml-cuda/sumrows.cu @@ -1,25 +1,9 @@ #include "sumrows.cuh" -static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) { - const int row = blockIdx.x; - const int col = threadIdx.x; - - float sum = 0.0f; - for (int i = col; i < ncols; i += blockDim.x) { - sum += x[row * ncols + i]; - } - - sum = warp_reduce_sum(sum); - - if (col == 0) { - dst[row] = sum; - } -} - void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { const dim3 block_dims(WARP_SIZE, 1, 1); const dim3 block_nums(nrows, 1, 1); - k_sum_rows_f32<<>>(x, dst, ncols); + reduce_rows_f32<<>>(x, dst, ncols); } void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -35,5 +19,8 @@ void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const int64_t ncols = src0->ne[0]; const int64_t nrows = ggml_nrows(src0); - sum_rows_f32_cuda(src0_d, dst_d, ncols, nrows, stream); + const dim3 block_dims(WARP_SIZE, 1, 1); + const dim3 block_nums(nrows, 1, 1); + + reduce_rows_f32<<>>(src0_d, dst_d, ncols); } diff --git a/ggml/src/ggml-cuda/sumrows.cuh b/ggml/src/ggml-cuda/sumrows.cuh index 191db1c13..3431c599b 100644 --- a/ggml/src/ggml-cuda/sumrows.cuh +++ b/ggml/src/ggml-cuda/sumrows.cuh @@ -1,5 +1,4 @@ #include "common.cuh" void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream); - void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m index d03e8ad77..c403e0132 100644 --- a/ggml/src/ggml-metal/ggml-metal.m +++ b/ggml/src/ggml-metal/ggml-metal.m @@ -48,22 +48,28 @@ static struct ggml_backend_metal_device_context { int mtl_device_ref_count; id mtl_library; + NSLock * mtl_lock; + bool has_simdgroup_reduction; bool has_simdgroup_mm; bool has_residency_sets; bool has_bfloat; bool use_bfloat; + size_t max_size; + char name[128]; } g_ggml_ctx_dev_main = { /*.mtl_device =*/ nil, /*.mtl_device_ref_count =*/ 0, /*.mtl_library =*/ nil, + /*.mtl_lock =*/ nil, /*.has_simdgroup_reduction =*/ false, /*.has_simdgroup_mm =*/ false, /*.has_residency_sets =*/ false, /*.has_bfloat =*/ false, /*.use_bfloat =*/ false, + /*.max_size =*/ 0, /*.name =*/ "", }; @@ -71,6 +77,10 @@ static struct ggml_backend_metal_device_context { static id ggml_backend_metal_device_acq(struct ggml_backend_metal_device_context * ctx) { assert(ctx != NULL); + if (ctx->mtl_lock == nil) { + ctx->mtl_lock = [[NSLock alloc] init]; + } + if (ctx->mtl_device == nil) { ctx->mtl_device = MTLCreateSystemDefaultDevice(); } @@ -94,6 +104,8 @@ static id ggml_backend_metal_device_acq(struct ggml_backend_metal_dev ctx->use_bfloat = false; #endif + ctx->max_size = ctx->mtl_device.maxBufferLength; + strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1); } @@ -110,6 +122,11 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte ctx->mtl_device_ref_count--; if (ctx->mtl_device_ref_count == 0) { + if (ctx->mtl_lock) { + [ctx->mtl_lock release]; + ctx->mtl_lock = nil; + } + if (ctx->mtl_library) { [ctx->mtl_library release]; ctx->mtl_library = nil; @@ -978,7 +995,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context)); struct ggml_backend_metal_device_context * ctx_dev = dev->context; - id device = ggml_backend_metal_device_acq(ctx_dev); + id device = ctx_dev->mtl_device; GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]); @@ -992,9 +1009,16 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); // load library - if (ctx_dev->mtl_library == nil) { - ctx_dev->mtl_library = ggml_metal_load_library(device, ctx_dev->use_bfloat); + { + [ctx_dev->mtl_lock lock]; + + if (ctx_dev->mtl_library == nil) { + ctx_dev->mtl_library = ggml_metal_load_library(device, ctx_dev->use_bfloat); + } + + [ctx_dev->mtl_lock unlock]; } + id metal_library = ctx_dev->mtl_library; if (metal_library == nil) { GGML_LOG_ERROR("%s: error: metal library is nil\n", __func__); @@ -5313,7 +5337,6 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) } ggml_backend_metal_buffer_rset_free(ctx); - ggml_backend_metal_device_rel(buffer->buft->device->context); if (ctx->owned) { #if TARGET_OS_OSX @@ -5422,7 +5445,10 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba } struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)buft->device->context; - id device = ggml_backend_metal_device_acq(ctx_dev); + + GGML_ASSERT(ctx_dev->mtl_device != nil); + + id device = ctx_dev->mtl_device; ctx->all_data = ggml_metal_host_malloc(size_aligned); ctx->all_size = size_aligned; @@ -5445,14 +5471,12 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) { GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); free(ctx); - ggml_backend_metal_device_rel(ctx_dev); return NULL; } if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) { GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__); free(ctx); - ggml_backend_metal_device_rel(ctx_dev); return NULL; } @@ -5463,17 +5487,14 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 32; + GGML_UNUSED(buft); } static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - id device = ggml_backend_metal_device_acq(buft->device->context); - const size_t max_size = device.maxBufferLength; - ggml_backend_metal_device_rel(buft->device->context); + const size_t max_size = ((struct ggml_backend_metal_device_context *)buft->device->context)->max_size; return max_size; - - GGML_UNUSED(buft); } static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { @@ -5546,7 +5567,10 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz } struct ggml_backend_metal_device_context * ctx_dev = &g_ggml_ctx_dev_main; - id device = ggml_backend_metal_device_acq(ctx_dev); + + GGML_ASSERT(ctx_dev->mtl_device != nil); + + id device = ctx_dev->mtl_device; // the buffer fits into the max buffer size allowed by the device if (size_aligned <= device.maxBufferLength) { @@ -5602,7 +5626,6 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) { GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__); free(ctx); - ggml_backend_metal_device_rel(ctx_dev); return NULL; } @@ -5618,10 +5641,8 @@ static const char * ggml_backend_metal_name(ggml_backend_t backend) { } static void ggml_backend_metal_free(ggml_backend_t backend) { - struct ggml_backend_metal_context * ctx = backend->context; - struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; + struct ggml_backend_metal_context * ctx = backend->context; - ggml_backend_metal_device_rel(ctx_dev); ggml_metal_free(ctx); free(backend); @@ -5761,6 +5782,8 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; + GGML_ASSERT(ctx_dev->mtl_device != nil); + return [ctx_dev->mtl_device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; } @@ -5780,10 +5803,7 @@ static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) { } static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) { - // acq/rel just to populate ctx->name in case it hasn't been done yet struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; - ggml_backend_metal_device_acq(ctx_dev); - ggml_backend_metal_device_rel(ctx_dev); return ctx_dev->name; } @@ -5791,12 +5811,10 @@ static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { if (@available(macOS 10.12, iOS 16.0, *)) { struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; - id device = ggml_backend_metal_device_acq(ctx_dev); + id device = ctx_dev->mtl_device; *total = device.recommendedMaxWorkingSetSize; *free = *total - device.currentAllocatedSize; - - ggml_backend_metal_device_rel(ctx_dev); } else { *free = 1; *total = 1; @@ -5874,7 +5892,10 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back } struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; - id device = ggml_backend_metal_device_acq(ctx_dev); + + GGML_ASSERT(ctx_dev->mtl_device != nil); + + id device = ctx_dev->mtl_device; // the buffer fits into the max buffer size allowed by the device if (size_aligned <= device.maxBufferLength) { @@ -5930,7 +5951,6 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) { GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__); free(ctx); - ggml_backend_metal_device_rel(ctx_dev); return NULL; } @@ -5944,8 +5964,9 @@ static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const } static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name || - buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name; + return + buft->iface.get_name == ggml_backend_metal_buffer_type_get_name || + buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name; GGML_UNUSED(dev); } @@ -6030,8 +6051,19 @@ static struct ggml_backend_reg_i ggml_backend_metal_reg_i = { /* .get_proc_address = */ ggml_backend_metal_get_proc_address, }; +// called upon program exit +static void ggml_metal_cleanup(void) { + ggml_backend_metal_device_rel(&g_ggml_ctx_dev_main); +} + +// TODO: make thread-safe ggml_backend_reg_t ggml_backend_metal_reg(void) { - // TODO: make this thread-safe somehow? + ggml_backend_metal_device_acq(&g_ggml_ctx_dev_main); + + // register cleanup callback + // TODO: not ideal, but not sure if there is a better way to do this in Objective-C + atexit(ggml_metal_cleanup); + { g_ggml_backend_metal_reg = (struct ggml_backend_reg) { /* .api_version = */ GGML_BACKEND_API_VERSION, diff --git a/ggml/src/ggml-sycl/binbcast.cpp b/ggml/src/ggml-sycl/binbcast.cpp index 0a3883ae1..741630dba 100644 --- a/ggml/src/ggml-sycl/binbcast.cpp +++ b/ggml/src/ggml-sycl/binbcast.cpp @@ -225,9 +225,9 @@ struct bin_bcast_sycl { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * - sycl::range<3>(1, 1, block_size), + sycl_parallel_for( + stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * sycl::range<3>(1, 1, block_size), sycl::range<3>(1, 1, block_size)), [=](sycl::nd_item<3> item_ct1) { k_bin_bcast_unravel( @@ -246,9 +246,8 @@ struct bin_bcast_sycl { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_bin_bcast(src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, ne10, ne11, ne12, ne13, s1, s2, s3, s01, s02, s03, s11, s12, s13, diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp index 7aa91c861..3501484a1 100644 --- a/ggml/src/ggml-sycl/concat.cpp +++ b/ggml/src/ggml-sycl/concat.cpp @@ -89,33 +89,24 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst, sycl::range<3> gridDim(ne2, ne1, num_blocks); switch (dim) { case 0: - stream->parallel_for( - sycl::nd_range<3>(gridDim * - sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1); - }); - break; + sycl_parallel_for(stream, + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1); }); + break; case 1: - stream->parallel_for( - sycl::nd_range<3>(gridDim * - sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1); - }); - break; + sycl_parallel_for(stream, + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1); }); + break; // dim >=2 will be dispatched to the default path default: - stream->parallel_for( - sycl::nd_range<3>(gridDim * - sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1); - }); - break; + sycl_parallel_for(stream, + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1); }); + break; } } @@ -129,33 +120,29 @@ static void concat_f32_sycl_non_cont( int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2, uint64_t nb3, int32_t dim) { sycl::range<3> gridDim(ne3, ne2, ne1); - stream->parallel_for( - sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - int64_t i3 = item_ct1.get_group(0); - int64_t i2 = item_ct1.get_group(1); - int64_t i1 = item_ct1.get_group(2); + sycl_parallel_for(stream, sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + int64_t i3 = item_ct1.get_group(0); + int64_t i2 = item_ct1.get_group(1); + int64_t i1 = item_ct1.get_group(2); - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); + int64_t o[4] = { 0, 0, 0, 0 }; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); - const float *x; + const float * x; - for (int i0 = item_ct1.get_local_id(2); i0 < ne0; - i0 += item_ct1.get_local_range(2)) { + for (int i0 = item_ct1.get_local_id(2); i0 < ne0; i0 += item_ct1.get_local_range(2)) { if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const float *)(src0 + (i3)*nb03 + (i2)*nb02 + (i1)*nb01 + - (i0)*nb00); + x = (const float *) (src0 + (i3) *nb03 + (i2) *nb02 + (i1) *nb01 + (i0) *nb00); } else { - x = (const float *)(src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 + - (i1 - o[1]) * nb11 + (i0 - o[0]) * nb10); + x = (const float *) (src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 + (i1 - o[1]) * nb11 + + (i0 - o[0]) * nb10); } float *y = (float *)(dst + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0); *y = *x; - } - }); + } + }); } void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { diff --git a/ggml/src/ggml-sycl/conv.cpp b/ggml/src/ggml-sycl/conv.cpp index 475bd34a2..c2f991e8d 100644 --- a/ggml/src/ggml-sycl/conv.cpp +++ b/ggml/src/ggml-sycl/conv.cpp @@ -59,16 +59,10 @@ static void conv_transpose_1d_f32_f32_sycl( const int num_blocks = (output_size + SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE - 1) / SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE; const sycl::range<3> block_dims(1, 1, SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE); const sycl::range<3> block_nums(1, 1, num_blocks); - stream->parallel_for( - sycl::nd_range<3>( - block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - conv_transpose_1d_kernel( - s0, output_size, - src0_ne0, src0_ne1, src0_ne2, - src1_ne0, dst_ne0, - src0, src1, dst, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { + conv_transpose_1d_kernel(s0, output_size, src0_ne0, src0_ne1, src0_ne2, src1_ne0, dst_ne0, src0, src1, dst, + item_ct1); + }); } void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index 96d2583b1..0ef567122 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -33,14 +33,11 @@ static void dequantize_block_sycl(const void *__restrict__ vx, { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>( - sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block(vx, y, k, item_ct1); - }); + sycl_parallel_for( + stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block(vx, y, k, item_ct1); }); } } @@ -53,24 +50,18 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 64), - sycl::range<3>(1, 1, 64)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q2_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q2_K(vx, y, item_ct1); }); } #else { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q2_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q2_K(vx, y, item_ct1); }); } #endif @@ -85,24 +76,18 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 64), - sycl::range<3>(1, 1, 64)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q3_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q3_K(vx, y, item_ct1); }); } #else { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q3_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q3_K(vx, y, item_ct1); }); } #endif } @@ -116,12 +101,9 @@ static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q4_0(vx, y, nb32, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q4_0(vx, y, nb32, item_ct1); }); } } @@ -135,13 +117,12 @@ static void dequantize_row_q4_0_sycl_reorder(const void *vx, dst_t *y, const int int constexpr WARP_K = WARP_SIZE * QK4_0; const int n_warp = (k + WARP_K - 1) / WARP_K; GGML_ASSERT(k % 2 == 0); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, n_warp) * - sycl::range<3>(1, 1, WARP_SIZE), - sycl::range<3>(1, 1, WARP_SIZE)), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ - dequantize_block_q4_0_reorder(vx, y, k, item_ct1); - }); - + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, n_warp) * sycl::range<3>(1, 1, WARP_SIZE), + sycl::range<3>(1, 1, WARP_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_block_q4_0_reorder(vx, y, k, item_ct1); + }); } template @@ -153,12 +134,9 @@ static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q4_1(vx, y, nb32, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q4_1(vx, y, nb32, item_ct1); }); } } @@ -171,14 +149,13 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor scale_local_acc(sycl::range<1>(12), cgh); - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q4_K(vx, y, get_pointer(scale_local_acc), item_ct1); - }); + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q4_K(vx, y, get_pointer(scale_local_acc), item_ct1); + }); }); } } @@ -191,13 +168,13 @@ static void dequantize_row_q4_K_sycl_reorder(const void * vx, dst_t * y, const i dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - stream->submit([&](sycl::handler & cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor scale_local_acc(sycl::range<1>(12), cgh); - cgh.parallel_for(sycl::nd_range<1>(sycl::range<1>(global_size), sycl::range<1>(local_size)), - [=](sycl::nd_item<1> item_ct1) { - dequantize_block_q4_K_reorder(vx, y, get_pointer(scale_local_acc), item_ct1, nb); - }); + sycl_parallel_for<1>(cgh, sycl::nd_range<1>(sycl::range<1>(global_size), sycl::range<1>(local_size)), + [=](sycl::nd_item<1> item_ct1) { + dequantize_block_q4_K_reorder(vx, y, get_pointer(scale_local_acc), item_ct1, nb); + }); }); } @@ -210,24 +187,18 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 64), - sycl::range<3>(1, 1, 64)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q5_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q5_K(vx, y, item_ct1); }); } #else { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q5_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q5_K(vx, y, item_ct1); }); } #endif @@ -242,24 +213,18 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 64), - sycl::range<3>(1, 1, 64)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q6_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K(vx, y, item_ct1); }); } #else { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q6_K(vx, y, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K(vx, y, item_ct1); }); } #endif @@ -271,9 +236,9 @@ static void dequantize_row_q6_K_sycl_reorder(const void * vx, dst_t * y, const i dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)), - [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K_reorder(vx, y, item_ct1, nb); }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K_reorder(vx, y, item_ct1, nb); }); } template @@ -284,15 +249,10 @@ static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq1_s( - vx, y, item_ct1, iq1s_grid_gpu - ); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq1_s(vx, y, item_ct1, iq1s_grid_gpu); }); }); } } @@ -305,15 +265,10 @@ static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq1_m( - vx, y, item_ct1, iq1s_grid_gpu - ); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq1_m(vx, y, item_ct1, iq1s_grid_gpu); }); }); } } @@ -326,15 +281,12 @@ static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int64_t dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq2_xxs( - vx, y, item_ct1, iq2xxs_grid, - ksigns_iq2xs, kmask_iq2xs); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq2_xxs(vx, y, item_ct1, iq2xxs_grid, ksigns_iq2xs, kmask_iq2xs); + }); }); } } @@ -347,15 +299,12 @@ static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int64_t k dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq2_xs( - vx, y, item_ct1, iq2xs_grid, - ksigns_iq2xs, kmask_iq2xs); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq2_xs(vx, y, item_ct1, iq2xs_grid, ksigns_iq2xs, kmask_iq2xs); + }); }); } } @@ -368,13 +317,10 @@ static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq2_s(vx, y, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq2_s(vx, y, item_ct1); }); }); } } @@ -388,15 +334,12 @@ static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int64_t dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq3_xxs( - vx, y, item_ct1, iq3xxs_grid, - ksigns_iq2xs, kmask_iq2xs); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_iq3_xxs(vx, y, item_ct1, iq3xxs_grid, ksigns_iq2xs, kmask_iq2xs); + }); }); } } @@ -409,14 +352,10 @@ static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int64_t k, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq3_s( - vx, y, item_ct1, kmask_iq2xs, iq3s_grid); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq3_s(vx, y, item_ct1, kmask_iq2xs, iq3s_grid); }); }); } } @@ -432,14 +371,11 @@ static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int64_t k dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq4_xs(vx, y, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, + sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq4_xs(vx, y, item_ct1); }); }); } #endif @@ -453,14 +389,11 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_iq4_nl(vx, y, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for( + cgh, + sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq4_nl(vx, y, item_ct1); }); }); } } diff --git a/ggml/src/ggml-sycl/cpy.cpp b/ggml/src/ggml-sycl/cpy.cpp index bec137140..1ffd7f122 100644 --- a/ggml/src/ggml-sycl/cpy.cpp +++ b/ggml/src/ggml-sycl/cpy.cpp @@ -413,7 +413,8 @@ static void ggml_cpy_f16_f32_sycl(const char * cx, char * cdst, const int ne, co { dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - stream->parallel_for( + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { @@ -431,7 +432,8 @@ static void ggml_cpy_f32_f32_sycl(const char * cx, char * cdst, const int ne, co { dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - stream->parallel_for( + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { @@ -449,7 +451,8 @@ static void ggml_cpy_f32_f16_sycl(const char * cx, char * cdst, const int ne, co { dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - stream->parallel_for( + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { @@ -465,11 +468,11 @@ static void ggml_cpy_f32_q8_0_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { GGML_ASSERT(ne % QK8_0 == 0); const int num_blocks = ne / QK8_0; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } static void ggml_cpy_q8_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, @@ -477,11 +480,11 @@ static void ggml_cpy_q8_0_f32_sycl(const char * cx, char * cdst, const int ne, c const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_q_f32(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_q_f32(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, @@ -490,11 +493,11 @@ static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { GGML_ASSERT(ne % QK4_0 == 0); const int num_blocks = ne / QK4_0; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } static void ggml_cpy_q4_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, @@ -502,8 +505,9 @@ static void ggml_cpy_q4_0_f32_sycl(const char * cx, char * cdst, const int ne, c const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { cpy_q_f32, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -516,11 +520,11 @@ static void ggml_cpy_f32_q4_1_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { GGML_ASSERT(ne % QK4_1 == 0); const int num_blocks = ne / QK4_1; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } static void ggml_cpy_q4_1_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, @@ -528,8 +532,9 @@ static void ggml_cpy_q4_1_f32_sycl(const char * cx, char * cdst, const int ne, c const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { cpy_q_f32, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -542,11 +547,11 @@ static void ggml_cpy_f32_q5_0_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { GGML_ASSERT(ne % QK5_0 == 0); const int num_blocks = ne / QK5_0; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } static void ggml_cpy_q5_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, @@ -554,8 +559,9 @@ static void ggml_cpy_q5_0_f32_sycl(const char * cx, char * cdst, const int ne, c const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { cpy_q_f32, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -568,11 +574,11 @@ static void ggml_cpy_f32_q5_1_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { GGML_ASSERT(ne % QK5_1 == 0); const int num_blocks = ne / QK5_1; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, @@ -580,8 +586,9 @@ static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, c const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { cpy_q_f32, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -594,11 +601,11 @@ static void ggml_cpy_f32_iq4_nl_sycl(const char * cx, char * cdst, const int ne, const int nb12, const int nb13, queue_ptr stream) { GGML_ASSERT(ne % QK4_NL == 0); const int num_blocks = ne / QK4_NL; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, - ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, @@ -609,7 +616,8 @@ static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, co { dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - stream->parallel_for( + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { @@ -628,7 +636,8 @@ static void ggml_cpy_i16_i16_sycl(const char * cx, char * cdst, const int ne, co // dpct::has_capability_or_fail(stream->get_device(), // {sycl::aspect::fp16}); - stream->parallel_for( + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { @@ -647,7 +656,8 @@ static void ggml_cpy_i32_i32_sycl(const char * cx, char * cdst, const int ne, co // dpct::has_capability_or_fail(stream->get_device(), // {sycl::aspect::fp16}); - stream->parallel_for( + sycl_parallel_for( + stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { @@ -662,11 +672,13 @@ static void ggml_cpy_q8_0_q8_0(const char * cx, char * cdst, const int ne, const const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, + ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } @@ -675,11 +687,13 @@ static void ggml_cpy_q5_0_q5_0(const char * cx, char * cdst, const int ne, const const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, + ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } @@ -689,11 +703,13 @@ static void ggml_cpy_q5_1_q5_1(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, + ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } @@ -702,10 +718,13 @@ static void ggml_cpy_q4_0_q4_0(const char * cx, char * cdst, const int ne, const const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, + ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } @@ -715,10 +734,13 @@ static void ggml_cpy_q4_1_q4_1(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, + ne12, nb10, nb11, nb12, nb13, item_ct1); + }); } void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1) try { diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp index 4f2760110..70579c0c3 100644 --- a/ggml/src/ggml-sycl/dmmv.cpp +++ b/ggml/src/ggml-sycl/dmmv.cpp @@ -208,12 +208,10 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols, - nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols, nrows, item_ct1); + }); } } @@ -877,12 +875,11 @@ static void dequantize_mul_mat_vec_q4_0_sycl_reorder(const void *vx, const dfloa dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec_reorder( - vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_mul_mat_vec_reorder(vx, y, dst, ncols, + nrows, item_ct1); + }); } } @@ -900,12 +897,10 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec( - vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_mul_mat_vec(vx, y, dst, ncols, nrows, item_ct1); + }); } } @@ -921,12 +916,10 @@ static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec( - vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_mul_mat_vec(vx, y, dst, ncols, nrows, item_ct1); + }); } } @@ -942,12 +935,10 @@ static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec( - vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_mul_mat_vec(vx, y, dst, ncols, nrows, item_ct1); + }); } } @@ -963,12 +954,10 @@ static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec( - vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_mul_mat_vec(vx, y, dst, ncols, nrows, item_ct1); + }); } } @@ -984,12 +973,10 @@ static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec( - vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + dequantize_mul_mat_vec(vx, y, dst, ncols, nrows, item_ct1); + }); } } @@ -1002,11 +989,10 @@ static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y, const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { - dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { + dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1); + }); } static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y, @@ -1018,11 +1004,10 @@ static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y, const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { - dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { + dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1); + }); } static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y, @@ -1034,11 +1019,10 @@ static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y, const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { - dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { + dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1); + }); } static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, @@ -1047,11 +1031,10 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, dpct::queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const sycl::range<3> block_dims(1, 1, QK_WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { - dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { + dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1); + }); } static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y, @@ -1063,11 +1046,10 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y, const int block_num_y = (nrows + ny - 1) / ny; const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { - dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { + dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1); + }); } void ggml_sycl_op_dequantize_mul_mat_vec( diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index d538965b0..27c727860 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -13,10 +13,10 @@ #ifndef GGML_SYCL_DPCT_HELPER_HPP #define GGML_SYCL_DPCT_HELPER_HPP +#include #include #include #include -#include #ifdef GGML_SYCL_USE_INTEL_ONEMKL #include @@ -118,6 +118,36 @@ inline auto get_onemath_backend(sycl::queue& queue) #endif } +#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS + namespace syclex = sycl::ext::oneapi::experimental; +#endif + +template +__dpct_inline__ void sycl_parallel_for(sycl::handler & cgh, sycl::nd_range nd_range, Func && func) { +#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS + syclex::nd_launch(cgh, nd_range, func); +#else + cgh.parallel_for(nd_range, func); +#endif +} + +template +__dpct_inline__ void sycl_parallel_for(sycl::queue * q, sycl::nd_range nd_range, Func && func) { +#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS + syclex::nd_launch(*q, nd_range, func); +#else + q->parallel_for(nd_range, func); +#endif +} + +template __dpct_inline__ void sycl_launch(sycl::queue * stream, Func && func) { +#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS + syclex::submit(*stream, func); +#else + stream->submit(func); +#endif +} + namespace dpct { typedef sycl::queue *queue_ptr; diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index 5b7c4f0b4..c56924ce8 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -329,60 +329,51 @@ static void acc_f32_sycl(const float *x, const float *y, float *dst, const int ne12, const int nb1, const int nb2, const int offset, queue_ptr stream) { int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, - item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, item_ct1); + }); } template static void gelu_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - gelu(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { gelu(x, dst, k, item_ct1); }); } template static void silu_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - silu(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { silu(x, dst, k, item_ct1); }); } template static void sgn_sycl(const T * x, T * dst, const int k, queue_ptr stream) { // hard code for now const int num_blocks = ceil_div(k, 256); - stream->parallel_for( - sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range(1, 1, 256)), sycl::range(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) { - sgn(x, dst, k, item_ct1); - }); + sycl_parallel_for( + stream, sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range(1, 1, 256)), sycl::range(1, 1, 256)), + [=](sycl::nd_item<3> item_ct1) { sgn(x, dst, k, item_ct1); }); } template static void abs_sycl(const T * x, T * dst, const int k, queue_ptr stream) { // hard code for now const int num_blocks = ceil_div(k, 256); - stream->parallel_for( - sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) { - abs_op(x, dst, k, item_ct1); - }); + sycl_parallel_for( + stream, + sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), + [=](sycl::nd_item<3> item_ct1) { abs_op(x, dst, k, item_ct1); }); } @@ -390,23 +381,20 @@ template static void elu_sycl(const T * x, T * dst, const int k, queue_ptr stream) { // hard code for now const int num_blocks = ceil_div(k, 256); - stream->parallel_for( - sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) { - elu_op(x, dst, k, item_ct1); - }); + sycl_parallel_for( + stream, + sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), + [=](sycl::nd_item<3> item_ct1) { elu_op(x, dst, k, item_ct1); }); } template static void gelu_quick_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - gelu_quick(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { gelu_quick(x, dst, k, item_ct1); }); } @@ -414,169 +402,133 @@ template static void gelu_erf_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - gelu_erf(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { gelu_erf(x, dst, k, item_ct1); }); } template static void tanh_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - tanh(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { tanh(x, dst, k, item_ct1); }); } template static void relu_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - relu(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { relu(x, dst, k, item_ct1); }); } template static void hardsigmoid_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), + sycl_parallel_for( + stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - hardsigmoid(x, dst, k, item_ct1); - }); + [=](sycl::nd_item<3> item_ct1) { hardsigmoid(x, dst, k, item_ct1); }); } template static void hardswish_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), + sycl_parallel_for( + stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - hardswish(x, dst, k, item_ct1); - }); + [=](sycl::nd_item<3> item_ct1) { hardswish(x, dst, k, item_ct1); }); } template static void exp_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - exp(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { exp(x, dst, k, item_ct1); }); } template static void log_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - log(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { log(x, dst, k, item_ct1); }); } template static void neg_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - neg(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { neg(x, dst, k, item_ct1); }); } template static void step_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - step(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { step(x, dst, k, item_ct1); }); } template static void sigmoid_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_SIGMOID_BLOCK_SIZE - 1) / SYCL_SIGMOID_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE), + sycl_parallel_for( + stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - sigmoid(x, dst, k, item_ct1); - }); + [=](sycl::nd_item<3> item_ct1) { sigmoid(x, dst, k, item_ct1); }); } template static void sqrt_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_SQRT_BLOCK_SIZE - 1) / SYCL_SQRT_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - sqrt(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { sqrt(x, dst, k, item_ct1); }); } template static void sin_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - sin(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { sin(x, dst, k, item_ct1); }); } template static void cos_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - cos(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { cos(x, dst, k, item_ct1); }); } template @@ -584,26 +536,20 @@ static void leaky_relu_sycl(const T *x, T *dst, const int k, const float negative_slope, queue_ptr stream) { const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - leaky_relu(x, dst, k, negative_slope, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { leaky_relu(x, dst, k, negative_slope, item_ct1); }); } template static void sqr_sycl(const T *x, T *dst, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - sqr(x, dst, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { sqr(x, dst, k, item_ct1); }); } template @@ -614,9 +560,8 @@ static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01, int dst_size = ne10 * ne11 * ne12 * ne13; int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), - [=](sycl::nd_item<1> item_ct1) { + sycl_parallel_for<1>( + stream, sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) { upscale(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1); }); } @@ -627,12 +572,10 @@ static void pad_sycl(const T *x, T *dst, const int ne00, const int ne1, const int ne2, queue_ptr stream) { int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; sycl::range<3> gridDim(ne2, ne1, num_blocks); - stream->parallel_for( - sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - pad(x, dst, ne0, ne00, ne01, ne02, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { pad(x, dst, ne0, ne00, ne01, ne02, item_ct1); }); } template @@ -640,13 +583,10 @@ static void clamp_sycl(const T *x, T *dst, const float min, const float max, const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - clamp(x, dst, min, max, k, item_ct1); - }); + sycl_parallel_for(stream, + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { clamp(x, dst, min, max, k, item_ct1); }); } inline void ggml_sycl_op_sgn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { diff --git a/ggml/src/ggml-sycl/getrows.cpp b/ggml/src/ggml-sycl/getrows.cpp index 4a7712781..9c76ffeb9 100644 --- a/ggml/src/ggml-sycl/getrows.cpp +++ b/ggml/src/ggml-sycl/getrows.cpp @@ -60,54 +60,6 @@ static void k_get_rows( dst_row[iybs + iqs + y_offset] = v.y(); } -template -static void k_get_rows_reorder( - const void * src0, const void *src0_dq, const int32_t * src1, dst_t * dst, - int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ - /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ - /*size_t s0,*/ size_t s1, size_t s2, size_t s3, - /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, - size_t s10, size_t s11, size_t s12, - const sycl::nd_item<3> &item_ct1/*, size_t s13*/) { - - const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) + - item_ct1.get_local_id(2)) * - 2; - const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + - item_ct1.get_local_id(0)) / - ne12; - const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + - item_ct1.get_local_id(0)) % - ne12; - - if (i00 >= ne00) { - return; - } - auto ncols = ne00; - const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; - - dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; - - const int src0_off = i01 * ncols + i00; - const int ib = src0_off / QK4_0; // block index - const int iqs = (i00%qk)/qr; // x quant index - const int iybs = i00 - i00%qk; // dst block start index - const int y_offset = qr == 1 ? 1 : qk/2; - - // dequantize - dfloat2 v; - dequantize_kernel_recorder((const void *)src0_dq, ib, (const void *)src0, src0_off/2, v); - - dst_row[iybs + iqs + 0] = v.x(); - dst_row[iybs + iqs + y_offset] = v.y(); - - GGML_UNUSED(nb01); - GGML_UNUSED(nb02); - GGML_UNUSED(nb03); -} - template static void k_get_rows_float( const src0_t * src0, const int32_t * src1, dst_t * dst, @@ -166,58 +118,15 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr GGML_ASSERT(ne00 % 2 == 0); - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_get_rows( - src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, - s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { + k_get_rows(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, s3, nb01, nb02, nb03, s10, s11, s12, + item_ct1); + }); GGML_UNUSED(dst); GGML_UNUSED(ctx); } -template -static void get_rows_sycl_reorder(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const void *src0_dd, - const int32_t *src1_dd, float *dst_dd, - queue_ptr stream) { - - GGML_TENSOR_BINARY_OP_LOCALS - - const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE); - const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE); - const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x); - - // strides in elements - //const size_t s0 = nb0 / ggml_element_size(dst); - const size_t s1 = nb1 / ggml_element_size(dst); - const size_t s2 = nb2 / ggml_element_size(dst); - const size_t s3 = nb3 / ggml_element_size(dst); - - const size_t s10 = nb10 / ggml_element_size(src1); - const size_t s11 = nb11 / ggml_element_size(src1); - const size_t s12 = nb12 / ggml_element_size(src1); - //const size_t s13 = nb13 / ggml_element_size(src1); - - GGML_ASSERT(ne00 % 2 == 0); - - const uint8_t* src0_q = (const uint8_t*)src0_dd; - const size_t ncols = ne00; - const size_t nrows = ne01; - const sycl::half* src0_dq = (const sycl::half*)(src0_q + nrows * ncols / 2); - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ - k_get_rows_reorder( - src0_dd, src0_dq, src1_dd, dst_dd, ne00, ne12, s1, s2, - s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); - }); - - GGML_UNUSED(dst); - GGML_UNUSED(ctx); -} - - template static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, @@ -245,9 +154,8 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); }); @@ -277,13 +185,8 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q4_0: - if (ctx.opt_feature.reorder && dst->op == GGML_OP_MUL_MAT) { - get_rows_sycl_reorder(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, - src1_i32, (float *)dst->data, ctx.stream()); - } else { - get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, - src1_i32, (float *)dst->data, ctx.stream()); - } + get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, + src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q4_1: get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 4b7610362..f25a96a62 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -1887,13 +1887,12 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, const size_t shared_mem = ncols_pad * sizeof(int); if (order == GGML_SORT_ORDER_ASC) { - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor dpct_local_acc_ct1( sycl::range<1>(shared_mem), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_argsort_f32_i32( x, dst, ncols, ncols_pad, item_ct1, dpct_local_acc_ct1.get_multi_ptr() @@ -1901,13 +1900,12 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, }); }); } else if (order == GGML_SORT_ORDER_DESC) { - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor dpct_local_acc_ct1( sycl::range<1>(shared_mem), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_argsort_f32_i32( x, dst, ncols, ncols_pad, item_ct1, dpct_local_acc_ct1.get_multi_ptr() @@ -1925,50 +1923,47 @@ static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols, const sycl::range<3> block_nums(1, nrows, 1); const size_t shared_mem = 256 * sizeof(float); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor shared_data( sycl::range<1>(shared_mem/sizeof(float)), cgh); sycl::local_accessor shared_indices( sycl::range<1>(shared_mem/sizeof(float)), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - const int tid = item_ct1.get_local_id(2); - const int row = item_ct1.get_global_id(1); + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { + const int tid = item_ct1.get_local_id(2); + const int row = item_ct1.get_global_id(1); - float max_val = -INFINITY; - int max_idx = -1; + float max_val = -INFINITY; + int max_idx = -1; - for (int col = tid; col < ncols; col += 256) { - float val = x[row * ncols + col]; - if (val > max_val) { - max_val = val; - max_idx = col; + for (int col = tid; col < ncols; col += 256) { + float val = x[row * ncols + col]; + if (val > max_val) { + max_val = val; + max_idx = col; + } + } + + shared_data[tid] = max_val; + shared_indices[tid] = max_idx; + item_ct1.barrier(sycl::access::fence_space::local_space); + + for (int stride = 256 / 2; stride > 0; stride >>= 1) { + if (tid < stride) { + float val1 = shared_data[tid]; + float val2 = shared_data[tid + stride]; + if (val2 > val1) { + shared_data[tid] = val2; + shared_indices[tid] = shared_indices[tid + stride]; } } - - shared_data[tid] = max_val; - shared_indices[tid] = max_idx; item_ct1.barrier(sycl::access::fence_space::local_space); + } - for (int stride = 256/2; stride > 0; stride >>= 1) { - if (tid < stride) { - float val1 = shared_data[tid]; - float val2 = shared_data[tid + stride]; - if (val2 > val1) { - shared_data[tid] = val2; - shared_indices[tid] = shared_indices[tid + stride]; - } - } - item_ct1.barrier(sycl::access::fence_space::local_space); - } - - - if (tid == 0) { - dst[row] = shared_indices[0]; - } - }); + if (tid == 0) { + dst[row] = shared_indices[0]; + } + }); }); } static void diag_mask_inf_f32_sycl(const float *x, float *dst, @@ -2952,7 +2947,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons void ** ptrs_dst_get = ptrs_dst.get(); size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : s12 * sizeof(sycl::half); size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : s13 * sizeof(sycl::half); - cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_compute_batched_ptrs(src0_f16, src1_f16, dst_ddf, ptrs_src_get, ptrs_dst_get, ne12, ne13, ne23, nb02, nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1); }); @@ -3456,7 +3451,7 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, { sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u)); sycl::range<3> grid_dims(1, n_ids, ids->ne[1]); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor src1_row_acc(cgh); char *__restrict src1_contiguous_get = @@ -3468,9 +3463,8 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, size_t ids_nb_ct6 = ids->nb[1]; size_t ids_nb_ct7 = ids->nb[0]; - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_copy_src1_to_contiguous( src1_original, src1_contiguous_get, dev_cur_src1_row_get, @@ -3501,15 +3495,14 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, { sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u)); sycl::range<3> grid_dims(1, 1, num_src1_rows); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { const char *__restrict dst_contiguous_get = dst_contiguous.get(); const mmid_row_mapping *__restrict dev_row_mapping_get = dev_row_mapping.get(); - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_copy_dst_from_contiguous(dst_original, dst_contiguous_get, dev_row_mapping_get, diff --git a/ggml/src/ggml-sycl/gla.cpp b/ggml/src/ggml-sycl/gla.cpp index 879184fdd..b40cbf1f1 100644 --- a/ggml/src/ggml-sycl/gla.cpp +++ b/ggml/src/ggml-sycl/gla.cpp @@ -11,13 +11,13 @@ static void gated_linear_attn_f32_kernel(const dpct::queue_ptr stream, u_int B, const u_int n_seq_tokens = T / B; sycl::range<1> block_dims((C / H)); sycl::range<1> grid_dims((B * H)); - stream->submit([&](sycl::handler & cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { /* local memory accessors*/ auto _k = sycl::local_accessor(sycl::range<1>(head_size), cgh); auto _r = sycl::local_accessor(sycl::range<1>(head_size), cgh); auto _td = sycl::local_accessor(sycl::range<1>(head_size), cgh); - cgh.parallel_for(sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) { + sycl_parallel_for<1>(cgh, sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) { u_int tid = item.get_local_id(0); u_int bid = item.get_group(0); diff --git a/ggml/src/ggml-sycl/im2col.cpp b/ggml/src/ggml-sycl/im2col.cpp index aa19c2527..52737cc74 100644 --- a/ggml/src/ggml-sycl/im2col.cpp +++ b/ggml/src/ggml-sycl/im2col.cpp @@ -70,7 +70,7 @@ static void im2col_sycl_internal(const float * x, T * dst, int64_t IW, int64_t I const int64_t CHW = IC * KH * KW; - stream->parallel_for(sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) { im2col_kernel(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, CHW, s0, s1, p0, p1, d0, d1, item_ct1); }); diff --git a/ggml/src/ggml-sycl/mmq.cpp b/ggml/src/ggml-sycl/mmq.cpp index ffb272aa2..c72fcd38e 100644 --- a/ggml/src/ggml-sycl/mmq.cpp +++ b/ggml/src/ggml-sycl/mmq.cpp @@ -1818,7 +1818,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_qs_q4_0_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_d_q4_0_acc_ct1( @@ -1829,9 +1829,8 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q4_0( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -1853,7 +1852,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_qs_q4_0_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_d_q4_0_acc_ct1( @@ -1864,9 +1863,8 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q4_0( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -1933,7 +1931,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_qs_q4_1_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh); sycl::local_accessor tile_x_dm_q4_1_acc_ct1( @@ -1944,9 +1942,8 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q4_1( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -1968,7 +1965,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_qs_q4_1_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh); sycl::local_accessor tile_x_dm_q4_1_acc_ct1( @@ -1979,9 +1976,8 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q4_1( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2048,7 +2044,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q5_0_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_d_q5_0_acc_ct1( @@ -2059,9 +2055,8 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q5_0( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2083,7 +2078,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q5_0_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_d_q5_0_acc_ct1( @@ -2094,9 +2089,8 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q5_0( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2163,7 +2157,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q5_1_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q5_1_acc_ct1( @@ -2174,9 +2168,8 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q5_1( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2198,7 +2191,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q5_1_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q5_1_acc_ct1( @@ -2209,9 +2202,8 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q5_1( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2278,7 +2270,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_qs_q8_0_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_d_q8_0_acc_ct1( @@ -2289,9 +2281,8 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q8_0( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2313,7 +2304,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_qs_q8_0_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_d_q8_0_acc_ct1( @@ -2324,9 +2315,8 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q8_0( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2393,7 +2383,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q2_K_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q2_K_acc_ct1( @@ -2406,9 +2396,8 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q2_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2431,7 +2420,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q2_K_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q2_K_acc_ct1( @@ -2444,9 +2433,8 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q2_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2516,7 +2504,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q3_K_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q3_K_acc_ct1( @@ -2531,9 +2519,8 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q3_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2557,7 +2544,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q3_K_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q3_K_acc_ct1( @@ -2572,9 +2559,8 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q3_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2644,7 +2630,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q4_K_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q4_K_acc_ct1( @@ -2657,9 +2643,8 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q4_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2682,7 +2667,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q4_K_acc_ct1( sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q4_K_acc_ct1( @@ -2695,9 +2680,8 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q4_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2765,7 +2749,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q5_K_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q5_K_acc_ct1( @@ -2778,9 +2762,8 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q5_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2803,7 +2786,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_q5_K_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_q5_K_acc_ct1( @@ -2816,9 +2799,8 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q5_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2886,7 +2868,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_acc_ct1( @@ -2899,9 +2881,8 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q6_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, @@ -2924,7 +2905,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor tile_x_ql_acc_ct1( sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); sycl::local_accessor tile_x_dm_acc_ct1( @@ -2937,9 +2918,8 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, sycl::local_accessor tile_y_ds_acc_ct1( sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { mul_mat_q6_K( vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, item_ct1, diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp index 5b7f06407..c21929d51 100644 --- a/ggml/src/ggml-sycl/mmvq.cpp +++ b/ggml/src/ggml-sycl/mmvq.cpp @@ -544,12 +544,12 @@ static void reorder_mul_mat_vec_q4_0_q8_1_sycl(const void * vx, const void * vy, const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, (block_num_y * WARP_SIZE)); const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE); - stream->submit([&](sycl::handler & cgh) { - cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size), - [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_reorder>(vx, vy, dst, ncols, nrows, - nd_item); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size), + [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_reorder>(vx, vy, dst, ncols, nrows, + nd_item); + }); }); } @@ -561,12 +561,12 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void * vx, const void * vy, float * const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - stream->submit([&](sycl::handler & cgh) { - cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -580,17 +580,12 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -604,17 +599,12 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -628,17 +618,12 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -652,17 +637,12 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -676,17 +656,12 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -700,17 +675,12 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -724,17 +694,12 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -750,12 +715,12 @@ static void reorder_mul_mat_vec_q4_k_q8_1_sycl(const void * vx, const void * vy, const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE); const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE); - stream->submit([&](sycl::handler & cgh) { - cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size), - [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_reorder>(vx, vy, dst, ncols, - nrows, nd_item); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size), + [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_reorder>(vx, vy, dst, ncols, nrows, + nd_item); + }); }); } @@ -769,17 +734,12 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -794,12 +754,12 @@ static void reorder_mul_mat_vec_q6_k_q8_1_sycl(const void * vx, const void * vy, const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE); const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE); - stream->submit([&](sycl::handler & cgh) { - cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size), - [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_reorder>(vx, vy, dst, ncols, nrows, - nd_item); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size), + [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_reorder>(vx, vy, dst, ncols, nrows, + nd_item); + }); }); } static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, @@ -811,17 +771,12 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); }); } } @@ -836,14 +791,12 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq2_xxs_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq2_xxs_q8_1(vx, vy, dst, ncols, + nrows, item_ct1); + }); }); } } @@ -857,14 +810,12 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - stream->submit([&](sycl::handler & cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq2_xs_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq2_xs_q8_1(vx, vy, dst, ncols, + nrows, item_ct1); + }); }); } } @@ -878,15 +829,12 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq2_s_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq2_s_q8_1(vx, vy, dst, ncols, nrows, + item_ct1); + }); }); } } @@ -900,15 +848,12 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq3_xxs_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq3_xxs_q8_1(vx, vy, dst, ncols, + nrows, item_ct1); + }); }); } } @@ -922,15 +867,12 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq3_s_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq3_s_q8_1(vx, vy, dst, ncols, nrows, + item_ct1); + }); }); } } @@ -944,15 +886,12 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq1_s_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq1_s_q8_1(vx, vy, dst, ncols, nrows, + item_ct1); + }); }); } } @@ -966,14 +905,12 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq1_m_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq1_m_q8_1(vx, vy, dst, ncols, nrows, + item_ct1); + }); }); } } @@ -987,15 +924,12 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq4_nl_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq4_nl_q8_1(vx, vy, dst, ncols, nrows, + item_ct1); + }); }); } } @@ -1009,15 +943,12 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy, const sycl::range<3> block_nums(1, 1, block_num_y); const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); { - - stream->submit([&](sycl::handler &cgh) { - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_q_iq4_xs_q8_1( - vx, vy, dst, ncols, nrows, item_ct1); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_iq4_xs_q8_1(vx, vy, dst, ncols, + nrows, item_ct1); + }); }); } } diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp index 4ec141684..79d846b41 100644 --- a/ggml/src/ggml-sycl/norm.cpp +++ b/ggml/src/ggml-sycl/norm.cpp @@ -254,14 +254,13 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i GGML_ASSERT(ncols % WARP_SIZE == 0); if (ncols < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); - stream->submit([&](sycl::handler& cgh) { - cgh.parallel_for( - sycl::nd_range<3>(global_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE); - }); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, + nullptr, WARP_SIZE); + }); + }); } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; @@ -272,16 +271,15 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor s_sum_acc_ct1( sycl::range<1>(work_group_size / WARP_SIZE), cgh); - cgh.parallel_for( - sycl::nd_range<3>(global_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size); - }); - }); + sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, + get_pointer(s_sum_acc_ct1), work_group_size); + }); + }); } } @@ -290,18 +288,14 @@ static void group_norm_f32_sycl(const float* x, float* dst, const int ne_elements, queue_ptr stream, int device) { if (group_size < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { const float eps_ct4 = eps; - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - group_norm_f32( - x, dst, group_size, ne_elements, eps_ct4, item_ct1, - nullptr, WARP_SIZE); - }); - }); + sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + group_norm_f32(x, dst, group_size, ne_elements, eps_ct4, item_ct1, nullptr, + WARP_SIZE); + }); + }); } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; @@ -313,22 +307,18 @@ static void group_norm_f32_sycl(const float* x, float* dst, info::device::max_work_group_size. Adjust the work-group size if needed. */ - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), cgh); const float eps_ct4 = eps; - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - group_norm_f32(x, dst, group_size, ne_elements, - eps_ct4, item_ct1, - get_pointer(s_sum_acc_ct1), work_group_size); - }); - }); + sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + group_norm_f32(x, dst, group_size, ne_elements, eps_ct4, item_ct1, + get_pointer(s_sum_acc_ct1), work_group_size); + }); + }); } } @@ -340,14 +330,13 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const const sycl::range<3> global_dims(nsamples, nchannels, nrows); if (ncols < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); - stream->submit([&](sycl::handler& cgh) { - cgh.parallel_for( - sycl::nd_range<3>(global_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE); - }); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, + nullptr, WARP_SIZE); + }); + }); } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; @@ -358,16 +347,15 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), cgh); - cgh.parallel_for( - sycl::nd_range<3>(global_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size); - }); - }); + sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, + get_pointer(s_sum_acc_ct1), work_group_size); + }); + }); } } @@ -378,16 +366,12 @@ static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols, // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); if (ncols < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); - stream->submit([&](sycl::handler& cgh) { - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - l2_norm_f32(x, dst, ncols, eps, item_ct1, - nullptr, WARP_SIZE); - }); - }); + sycl_launch(stream, [&](sycl::handler & cgh) { + sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + l2_norm_f32(x, dst, ncols, eps, item_ct1, nullptr, WARP_SIZE); + }); + }); } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; @@ -398,18 +382,15 @@ static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols, the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), cgh); - cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, - block_dims), - [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - l2_norm_f32(x, dst, ncols, eps, item_ct1, - get_pointer(s_sum_acc_ct1), work_group_size); - }); - }); + sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + l2_norm_f32(x, dst, ncols, eps, item_ct1, get_pointer(s_sum_acc_ct1), + work_group_size); + }); + }); } } diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp index 44473e1e5..e44c6b6ef 100644 --- a/ggml/src/ggml-sycl/rope.cpp +++ b/ggml/src/ggml-sycl/rope.cpp @@ -235,20 +235,22 @@ static void rope_norm_sycl(const T * x, T * dst, const int ne0, const int ne1, c the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_norm(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_norm(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, + attn_factor, corr_dims, theta_scale, freq_factors, item_ct1); + }); } else { /* DPCT1049:41: The work-group size passed to the SYCL kernel may exceed the limit. To get the device limit, query info::device::max_work_group_size. Adjust the work-group size if needed. */ - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_norm(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_norm(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, + attn_factor, corr_dims, theta_scale, freq_factors, item_ct1); + }); } } @@ -267,15 +269,17 @@ static void rope_neox_sycl(const T * x, T * dst, const int ne0, const int ne1, c dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); if (freq_factors == nullptr) { - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, + attn_factor, corr_dims, theta_scale, freq_factors, item_ct1); + }); } else { - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, + attn_factor, corr_dims, theta_scale, freq_factors, item_ct1); + }); } } @@ -298,12 +302,12 @@ static void rope_multi_sycl(const T * x, T * dst, const int ne0, const int ne1, } // launch kernel if (freq_factors == nullptr) { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) { rope_multi(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale, freq_factors, sections, item_ct1); }); } else { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) { rope_multi(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale, freq_factors, sections, item_ct1); }); @@ -333,12 +337,12 @@ static void rope_vision_sycl(const T * x, T * dst, const int ne0, const int ne1, } // launch kernel if (freq_factors == nullptr) { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) { rope_vision(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale, freq_factors, sections, item_ct1); }); } else { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) { rope_vision(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale, freq_factors, sections, item_ct1); }); diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp index 52fcf4b3d..7b60c292e 100644 --- a/ggml/src/ggml-sycl/softmax.cpp +++ b/ggml/src/ggml-sycl/softmax.cpp @@ -127,11 +127,11 @@ static void soft_max_f32_submitter(const float * x, const T * mask, float * dst, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, const size_t n_local_scratch, queue_ptr stream) { - stream->submit([&](sycl::handler &cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor local_buf_acc(n_local_scratch, cgh); - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), + sycl_parallel_for( + cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { soft_max_f32(x, mask, dst, ncols_par, nrows_y, scale, max_bias, m0, diff --git a/ggml/src/ggml-sycl/tsembd.cpp b/ggml/src/ggml-sycl/tsembd.cpp index f6ca626ea..721c8fa6f 100644 --- a/ggml/src/ggml-sycl/tsembd.cpp +++ b/ggml/src/ggml-sycl/tsembd.cpp @@ -45,14 +45,9 @@ static void timestep_embedding_f32_sycl( int num_blocks = (half_ceil + SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE; sycl::range<3> block_dims(1, 1, SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE); sycl::range<3> gridDim(1, ne00, num_blocks); - stream->parallel_for( - sycl::nd_range<3>( - gridDim * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - timestep_embedding_f32( - x, dst, nb1, dim, max_period, item_ct1 - ); - }); + sycl_parallel_for(stream, sycl::nd_range<3>(gridDim * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { + timestep_embedding_f32(x, dst, nb1, dim, max_period, item_ct1); + }); } void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { diff --git a/ggml/src/ggml-sycl/wkv.cpp b/ggml/src/ggml-sycl/wkv.cpp index c10e2f764..3ed5bbf35 100644 --- a/ggml/src/ggml-sycl/wkv.cpp +++ b/ggml/src/ggml-sycl/wkv.cpp @@ -207,12 +207,11 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { // Submit kernel if (C / H == WKV_BLOCK_SIZE) { - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor shared_mem_acc(shared_mem_size, cgh); - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { rwkv_wkv6_f32_kernel( B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d, item_ct1, (float*)shared_mem_acc.get_multi_ptr().get() @@ -220,12 +219,11 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { }); }); } else { - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor shared_mem_acc(shared_mem_size, cgh); - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { rwkv_wkv6_f32_kernel( B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d, item_ct1, (float*)shared_mem_acc.get_multi_ptr().get() @@ -264,12 +262,11 @@ void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { // Submit kernel if (C / H == WKV_BLOCK_SIZE) { - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor shared_mem_acc(shared_mem_size, cgh); - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { rwkv_wkv7_f32_kernel( B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d, item_ct1, (float*)shared_mem_acc.get_multi_ptr().get() @@ -277,12 +274,11 @@ void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { }); }); } else { - stream->submit([&](sycl::handler& cgh) { + sycl_launch(stream, [&](sycl::handler & cgh) { sycl::local_accessor shared_mem_acc(shared_mem_size, cgh); - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { + sycl_parallel_for( + cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { rwkv_wkv7_f32_kernel( B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d, item_ct1, (float*)shared_mem_acc.get_multi_ptr().get() diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 8d62303aa..99be5e45b 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -1041,6 +1041,14 @@ void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) { struct vk_instance_t { vk::Instance instance; + bool debug_utils_support = false; // VK_EXT_debug_utils enabled + PFN_vkSetDebugUtilsObjectNameEXT pfn_vkSetDebugUtilsObjectNameEXT = {}; + PFN_vkQueueBeginDebugUtilsLabelEXT pfn_vkQueueBeginDebugUtilsLabelEXT = {}; + PFN_vkQueueEndDebugUtilsLabelEXT pfn_vkQueueEndDebugUtilsLabelEXT = {}; + PFN_vkCmdBeginDebugUtilsLabelEXT pfn_vkCmdBeginDebugUtilsLabelEXT = {}; + PFN_vkCmdEndDebugUtilsLabelEXT pfn_vkCmdEndDebugUtilsLabelEXT = {}; + PFN_vkCmdInsertDebugUtilsLabelEXT pfn_vkCmdInsertDebugUtilsLabelEXT = {}; + std::vector device_indices; vk_device devices[GGML_VK_MAX_DEVICES]; }; @@ -1180,6 +1188,14 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin } pipeline->compiled = true; + if (vk_instance.debug_utils_support) { + vk::DebugUtilsObjectNameInfoEXT duoni; + duoni.objectType = vk::ObjectType::ePipeline; + duoni.pObjectName = pipeline->name.c_str(); + duoni.objectHandle = reinterpret_cast(static_cast(pipeline->pipeline)); + vk_instance.pfn_vkSetDebugUtilsObjectNameEXT(device->device, &static_cast(duoni)); + } + { std::lock_guard guard(device->mutex); device->pipelines.insert({ pipeline->name, pipeline }); @@ -3561,6 +3577,8 @@ static void ggml_vk_print_gpu_info(size_t idx) { static bool ggml_vk_instance_validation_ext_available(const std::vector& instance_extensions); static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions); +static bool ggml_vk_instance_debug_utils_ext_available(const std::vector & instance_extensions); + static void ggml_vk_instance_init() { if (vk_instance_initialized) { return; @@ -3581,7 +3599,7 @@ static void ggml_vk_instance_init() { #ifdef __APPLE__ const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions); #endif - + const bool debug_utils_ext = ggml_vk_instance_debug_utils_ext_available(instance_extensions) && getenv("GGML_VK_DEBUG_MARKERS") != nullptr; std::vector layers; if (validation_ext) { @@ -3596,6 +3614,9 @@ static void ggml_vk_instance_init() { extensions.push_back("VK_KHR_portability_enumeration"); } #endif + if (debug_utils_ext) { + extensions.push_back("VK_EXT_debug_utils"); + } vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); #ifdef __APPLE__ if (portability_enumeration_ext) { @@ -3619,6 +3640,18 @@ static void ggml_vk_instance_init() { vk_instance.instance = vk::createInstance(instance_create_info); vk_instance_initialized = true; + if (debug_utils_ext) { + vk_instance.debug_utils_support = true; + vk_instance.pfn_vkSetDebugUtilsObjectNameEXT = (PFN_vkSetDebugUtilsObjectNameEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkSetDebugUtilsObjectNameEXT"); + vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT = (PFN_vkQueueBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueBeginDebugUtilsLabelEXT"); + vk_instance.pfn_vkQueueEndDebugUtilsLabelEXT = (PFN_vkQueueEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueEndDebugUtilsLabelEXT"); + vk_instance.pfn_vkCmdBeginDebugUtilsLabelEXT = (PFN_vkCmdBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdBeginDebugUtilsLabelEXT"); + vk_instance.pfn_vkCmdEndDebugUtilsLabelEXT = (PFN_vkCmdEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdEndDebugUtilsLabelEXT"); + vk_instance.pfn_vkCmdInsertDebugUtilsLabelEXT = (PFN_vkCmdInsertDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdInsertDebugUtilsLabelEXT"); + + } + + size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size(); vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr; // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan @@ -9495,6 +9528,12 @@ static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer UNUSED(buft); } +static size_t ggml_backend_vk_host_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return vk_instance.devices[0]->suballocation_block_size; + + UNUSED(buft); +} + // Should be changed to return device-specific host buffer type // but that probably requires changes in llama.cpp ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { @@ -9503,7 +9542,7 @@ ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { /* .get_name = */ ggml_backend_vk_host_buffer_type_name, /* .alloc_buffer = */ ggml_backend_vk_host_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_vk_host_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_max_size = */ ggml_backend_vk_host_buffer_type_get_max_size, /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, @@ -9650,6 +9689,13 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + if (vk_instance.debug_utils_support) { + vk::DebugUtilsLabelEXT dul = {}; + dul.pLabelName = "ggml_backend_vk_graph_compute"; + dul.color = std::array{1.0f, 1.0f, 1.0f, 1.0f}; + vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue.queue, reinterpret_cast(&dul)); + } + uint64_t total_mat_mul_bytes = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false, false, false); @@ -10339,6 +10385,22 @@ static bool ggml_vk_instance_portability_enumeration_ext_available(const std::ve UNUSED(instance_extensions); } +// Extension availability +static bool ggml_vk_instance_debug_utils_ext_available( + const std::vector & instance_extensions) { + // Check for portability enumeration extension for MoltenVK support + for (const auto & properties : instance_extensions) { + if (strcmp("VK_EXT_debug_utils", properties.extensionName) == 0) { + return true; + } + } + + std::cerr << "ggml_vulkan: WARNING: Instance extension VK_EXT_debug_utils not found." << std::endl; + return false; + + UNUSED(instance_extensions); +} + static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch) { switch (props.vendorID) { case VK_VENDOR_ID_INTEL: diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index f3de7b7b0..31fb5d3f7 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -955,6 +955,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "UPSCALE", "PAD", "PAD_REFLECT_1D", + "ROLL", "ARANGE", "TIMESTEP_EMBEDDING", "ARGSORT", @@ -985,7 +986,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "OPT_STEP_ADAMW", }; -static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82"); +static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1050,6 +1051,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "upscale(x)", "pad(x)", "pad_reflect_1d(x)", + "roll(x)", "arange(start, stop, step)", "timestep_embedding(timesteps, dim, max_period)", "argsort(x)", @@ -1080,7 +1082,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "adamw(x)", }; -static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82"); +static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -4341,6 +4343,34 @@ struct ggml_tensor * ggml_pad_reflect_1d( return result; } +// ggml_roll + +struct ggml_tensor * ggml_roll( + struct ggml_context * ctx, + struct ggml_tensor * a, + int shift0, + int shift1, + int shift2, + int shift3) { + GGML_ASSERT(a->nb[0] == ggml_type_size(a->type)); + GGML_ASSERT(abs(shift0) < a->ne[0]); + GGML_ASSERT(abs(shift1) < a->ne[1]); + GGML_ASSERT(abs(shift2) < a->ne[2]); + GGML_ASSERT(abs(shift3) < a->ne[3]); + + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + ggml_set_op_params_i32(result, 0, shift0); + ggml_set_op_params_i32(result, 1, shift1); + ggml_set_op_params_i32(result, 2, shift2); + ggml_set_op_params_i32(result, 3, shift3); + + result->op = GGML_OP_ROLL; + result->src[0] = a; + + return result; +} + // ggml_arange struct ggml_tensor * ggml_arange( diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 42a05ebb5..cecda83a1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -199,6 +199,7 @@ class Keys: MASK_ID = "tokenizer.ggml.mask_token_id" ADD_BOS = "tokenizer.ggml.add_bos_token" ADD_EOS = "tokenizer.ggml.add_eos_token" + ADD_SEP = "tokenizer.ggml.add_sep_token" ADD_PREFIX = "tokenizer.ggml.add_space_prefix" REMOVE_EXTRA_WS = "tokenizer.ggml.remove_extra_whitespaces" PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index e1e8079f3..5a57bc20b 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -894,6 +894,9 @@ class GGUFWriter: def add_add_eos_token(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_EOS, value) + def add_add_sep_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_SEP, value) + def add_add_space_prefix(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index cca097986..3f541b0c0 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -7,7 +7,10 @@ import os from pathlib import Path from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable -from sentencepiece import SentencePieceProcessor +try: + from sentencepiece import SentencePieceProcessor +except ImportError: + SentencePieceProcessor = None import gguf @@ -116,6 +119,7 @@ class SpecialVocab: logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping') def _try_load_from_tokenizer_json(self, path: Path) -> bool: + tokenizer = None tokenizer_file = path / 'tokenizer.json' if tokenizer_file.is_file(): with open(tokenizer_file, encoding = 'utf-8') as f: @@ -149,11 +153,97 @@ class SpecialVocab: added_tokens = tokenizer.get('added_tokens', {}) else: added_tokens = {} + tokenizer_config = None tokenizer_config_file = path / 'tokenizer_config.json' - if not tokenizer_config_file.is_file(): + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, encoding = 'utf-8') as f: + tokenizer_config = json.load(f) + if tokenizer: + special_bos = (tokenizer_config or {}).get('bos_token') + special_cls = (tokenizer_config or {}).get('cls_token') + special_eos = (tokenizer_config or {}).get('eos_token') + special_sep = (tokenizer_config or {}).get('sep_token') + if not special_bos and special_cls and tokenizer_config: + tokenizer_config['bos_token'] = special_bos = special_cls + if not special_eos and special_sep and tokenizer_config: + tokenizer_config['eos_token'] = special_eos = special_sep + if post_processor := tokenizer.get('post_processor'): + for processor in post_processor.get('processors', [post_processor]): + if processor.get('type') == 'RobertaProcessing': + self.add_special_token['bos'] = True + self.add_special_token['eos'] = True + self.add_special_token['sep'] = True + if not special_cls and tokenizer_config: + special_cls = processor.get('cls', [special_bos])[0] + tokenizer_config['cls_token'] = special_cls + if not special_sep and tokenizer_config: + special_sep = processor.get('sep', [special_eos])[0] + tokenizer_config['sep_token'] = special_sep + continue + # Crude parsing of TemplateProcessing to determine if BOS/SEP/EOS should be added + # Only works with simple templates, **will** get it wrong on unusual sequences + if processor.get('type') == 'TemplateProcessing': + tmpl_single = processor.get('single', []) + tmpl_pair = processor.get('pair', []) + special_first = None + special_last = None + if len(tmpl_single) > 1: + if special_first := tmpl_single[0].get('SpecialToken', {}).get('id'): + if not tokenizer_config: + special_bos = special_first + self.add_special_token['bos'] = True if special_first in (special_bos, special_cls) else False + if special_first not in (special_bos, special_cls): + logger.warning(f'Unknown leading special token {special_first!r} in TemplateProcessing') + if special_last := tmpl_single[-1].get('SpecialToken', {}).get('id'): + if not tokenizer_config: + special_eos = special_last + elif special_last != special_eos: + if 'eot' not in self.special_token_types: + self.special_token_types = tuple(self.special_token_types) + ('eot', ) + tokenizer_config['eot_token'] = special_eos + elif 'eom' not in self.special_token_types: + self.special_token_types = tuple(self.special_token_types) + ('eom', ) + tokenizer_config['eom_token'] = special_eos + else: + logger.warning(f'Overriding EOS token {special_eos!r} with {special_last!r} without EOT/EOM fallback!') + tokenizer_config['eos_token'] = special_eos = special_last + self.add_special_token['eos'] = True if special_last == special_eos else False + if special_last != special_eos: + logger.warning(f'Unknown trailing special token {special_last!r} in TemplateProcessing') + if tmpl_pair: + seq_start = 1 if special_first and tmpl_pair[0].get('SpecialToken', {}).get('id') == special_first else 0 + seq_stop = -1 if special_last and tmpl_pair[-1].get('SpecialToken', {}).get('id') == special_last else None + if (special_first and seq_start == 0) or (special_last and seq_stop is None): + logger.warning('TemplateProcessing leading/trailing special tokens do not match TemplateProcessing') + if tmpl_pair := tmpl_pair[slice(seq_start, seq_stop)]: + tmpl_a = tmpl_pair[0].get('Sequence', {}).get('id') + tmpl_b = tmpl_pair[-1].get('Sequence', {}).get('id') + if tmpl_a != 'A' or tmpl_b != 'B': + logger.warning(f'Unknown sequence {tmpl_a}...{tmpl_b} in TemplateProcessing') + # A [sep] [eos] B + if tmpl_a == 'A' and tmpl_b == 'B' and (tmpl_pair := tmpl_pair[1:-1]): + add_sep = False + if special_entry := tmpl_pair[0].get('SpecialToken', {}).get('id'): + if special_entry in (special_sep, special_eos) and not special_last: + add_sep = True + if special_entry not in (special_sep, special_eos): + logger.warning(f'Unknown separator token {special_entry!r} in TemplateProcessing') + else: + logger.warning(f'Unknown middle sequence {tmpl_pair[0]!r} in TemplateProcessing') + if len(tmpl_pair) == 2: + if special_entry := tmpl_pair[1].get('SpecialToken', {}).get('id'): + if special_entry in (special_sep, special_eos): + add_sep = True + if special_entry not in (special_sep, special_eos): + logger.warning(f'Unknown second separator token {special_entry!r} in TemplateProcessing') + else: + logger.warning(f'Unknown second middle sequence {tmpl_pair[1]!r} in TemplateProcessing') + self.add_special_token['sep'] = add_sep + if add_sep and not special_sep and tokenizer_config: + tokenizer_config['sep_token'] = special_eos + continue + if not tokenizer_config: return True - with open(tokenizer_config_file, encoding = 'utf-8') as f: - tokenizer_config = json.load(f) chat_template_alt = None chat_template_file = path / 'chat_template.json' if chat_template_file.is_file(): @@ -302,6 +392,9 @@ class SentencePieceVocab(Vocab): name = "spm" def __init__(self, base_path: Path): + if SentencePieceProcessor is None: + raise RuntimeError("sentencepiece is not installed") + added_tokens: dict[str, int] = {} if (fname_tokenizer := base_path / 'tokenizer.model').exists(): # normal location diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index f11351cba..0f3a1eeee 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.17.0" +version = "0.17.1" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ @@ -22,7 +22,7 @@ python = ">=3.8" numpy = ">=1.17" tqdm = ">=4.27" pyyaml = ">=5.1" -sentencepiece = ">=0.1.98,<=0.2.0" +sentencepiece = { version = ">=0.1.98,<=0.2.0", optional = true } PySide6 = { version = "^6.9", python = ">=3.9,<3.14", optional = true } [tool.poetry.dev-dependencies] diff --git a/include/llama.h b/include/llama.h index 635508b10..3eda9bc68 100644 --- a/include/llama.h +++ b/include/llama.h @@ -390,6 +390,7 @@ extern "C" { void * imatrix; // pointer to importance matrix data void * kv_overrides; // pointer to vector containing overrides void * tensor_types; // pointer to vector containing tensor types + void * prune_layers; // pointer to vector containing layer indices to prune } llama_model_quantize_params; typedef struct llama_logit_bias { @@ -943,12 +944,14 @@ extern "C" { // Requires the context to have a memory. // For encode-decoder contexts, processes the batch using the decoder. // Positive return values does not mean a fatal error, but rather a warning. - // Upon non-zero return values, the memory state is restored to the state before this call + // Upon fatal-error or abort, the ubatches that managed to be been processed will remain in the memory state of the context + // To handle this correctly, query the memory state using llama_memory_seq_pos_min() and llama_memory_seq_pos_max() + // Upon other return values, the memory state is restored to the state before this call // 0 - success // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) - // 2 - aborted + // 2 - aborted (processed ubatches will remain in the context's memory) // -1 - invalid input batch - // < -1 - error + // < -1 - fatal error (processed ubatches will remain in the context's memory) LLAMA_API int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch); @@ -1044,6 +1047,7 @@ extern "C" { LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab); LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab); + LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab); LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab); LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab); @@ -1087,6 +1091,7 @@ extern "C" { /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. /// @return Returns the number of tokens on success, no more than n_tokens_max /// @return Returns a negative number on failure - the number of tokens that would have been returned + /// @return Returns INT32_MIN on overflow (e.g., tokenization result size exceeds int32_t limit) /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so. /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated /// as plaintext. Does not insert a leading space. diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index bb5d56a0e..bd1e04ed0 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -8cda0a3c19f2c7dc493887353c42f6956bc268b1 +9e4bee1c5afc2d677a5b32ecb90cbdb483e81fff diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 991b0df59..fb3f0c72e 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -200,6 +200,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" }, { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" }, { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" }, + { LLM_KV_TOKENIZER_ADD_SEP, "tokenizer.ggml.add_sep_token" }, { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" }, { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index f4cc19421..0083dc7b2 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -196,6 +196,7 @@ enum llm_kv { LLM_KV_TOKENIZER_MASK_ID, LLM_KV_TOKENIZER_ADD_BOS, LLM_KV_TOKENIZER_ADD_EOS, + LLM_KV_TOKENIZER_ADD_SEP, LLM_KV_TOKENIZER_ADD_PREFIX, LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp index 8b6d14fe8..401e11364 100644 --- a/src/llama-batch.cpp +++ b/src/llama-batch.cpp @@ -1,7 +1,6 @@ #include "llama-batch.h" #include "llama-impl.h" -#include "llama-cparams.h" #include "llama-vocab.h" #include "llama-memory.h" @@ -10,282 +9,7 @@ #include #include -llama_ubatch llama_sbatch::reserve_ubatch(size_t n_ubatch, bool has_embd) { - // clear empty sequences - // the previous ubatch is assumed to be gone, - // so nothing should refer to values in these sequences anymore. - for (size_t i = seq.size(); i-- > 0;) { - if (seq[i].length == 0) { - seq.pop_back(); - } else { - break; - } - } - - udatas.push_back({}); - - auto & udata = udatas.back(); - - udata.token.resize(!has_embd ? n_ubatch : 0); - udata.embd.resize(has_embd ? n_embd * n_ubatch : 0); - udata.pos.resize(n_ubatch); - udata.n_seq_id.resize(n_ubatch); - udata.seq_id.resize(n_ubatch); - udata.output.resize(n_ubatch); - - llama_ubatch ubatch = { - /*equal_seqs =*/ true, - /*n_tokens =*/ 0, - /*n_seq_tokens =*/ 0, - /*n_seqs =*/ 0, - /*token =*/ !has_embd ? udata.token.data() : nullptr, - /*embd =*/ has_embd ? udata.embd.data() : nullptr, - /*pos =*/ udata.pos.data(), - /*n_seq_id =*/ udata.n_seq_id.data(), - /*seq_id =*/ udata.seq_id.data(), - /*output =*/ udata.output.data(), - }; - - return ubatch; -} - -void llama_sbatch::add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & seq, size_t length) { - GGML_ASSERT(batch != nullptr); - GGML_ASSERT(length <= seq.length); - // Can only add sequences of equal lengths to a batch, - // otherwise it isn't clear to which sequence a token belongs - GGML_ASSERT(seq.n_seq_id == 0 || ubatch.n_seqs == 0 || length == (size_t) ubatch.n_tokens / ubatch.n_seqs); - GGML_ASSERT((seq.n_seq_id != 0) == ubatch.equal_seqs); - // NOTE: loops are separated for cache-friendliness - if (batch->token) { - if (ubatch.equal_seqs) { - for (size_t i = 0; i < length; ++i) { - ubatch.token[ubatch.n_tokens + i] = batch->token[ids[seq.offset + i]]; - } - } else { - // simple split - ubatch.token = batch->token + seq.offset; - } - } else { - ubatch.token = nullptr; - } - if (batch->embd) { - if (ubatch.equal_seqs) { - for (size_t i = 0; i < length; ++i) { - memcpy( - ubatch.embd + (n_embd * (ubatch.n_tokens + i)), - batch->embd + (n_embd * ids[seq.offset + i]), - n_embd * sizeof(float) - ); - } - } else { - // simple split - ubatch.embd = batch->embd + (n_embd * seq.offset); - } - } else { - ubatch.embd = nullptr; - } - if (ubatch.equal_seqs) { - for (size_t i = 0; i < length; ++i) { - ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; - } - } else { - // simple split - ubatch.pos = batch->pos + seq.offset; - } - if (ubatch.equal_seqs) { - ubatch.n_seq_id[ubatch.n_seqs] = seq.n_seq_id; - if (seq.seq_id) { - ubatch.seq_id[ubatch.n_seqs] = seq.seq_id; - } - } else { - // simple split - if (batch->n_seq_id) { - ubatch.n_seq_id = batch->n_seq_id + seq.offset; - } else { - for (size_t i = 0; i < length; ++i) { - ubatch.n_seq_id[ubatch.n_seqs + i] = 1; - } - } - if (batch->seq_id) { - ubatch.seq_id = batch->seq_id + seq.offset; - } - } - if (batch->logits) { - if (ubatch.equal_seqs) { - for (size_t i = 0; i < length; ++i) { - size_t id = ids[seq.offset + i]; - int8_t is_output = batch->logits[id]; - ubatch.output[ubatch.n_tokens + i] = is_output; - if (is_output) { out_ids.push_back(id); } - } - } else { - // simple split - ubatch.output = batch->logits + seq.offset; - for (size_t i = 0; i < length; ++i) { - if (ubatch.output[i] != 0) { out_ids.push_back(seq.offset + i); } - } - } - } else { - // only get last output - for (size_t i = 0; i < length; ++i) { - size_t id = ids[seq.offset + i]; - int8_t is_last = id == ids.size() - 1; - ubatch.output[ubatch.n_tokens + i] = is_last; - if (is_last) { out_ids.push_back(id); } - } - } - if (ubatch.n_tokens == 0 && ubatch.n_seqs == 0) { - ubatch.n_seq_tokens = ubatch.equal_seqs ? length : 1; - } - ubatch.n_tokens += length; - ubatch.n_seqs += ubatch.equal_seqs ? 1 : length; // virtual sequences for simple splits - seq.offset += length; - seq.length -= length; - n_tokens -= length; - GGML_ASSERT(ubatch.n_tokens == ubatch.n_seq_tokens * ubatch.n_seqs); -} - -llama_ubatch llama_sbatch::split_simple(size_t n_ubatch) { - n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch; - llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr); - ubatch.equal_seqs = false; - if (!seq.empty()) { - llama_sbatch_seq & s = seq[0]; - size_t length = s.length < n_ubatch ? s.length : n_ubatch; - GGML_ASSERT(seq.size() == 1 && s.n_seq_id == 0); // don't mix with other splits - add_seq_to_ubatch(ubatch, s, length); - } - return ubatch; -} - -llama_ubatch llama_sbatch::split_equal(size_t n_ubatch) { - n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch; - llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr); - if (!seq.empty()) { - size_t length = 0; - size_t n_tokens_in_ubatch = 0; - GGML_ASSERT(seq[0].n_seq_id > 0); // should not be mixed with simple splits - // smallest first, because it's easier to split this way; - // starting from the end to pop in constant time. - for (size_t i = seq.size(); i-- > 0;) { - llama_sbatch_seq & s = seq[i]; - GGML_ASSERT(s.length > 0); - if (length == 0) { - length = s.length < n_ubatch ? s.length : n_ubatch; - } - add_seq_to_ubatch(ubatch, s, length); - n_tokens_in_ubatch += length; - // shared prompts can't be mixed with any of their sequences, - // so it's safer to compute them in their own ubatch - if (s.n_seq_id > 1) { break; } - // stop when there isn't enough space for another sequence - if (length + n_tokens_in_ubatch > n_ubatch) { break; } - } - } - return ubatch; -} - -llama_ubatch llama_sbatch::split_seq(size_t n_ubatch) { - n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch; - llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr); - if (!seq.empty()) { - llama_sbatch_seq & s = seq[seq.size() - 1]; - size_t length = s.length < n_ubatch ? s.length : n_ubatch; - GGML_ASSERT(s.n_seq_id > 0); // should not be mixed with simple splits - add_seq_to_ubatch(ubatch, s, length); - } - return ubatch; -} - -llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split) { - GGML_ASSERT(batch.n_tokens >= 0); - this->batch = &batch; - this->n_embd = n_embd; - - n_tokens = batch.n_tokens; - ids.resize(n_tokens); - out_ids.clear(); - // TODO: reserve out_ids and seq - - for (size_t i = 0; i < n_tokens; ++i) { - ids[i] = i; - } - - if (simple_split) { - seq.resize(1); - llama_sbatch_seq & s = seq[0]; - s.n_seq_id = 0; - s.seq_id = nullptr; - s.offset = 0; - s.length = n_tokens; - return; - } - - std::sort(ids.begin(), ids.end(), - [&batch](size_t a, size_t b) { - int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1; - int32_t n_seq_b = batch.n_seq_id ? batch.n_seq_id[b] : 1; - // sort by seq_id, then by pos - if (n_seq_a == n_seq_b) { - if (batch.seq_id) { - for (int32_t i = 0; i < n_seq_a; ++i) { - llama_seq_id seq_id_a = batch.seq_id[a][i]; - llama_seq_id seq_id_b = batch.seq_id[b][i]; - // smaller seq_ids go first - if (seq_id_a != seq_id_b) { - return seq_id_a < seq_id_b; - } - } - } - // when all else is equal, sort by pos - if (batch.pos) { - return batch.pos[a] < batch.pos[b]; - } - // no pos, sort by id - return a < b; - } - // shared prompts go first - return n_seq_a > n_seq_b; - } - ); - - // init seq - llama_sbatch_seq * last_seq = nullptr; - - for (size_t i = 0; i < n_tokens; ++i) { - const size_t bi = ids[i]; - const int32_t n_seqs = batch.n_seq_id[bi]; - llama_seq_id * seq_ids = batch.seq_id[bi]; - if (last_seq != nullptr) { - bool same = n_seqs == last_seq->n_seq_id; - for (int32_t j = 0; same && j < n_seqs; ++j) { - if (seq_ids[j] != last_seq->seq_id[j]) { - same = false; - } - } - if (same) { - last_seq->length += 1; - continue; - } - } - llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1}; - seq.push_back(new_seq); - last_seq = &seq.back(); - } - - // keep shared prompts first at the end, then sort by length descending. - std::sort(seq.begin(), seq.end(), - [](llama_sbatch_seq & a, llama_sbatch_seq & b) { - if (a.n_seq_id == b.n_seq_id) { - return a.length > b.length; - } - return a.n_seq_id < b.n_seq_id; - } - ); -} - -llama_batch_allocr::llama_batch_allocr() { +llama_batch_allocr::llama_batch_allocr(uint32_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) { const char * LLAMA_BATCH_DEBUG = getenv("LLAMA_BATCH_DEBUG"); debug = LLAMA_BATCH_DEBUG ? atoi(LLAMA_BATCH_DEBUG) : 0; @@ -294,17 +18,22 @@ llama_batch_allocr::llama_batch_allocr() { for (auto & cur : seq_cpl) { cur.resize(LLAMA_MAX_SEQ); } + + seq_idx.resize(LLAMA_MAX_SEQ, -1); } bool llama_batch_allocr::init( const llama_batch & batch_inp, const llama_vocab & vocab, const llama_memory_i * memory, - bool embd_all) { + uint32_t n_embd, + bool output_all) { clear(); batch = batch_inp; + this->vocab = &vocab; + GGML_ASSERT(batch.n_tokens > 0); // @@ -359,6 +88,7 @@ bool llama_batch_allocr::init( llama_pos p0[LLAMA_MAX_SEQ]; for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { if (!memory) { + // if no memory -> start from 0 p0[s] = 0; } else { p0[s] = memory->seq_pos_max(s) + 1; @@ -370,8 +100,11 @@ bool llama_batch_allocr::init( pos[i] = p0[seq_id]; + // update the starting position for all sequences that are assigned to the this token for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) { - p0[batch.seq_id[i][s]] = pos[i] + 1; + const llama_seq_id seq_id = batch.seq_id[i][s]; + + p0[seq_id] = pos[i] + 1; } } @@ -379,7 +112,7 @@ bool llama_batch_allocr::init( } if (!batch.logits) { - if (embd_all) { + if (output_all) { // return the output for all tokens output.resize(batch.n_tokens, true); } else { @@ -389,7 +122,7 @@ bool llama_batch_allocr::init( } batch.logits = output.data(); - } else if (embd_all) { + } else if (output_all) { bool warn = false; for (int32_t i = 0; i < batch.n_tokens; ++i) { @@ -410,6 +143,9 @@ bool llama_batch_allocr::init( // compute stats // + this->n_embd = n_embd; + + // count the outputs in this batch for (int32_t i = 0; i < batch.n_tokens; ++i) { n_outputs += batch.logits[i] != 0; } @@ -417,85 +153,86 @@ bool llama_batch_allocr::init( // determine coupled sequences // these are pairs of sequences that have at least one token in the input batch that is assigned to both of them for (int32_t i = 0; i < batch.n_tokens; ++i) { + const llama_seq_id s0 = batch.seq_id[i][0]; + for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) { - seq_pos[batch.seq_id[i][s]].insert(batch.pos[i]); + const llama_seq_id s1 = batch.seq_id[i][s]; + + seq_pos[s1].insert(batch.pos[i]); if (s > 0) { - const llama_seq_id s0 = batch.seq_id[i][0]; - const llama_seq_id s1 = batch.seq_id[i][s]; - // mark that sequence s1 is coupled to s0 seq_cpl[s1][s0] = true; - // note: the other way around is not necessary for now + // note: tracking the other way around is not necessary for now //seq_cpl[s0][s1] = true; } } } + // precompute the sequence sets for each token and determine the unique sequence ids that participate in the batch + { + seq_set_t seq_set_unq; + + for (int32_t i = 0; i < batch.n_tokens; ++i) { + seq_set_t cur; + for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) { + const llama_seq_id seq_id = batch.seq_id[i][s]; + + cur .set(seq_id); + seq_set_unq.set(seq_id); + } + + seq_set.push_back(cur); + seq_set_map[cur].push_back(i); + } + + for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { + if (seq_set_unq.test(s)) { + seq_idx[s] = seq_id_unq.size(); + seq_id_unq.push_back(s); + } + } + } + if (debug > 0) { LLAMA_LOG_DEBUG("%s: input batch info:\n", __func__); - LLAMA_LOG_DEBUG("%s: n_tokens = %d\n", __func__, batch.n_tokens); - LLAMA_LOG_DEBUG("%s: token = %p\n", __func__, (void *) batch.token); - LLAMA_LOG_DEBUG("%s: embd = %p\n", __func__, (void *) batch.embd); - LLAMA_LOG_DEBUG("%s: pos = %p\n", __func__, (void *) batch.pos); - LLAMA_LOG_DEBUG("%s: n_seq_id = %p\n", __func__, (void *) batch.n_seq_id); - LLAMA_LOG_DEBUG("%s: seq_id = %p\n", __func__, (void *) batch.seq_id); - LLAMA_LOG_DEBUG("%s: logits = %p\n", __func__, (void *) batch.logits); - LLAMA_LOG_DEBUG("%s: n_outputs = %d\n", __func__, n_outputs); - if (debug > 1) { - int seq_id_max = 0; - for (int32_t i = 0; i < batch.n_tokens; ++i) { - for (int s = 0; s < batch.n_seq_id[i]; ++s) { - for (int s = 0; s < batch.n_seq_id[i]; ++s) { - seq_id_max = std::max(seq_id_max, batch.seq_id[i][s]); - } + llama_ubatch ubatch { + /*.equal_seqs =*/ false, + /*.n_tokens =*/ (uint32_t) batch.n_tokens, + /*.n_seq_tokens =*/ (uint32_t) 1, + /*.n_seqs =*/ (uint32_t) batch.n_tokens, + /*.n_seqs_unq =*/ (uint32_t) this->seq_id_unq.size(), + /*.token =*/ batch.token, + /*.embd =*/ batch.embd, + /*.pos =*/ batch.pos, + /*.n_seq_id =*/ batch.n_seq_id, + /*.seq_id =*/ batch.seq_id, + /*.seq_id_unq =*/ this->seq_id_unq.data(), + /*.seq_idx =*/ this->seq_idx.data(), + /*.output =*/ batch.logits, + }; + + ubatch_print(ubatch, debug); + + LLAMA_LOG_DEBUG("%s: seq = [\n", __func__); + for (int s0 = 0; s0 < (int) seq_pos.size(); ++s0) { + if (seq_pos[s0].empty()) { + continue; + } + + std::stringstream ss; + for (int s1 = 0; s1 < (int) seq_cpl[s0].size(); ++s1) { + if (seq_cpl[s0][s1]) { + ss << s1 << " "; } } - ++seq_id_max; - LLAMA_LOG_DEBUG("%s: token = [\n", __func__); - for (int32_t i = 0; i < batch.n_tokens; ++i) { - std::vector seq_id(seq_id_max); - - for (int s = 0; s < batch.n_seq_id[i]; ++s) { - seq_id[batch.seq_id[i][s]] = 1; - } - - std::stringstream ss; - for (int s = 0; s < seq_id_max; ++s) { - if (seq_id[s]) { - ss << s%10; - } else { - ss << "."; - } - } - - LLAMA_LOG_DEBUG("%s: %4d: id = %6d (%16s), pos = %4d, n_seq_id = %2d, seq_id = [%s], output = %d\n", - __func__, i, batch.token[i], vocab.token_to_piece(batch.token[i]).c_str(), - batch.pos[i], batch.n_seq_id[i], ss.str().c_str(), batch.logits[i]); - } - LLAMA_LOG_DEBUG("%s: ]\n", __func__); - - LLAMA_LOG_DEBUG("%s: seq = [\n", __func__); - for (int s0 = 0; s0 < (int) seq_pos.size(); ++s0) { - if (seq_pos[s0].empty()) { - continue; - } - - std::stringstream ss; - for (int s1 = 0; s1 < (int) seq_cpl[s0].size(); ++s1) { - if (seq_cpl[s0][s1]) { - ss << s1 << " "; - } - } - - LLAMA_LOG_DEBUG("%s: %4d: pos = [%4d, %4d], cpl = %s\n", - __func__, s0, seq_pos_min(s0), seq_pos_max(s0), ss.str().empty() ? "-" : ss.str().c_str()); - } - LLAMA_LOG_DEBUG("%s: ]\n", __func__); + LLAMA_LOG_DEBUG("%s: %4d: pos = [%4d, %4d], cpl = %s\n", + __func__, s0, seq_pos_min(s0), seq_pos_max(s0), ss.str().empty() ? "-" : ss.str().c_str()); } + LLAMA_LOG_DEBUG("%s: ]\n", __func__); } // @@ -507,9 +244,33 @@ bool llama_batch_allocr::init( continue; } - if (memory && seq_pos_min(s) != memory->seq_pos_max(s) + 1) { - LLAMA_LOG_ERROR("%s: sequence %d does not start from the last position stored in the memory\n", __func__, s); - return false; + if (memory) { + bool ok = true; + + if (batch.token) { + if (seq_pos_min(s) != memory->seq_pos_max(s) + 1) { + ok = false; + } + } else { + assert(batch.embd); + + // for embeddings (typically used as vision input), we allow them to have repeating positions + // ref: https://github.com/ggml-org/llama.cpp/issues/13694#issuecomment-2983871762 + if (seq_pos_min(s) != memory->seq_pos_max(s) && seq_pos_min(s) != memory->seq_pos_max(s) + 1) { + ok = false; + } + } + + if (!ok) { + LLAMA_LOG_ERROR( + "%s: the tokens of sequence %d in the input batch have inconsistent sequence positions:\n" + " - the last position stored in the memory module of the context (i.e. the KV cache) for sequence %d is X = %d\n" + " - the tokens for sequence %d in the input batch have a starting position of Y = %d\n" + " it is required that the sequence positions remain consecutive: Y = X + 1\n", + __func__, s, s, memory->seq_pos_max(s), s, seq_pos_min(s)); + + return false; + } } if (seq_pos_max(s) - seq_pos_min(s) + 1 > (int) seq_pos[s].size()) { @@ -532,17 +293,120 @@ bool llama_batch_allocr::init( } } + // disallow partial sequence sub-sets: + // + // invalid: x + // i: 0 1 2 ... + // --------------------------------------- + // seq_id[i][0]: 0 0 1 + // seq_id[i][1]: 1 1 2 + // seq_id[i][2]: 2 + // + // disallow decreasing sequence positions: + // + // invalid: x + // i: 0 1 2 3 4 5 6 ... + // --------------------------------------- + // pos[i]: 4 5 0 1 6 2 3 + // seq_id[i][0]: 0 0 1 1 0 1 0 + // + { + seq_set_t cur_seq_set[LLAMA_MAX_SEQ]; + for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { + cur_seq_set[s].set(); + } + + llama_pos cur_seq_pos[LLAMA_MAX_SEQ]; + for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { + cur_seq_pos[s] = -1; + } + + for (int32_t i = 0; i < batch.n_tokens; ++i) { + const llama_pos pos = batch.pos[i]; + + for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) { + const llama_seq_id seq_id = batch.seq_id[i][s]; + + cur_seq_set[seq_id] &= seq_set[i]; + + if (cur_seq_set[seq_id].none()) { + LLAMA_LOG_ERROR("%s: sequence %d belongs to incompatible sequence sets (not allowed)\n", __func__, seq_id); + return false; + } + + if (pos < cur_seq_pos[seq_id]) { + LLAMA_LOG_ERROR("%s: sequence %d positions are decreasing (not allowed)\n", __func__, seq_id); + return false; + } + } + } + } + + split_reset(); + return true; } +llama_ubatch llama_batch_allocr::ubatch_reserve(uint32_t n_seq_tokens, uint32_t n_seqs) { + const uint32_t n_tokens = n_seq_tokens*n_seqs; + + clear(); + split_reset(); + + ubatches.emplace_back(); + + auto & ubatch = ubatches.back(); + + ubatch.token .resize(n_tokens); + ubatch.embd .clear(); + ubatch.pos .resize(n_tokens); + ubatch.n_seq_id .resize(n_tokens); + ubatch.seq_id .resize(n_tokens); + ubatch.seq_id_unq.resize(0); + ubatch.seq_idx .resize(LLAMA_MAX_SEQ, -1); + ubatch.output .resize(n_tokens); + + for (uint32_t s = 0; s < n_seqs; ++s) { + ubatch.seq_idx[s] = s; + ubatch.seq_id_unq.push_back(s); + } + + llama_ubatch res { + /*.equal_seqs =*/ true, + /*.n_tokens =*/ n_tokens, + /*.n_seq_tokens =*/ n_seq_tokens, + /*.n_seqs =*/ n_seqs, + /*.n_seqs_unq =*/ n_seqs, + + /*.token =*/ ubatch.token.data(), + /*.embd =*/ nullptr, + /*.pos =*/ ubatch.pos.data(), + /*.n_seq_id =*/ ubatch.n_seq_id.data(), + /*.seq_id =*/ ubatch.seq_id.data(), + /*.seq_id_unq =*/ ubatch.seq_id_unq.data(), + /*.seq_idx =*/ ubatch.seq_idx.data(), + /*.output =*/ ubatch.output.data(), + }; + + return res; +} + const llama_batch & llama_batch_allocr::get_batch() const { return batch; } +uint32_t llama_batch_allocr::get_n_tokens() const { + return batch.n_tokens; +} + uint32_t llama_batch_allocr::get_n_outputs() const { return n_outputs; } +std::vector & llama_batch_allocr::get_out_ids() { + return out_ids; +} + llama_pos llama_batch_allocr::seq_pos_min(llama_seq_id seq_id) const { return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].begin(); } @@ -551,14 +415,188 @@ llama_pos llama_batch_allocr::seq_pos_max(llama_seq_id seq_id) const { return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].rbegin(); } +void llama_batch_allocr::split_reset() { + out_ids.clear(); + + used.clear(); + used.resize(get_n_tokens(), false); + + ubatches.clear(); +} + +llama_ubatch llama_batch_allocr::split_simple(uint32_t n_ubatch) { + // find the first unused token + uint32_t cur_idx = 0; + while (cur_idx < used.size() && used[cur_idx]) { + ++cur_idx; + } + + // we are done + if (cur_idx >= used.size()) { + return {}; + } + + std::vector idxs; + + while (true) { + idxs.push_back(cur_idx); + + used[cur_idx] = true; + + ++cur_idx; + + if (cur_idx >= used.size()) { + break; + } + + if (idxs.size() >= n_ubatch) { + break; + } + } + + return ubatch_add(idxs, idxs.size(), false); +} + +llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch) { + std::vector cur_seq_set; + + // determine the non-overlapping sequence sets participating in this ubatch + for (int32_t i = 0; i < batch.n_tokens; ++i) { + if (used[i]) { + continue; + } + + bool add = true; + + for (uint32_t s = 0; s < cur_seq_set.size(); ++s) { + // no overlap with existing sequence sets: + if (!(cur_seq_set[s] & seq_set[i]).none()) { + add = false; + break; + } + } + + if (add) { + cur_seq_set.push_back(seq_set[i]); + + if (cur_seq_set.size() > n_ubatch) { + break; + } + } + } + + const uint32_t n_seqs = cur_seq_set.size(); + + // we are done + if (n_seqs == 0) { + return {}; + } + + // the current batch index of each sequence set + std::vector cur_idx(n_seqs, 0); + + for (uint32_t s = 0; s < n_seqs; ++s) { + while (used[seq_set_map[cur_seq_set[s]][cur_idx[s]]]) { + ++cur_idx[s]; + } + } + + // the list of batch indices for each sequence set + // at the end we will concat these to get the final ubatch + std::vector idxs_per_seq(n_seqs); + + while (true) { + // we can only add new n_seq_tokens tokens if all the sequence sets have at least one more unused token and + // if we haven't reached n_ubatch + bool can_expand = true; + + for (uint32_t s = 0; s < n_seqs; ++s) { + if (cur_idx[s] >= (int32_t) seq_set_map[cur_seq_set[s]].size()) { + can_expand = false; + break; + } + } + + if (!can_expand) { + break; + } + + for (uint32_t s = 0; s < n_seqs; ++s) { + const int32_t idx = seq_set_map[cur_seq_set[s]][cur_idx[s]]; + + idxs_per_seq[s].push_back(idx); + + used[idx] = true; + + ++cur_idx[s]; + } + + if ((idxs_per_seq[0].size() + 1)*n_seqs > n_ubatch) { + break; + } + } + + // concat the per-sequence-set lists + std::vector idxs; + + for (uint32_t s = 0; s < n_seqs; ++s) { + idxs.insert(idxs.end(), idxs_per_seq[s].begin(), idxs_per_seq[s].end()); + } + + return ubatch_add(idxs, n_seqs, true); +} + +llama_ubatch llama_batch_allocr::split_seq(uint32_t n_ubatch) { + // find the first unused token + uint32_t cur_idx = 0; + while (cur_idx < used.size() && used[cur_idx]) { + ++cur_idx; + } + + // we are done + if (cur_idx >= used.size()) { + return {}; + } + + // this is the starting sequence set + // we allow adding tokens only if their sequence set is a subset of the current sequence set + auto cur_seq_set = seq_set[cur_idx]; + + std::vector idxs; + + while (true) { + idxs.push_back(cur_idx); + + used[cur_idx] = true; + + if (idxs.size() >= n_ubatch) { + break; + } + + do { + ++cur_idx; + } while (cur_idx < get_n_tokens() && (used[cur_idx] || ((cur_seq_set & seq_set[cur_idx]) != seq_set[cur_idx]))); + + if (cur_idx == get_n_tokens()) { + break; + } + + cur_seq_set = seq_set[cur_idx]; + } + + return ubatch_add(idxs, 1, true); +} + void llama_batch_allocr::clear() { n_outputs = 0; batch = {}; - pos.clear(); - n_seq_id.clear(); - seq_id.clear(); - output.clear(); + + pos .clear(); + n_seq_id .clear(); + seq_id .clear(); + seq_id_unq.clear(); + output .clear(); for (auto & cur : seq_pos) { cur.clear(); @@ -567,6 +605,177 @@ void llama_batch_allocr::clear() { for (auto & cur : seq_cpl) { std::fill(cur.begin(), cur.end(), false); } + + seq_set.clear(); + + seq_set_map.clear(); + + std::fill(seq_idx.begin(), seq_idx.end(), -1); +} + +llama_ubatch llama_batch_allocr::ubatch_add(const std::vector & idxs, uint32_t n_seqs, bool equal_seqs) { + const uint32_t n_tokens = idxs.size(); + + assert(n_tokens%n_seqs == 0); + + ubatches.emplace_back(); + + auto & ubatch = ubatches.back(); + + const int32_t n_pos_cur = batch.embd ? n_pos_per_embd : 1; + + const int64_t n_embd_all = batch.embd ? (int64_t) n_tokens*n_embd : 0; + const int64_t n_pos_all = (int64_t) n_tokens*n_pos_cur; + + ubatch.token .resize(n_tokens); + ubatch.embd .resize(n_embd_all); + ubatch.pos .resize(n_pos_all); + ubatch.n_seq_id .resize(n_tokens); + ubatch.seq_id .resize(n_tokens); + ubatch.seq_id_unq.resize(0); + ubatch.seq_idx .resize(LLAMA_MAX_SEQ, -1); + ubatch.output .resize(n_tokens); + + seq_set_t seq_set_unq; + + for (size_t i = 0; i < idxs.size(); ++i) { + if (batch.token) { + ubatch.token[i] = batch.token[idxs[i]]; + } + + if (batch.embd) { + memcpy(ubatch.embd.data() + i*n_embd, batch.embd + (int64_t) idxs[i]*n_embd, n_embd*sizeof(float)); + } + + for (int j = 0; j < n_pos_cur; ++j) { + ubatch.pos[j*n_tokens + i] = batch.pos[j*batch.n_tokens + idxs[i]]; + } + + ubatch.n_seq_id[i] = batch.n_seq_id[idxs[i]]; + ubatch.seq_id[i] = batch.seq_id[idxs[i]]; + ubatch.output[i] = batch.logits[idxs[i]]; + + for (int s = 0; s < ubatch.n_seq_id[i]; ++s) { + seq_set_unq.set(ubatch.seq_id[i][s]); + } + + if (ubatch.output[i]) { + out_ids.push_back(idxs[i]); + } + } + + for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { + if (seq_set_unq.test(s)) { + ubatch.seq_idx[s] = ubatch.seq_id_unq.size(); + ubatch.seq_id_unq.push_back(s); + } + } + + llama_ubatch res { + /*.equal_seqs =*/ equal_seqs, + /*.n_tokens =*/ n_tokens, + /*.n_seq_tokens =*/ n_tokens/n_seqs, + /*.n_seqs =*/ n_seqs, + /*.n_seqs_unq =*/ (uint32_t) ubatch.seq_id_unq.size(), + + /*.token =*/ batch.token ? ubatch.token.data() : nullptr, + /*.embd =*/ batch.embd ? ubatch.embd.data() : nullptr, + /*.pos =*/ ubatch.pos.data(), + /*.n_seq_id =*/ ubatch.n_seq_id.data(), + /*.seq_id =*/ ubatch.seq_id.data(), + /*.seq_id_unq =*/ ubatch.seq_id_unq.data(), + /*.seq_idx =*/ ubatch.seq_idx.data(), + /*.output =*/ ubatch.output.data(), + }; + + if (debug > 0) { + LLAMA_LOG_DEBUG("%s: added ubatch %d to split:\n", __func__, (int) ubatches.size() - 1); + + ubatch_print(res, debug); + } + + return res; +} + +void llama_batch_allocr::ubatch_print(const llama_ubatch & ubatch, int debug) { + if (debug > 0) { + LLAMA_LOG_DEBUG("%s: equal_seqs = %d\n", __func__, ubatch.equal_seqs); + LLAMA_LOG_DEBUG("%s: n_tokens = %d\n", __func__, ubatch.n_tokens); + LLAMA_LOG_DEBUG("%s: n_seq_tokens = %d\n", __func__, ubatch.n_seq_tokens); + LLAMA_LOG_DEBUG("%s: n_seqs = %d\n", __func__, ubatch.n_seqs); + LLAMA_LOG_DEBUG("%s: n_seqs_unq = %d\n", __func__, ubatch.n_seqs_unq); + + std::stringstream ss_seq_id_unq; + std::stringstream ss_seq_idx; + + ss_seq_id_unq << "[ "; + ss_seq_idx << "["; + + for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) { + ss_seq_id_unq << ubatch.seq_id_unq[s] << " "; + } + + for (uint32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { + if (ubatch.seq_idx[s] >= 0) { + ss_seq_idx << ubatch.seq_idx[s]%10; + } else { + ss_seq_idx << "."; + } + } + + ss_seq_id_unq << "]"; + ss_seq_idx << "]"; + + LLAMA_LOG_DEBUG("%s: token = %p\n", __func__, (void *) ubatch.token); + LLAMA_LOG_DEBUG("%s: embd = %p\n", __func__, (void *) ubatch.embd); + LLAMA_LOG_DEBUG("%s: pos = %p\n", __func__, (void *) ubatch.pos); + LLAMA_LOG_DEBUG("%s: n_seq_id = %p\n", __func__, (void *) ubatch.n_seq_id); + LLAMA_LOG_DEBUG("%s: seq_id = %p\n", __func__, (void *) ubatch.seq_id); + LLAMA_LOG_DEBUG("%s: seq_id_unq = %s\n", __func__, ss_seq_id_unq.str().c_str()); + LLAMA_LOG_DEBUG("%s: seq_idx = %s\n", __func__, ss_seq_idx.str().c_str()); + LLAMA_LOG_DEBUG("%s: output = %p\n", __func__, (void *) ubatch.output); + LLAMA_LOG_DEBUG("%s: n_outputs = %d\n", __func__, n_outputs); + + if (debug > 1) { + int seq_id_max = 0; + for (uint32_t i = 0; i < ubatch.n_tokens; ++i) { + for (int s = 0; s < ubatch.n_seq_id[i]; ++s) { + for (int s = 0; s < ubatch.n_seq_id[i]; ++s) { + seq_id_max = std::max(seq_id_max, ubatch.seq_id[i][s]); + } + } + } + ++seq_id_max; + + LLAMA_LOG_DEBUG("%s: token = [\n", __func__); + for (uint32_t i = 0; i < ubatch.n_tokens; ++i) { + std::vector seq_id(seq_id_max); + + for (int s = 0; s < ubatch.n_seq_id[i]; ++s) { + seq_id[ubatch.seq_id[i][s]] = 1; + } + + std::stringstream ss; + for (int s = 0; s < seq_id_max; ++s) { + if (seq_id[s]) { + ss << s%10; + } else { + ss << "."; + } + } + + if (ubatch.token) { + LLAMA_LOG_DEBUG("%s: %4d: id = %6d (%16s), pos = %4d, n_seq_id = %2d, seq_id = [%s], output = %d\n", + __func__, i, ubatch.token[i], vocab->token_to_piece(ubatch.token[i]).c_str(), + ubatch.pos[i], ubatch.n_seq_id[i], ss.str().c_str(), ubatch.output[i]); + } else { + LLAMA_LOG_DEBUG("%s: %4d: [embd], pos = %4d, n_seq_id = %2d, seq_id = [%s], output = %d\n", + __func__, i, ubatch.pos[i], ubatch.n_seq_id[i], ss.str().c_str(), ubatch.output[i]); + } + } + LLAMA_LOG_DEBUG("%s: ]\n", __func__); + } + } } // @@ -577,25 +786,25 @@ struct llama_batch llama_batch_get_one( llama_token * tokens, int32_t n_tokens) { return { - /*n_tokens =*/ n_tokens, - /*tokens =*/ tokens, - /*embd =*/ nullptr, - /*pos =*/ nullptr, - /*n_seq_id =*/ nullptr, - /*seq_id =*/ nullptr, - /*logits =*/ nullptr, + /*n_tokens =*/ n_tokens, + /*tokens =*/ tokens, + /*embd =*/ nullptr, + /*pos =*/ nullptr, + /*n_seq_id =*/ nullptr, + /*seq_id =*/ nullptr, + /*logits =*/ nullptr, }; } struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) { llama_batch batch = { - /*n_tokens =*/ 0, - /*tokens =*/ nullptr, - /*embd =*/ nullptr, - /*pos =*/ nullptr, - /*n_seq_id =*/ nullptr, - /*seq_id =*/ nullptr, - /*logits =*/ nullptr, + /*n_tokens =*/ 0, + /*tokens =*/ nullptr, + /*embd =*/ nullptr, + /*pos =*/ nullptr, + /*n_seq_id =*/ nullptr, + /*seq_id =*/ nullptr, + /*logits =*/ nullptr, }; if (embd) { diff --git a/src/llama-batch.h b/src/llama-batch.h index a555c1572..d2c537618 100644 --- a/src/llama-batch.h +++ b/src/llama-batch.h @@ -2,86 +2,44 @@ #include "llama.h" +#include "llama-cparams.h" + #include #include #include +#include +#include -// very similar to llama_batch, -// but has more metadata about sequences +// keep this struct lightweight +// it points to data in `llama_batch_allocr` struct llama_ubatch { bool equal_seqs; // TODO: whole_seqs for embeddings? uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs) - uint32_t n_seq_tokens; // tokens per sequence - uint32_t n_seqs; + uint32_t n_seq_tokens; // tokens per sequence set + uint32_t n_seqs; // sequence sets in the ubatch + uint32_t n_seqs_unq; // unique sequence ids in the ubatch - llama_token * token; // [n_tokens] - float * embd; // [n_embd, n_tokens] - llama_pos * pos; // [n_tokens] - int32_t * n_seq_id; // [n_seqs] - llama_seq_id ** seq_id; // [n_seqs] - int8_t * output; // [n_tokens] + // seq_id_unq: unique sequence ids in the ubatch + // seq_idx: indices of the unique sequence ids in the ubatch in [0, n_seqs_unq) + // used for extracting sequence pooled embeddings + + // // size | idx | val + llama_token * token; // [n_tokens] | i | id, token + float * embd; // [n_embd, n_tokens] | i | embd + llama_pos * pos; // [n_tokens] | i | pos + int32_t * n_seq_id; // [n_tokens] | i | - + llama_seq_id ** seq_id; // [n_tokens] | s | s0, s1, seq_id + llama_seq_id * seq_id_unq; // [n_seqs_unq] | s | seq_id + int32_t * seq_idx; // [LLAMA_MAX_SEQ] | - | seq_idx + int8_t * output; // [n_tokens] | i | - }; -struct llama_sbatch_seq { - int32_t n_seq_id; - - llama_seq_id * seq_id; - - size_t offset; - size_t length; -}; - -// sequence-length-aware batch splitting -struct llama_sbatch { - // tokens left in this batch - size_t n_tokens; - - size_t n_embd; - - // sorted indices into the batch - std::vector ids; - // batch indices of the output - std::vector out_ids; - std::vector seq; - - const llama_batch * batch = nullptr; - - // buffers for the ubatches - // TODO: very hacky, this needs a complete rework - struct ubatch_data { - std::vector token; - std::vector embd; - std::vector pos; - std::vector n_seq_id; - std::vector seq_id; - std::vector output; - }; - - std::vector udatas; - - llama_ubatch reserve_ubatch(size_t n_ubatch, bool has_embd = false); - - void add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & seq, size_t length); - - // simple split, unknown number of sequences of unequal lengths - llama_ubatch split_simple(size_t n_ubatch); - - // make batches of equal-length sequences - llama_ubatch split_equal(size_t n_ubatch); - - // sequence-wise split - llama_ubatch split_seq(size_t n_ubatch); - - llama_sbatch() = default; - llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false); -}; - -// a helper for sanitizing and fulfilling a batch +// a helper for sanitizing, fulfilling and splitting a batch class llama_batch_allocr { public: - llama_batch_allocr(); + llama_batch_allocr(uint32_t n_pos_per_embd); // sanitize and auto-gen missing data in the input batch // memory is optional. if provided will be used to check for sequence continuity and to determine the positions @@ -89,20 +47,57 @@ public: const llama_batch & batch_inp, const llama_vocab & vocab, const llama_memory_i * memory, - bool embd_all); + uint32_t n_embd, + bool output_all); const llama_batch & get_batch() const; + uint32_t get_n_tokens() const; uint32_t get_n_outputs() const; + // the array of output indices in the order they were encountered during the ubatch splitting + std::vector & get_out_ids(); + + // min/max positions of each sequence in the current ubatch llama_pos seq_pos_min(llama_seq_id seq_id) const; llama_pos seq_pos_max(llama_seq_id seq_id) const; + // call once before splitting the batch to reset the internal state + void split_reset(); + + // simple split, unknown number of sequence sets of unequal lengths + llama_ubatch split_simple(uint32_t n_ubatch); + + // make ubatches of equal-length sequences sets + llama_ubatch split_equal(uint32_t n_ubatch); + + // sequence-set-wise split - each ubatch contains a single sequence-set + llama_ubatch split_seq(uint32_t n_ubatch); + + // a helper method for creating a well-defined ubatch of tokens + // TODO: support embeddings if needed in the future + llama_ubatch ubatch_reserve(uint32_t n_seq_tokens, uint32_t n_seqs); + private: void clear(); + // create the next ubatch based on the provided batch indices (idxs) and the number of sequence sets (n_seqs) + // return llama_ubatch.n_tokens == 0 if the entire batch was consumed + llama_ubatch ubatch_add(const std::vector & idxs, uint32_t n_seqs, bool equal_seqs); + + // for debugging, start with LLAMA_BATCH_DEBUG=2 + void ubatch_print(const llama_ubatch & ubatch, int debug); + llama_batch batch; + // only for debugging purposes + const llama_vocab * vocab; + + // TODO: this is more of a temporary solution until we have a better way to handle multiple positions per token/embd + // ref: https://github.com/ggml-org/llama.cpp/issues/13694#issuecomment-2983871762 + const uint32_t n_pos_per_embd; + + uint32_t n_embd; uint32_t n_outputs; std::array seq_id_0 = { 0 }; // default sequence id @@ -110,10 +105,43 @@ private: std::vector pos; std::vector n_seq_id; std::vector seq_id; + std::vector seq_id_unq; + std::vector seq_idx; std::vector output; - std::vector> seq_pos; // seq_pos[s]: the set of positions in sequence s - std::vector> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1 + using pos_set_t = std::set; + using seq_cpl_t = std::vector; + + std::vector seq_pos; // seq_pos[s]: the set of positions in sequence s + std::vector seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1 + + using idx_vec_t = std::vector; + using seq_set_t = std::bitset; + + std::vector seq_set; // seq_set[i]: the sequence set of token i + + std::unordered_map seq_set_map; // the indices at which the sequence set appears + + // batch indices of the output + std::vector out_ids; + + // used[i] indicates if token i has already been used in a previous ubatch + std::vector used; + + // llama_ubatch points to this data: + struct ubatch { + std::vector token; + std::vector embd; + std::vector pos; + std::vector n_seq_id; + std::vector seq_id; + std::vector seq_id_unq; + std::vector seq_idx; + std::vector output; + }; + + // current splitting state: + std::vector ubatches; int debug; }; diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index 0839cad3e..5d317f4ee 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -528,12 +528,17 @@ int32_t llm_chat_apply_template( } } else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) { // this template requires the model to have "\n\n" as EOT token - for (auto message : chat) { - std::string role(message->role); - if (role == "user") { - ss << "User: " << message->content << "\n\nAssistant:"; - } else { - ss << message->content << "\n\n"; + for (size_t i = 0; i < chat.size(); i++) { + std::string role(chat[i]->role); + if (role == "system") { + ss << "System: " << trim(chat[i]->content) << "\n\n"; + } else if (role == "user") { + ss << "User: " << trim(chat[i]->content) << "\n\n"; + if (i == chat.size() - 1) { + ss << "Assistant:"; + } + } else if (role == "assistant") { + ss << "Assistant: " << trim(chat[i]->content) << "\n\n"; } } } else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) { diff --git a/src/llama-context.cpp b/src/llama-context.cpp index f56a58e9b..06e93b19c 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -20,7 +20,7 @@ llama_context::llama_context( const llama_model & model, llama_context_params params) : model(model), - batch_allocr(std::make_unique()) { + balloc(std::make_unique(model.hparams.n_pos_per_embd())) { LLAMA_LOG_INFO("%s: constructing llama_context\n", __func__); t_start_us = model.t_start_us; @@ -280,8 +280,8 @@ llama_context::llama_context( // simulate full KV cache - const auto mstate = memory->init_full(); - if (!mstate) { + const auto mctx = memory->init_full(); + if (!mctx) { throw std::runtime_error("failed to initialize KV cache"); } @@ -289,7 +289,7 @@ llama_context::llama_context( // reserve pp graph first so that buffers are only allocated once { - auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get()); + auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get()); if (!gf) { throw std::runtime_error("failed to allocate compute pp buffers"); } @@ -300,7 +300,7 @@ llama_context::llama_context( // reserve with tg graph to get the number of splits and nodes { - auto * gf = graph_reserve(1, 1, 1, mstate.get()); + auto * gf = graph_reserve(1, 1, 1, mctx.get()); if (!gf) { throw std::runtime_error("failed to allocate compute tg buffers"); } @@ -311,7 +311,7 @@ llama_context::llama_context( // reserve again with pp graph to avoid ggml-alloc reallocations during inference { - auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get()); + auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get()); if (!gf) { throw std::runtime_error("failed to allocate compute pp buffers"); } @@ -444,8 +444,8 @@ bool llama_context::kv_self_update(bool optimize) { optimize |= memory_force_optimize; memory_force_optimize = false; - const auto mstate = memory->init_update(this, optimize); - switch (mstate->get_status()) { + const auto mctx = memory->init_update(this, optimize); + switch (mctx->get_status()) { case LLAMA_MEMORY_STATUS_SUCCESS: { // noop @@ -463,22 +463,22 @@ bool llama_context::kv_self_update(bool optimize) { } } - if (!mstate->apply()) { + if (!mctx->apply()) { LLAMA_LOG_ERROR("%s: failed to apply memory update\n", __func__); } } // if the memory module did any computation, we have to reserve a new worst-case graph { - const auto mstate = memory->init_full(); - if (!mstate) { - throw std::runtime_error("failed to initialize memory state"); + const auto mctx = memory->init_full(); + if (!mctx) { + throw std::runtime_error("failed to initialize memory context"); } const uint32_t n_seqs = cparams.n_seq_max; const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch); - auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get()); + auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get()); if (!gf) { LLAMA_LOG_ERROR("%s: failed to reserve graph after the memory update\n", __func__); } @@ -678,9 +678,9 @@ bool llama_context::apply_adapter_cvec( return cvec.apply(model, data, len, n_embd, il_start, il_end); } -llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_state_i * mstate, ggml_status & ret) { - if (mstate && !mstate->apply()) { - LLAMA_LOG_ERROR("%s: failed to apply memory state\n", __func__); +llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_context_i * mctx, ggml_status & ret) { + if (mctx && !mctx->apply()) { + LLAMA_LOG_ERROR("%s: failed to apply memory context\n", __func__); ret = GGML_STATUS_FAILED; return nullptr; } @@ -692,7 +692,7 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, return nullptr; } - auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mstate); + auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mctx); if (!res) { LLAMA_LOG_ERROR("%s: failed to build graph\n", __func__); ret = GGML_STATUS_FAILED; @@ -722,22 +722,26 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, } int llama_context::encode(const llama_batch & batch_inp) { + GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT + if (batch_inp.n_tokens == 0) { LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); return -1; } + const auto & hparams = model.hparams; + + const int64_t n_embd = hparams.n_embd; + // note: during encode, we always pass the full sequence starting from pos = 0 - if (!batch_allocr->init(batch_inp, model.vocab, nullptr, true)) { + if (!balloc->init(batch_inp, model.vocab, nullptr, n_embd, true)) { LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__); return -1; } - const llama_batch & batch = batch_allocr->get_batch(); + const uint32_t n_tokens = balloc->get_n_tokens(); - const uint32_t n_tokens = batch.n_tokens; - - GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT + const llama_ubatch ubatch = balloc->split_simple(n_tokens); // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens"); @@ -751,14 +755,6 @@ int llama_context::encode(const llama_batch & batch_inp) { n_queued_tokens += n_tokens; - const auto & hparams = model.hparams; - - const int64_t n_embd = hparams.n_embd; - - llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true); - - const llama_ubatch ubatch = sbatch.split_simple(n_tokens); - // reserve output buffer if (output_reserve(n_tokens) < n_tokens) { LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens); @@ -817,34 +813,28 @@ int llama_context::encode(const llama_batch & batch_inp) { { // extract sequence embeddings auto & embd_seq_out = embd_seq; - embd_seq_out.clear(); - GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits + for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) { + const llama_seq_id seq_id = ubatch.seq_id_unq[s]; + const int32_t seq_idx = ubatch.seq_idx[seq_id]; - // TODO: fix indexing [UBATCH_IDX] - for (uint32_t i = 0; i < n_tokens; i++) { - const llama_seq_id seq_id = ubatch.seq_id[i][0]; - if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { - continue; - } embd_seq_out[seq_id].resize(n_embd); - ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float)); + ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float)); } } break; case LLAMA_POOLING_TYPE_RANK: { // extract the rerank score - n_cls_out floats per sequence auto & embd_seq_out = embd_seq; + const uint32_t n_cls_out = hparams.n_cls_out; - // TODO: fix indexing [UBATCH_IDX] - for (uint32_t s = 0; s < ubatch.n_seqs; ++s) { - const llama_seq_id seq_id = ubatch.seq_id[s][0]; - if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { - continue; - } + for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) { + const llama_seq_id seq_id = ubatch.seq_id_unq[s]; + const int32_t seq_idx = ubatch.seq_idx[seq_id]; + embd_seq_out[seq_id].resize(n_cls_out); - ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_id)*sizeof(float), n_cls_out*sizeof(float)); + ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float)); } } break; case LLAMA_POOLING_TYPE_UNSPECIFIED: @@ -869,12 +859,16 @@ int llama_context::encode(const llama_batch & batch_inp) { cross.v_embd.resize(cross.n_embd*cross.n_enc); memcpy(cross.v_embd.data(), embd, ggml_nbytes(t_embd)); + const auto & batch = balloc->get_batch(); + // remember the sequence ids used during the encoding - needed for cross attention later cross.seq_ids_enc.resize(n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { cross.seq_ids_enc[i].clear(); + for (int s = 0; s < batch.n_seq_id[i]; s++) { - llama_seq_id seq_id = batch.seq_id[i][s]; + const llama_seq_id seq_id = batch.seq_id[i][s]; + cross.seq_ids_enc[i].insert(seq_id); } } @@ -884,6 +878,8 @@ int llama_context::encode(const llama_batch & batch_inp) { } int llama_context::decode(const llama_batch & batch_inp) { + GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT + if (!memory) { LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__); return encode(batch_inp); @@ -894,29 +890,24 @@ int llama_context::decode(const llama_batch & batch_inp) { return -1; } - // when computing embeddings, all tokens are output - const bool embd_all = cparams.embeddings; - - if (!batch_allocr->init(batch_inp, model.vocab, memory.get(), embd_all)) { - LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__); - return -1; - } - - const llama_batch & batch = batch_allocr->get_batch(); - const auto & vocab = model.vocab; const auto & hparams = model.hparams; const int32_t n_vocab = vocab.n_tokens(); const int64_t n_embd = hparams.n_embd; - const uint32_t n_tokens_all = batch.n_tokens; + // when computing embeddings, all tokens are output + const bool output_all = cparams.embeddings; - GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT + if (!balloc->init(batch_inp, vocab, memory.get(), n_embd, output_all)) { + LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__); + return -1; + } - const uint32_t n_outputs_all = batch_allocr->get_n_outputs(); + const uint32_t n_tokens_all = balloc->get_n_tokens(); + const uint32_t n_outputs_all = balloc->get_n_outputs(); - if (embd_all) { + if (output_all) { // require that all tokens are output if (n_outputs_all != n_tokens_all) { LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %d, n_tokens_all = %d)\n", @@ -942,21 +933,21 @@ int llama_context::decode(const llama_batch & batch_inp) { // handle any pending defrags/shifts kv_self_update(false); - llama_memory_state_ptr mstate; + llama_memory_context_ptr mctx; while (true) { - mstate = memory->init_batch(batch, cparams.n_ubatch, embd_all); - if (!mstate) { + mctx = memory->init_batch(*balloc, cparams.n_ubatch, output_all); + if (!mctx) { return -2; } - switch (mstate->get_status()) { + switch (mctx->get_status()) { case LLAMA_MEMORY_STATUS_SUCCESS: { } break; case LLAMA_MEMORY_STATUS_NO_UPDATE: { - LLAMA_LOG_ERROR("%s: unexpected memory state status: %d\n", __func__, mstate->get_status()); + LLAMA_LOG_ERROR("%s: unexpected memory context status: %d\n", __func__, mctx->get_status()); return -2; } @@ -966,19 +957,19 @@ int llama_context::decode(const llama_batch & batch_inp) { did_optimize = true; if (kv_self_update(true)) { - LLAMA_LOG_DEBUG("%s: retrying batch size %d after cache optimization\n", __func__, batch.n_tokens); + LLAMA_LOG_DEBUG("%s: retrying batch size %d after cache optimization\n", __func__, balloc->get_n_tokens()); continue; } } - LLAMA_LOG_WARN("%s: failed to find a memory slot for batch of size %d\n", __func__, batch.n_tokens); + LLAMA_LOG_WARN("%s: failed to find a memory slot for batch of size %d\n", __func__, balloc->get_n_tokens()); return 1; } case LLAMA_MEMORY_STATUS_FAILED_COMPUTE: { - LLAMA_LOG_ERROR("%s: compute failed while preparing batch of size %d\n", __func__, batch.n_tokens); + LLAMA_LOG_ERROR("%s: compute failed while preparing batch of size %d\n", __func__, balloc->get_n_tokens()); return -2; } @@ -996,7 +987,7 @@ int llama_context::decode(const llama_batch & batch_inp) { int64_t n_outputs_prev = 0; do { - const auto & ubatch = mstate->get_ubatch(); + const auto & ubatch = mctx->get_ubatch(); // count the outputs in this ubatch { @@ -1005,7 +996,6 @@ int llama_context::decode(const llama_batch & batch_inp) { if (n_outputs_all == n_tokens_all) { n_outputs_new = ubatch.n_tokens; } else { - GGML_ASSERT(ubatch.output); for (uint32_t i = 0; i < ubatch.n_tokens; i++) { n_outputs_new += (int32_t) (ubatch.output[i] != 0); } @@ -1019,7 +1009,7 @@ int llama_context::decode(const llama_batch & batch_inp) { ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data); ggml_status status; - const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mstate.get(), status); + const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mctx.get(), status); if (!res) { // the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache @@ -1028,7 +1018,6 @@ int llama_context::decode(const llama_batch & batch_inp) { pos_min[s] = std::numeric_limits::max(); } - // TODO: fix sequence indexing for (uint32_t i = 0; i < ubatch.n_tokens; ++i) { const auto & seq_id = ubatch.seq_id[i][0]; @@ -1105,27 +1094,27 @@ int llama_context::decode(const llama_batch & batch_inp) { // extract sequence embeddings (cleared before processing each batch) auto & embd_seq_out = embd_seq; - for (uint32_t s = 0; s < ubatch.n_seqs; ++s) { - const llama_seq_id seq_id = ubatch.seq_id[s][0]; - if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { - continue; - } + for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) { + const llama_seq_id seq_id = ubatch.seq_id_unq[s]; + const int32_t seq_idx = ubatch.seq_idx[seq_id]; + embd_seq_out[seq_id].resize(n_embd); - ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float)); + ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float)); } } break; case LLAMA_POOLING_TYPE_RANK: { - // extract the rerank score - a single float per sequence + // extract the rerank score - n_cls_out floats per sequence auto & embd_seq_out = embd_seq; - for (uint32_t s = 0; s < ubatch.n_seqs; ++s) { - const llama_seq_id seq_id = ubatch.seq_id[s][0]; - if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { - continue; - } - embd_seq_out[seq_id].resize(1); - ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float)); + const uint32_t n_cls_out = hparams.n_cls_out; + + for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) { + const llama_seq_id seq_id = ubatch.seq_id_unq[s]; + const int32_t seq_idx = ubatch.seq_idx[seq_id]; + + embd_seq_out[seq_id].resize(n_cls_out); + ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float)); } } break; case LLAMA_POOLING_TYPE_UNSPECIFIED: @@ -1136,7 +1125,7 @@ int llama_context::decode(const llama_batch & batch_inp) { } n_outputs_prev += n_outputs; - } while (mstate->next()); + } while (mctx->next()); // set to total number of outputs in the batch, for use in llama_get_logits_ith n_outputs = n_outputs_all; @@ -1145,7 +1134,7 @@ int llama_context::decode(const llama_batch & batch_inp) { if (n_outputs > 0) { bool sorted_output = true; - auto & out_ids = mstate->out_ids(); + auto & out_ids = balloc->get_out_ids(); GGML_ASSERT(out_ids.size() == (size_t) n_outputs); @@ -1302,7 +1291,7 @@ ggml_cgraph * llama_context::graph_init() { return ggml_new_graph_custom(ctx_compute.get(), graph_max_nodes(), false); } -ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate) { +ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx) { LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs); if (n_tokens % n_seqs != 0) { @@ -1318,11 +1307,11 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u this->n_outputs = n_outputs; - llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph - llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; + llama_batch_allocr balloc(model.hparams.n_pos_per_embd()); + llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs); auto * gf = graph_init(); - auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate); + auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx); this->n_outputs = save_n_outputs; @@ -1343,11 +1332,11 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u } llm_graph_result_ptr llama_context::graph_build( - ggml_context * ctx, - ggml_cgraph * gf, - const llama_ubatch & ubatch, - llm_graph_type gtype, - const llama_memory_state_i * mstate) { + ggml_context * ctx, + ggml_cgraph * gf, + const llama_ubatch & ubatch, + llm_graph_type gtype, + const llama_memory_context_i * mctx) { return model.build_graph( { /*.ctx =*/ ctx, @@ -1359,7 +1348,7 @@ llm_graph_result_ptr llama_context::graph_build( /*.backend_cpu =*/ backend_cpu, /*.cvec =*/ &cvec, /*.loras =*/ &loras, - /*.mstate =*/ mstate, + /*.mctx =*/ mctx, /*.cross =*/ &cross, /*.n_outputs =*/ n_outputs, /*.cb =*/ graph_get_cb(), @@ -2039,7 +2028,12 @@ void llama_context::opt_epoch_iter( batch.logits [pos_batch] = true; } - const auto n_tokens_all = batch.n_tokens; + if (!balloc->init(batch, model.vocab, nullptr, model.hparams.n_embd, true)) { + LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__); + return; + } + + const uint32_t n_tokens_all = balloc->get_n_tokens(); n_queued_tokens += n_tokens_all; @@ -2047,8 +2041,8 @@ void llama_context::opt_epoch_iter( uint32_t n_outputs_all = n_tokens_all; - auto mstate = memory->init_batch(batch, cparams.n_ubatch, true); - if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) { + auto mctx = memory->init_batch(*balloc, cparams.n_ubatch, true); + if (!mctx || mctx->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) { LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__); break; } @@ -2061,17 +2055,17 @@ void llama_context::opt_epoch_iter( uint32_t pos_batch = 0; do { - const auto & ubatch = mstate->get_ubatch(); + const auto & ubatch = mctx->get_ubatch(); n_outputs = ubatch.n_tokens; - if (!mstate->apply()) { - LLAMA_LOG_ERROR("%s: failed to update the memory state\n", __func__); + if (!mctx->apply()) { + LLAMA_LOG_ERROR("%s: failed to update the memory context\n", __func__); break; } auto * gf = graph_init(); - auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate.get()); + auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx.get()); struct ggml_context * ctx_compute_opt; { @@ -2106,7 +2100,7 @@ void llama_context::opt_epoch_iter( ggml_free(ctx_compute_opt); pos_batch += ubatch.n_tokens; - } while (mstate->next()); + } while (mctx->next()); } } diff --git a/src/llama-context.h b/src/llama-context.h index 040f03ae4..9ce05715a 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -18,7 +18,7 @@ class llama_io_read_i; class llama_io_write_i; struct llama_memory_i; -struct llama_memory_state_i; +struct llama_memory_context_i; struct llama_context { // init scheduler and compute buffers, reserve worst-case graphs @@ -93,14 +93,14 @@ struct llama_context { int32_t il_end); // process a single ubatch with a specific graph type - // if memory_state is provided, it will be applied first to the context's memory + // if memory_context is provided, it will be applied first to the context's memory // ret contains the status of the graph computation // returns nullptr only if ret != GGML_STATUS_SUCCESS llm_graph_result_ptr process_ubatch( - const llama_ubatch & ubatch, - llm_graph_type gtype, - llama_memory_state_i * mstate, - ggml_status & ret); + const llama_ubatch & ubatch, + llm_graph_type gtype, + llama_memory_context_i * mctx, + ggml_status & ret); int encode(const llama_batch & batch_inp); int decode(const llama_batch & batch_inp); @@ -197,15 +197,15 @@ public: ggml_status graph_compute(ggml_cgraph * gf, bool batched); // reserve a graph with a dummy ubatch of the specified size - ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate); + ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx); private: llm_graph_result_ptr graph_build( - ggml_context * ctx, - ggml_cgraph * gf, - const llama_ubatch & ubatch, - llm_graph_type gtype, - const llama_memory_state_i * mstate); + ggml_context * ctx, + ggml_cgraph * gf, + const llama_ubatch & ubatch, + llm_graph_type gtype, + const llama_memory_context_i * mctx); llm_graph_cb graph_get_cb() const; @@ -247,7 +247,7 @@ private: std::map> embd_seq; // reuse the batch_allocr to avoid unnecessary memory allocations - std::unique_ptr batch_allocr; + std::unique_ptr balloc; uint32_t n_outputs = 0; // number of actually-used outputs in the current ubatch or last logical batch diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index e5fb87ee0..a8e7b4cbb 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -87,41 +87,33 @@ void llm_graph_input_pos_bucket::set_input(const llama_ubatch * ubatch) { void llm_graph_input_pos_bucket_kv::set_input(const llama_ubatch * ubatch) { if (pos_bucket) { - kv_state->set_input_pos_bucket(pos_bucket, ubatch); + mctx->set_input_pos_bucket(pos_bucket, ubatch); } } void llm_graph_input_out_ids::set_input(const llama_ubatch * ubatch) { - if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) { - //GGML_ASSERT(out_ids && "every model that can must skip unused outputs"); + GGML_ASSERT(out_ids); - if (!out_ids) { - LLAMA_LOG_WARN("%s: 'out_ids' is not created\n", __func__); - } else { - const int64_t n_tokens = ubatch->n_tokens; + const int64_t n_tokens = ubatch->n_tokens; - GGML_ASSERT(ggml_backend_buffer_is_host(out_ids->buffer)); - int32_t * data = (int32_t *) out_ids->data; + GGML_ASSERT(ggml_backend_buffer_is_host(out_ids->buffer)); + int32_t * data = (int32_t *) out_ids->data; - if (n_outputs == n_tokens) { - for (int i = 0; i < n_tokens; ++i) { - data[i] = i; - } - } else if (ubatch->output) { - int32_t n_outputs = 0; - for (int i = 0; i < n_tokens; ++i) { - if (ubatch->output[i]) { - data[n_outputs++] = i; - } - } - // the graph needs to have been passed the correct number of outputs - GGML_ASSERT(n_outputs == n_outputs); - } else if (n_outputs == 1) { - // only keep last output - data[0] = n_tokens - 1; - } else { - GGML_ASSERT(n_outputs == 0); - } + if (n_outputs == n_tokens) { + for (int i = 0; i < n_tokens; ++i) { + data[i] = i; + } + + return; + } + + GGML_ASSERT(ubatch->output); + + int n_outputs = 0; + + for (int i = 0; i < n_tokens; ++i) { + if (ubatch->output[i]) { + data[n_outputs++] = i; } } } @@ -130,110 +122,97 @@ void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) { if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = ubatch->n_tokens; const int64_t n_seq_tokens = ubatch->n_seq_tokens; - const int64_t n_seqs = ubatch->n_seqs; + const int64_t n_seqs_unq = ubatch->n_seqs_unq; GGML_ASSERT(mean); GGML_ASSERT(ggml_backend_buffer_is_host(mean->buffer)); float * data = (float *) mean->data; - memset(mean->data, 0, n_tokens * n_tokens * ggml_element_size(mean)); + memset(mean->data, 0, n_tokens*n_seqs_unq*ggml_element_size(mean)); - std::vector sum(n_tokens, 0); + std::vector sums(n_seqs_unq, 0); + for (int i = 0; i < n_tokens; i += n_seq_tokens) { + for (int s = 0; s < ubatch->n_seq_id[i]; ++s) { + const llama_seq_id seq_id = ubatch->seq_id[i][s]; + const int32_t seq_idx = ubatch->seq_idx[seq_id]; - // TODO: fix indexing [UBATCH_IDX] - for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch->seq_id[s][0]; - - // TODO: adapt limits to n_seqs when ubatch->equal_seqs is true - GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); - - sum[seq_id] += ubatch->n_seq_tokens; - } - - std::vector div(n_tokens, 0.0f); - for (int i = 0; i < n_tokens; ++i) { - const uint64_t s = sum[i]; - if (s > 0) { - div[i] = 1.0f/float(s); + sums[seq_idx] += ubatch->n_seq_tokens; } } - // TODO: fix indexing [UBATCH_IDX] - for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch->seq_id[s][0]; + std::vector div(n_seqs_unq, 0.0f); + for (int s = 0; s < n_seqs_unq; ++s) { + const uint64_t sum = sums[s]; + if (sum > 0) { + div[s] = 1.0f/float(sum); + } + } - for (int i = 0; i < n_seq_tokens; ++i) { - data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id]; + for (int i = 0; i < n_tokens; i += n_seq_tokens) { + for (int s = 0; s < ubatch->n_seq_id[i]; ++s) { + const llama_seq_id seq_id = ubatch->seq_id[i][s]; + const int32_t seq_idx = ubatch->seq_idx[seq_id]; + + for (int j = 0; j < n_seq_tokens; ++j) { + data[seq_idx*n_tokens + i + j] = div[seq_idx]; + } } } } } void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) { - if (cparams.embeddings && ( - cparams.pooling_type == LLAMA_POOLING_TYPE_CLS || - cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) { - const int64_t n_tokens = ubatch->n_tokens; - const int64_t n_seq_tokens = ubatch->n_seq_tokens; - const int64_t n_seqs = ubatch->n_seqs; + const int64_t n_tokens = ubatch->n_tokens; + const int64_t n_seq_tokens = ubatch->n_seq_tokens; + const int64_t n_seqs_unq = ubatch->n_seqs_unq; + if (cparams.embeddings && ( + cparams.pooling_type == LLAMA_POOLING_TYPE_CLS || + cparams.pooling_type == LLAMA_POOLING_TYPE_RANK + )) { GGML_ASSERT(cls); GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer)); uint32_t * data = (uint32_t *) cls->data; - memset(cls->data, 0, n_tokens * ggml_element_size(cls)); + memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls)); - // TODO: fix indexing [UBATCH_IDX] - for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch->seq_id[s][0]; + for (int i = 0; i < n_tokens; i += n_seq_tokens) { + for (int s = 0; s < ubatch->n_seq_id[i]; ++s) { + const llama_seq_id seq_id = ubatch->seq_id[i][s]; + const int32_t seq_idx = ubatch->seq_idx[seq_id]; - // TODO: adapt limits to n_seqs when ubatch->equal_seqs is true - GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK"); - - for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = ubatch->pos[s*n_seq_tokens + i]; - - if (pos == 0) { - data[seq_id] = s*n_seq_tokens + i; - } + data[seq_idx] = i; } } } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) { - const int64_t n_tokens = ubatch->n_tokens; - const int64_t n_seq_tokens = ubatch->n_seq_tokens; - const int64_t n_seqs = ubatch->n_seqs; - GGML_ASSERT(cls); GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer)); uint32_t * data = (uint32_t *) cls->data; - memset(cls->data, 0, n_tokens * ggml_element_size(cls)); + memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls)); - std::vector last_pos(n_tokens, -1); - std::vector last_row(n_tokens, -1); + std::vector last_pos(n_seqs_unq, -1); + std::vector last_row(n_seqs_unq, -1); - // TODO: fix indexing [UBATCH_IDX] - for (int s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch->seq_id[s][0]; + for (int i = 0; i < n_tokens; ++i) { + const llama_pos pos = ubatch->pos[i]; - // TODO: adapt limits to n_seqs when ubatch->equal_seqs is true - GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST"); + for (int s = 0; s < ubatch->n_seq_id[i]; ++s) { + const llama_seq_id seq_id = ubatch->seq_id[i][s]; + const int32_t seq_idx = ubatch->seq_idx[seq_id]; - for (int i = 0; i < n_seq_tokens; ++i) { - const llama_pos pos = ubatch->pos[s*n_seq_tokens + i]; - - if (pos >= last_pos[seq_id]) { - last_pos[seq_id] = pos; - last_row[seq_id] = s*n_seq_tokens + i; + if (pos >= last_pos[seq_idx]) { + last_pos[seq_idx] = pos; + last_row[seq_idx] = i; } } } - for (int i = 0; i < n_tokens; ++i) { - if (last_row[i] >= 0) { - data[i] = last_row[i]; + for (int s = 0; s < n_seqs_unq; ++s) { + if (last_row[s] >= 0) { + data[s] = last_row[s]; } } } @@ -242,7 +221,7 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) { void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) { GGML_UNUSED(ubatch); - const int64_t n_rs = mem_state->get_n_rs(); + const int64_t n_rs = mctx->get_n_rs(); if (s_copy) { GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer)); @@ -250,7 +229,7 @@ void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) { // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n for (uint32_t i = 0; i < n_rs; ++i) { - data[i] = mem_state->s_copy(i); + data[i] = mctx->s_copy(i); } } } @@ -266,89 +245,36 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) { } void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) { - if (kq_mask) { - if (cparams.causal_attn) { - const int64_t n_kv = ubatch->n_tokens; - const int64_t n_tokens = ubatch->n_tokens; - const int64_t n_seq_tokens = ubatch->n_seq_tokens; - const int64_t n_seqs = ubatch->n_seqs; + const int64_t n_kv = ubatch->n_tokens; + const int64_t n_tokens = ubatch->n_tokens; - GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer)); - float * data = (float *) kq_mask->data; + GGML_ASSERT(kq_mask); + GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer)); - for (int h = 0; h < 1; ++h) { - for (int s1 = 0; s1 < n_seqs; ++s1) { - const llama_seq_id seq_id = ubatch->seq_id[s1][0]; + float * data = (float *) kq_mask->data; - for (int j = 0; j < n_seq_tokens; ++j) { - const int32_t tj = s1*n_seq_tokens + j; + for (int h = 0; h < 1; ++h) { + for (int i1 = 0; i1 < n_tokens; ++i1) { + const llama_seq_id s1 = ubatch->seq_id[i1][0]; - for (int s0 = 0; s0 < n_seqs; ++s0) { - for (int i = 0; i < n_seq_tokens; ++i) { - const int32_t ti = s0*n_seq_tokens + i; - float f = -INFINITY; + for (int i0 = 0; i0 < n_tokens; ++i0) { + float f = -INFINITY; - // TODO: fix indexing [UBATCH_IDX] - for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) { - if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) { - if (hparams.use_alibi) { - f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]); - } else { - f = 0.0f; - } - break; - } - } + for (int s = 0; s < ubatch->n_seq_id[i0]; ++s) { + const llama_seq_id s0 = ubatch->seq_id[i0][0]; - data[h*(n_kv*n_tokens) + tj*n_kv + ti] = f; - } + // TODO: reimplement this like in llama_kv_cache_unified + if (s0 == s1 && (!cparams.causal_attn || ubatch->pos[i0] <= ubatch->pos[i1])) { + if (hparams.use_alibi) { + f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]); + } else { + f = 0.0f; } + break; } } - } - } else { - const int64_t n_tokens = ubatch->n_tokens; - const int64_t n_seq_tokens = ubatch->n_seq_tokens; - const int64_t n_seqs = ubatch->n_seqs; - const int64_t n_stride = ubatch->n_tokens; - GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer)); - - float * data = (float *) kq_mask->data; - - for (int h = 0; h < 1; ++h) { - for (int s1 = 0; s1 < n_seqs; ++s1) { - const llama_seq_id seq_id = ubatch->seq_id[s1][0]; - - for (int j = 0; j < n_seq_tokens; ++j) { - const int32_t tj = s1*n_seq_tokens + j; - - for (int s0 = 0; s0 < n_seqs; ++s0) { - for (int i = 0; i < n_seq_tokens; ++i) { - const int32_t ti = s0*n_seq_tokens + i; - float f = -INFINITY; - - // TODO: fix indexing [UBATCH_IDX] - for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) { - if (ubatch->seq_id[s0][s] == seq_id) { - if (hparams.use_alibi) { - f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]); - } else { - f = 0.0f; - } - break; - } - } - - data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f; - } - } - - for (int i = n_tokens; i < n_stride; ++i) { - data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY; - } - } - } + data[h*(n_kv*n_tokens) + i1*n_kv + i0] = f; } } } @@ -356,49 +282,51 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) { void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) { if (self_kq_mask) { - kv_state->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); + mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); } } void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) { if (self_kq_mask) { - kv_state->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); + mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); } if (self_kq_mask_swa) { - kv_state->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn); + mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn); } } void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) { - if (cross_kq_mask) { - const int64_t n_enc = cross_kq_mask->ne[0]; - const int64_t n_tokens = ubatch->n_tokens; + GGML_ASSERT(cross_kq_mask); - GGML_ASSERT(ggml_backend_buffer_is_host(cross_kq_mask->buffer)); - GGML_ASSERT(!ubatch->equal_seqs); // TODO: use ubatch->n_seqs instead of failing + const int64_t n_enc = cross_kq_mask->ne[0]; + const int64_t n_tokens = ubatch->n_tokens; - float * data = (float *) cross_kq_mask->data; + GGML_ASSERT(ggml_backend_buffer_is_host(cross_kq_mask->buffer)); + GGML_ASSERT(!ubatch->equal_seqs); // TODO: use ubatch->n_seqs instead of failing - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - for (int i = 0; i < n_enc; ++i) { - float f = -INFINITY; - // TODO: fix indexing [UBATCH_IDX] - for (int s = 0; s < ubatch->n_seq_id[j]; ++s) { - const llama_seq_id seq_id = ubatch->seq_id[j][s]; - if (cross->seq_ids_enc[i].find(seq_id) != cross->seq_ids_enc[i].end()) { - f = 0.0f; - } + float * data = (float *) cross_kq_mask->data; + + for (int h = 0; h < 1; ++h) { + for (int i = 0; i < n_tokens; ++i) { + for (int j = 0; j < n_enc; ++j) { + float f = -INFINITY; + + for (int s = 0; s < ubatch->n_seq_id[i]; ++s) { + const llama_seq_id seq_id = ubatch->seq_id[i][s]; + + if (cross->seq_ids_enc[j].find(seq_id) != cross->seq_ids_enc[j].end()) { + f = 0.0f; } - data[h*(n_enc*n_tokens) + j*n_enc + i] = f; } - } - for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) { - for (int j = 0; j < n_enc; ++j) { - data[h*(n_enc*n_tokens) + i*n_enc + j] = -INFINITY; - } + data[h*(n_enc*n_tokens) + i*n_enc + j] = f; + } + } + + for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) { + for (int j = 0; j < n_enc; ++j) { + data[h*(n_enc*n_tokens) + i*n_enc + j] = -INFINITY; } } } @@ -406,10 +334,10 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) { void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) { if (self_kq_mask) { - mem_state->get_state_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); + mctx->get_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); } - const int64_t n_rs = mem_state->get_state_recr()->get_n_rs(); + const int64_t n_rs = mctx->get_recr()->get_n_rs(); if (s_copy) { GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer)); @@ -417,7 +345,7 @@ void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) { // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n for (uint32_t i = 0; i < n_rs; ++i) { - data[i] = mem_state->get_state_recr()->s_copy(i); + data[i] = mctx->get_recr()->s_copy(i); } } } @@ -461,16 +389,12 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) : backend_cpu (params.backend_cpu), cvec (params.cvec), loras (params.loras), - mstate (params.mstate), + mctx (params.mctx), cross (params.cross), cb_func (params.cb), res (std::make_unique()) { } -int64_t llm_graph_context::n_pos_per_embd() const { - return hparams.rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1; -} - void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const { if (cb_func) { cb_func(ubatch, cur, name, il); @@ -915,11 +839,11 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const { } ggml_tensor * llm_graph_context::build_inp_pos() const { - auto inp = std::make_unique(n_pos_per_embd()); + auto inp = std::make_unique(hparams.n_pos_per_embd()); auto & cur = inp->pos; - cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_embd()); + cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, (int64_t)n_tokens*hparams.n_pos_per_embd()); ggml_set_input(cur); res->add_input(std::move(inp)); @@ -942,6 +866,14 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const { } ggml_tensor * llm_graph_context::build_inp_out_ids() const { + // note: when all tokens are output, we could skip this optimization to spare the ggml_get_rows() calls, + // but this would make the graph topology depend on the number of output tokens, which can interere with + // features that require constant topology such as pipline parallelism + // ref: https://github.com/ggml-org/llama.cpp/pull/14275#issuecomment-2987424471 + //if (n_outputs < n_tokens) { + // return nullptr; + //} + auto inp = std::make_unique(hparams, cparams, n_outputs); auto & cur = inp->out_ids; @@ -959,7 +891,7 @@ ggml_tensor * llm_graph_context::build_inp_mean() const { auto & cur = inp->mean; - cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); + cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, ubatch.n_seqs_unq); ggml_set_input(cur); res->add_input(std::move(inp)); @@ -972,7 +904,7 @@ ggml_tensor * llm_graph_context::build_inp_cls() const { auto & cur = inp->cls; - cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_seqs_unq); ggml_set_input(cur); res->add_input(std::move(inp)); @@ -1018,11 +950,11 @@ ggml_tensor * llm_graph_context::build_inp_pos_bucket_enc() const { } ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); - auto inp = std::make_unique(hparams, kv_state); + auto inp = std::make_unique(hparams, mctx_cur); - const auto n_kv = kv_state->get_n_kv(); + const auto n_kv = mctx_cur->get_n_kv(); auto & cur = inp->pos_bucket; @@ -1050,14 +982,14 @@ ggml_tensor * llm_graph_context::build_pos_bias(ggml_tensor * pos_bucket, ggml_t } llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const { - const auto * mem_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); - auto inp = std::make_unique(hparams, cparams, mem_state); + auto inp = std::make_unique(hparams, cparams, mctx_cur); { GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Hybrid recurrent is not supported with SWA attention layers"); - const auto n_kv = inp->mem_state->get_state_attn()->get_n_kv(); + const auto n_kv = inp->mctx->get_attn()->get_n_kv(); inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); //cb(inp->self_kq_mask, "KQ_mask", -1); @@ -1067,7 +999,7 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const { } { - const auto n_rs = mem_state->get_state_recr()->get_n_rs(); + const auto n_rs = mctx_cur->get_recr()->get_n_rs(); inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs); ggml_set_input(inp->s_copy); @@ -1251,14 +1183,14 @@ ggml_tensor * llm_graph_context::build_attn( } llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); - auto inp = std::make_unique(hparams, cparams, kv_state); + auto inp = std::make_unique(hparams, cparams, mctx_cur); { GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA"); - const auto n_kv = kv_state->get_n_kv(); + const auto n_kv = mctx_cur->get_n_kv(); inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); //cb(inp->self_kq_mask, "KQ_mask", -1); @@ -1288,19 +1220,19 @@ ggml_tensor * llm_graph_context::build_attn( ggml_build_forward_expand(gf, k_cur); ggml_build_forward_expand(gf, v_cur); - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); // store to KV cache { - ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il)); - ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il)); } const auto & kq_mask = inp->get_kq_mask(); ggml_tensor * q = q_cur; - ggml_tensor * k = kv_state->get_k(ctx0, il); - ggml_tensor * v = kv_state->get_v(ctx0, il); + ggml_tensor * k = mctx_cur->get_k(ctx0, il); + ggml_tensor * v = mctx_cur->get_v(ctx0, il); ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale); cb(cur, "kqv_out", il); @@ -1338,23 +1270,23 @@ ggml_tensor * llm_graph_context::build_attn( ggml_build_forward_expand(gf, k_cur); ggml_build_forward_expand(gf, v_cur); - const auto * kv_state_iswa = static_cast(mstate); + const auto * mctx_iswa = static_cast(mctx); const bool is_swa = hparams.is_swa(il); - const auto * kv_state = is_swa ? kv_state_iswa->get_swa() : kv_state_iswa->get_base(); + const auto * mctx_cur = is_swa ? mctx_iswa->get_swa() : mctx_iswa->get_base(); // store to KV cache { - ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il)); - ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il)); } const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask(); ggml_tensor * q = q_cur; - ggml_tensor * k = kv_state->get_k(ctx0, il); - ggml_tensor * v = kv_state->get_v(ctx0, il); + ggml_tensor * k = mctx_cur->get_k(ctx0, il); + ggml_tensor * v = mctx_cur->get_v(ctx0, il); ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale); cb(cur, "kqv_out", il); @@ -1447,19 +1379,19 @@ ggml_tensor * llm_graph_context::build_attn( ggml_build_forward_expand(gf, k_cur); ggml_build_forward_expand(gf, v_cur); - const auto * kv_state = static_cast(mstate)->get_state_attn(); + const auto * mctx_cur = static_cast(mctx)->get_attn(); // store to KV cache { - ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il)); - ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il)); } const auto & kq_mask = inp->get_kq_mask(); ggml_tensor * q = q_cur; - ggml_tensor * k = kv_state->get_k(ctx0, il); - ggml_tensor * v = kv_state->get_v(ctx0, il); + ggml_tensor * k = mctx_cur->get_k(ctx0, il); + ggml_tensor * v = mctx_cur->get_v(ctx0, il); ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale); cb(cur, "kqv_out", il); @@ -1480,12 +1412,12 @@ ggml_tensor * llm_graph_context::build_attn( } llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); - auto inp = std::make_unique(hparams, cparams, kv_state); + auto inp = std::make_unique(hparams, cparams, mctx_cur); { - const auto n_kv = kv_state->get_base()->get_n_kv(); + const auto n_kv = mctx_cur->get_base()->get_n_kv(); inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); //cb(inp->self_kq_mask, "KQ_mask", -1); @@ -1497,7 +1429,7 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif { GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified for non-SWA"); - const auto n_kv = kv_state->get_swa()->get_n_kv(); + const auto n_kv = mctx_cur->get_swa()->get_n_kv(); inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); //cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1); @@ -1545,11 +1477,11 @@ ggml_tensor * llm_graph_context::build_rs( } llm_graph_input_rs * llm_graph_context::build_rs_inp() const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); - auto inp = std::make_unique(kv_state); + auto inp = std::make_unique(mctx_cur); - const auto n_rs = kv_state->get_n_rs(); + const auto n_rs = mctx_cur->get_n_rs(); inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs); ggml_set_input(inp->s_copy); @@ -1564,7 +1496,7 @@ ggml_tensor * llm_graph_context::build_rs( int32_t state_size, int32_t n_seqs, const llm_graph_get_rows_fn & get_state_rows) const { - const auto * kv_state = static_cast(mstate); + const auto * kv_state = static_cast(mctx); return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows); } @@ -1576,7 +1508,7 @@ ggml_tensor * llm_graph_context::build_rs( int32_t state_size, int32_t n_seqs, const llm_graph_get_rows_fn & get_state_rows) const { - const auto * kv_state = static_cast(mstate)->get_state_recr(); + const auto * kv_state = static_cast(mctx)->get_recr(); return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows); } @@ -1586,13 +1518,13 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load( ggml_cgraph * gf, const llama_ubatch & ubatch, int il) const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); const auto token_shift_count = hparams.token_shift_count; const int64_t n_seqs = ubatch.n_seqs; - ggml_tensor * token_shift_all = kv_state->get_r_l(il); + ggml_tensor * token_shift_all = mctx_cur->get_r_l(il); ggml_tensor * token_shift = build_rs( inp, gf, token_shift_all, @@ -1607,19 +1539,19 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store( ggml_tensor * token_shift, const llama_ubatch & ubatch, int il) const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); const auto token_shift_count = hparams.token_shift_count; const auto n_embd = hparams.n_embd; const int64_t n_seqs = ubatch.n_seqs; - const auto kv_head = kv_state->get_head(); + const auto kv_head = mctx_cur->get_head(); return ggml_cpy( ctx0, ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * token_shift_count, 0), - ggml_view_1d(ctx0, kv_state->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(kv_state->get_r_l(il))) + ggml_view_1d(ctx0, mctx_cur->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(mctx_cur->get_r_l(il))) ); } diff --git a/src/llama-graph.h b/src/llama-graph.h index b2aac72a7..84f77be59 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -17,12 +17,12 @@ struct ggml_tensor; struct llama_ubatch; struct llama_cparams; -struct llama_memory_state_i; +struct llama_memory_context_i; -class llama_kv_cache_unified_state; -class llama_kv_cache_unified_iswa_state; -class llama_memory_recurrent_state; -class llama_memory_hybrid_state; +class llama_kv_cache_unified_context; +class llama_kv_cache_unified_iswa_context; +class llama_memory_recurrent_context; +class llama_memory_hybrid_context; // certain models (typically multi-modal) can produce different types of graphs enum llm_graph_type { @@ -95,14 +95,14 @@ public: class llm_graph_input_pos : public llm_graph_input_i { public: - llm_graph_input_pos(int64_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {} + llm_graph_input_pos(uint32_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {} virtual ~llm_graph_input_pos() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * pos = nullptr; // I32 [n_batch] - const int64_t n_pos_per_embd = 1; + const uint32_t n_pos_per_embd = 1; }; // temperature tuning, used by llama4 @@ -136,7 +136,7 @@ class llm_graph_input_pos_bucket_kv : public llm_graph_input_i { public: llm_graph_input_pos_bucket_kv( const llama_hparams & hparams, - const llama_kv_cache_unified_state * kv_state) : hparams(hparams), kv_state(kv_state) {} + const llama_kv_cache_unified_context * mctx) : hparams(hparams), mctx(mctx) {} virtual ~llm_graph_input_pos_bucket_kv() = default; void set_input(const llama_ubatch * ubatch) override; @@ -144,7 +144,8 @@ public: ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch] const llama_hparams & hparams; - const llama_kv_cache_unified_state * kv_state; + + const llama_kv_cache_unified_context * mctx; }; class llm_graph_input_out_ids : public llm_graph_input_i { @@ -191,14 +192,14 @@ public: class llm_graph_input_rs : public llm_graph_input_i { public: - llm_graph_input_rs(const llama_memory_recurrent_state * mem_state) : mem_state(mem_state) {} + llm_graph_input_rs(const llama_memory_recurrent_context * mctx) : mctx(mctx) {} virtual ~llm_graph_input_rs() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * s_copy; // I32 [kv_size] - const llama_memory_recurrent_state * mem_state; + const llama_memory_recurrent_context * mctx; }; class llm_graph_input_cross_embd : public llm_graph_input_i { @@ -238,10 +239,10 @@ public: llm_graph_input_attn_kv_unified( const llama_hparams & hparams, const llama_cparams & cparams, - const llama_kv_cache_unified_state * kv_state) : + const llama_kv_cache_unified_context * mctx) : hparams(hparams), cparams(cparams), - kv_state(kv_state) { + mctx(mctx) { } ~llm_graph_input_attn_kv_unified() = default; @@ -255,7 +256,7 @@ public: const llama_hparams & hparams; const llama_cparams & cparams; - const llama_kv_cache_unified_state * kv_state; + const llama_kv_cache_unified_context * mctx; }; class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i { @@ -263,10 +264,10 @@ public: llm_graph_input_attn_kv_unified_iswa( const llama_hparams & hparams, const llama_cparams & cparams, - const llama_kv_cache_unified_iswa_state * kv_state) : + const llama_kv_cache_unified_iswa_context * mctx) : hparams(hparams), cparams(cparams), - kv_state(kv_state) { + mctx(mctx) { } ~llm_graph_input_attn_kv_unified_iswa() = default; @@ -283,7 +284,7 @@ public: const llama_hparams & hparams; const llama_cparams & cparams; - const llama_kv_cache_unified_iswa_state * kv_state; + const llama_kv_cache_unified_iswa_context * mctx; }; class llm_graph_input_attn_cross : public llm_graph_input_i { @@ -306,10 +307,10 @@ public: llm_graph_input_mem_hybrid( const llama_hparams & hparams, const llama_cparams & cparams, - const llama_memory_hybrid_state * mem_state) : + const llama_memory_hybrid_context * mctx) : hparams(hparams), cparams(cparams), - mem_state(mem_state) { + mctx(mctx) { } virtual ~llm_graph_input_mem_hybrid() = default; @@ -325,7 +326,7 @@ public: const llama_hparams & hparams; const llama_cparams & cparams; - const llama_memory_hybrid_state * mem_state; + const llama_memory_hybrid_context * mctx; }; // @@ -401,10 +402,10 @@ struct llm_graph_params { ggml_backend_sched_t sched; ggml_backend_t backend_cpu; - const llama_adapter_cvec * cvec; - const llama_adapter_loras * loras; - const llama_memory_state_i * mstate; - const llama_cross * cross; + const llama_adapter_cvec * cvec; + const llama_adapter_loras * loras; + const llama_memory_context_i * mctx; + const llama_cross * cross; uint32_t n_outputs; @@ -456,10 +457,10 @@ struct llm_graph_context { ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove? - const llama_adapter_cvec * cvec; - const llama_adapter_loras * loras; - const llama_memory_state_i * mstate; - const llama_cross * cross; + const llama_adapter_cvec * cvec; + const llama_adapter_loras * loras; + const llama_memory_context_i * mctx; + const llama_cross * cross; const llm_graph_cb & cb_func; @@ -467,8 +468,6 @@ struct llm_graph_context { llm_graph_context(const llm_graph_params & params); - int64_t n_pos_per_embd() const; - void cb(ggml_tensor * cur, const char * name, int il) const; // diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index 08d43c2b2..86c814d51 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -91,6 +91,10 @@ bool llama_hparams::is_recurrent(uint32_t il) const { return recurrent_layer_arr[il]; } +uint32_t llama_hparams::n_pos_per_embd() const { + return rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1; +} + bool llama_hparams::is_swa(uint32_t il) const { if (il < n_layer) { return swa_layers[il]; diff --git a/src/llama-hparams.h b/src/llama-hparams.h index ab1f3d254..238519b01 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -193,6 +193,8 @@ struct llama_hparams { // whether or not the given layer is recurrent (for hybrid models) bool is_recurrent(uint32_t il) const; + uint32_t n_pos_per_embd() const; + bool is_swa(uint32_t il) const; }; diff --git a/src/llama-kv-cache-unified-iswa.cpp b/src/llama-kv-cache-unified-iswa.cpp index a869b1de8..b9169299c 100644 --- a/src/llama-kv-cache-unified-iswa.cpp +++ b/src/llama-kv-cache-unified-iswa.cpp @@ -95,19 +95,22 @@ llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const { return kv_swa->seq_pos_max(seq_id); } -llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) { +llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) { GGML_UNUSED(embd_all); // first try simple split do { - auto sbatch = llama_sbatch(batch, hparams.n_embd, true); + balloc.split_reset(); std::vector ubatches; + while (true) { + auto ubatch = balloc.split_simple(n_ubatch); - while (sbatch.n_tokens > 0) { - auto ubatch = sbatch.split_simple(n_ubatch); + if (ubatch.n_tokens == 0) { + break; + } - ubatches.push_back(ubatch); + ubatches.push_back(std::move(ubatch)); // NOLINT } auto heads_base = kv_base->prepare(ubatches); @@ -122,20 +125,23 @@ llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch assert(heads_base.size() == heads_swa.size()); - return std::make_unique( - this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches)); + return std::make_unique( + this, std::move(heads_base), std::move(heads_swa), std::move(ubatches)); } while (false); // if it fails, try equal split do { - auto sbatch = llama_sbatch(batch, hparams.n_embd, false); + balloc.split_reset(); std::vector ubatches; + while (true) { + auto ubatch = balloc.split_equal(n_ubatch); - while (sbatch.n_tokens > 0) { - auto ubatch = sbatch.split_equal(n_ubatch); + if (ubatch.n_tokens == 0) { + break; + } - ubatches.push_back(ubatch); + ubatches.push_back(std::move(ubatch)); // NOLINT } auto heads_base = kv_base->prepare(ubatches); @@ -150,22 +156,22 @@ llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch assert(heads_base.size() == heads_swa.size()); - return std::make_unique( - this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches)); + return std::make_unique( + this, std::move(heads_base), std::move(heads_swa), std::move(ubatches)); } while (false); // TODO: if we fail again, we should attempt different splitting strategies // but to do that properly, we first have to refactor the batches to be more flexible - return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); + return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); } -llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() { - return std::make_unique(this); +llama_memory_context_ptr llama_kv_cache_unified_iswa::init_full() { + return std::make_unique(this); } -llama_memory_state_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) { - return std::make_unique(this, lctx, optimize); +llama_memory_context_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) { + return std::make_unique(this, lctx, optimize); } bool llama_kv_cache_unified_iswa::get_can_shift() const { @@ -191,48 +197,46 @@ llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const { } // -// llama_kv_cache_unified_iswa_state +// llama_kv_cache_unified_iswa_context // -llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {} +llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(llama_memory_status status) : status(status) {} -llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state( +llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context( llama_kv_cache_unified_iswa * kv) : - state_base(kv->get_base()->init_full()), - state_swa (kv->get_swa ()->init_full()), - status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) { + ctx_base(kv->get_base()->init_full()), + ctx_swa (kv->get_swa ()->init_full()), + status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) { } -llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state( +llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context( llama_kv_cache_unified_iswa * kv, llama_context * lctx, bool optimize) : - state_base(kv->get_base()->init_update(lctx, optimize)), - state_swa (kv->get_swa ()->init_update(lctx, optimize)), - status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) { + ctx_base(kv->get_base()->init_update(lctx, optimize)), + ctx_swa (kv->get_swa ()->init_update(lctx, optimize)), + status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) { } -llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state( +llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context( llama_kv_cache_unified_iswa * kv, - llama_sbatch sbatch, std::vector heads_base, std::vector heads_swa, std::vector ubatches) : - sbatch(std::move(sbatch)), ubatches(std::move(ubatches)), // note: here we copy the ubatches. not sure if this is ideal - state_base(new llama_kv_cache_unified_state(kv->get_base(), {}, std::move(heads_base), this->ubatches)), - state_swa (new llama_kv_cache_unified_state(kv->get_swa (), {}, std::move(heads_swa), this->ubatches)), - status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) { + ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(heads_base), this->ubatches)), + ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(heads_swa), this->ubatches)), + status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) { } -llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default; +llama_kv_cache_unified_iswa_context:: ~llama_kv_cache_unified_iswa_context() = default; -bool llama_kv_cache_unified_iswa_state::next() { +bool llama_kv_cache_unified_iswa_context::next() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - state_base->next(); - state_swa ->next(); + ctx_base->next(); + ctx_swa ->next(); if (++i_next >= ubatches.size()) { return false; @@ -241,41 +245,35 @@ bool llama_kv_cache_unified_iswa_state::next() { return true; } -bool llama_kv_cache_unified_iswa_state::apply() { +bool llama_kv_cache_unified_iswa_context::apply() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); bool res = true; - res = res & state_base->apply(); - res = res & state_swa ->apply(); + res = res & ctx_base->apply(); + res = res & ctx_swa ->apply(); return res; } -std::vector & llama_kv_cache_unified_iswa_state::out_ids() { - assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - - return sbatch.out_ids; -} - -llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const { +llama_memory_status llama_kv_cache_unified_iswa_context::get_status() const { return status; } -const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const { +const llama_ubatch & llama_kv_cache_unified_iswa_context::get_ubatch() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return ubatches[i_next]; } -const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const { +const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_base() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - return static_cast(state_base.get()); + return static_cast(ctx_base.get()); } -const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const { +const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_swa() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - return static_cast(state_swa.get()); + return static_cast(ctx_swa.get()); } diff --git a/src/llama-kv-cache-unified-iswa.h b/src/llama-kv-cache-unified-iswa.h index 813eaf39b..46c1ed614 100644 --- a/src/llama-kv-cache-unified-iswa.h +++ b/src/llama-kv-cache-unified-iswa.h @@ -31,14 +31,14 @@ public: // llama_memory_i // - llama_memory_state_ptr init_batch( - const llama_batch & batch, + llama_memory_context_ptr init_batch( + llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) override; - llama_memory_state_ptr init_full() override; + llama_memory_context_ptr init_full() override; - llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override; + llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override; bool get_can_shift() const override; @@ -72,62 +72,57 @@ private: std::unique_ptr kv_swa; }; -class llama_kv_cache_unified_iswa_state : public llama_memory_state_i { +class llama_kv_cache_unified_iswa_context : public llama_memory_context_i { public: // used for errors - llama_kv_cache_unified_iswa_state(llama_memory_status status); + llama_kv_cache_unified_iswa_context(llama_memory_status status); - // used to create a full-cache state - llama_kv_cache_unified_iswa_state( + // used to create a full-cache context + llama_kv_cache_unified_iswa_context( llama_kv_cache_unified_iswa * kv); - // used to create an update state - llama_kv_cache_unified_iswa_state( + // used to create an update context + llama_kv_cache_unified_iswa_context( llama_kv_cache_unified_iswa * kv, llama_context * lctx, bool optimize); - // used to create a state from a batch - llama_kv_cache_unified_iswa_state( + // used to create a batch processing context from a batch + llama_kv_cache_unified_iswa_context( llama_kv_cache_unified_iswa * kv, - llama_sbatch sbatch, std::vector heads_base, std::vector heads_swa, std::vector ubatches); - virtual ~llama_kv_cache_unified_iswa_state(); + virtual ~llama_kv_cache_unified_iswa_context(); // - // llama_memory_state_i + // llama_memory_context_i // bool next() override; bool apply() override; - std::vector & out_ids() override; - llama_memory_status get_status() const override; const llama_ubatch & get_ubatch() const override; // - // llama_kv_cache_unified_iswa_state specific API + // llama_kv_cache_unified_iswa_context specific API // - const llama_kv_cache_unified_state * get_base() const; - const llama_kv_cache_unified_state * get_swa() const; + const llama_kv_cache_unified_context * get_base() const; + const llama_kv_cache_unified_context * get_swa() const; private: //llama_kv_cache_unified_iswa * kv; - llama_sbatch sbatch; - // the index of the next ubatch to process size_t i_next = 0; std::vector ubatches; - const llama_memory_state_ptr state_base; - const llama_memory_state_ptr state_swa; + const llama_memory_context_ptr ctx_base; + const llama_memory_context_ptr ctx_swa; const llama_memory_status status; }; diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp index d44122889..b506d32ed 100644 --- a/src/llama-kv-cache-unified.cpp +++ b/src/llama-kv-cache-unified.cpp @@ -307,18 +307,24 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const { return cells.seq_pos_max(seq_id); } -llama_memory_state_ptr llama_kv_cache_unified::init_batch( - const llama_batch & batch, +llama_memory_context_ptr llama_kv_cache_unified::init_batch( + llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) { GGML_UNUSED(embd_all); do { - auto sbatch = llama_sbatch(batch, hparams.n_embd, true); + balloc.split_reset(); std::vector ubatches; - while (sbatch.n_tokens > 0) { - ubatches.push_back(sbatch.split_simple(n_ubatch)); + while (true) { + auto ubatch = balloc.split_simple(n_ubatch); + + if (ubatch.n_tokens == 0) { + break; + } + + ubatches.push_back(std::move(ubatch)); // NOLINT } auto heads = prepare(ubatches); @@ -326,18 +332,18 @@ llama_memory_state_ptr llama_kv_cache_unified::init_batch( break; } - return std::make_unique( - this, std::move(sbatch), std::move(heads), std::move(ubatches)); + return std::make_unique( + this, std::move(heads), std::move(ubatches)); } while (false); - return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); + return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); } -llama_memory_state_ptr llama_kv_cache_unified::init_full() { - return std::make_unique(this); +llama_memory_context_ptr llama_kv_cache_unified::init_full() { + return std::make_unique(this); } -llama_memory_state_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) { +llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) { bool do_shift = get_has_shift(); defrag_info dinfo; @@ -367,7 +373,7 @@ llama_memory_state_ptr llama_kv_cache_unified::init_update(llama_context * lctx, } } - return std::make_unique(this, lctx, do_shift, std::move(dinfo)); + return std::make_unique(this, lctx, do_shift, std::move(dinfo)); } llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::vector & ubatches) { @@ -644,12 +650,6 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const { } void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch) { - if (debug > 0) { - LLAMA_LOG_DEBUG("%s: ubatch info:\n", __func__); - LLAMA_LOG_DEBUG("%s: n_tokens = %d, equal_seqs = %d\n", __func__, ubatch.n_tokens, ubatch.equal_seqs); - LLAMA_LOG_DEBUG("%s: n_seq_tokens = %d, n_seqs = %d\n", __func__, ubatch.n_seq_tokens, ubatch.n_seqs); - } - // keep track of the max sequence position that we would overwrite with this ubatch // for non-SWA cache, this would be always empty llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ]; @@ -657,27 +657,22 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch seq_pos_max_rm[s] = -1; } - for (uint32_t s = 0; s < ubatch.n_seqs; ++s) { - for (uint32_t j = 0; j < ubatch.n_seq_tokens; ++j) { - const uint32_t idx = s*ubatch.n_seq_tokens + j; + for (uint32_t i = 0; i < ubatch.n_tokens; ++i) { + if (!cells.is_empty(head_cur + i)) { + assert(cells.seq_count(head_cur + i) == 1); - if (!cells.is_empty(head_cur + idx)) { - assert(cells.seq_count(head_cur + idx) == 1); + const llama_seq_id seq_id = cells.seq_get(head_cur + i); + const llama_pos pos = cells.pos_get(head_cur + i); - const llama_seq_id seq_id = cells.seq_get(head_cur + idx); - const llama_pos pos = cells.pos_get(head_cur + idx); + seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos); - seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos); + cells.rm(head_cur + i); + } - cells.rm(head_cur + idx); - } + cells.pos_set(head_cur + i, ubatch.pos[i]); - cells.pos_set(head_cur + idx, ubatch.pos[idx]); - - // TODO: fix indexing [UBATCH_IDX] - for (int32_t i = 0; i < ubatch.n_seq_id[s]; i++) { - cells.seq_add(head_cur + idx, ubatch.seq_id[s][i]); - } + for (int32_t s = 0; s < ubatch.n_seq_id[i]; s++) { + cells.seq_add(head_cur + i, ubatch.seq_id[i][s]); } } @@ -696,6 +691,7 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch seq_rm(s, cells.seq_pos_min(s), seq_pos_max_rm[s] + 1); } } + // move the head at the end of the slot head = head_cur + ubatch.n_tokens; } @@ -792,9 +788,7 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_ } void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const { - const uint32_t n_tokens = ubatch->n_tokens; - const uint32_t n_seq_tokens = ubatch->n_seq_tokens; - const uint32_t n_seqs = ubatch->n_seqs; + const uint32_t n_tokens = ubatch->n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer)); float * data = (float *) dst->data; @@ -814,52 +808,48 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub // xxxxx----- // To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615 for (uint32_t h = 0; h < 1; ++h) { - for (uint32_t s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch->seq_id[s][0]; + for (uint32_t i = 0; i < n_tokens; ++i) { + const llama_seq_id seq_id = ubatch->seq_id[i][0]; - for (uint32_t j = 0; j < n_seq_tokens; ++j) { - const uint32_t idx = s*n_seq_tokens + j; + const llama_pos p1 = ubatch->pos[i]; - const llama_pos p1 = ubatch->pos[idx]; + for (uint32_t j = 0; j < n_kv; ++j) { + float f = 0.0f; - for (uint32_t i = 0; i < n_kv; ++i) { - float f = 0.0f; + bool masked = false; - bool masked = false; + if (cells.is_empty(j)) { + masked = true; + } else { + const llama_pos p0 = cells.pos_get(j); - if (cells.is_empty(i)) { - masked = true; - } else { - const llama_pos p0 = cells.pos_get(i); + // mask the token if not the same sequence + masked = masked || (!cells.seq_has(j, seq_id)); - // mask the token if not the same sequence - masked = masked || (!cells.seq_has(i, seq_id)); + // mask future tokens + masked = masked || (causal_attn && p0 > p1); - // mask future tokens - masked = masked || (causal_attn && p0 > p1); + // apply SWA if any + masked = masked || (is_masked_swa(p0, p1)); - // apply SWA if any - masked = masked || (is_masked_swa(p0, p1)); - - if (!masked && hparams.use_alibi) { - f = -std::abs(p0 - p1); - } + if (!masked && hparams.use_alibi) { + f = -std::abs(p0 - p1); } - - if (masked) { - f = -INFINITY; - } - - data[h*(n_kv*n_tokens) + idx*n_kv + i] = f; } + + if (masked) { + f = -INFINITY; + } + + data[h*(n_kv*n_tokens) + i*n_kv + j] = f; } } // mask padded tokens if (data) { - for (uint32_t j = n_tokens; j < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++j) { - for (uint32_t i = 0; i < n_kv; ++i) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY; + for (uint32_t i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) { + for (uint32_t j = 0; j < n_kv; ++j) { + data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY; } } } @@ -887,12 +877,12 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama const int32_t n_kv = dst->ne[0]; for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - for (int i = 0; i < n_kv; ++i) { + for (int i = 0; i < n_tokens; ++i) { + for (int j = 0; j < n_kv; ++j) { // the position when the cells is empty is irrelevant - it will be masked out later in the attention - const llama_pos p0 = cells.is_empty(i) ? -1 : cells.pos_get(i); + const llama_pos p0 = cells.is_empty(j) ? -1 : cells.pos_get(j); - data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(p0, ubatch->pos[j], hparams.n_rel_attn_bkts, false); + data[h*(n_kv*n_tokens) + i*n_kv + j] = llama_relative_position_bucket(p0, ubatch->pos[i], hparams.n_rel_attn_bkts, false); } } } @@ -1509,12 +1499,9 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell seq_rm(dest_seq_id, -1, -1); - llama_sbatch sbatch; - llama_ubatch ubatch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false); + llama_batch_allocr balloc(hparams.n_pos_per_embd()); - ubatch.n_tokens = cell_count; - ubatch.n_seq_tokens = cell_count; - ubatch.n_seqs = 1; + llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1); for (uint32_t i = 0; i < cell_count; ++i) { llama_pos pos; @@ -1723,18 +1710,18 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell } // -// llama_kv_cache_unified_state +// llama_kv_cache_unified_context // -llama_kv_cache_unified_state::llama_kv_cache_unified_state(llama_memory_status status) : status(status) {} +llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_status status) : status(status) {} -llama_kv_cache_unified_state::llama_kv_cache_unified_state( +llama_kv_cache_unified_context::llama_kv_cache_unified_context( llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) { n_kv = kv->get_size(); head = 0; } -llama_kv_cache_unified_state::llama_kv_cache_unified_state( +llama_kv_cache_unified_context::llama_kv_cache_unified_context( llama_kv_cache_unified * kv, llama_context * lctx, bool do_shift, @@ -1744,16 +1731,15 @@ llama_kv_cache_unified_state::llama_kv_cache_unified_state( } } -llama_kv_cache_unified_state::llama_kv_cache_unified_state( +llama_kv_cache_unified_context::llama_kv_cache_unified_context( llama_kv_cache_unified * kv, - llama_sbatch sbatch, llama_kv_cache_unified::ubatch_heads heads, - std::vector ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), sbatch(std::move(sbatch)), heads(std::move(heads)), ubatches(std::move(ubatches)) { + std::vector ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), heads(std::move(heads)), ubatches(std::move(ubatches)) { } -llama_kv_cache_unified_state::~llama_kv_cache_unified_state() = default; +llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default; -bool llama_kv_cache_unified_state::next() { +bool llama_kv_cache_unified_context::next() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); if (++i_next >= ubatches.size()) { @@ -1763,7 +1749,7 @@ bool llama_kv_cache_unified_state::next() { return true; } -bool llama_kv_cache_unified_state::apply() { +bool llama_kv_cache_unified_context::apply() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); // no ubatches -> this is a KV cache update @@ -1781,51 +1767,45 @@ bool llama_kv_cache_unified_state::apply() { return true; } -std::vector & llama_kv_cache_unified_state::out_ids() { - assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - - return sbatch.out_ids; -} - -llama_memory_status llama_kv_cache_unified_state::get_status() const { +llama_memory_status llama_kv_cache_unified_context::get_status() const { return status; } -const llama_ubatch & llama_kv_cache_unified_state::get_ubatch() const { +const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return ubatches[i_next]; } -uint32_t llama_kv_cache_unified_state::get_n_kv() const { +uint32_t llama_kv_cache_unified_context::get_n_kv() const { return n_kv; } -ggml_tensor * llama_kv_cache_unified_state::get_k(ggml_context * ctx, int32_t il) const { +ggml_tensor * llama_kv_cache_unified_context::get_k(ggml_context * ctx, int32_t il) const { return kv->get_k(ctx, il, n_kv); } -ggml_tensor * llama_kv_cache_unified_state::get_v(ggml_context * ctx, int32_t il) const { +ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t il) const { return kv->get_v(ctx, il, n_kv); } -ggml_tensor * llama_kv_cache_unified_state::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const { +ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const { return kv->cpy_k(ctx, k_cur, il, head); } -ggml_tensor * llama_kv_cache_unified_state::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const { +ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const { return kv->cpy_v(ctx, v_cur, il, head); } -void llama_kv_cache_unified_state::set_input_k_shift(ggml_tensor * dst) const { +void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const { kv->set_input_k_shift(dst); } -void llama_kv_cache_unified_state::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const { +void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const { kv->set_input_kq_mask(dst, ubatch, causal_attn); } -void llama_kv_cache_unified_state::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const { +void llama_kv_cache_unified_context::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const { kv->set_input_pos_bucket(dst, ubatch); } diff --git a/src/llama-kv-cache-unified.h b/src/llama-kv-cache-unified.h index d96571d95..4c53f1273 100644 --- a/src/llama-kv-cache-unified.h +++ b/src/llama-kv-cache-unified.h @@ -56,14 +56,14 @@ public: // llama_memory_i // - llama_memory_state_ptr init_batch( - const llama_batch & batch, + llama_memory_context_ptr init_batch( + llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) override; - llama_memory_state_ptr init_full() override; + llama_memory_context_ptr init_full() override; - llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override; + llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override; bool get_can_shift() const override; @@ -208,49 +208,46 @@ private: bool state_read_data(llama_io_read_i & io, uint32_t cell_count); }; -class llama_kv_cache_unified_state : public llama_memory_state_i { +class llama_kv_cache_unified_context : public llama_memory_context_i { public: // some shorthands using ubatch_heads = llama_kv_cache_unified::ubatch_heads; using defrag_info = llama_kv_cache_unified::defrag_info; // used for errors - llama_kv_cache_unified_state(llama_memory_status status); + llama_kv_cache_unified_context(llama_memory_status status); - // used to create a full-cache state - llama_kv_cache_unified_state( + // used to create a full-cache context + llama_kv_cache_unified_context( llama_kv_cache_unified * kv); - // used to create an update state - llama_kv_cache_unified_state( + // used to create an update context + llama_kv_cache_unified_context( llama_kv_cache_unified * kv, llama_context * lctx, bool do_shift, defrag_info dinfo); - // used to create a decode state from a batch - llama_kv_cache_unified_state( + // used to create a batch procesing context from a batch + llama_kv_cache_unified_context( llama_kv_cache_unified * kv, - llama_sbatch sbatch, ubatch_heads heads, std::vector ubatches); - virtual ~llama_kv_cache_unified_state(); + virtual ~llama_kv_cache_unified_context(); // - // llama_memory_state_i + // llama_memory_context_i // bool next() override; bool apply() override; - std::vector & out_ids() override; - llama_memory_status get_status() const override; const llama_ubatch & get_ubatch() const override; // - // llama_kv_cache_unified_state specific API + // llama_kv_cache_unified_context specific API // uint32_t get_n_kv() const; @@ -275,7 +272,7 @@ private: llama_context * lctx; // - // update state + // update context // bool do_shift = false; @@ -283,11 +280,9 @@ private: defrag_info dinfo; // - // batch processing state + // batch processing context // - llama_sbatch sbatch; - // the index of the next ubatch to process size_t i_next = 0; diff --git a/src/llama-kv-cells.h b/src/llama-kv-cells.h index 1d4e70f4d..c95d63594 100644 --- a/src/llama-kv-cells.h +++ b/src/llama-kv-cells.h @@ -7,6 +7,7 @@ #include #include #include +#include // meta information about KV cells that can be part of multiple sequences at the same time // TODO: add unit tests @@ -164,7 +165,7 @@ public: assert(seq_id >= 0); seq[i].reset(seq_id); - seq_pos[seq_id].erase(pos[i]); + seq_pos_dec(seq_id, pos[i]); if (seq[i].none()) { pos[i] = -1; @@ -187,7 +188,7 @@ public: seq[i].reset(); seq[i].set(seq_id); - seq_pos[seq_id].insert(pos[i]); + seq_pos_inc(seq_id, pos[i]); return false; } @@ -232,7 +233,7 @@ public: assert(!seq[i].test(seq_id)); seq[i].set(seq_id); - seq_pos[seq_id].insert(pos[i]); + seq_pos_inc(seq_id, pos[i]); } // return the sequence id of this cell @@ -259,7 +260,9 @@ public: return -1; } - return *seq_pos[seq_id].begin(); + assert(seq_pos[seq_id].begin()->second > 0); + + return seq_pos[seq_id].begin()->first; } // the maximum position of sequence seq_id currently present in any of the cells @@ -272,7 +275,9 @@ public: return -1; } - return *seq_pos[seq_id].rbegin(); + assert(seq_pos[seq_id].rbegin()->second > 0); + + return seq_pos[seq_id].rbegin()->first; } // note: call only if the cell is not empty @@ -384,22 +389,41 @@ private: // std::vector shift; - using bits_t = std::bitset; + using seq_set_t = std::bitset; // the bitset seq[i] tells us which sequences are currently occupying the i-th cell - std::vector seq; + std::vector seq; - // the set seq_pos[s] tells us which positions are currently present for sequence s + // the set seq_pos[s][p] tells us how many times the position p is currently present for sequence s + // if the position p is not present, seq_pos[s][p] is not set // this way seq_pos[s].begin() and seq_pos[s].rbegin() give us the min/max positions currently in the cache - std::set seq_pos[LLAMA_MAX_SEQ]; + // + // note that we cannot a use an std::set because in some cases a position can occur more than once for the same seq: + // - during performing a cache reuse via (rm + add) + // - some vision models have input embeddings with repeating positions + // + std::map seq_pos[LLAMA_MAX_SEQ]; // helper functions for updating `seq_pos`, once cell at a time: + void seq_pos_dec(llama_seq_id s, llama_pos p) { + auto it = seq_pos[s].find(p); + assert(it != seq_pos[s].end()); + + if (--it->second == 0) { + seq_pos[s].erase(it); + } + } + + void seq_pos_inc(llama_seq_id s, llama_pos p) { + seq_pos[s][p]++; + } + // remove cell i void seq_pos_rm(uint32_t i) { for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (seq[i].test(s)) { - seq_pos[s].erase(pos[i]); + seq_pos_dec(s, pos[i]); } } } @@ -408,7 +432,7 @@ private: void seq_pos_add(uint32_t i) { for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (seq[i].test(s)) { - seq_pos[s].insert(pos[i]); + seq_pos_inc(s, pos[i]); } } } diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp index d4b260db4..15cde98d1 100644 --- a/src/llama-memory-hybrid.cpp +++ b/src/llama-memory-hybrid.cpp @@ -32,7 +32,7 @@ llama_memory_hybrid::llama_memory_hybrid( mem_attn(new llama_kv_cache_unified( model, filter_attn == nullptr ? - [&](int32_t il) { return !model.hparams.is_recurrent(il); } + [&](int32_t il) { return !hparams.is_recurrent(il); } : filter_attn, type_k, type_v, @@ -47,7 +47,7 @@ llama_memory_hybrid::llama_memory_hybrid( mem_recr(new llama_memory_recurrent( model, filter_recr == nullptr ? - [&](int32_t il) { return model.hparams.is_recurrent(il); } + [&](int32_t il) { return hparams.is_recurrent(il); } : filter_recr, type_r, type_s, @@ -56,50 +56,57 @@ llama_memory_hybrid::llama_memory_hybrid( n_seq_max )) {} -llama_memory_state_ptr llama_memory_hybrid::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled) { +llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) { + do { + balloc.split_reset(); - // since this includes a recurrent cache, we cannot use split_simple - auto sbatch = llama_sbatch(batch, hparams.n_embd, false); + // follow the recurrent pattern for creating the ubatch splits + std::vector ubatches; - // follow the recurrent pattern for creating the ubatch splits - std::vector ubatches; - while (sbatch.n_tokens > 0) { - llama_ubatch ubatch; + while (true) { + llama_ubatch ubatch; - if (embd_pooled) { - // Pooled embeddings cannot be split across ubatches (yet) - ubatch = sbatch.split_seq(n_ubatch); - } else { - ubatch = sbatch.split_equal(n_ubatch); + if (embd_all) { + // if all tokens are output, split by sequence + ubatch = balloc.split_seq(n_ubatch); + } else { + ubatch = balloc.split_equal(n_ubatch); + } + + if (ubatch.n_tokens == 0) { + break; + } + + ubatches.push_back(std::move(ubatch)); // NOLINT } - ubatches.push_back(ubatch); - } + // prepare the recurrent batches first + if (!mem_recr->prepare(ubatches)) { + // TODO: will the recurrent cache be in an undefined context at this point? + LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__); + return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); + } - // prepare the recurrent batches first - if (!mem_recr->prepare(ubatches)) { - // TODO: will the recurrent cache be in an undefined state at this point? - LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__); - return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); - } + // prepare the attention cache + auto heads_attn = mem_attn->prepare(ubatches); + if (heads_attn.empty()) { + LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__); + return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); + } - // prepare the attention cache - auto heads_attn = mem_attn->prepare(ubatches); - if (heads_attn.empty()) { - LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__); - return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); - } + return std::make_unique( + this, std::move(heads_attn), std::move(ubatches)); + } while(false); - return std::make_unique( - this, std::move(sbatch), std::move(heads_attn), std::move(ubatches)); + return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); } -llama_memory_state_ptr llama_memory_hybrid::init_full() { - return std::make_unique(this); +llama_memory_context_ptr llama_memory_hybrid::init_full() { + return std::make_unique(this); } -llama_memory_state_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) { - return std::make_unique(this, lctx, optimize); +llama_memory_context_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) { + return std::make_unique(this, lctx, optimize); } bool llama_memory_hybrid::get_can_shift() const { @@ -169,41 +176,39 @@ llama_memory_recurrent * llama_memory_hybrid::get_mem_recr() const { return mem_recr.get(); } -llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_status status) : status(status) {} +llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_status status) : status(status) {} -llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_hybrid * mem) : - state_attn(mem->get_mem_attn()->init_full()), - state_recr(mem->get_mem_recr()->init_full()), - status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) { +llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_hybrid * mem) : + ctx_attn(mem->get_mem_attn()->init_full()), + ctx_recr(mem->get_mem_recr()->init_full()), + status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) { } -llama_memory_hybrid_state::llama_memory_hybrid_state( +llama_memory_hybrid_context::llama_memory_hybrid_context( llama_memory_hybrid * mem, llama_context * lctx, bool optimize) : - state_attn(mem->get_mem_attn()->init_update(lctx, optimize)), - state_recr(mem->get_mem_recr()->init_update(lctx, optimize)), - status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) { + ctx_attn(mem->get_mem_attn()->init_update(lctx, optimize)), + ctx_recr(mem->get_mem_recr()->init_update(lctx, optimize)), + status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) { } -llama_memory_hybrid_state::llama_memory_hybrid_state( +llama_memory_hybrid_context::llama_memory_hybrid_context( llama_memory_hybrid * mem, - llama_sbatch sbatch, std::vector heads_attn, std::vector ubatches) : - sbatch(std::move(sbatch)), ubatches(std::move(ubatches)), // note: here we copy the ubatches. not sure if this is ideal - state_attn(new llama_kv_cache_unified_state(mem->get_mem_attn(), {}, std::move(heads_attn), this->ubatches)), - state_recr(new llama_memory_recurrent_state(mem->get_mem_recr(), {}, this->ubatches)), - status(LLAMA_MEMORY_STATUS_SUCCESS) { + ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)), + ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)), + status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) { } -bool llama_memory_hybrid_state::next() { +bool llama_memory_hybrid_context::next() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - state_attn->next(); - state_recr->next(); + ctx_attn->next(); + ctx_recr->next(); if (++i_next >= ubatches.size()) { return false; @@ -212,36 +217,30 @@ bool llama_memory_hybrid_state::next() { return true; } -bool llama_memory_hybrid_state::apply() { +bool llama_memory_hybrid_context::apply() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); bool res = true; - res = res & state_attn->apply(); - res = res & state_recr->apply(); + res = res & ctx_attn->apply(); + res = res & ctx_recr->apply(); return res; } -std::vector & llama_memory_hybrid_state::out_ids() { - assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - - return sbatch.out_ids; -} - -llama_memory_status llama_memory_hybrid_state::get_status() const { +llama_memory_status llama_memory_hybrid_context::get_status() const { return status; } -const llama_ubatch & llama_memory_hybrid_state::get_ubatch() const { +const llama_ubatch & llama_memory_hybrid_context::get_ubatch() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return ubatches[i_next]; } -const llama_kv_cache_unified_state * llama_memory_hybrid_state::get_state_attn() const { - return static_cast(state_attn.get()); +const llama_kv_cache_unified_context * llama_memory_hybrid_context::get_attn() const { + return static_cast(ctx_attn.get()); } -const llama_memory_recurrent_state * llama_memory_hybrid_state::get_state_recr() const { - return static_cast(state_recr.get()); +const llama_memory_recurrent_context * llama_memory_hybrid_context::get_recr() const { + return static_cast(ctx_recr.get()); } diff --git a/src/llama-memory-hybrid.h b/src/llama-memory-hybrid.h index b5700c522..f0c2420e9 100644 --- a/src/llama-memory-hybrid.h +++ b/src/llama-memory-hybrid.h @@ -49,14 +49,14 @@ public: // llama_memory_i // - llama_memory_state_ptr init_batch( - const llama_batch & batch, + llama_memory_context_ptr init_batch( + llama_batch_allocr & balloc, uint32_t n_ubatch, - bool embd_pooled) override; + bool embd_all) override; - llama_memory_state_ptr init_full() override; + llama_memory_context_ptr init_full() override; - llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override; + llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override; bool get_can_shift() const override; @@ -90,54 +90,49 @@ private: const std::unique_ptr mem_recr; }; -class llama_memory_hybrid_state : public llama_memory_state_i { +class llama_memory_hybrid_context : public llama_memory_context_i { public: // init failure - explicit llama_memory_hybrid_state(llama_memory_status status); + explicit llama_memory_hybrid_context(llama_memory_status status); // init full - explicit llama_memory_hybrid_state(llama_memory_hybrid * mem); + explicit llama_memory_hybrid_context(llama_memory_hybrid * mem); // init update - explicit llama_memory_hybrid_state( + explicit llama_memory_hybrid_context( llama_memory_hybrid * mem, llama_context * lctx, bool optimize); // init success - llama_memory_hybrid_state( + llama_memory_hybrid_context( llama_memory_hybrid * mem, - llama_sbatch sbatch, std::vector heads_attn, std::vector ubatches); - ~llama_memory_hybrid_state() = default; + ~llama_memory_hybrid_context() = default; bool next() override; bool apply() override; - std::vector & out_ids() override; - llama_memory_status get_status() const override; const llama_ubatch & get_ubatch() const override; // - // llama_memory_hybrid_state + // llama_memory_hybrid_context // - const llama_kv_cache_unified_state * get_state_attn() const; - const llama_memory_recurrent_state * get_state_recr() const; + const llama_kv_cache_unified_context * get_attn() const; + const llama_memory_recurrent_context * get_recr() const; private: - llama_sbatch sbatch; - // the index of the next ubatch to process size_t i_next = 0; std::vector ubatches; - const llama_memory_state_ptr state_attn; - const llama_memory_state_ptr state_recr; + const llama_memory_context_ptr ctx_attn; + const llama_memory_context_ptr ctx_recr; const llama_memory_status status; }; diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp index c4f9a6f1d..1b1e95d56 100644 --- a/src/llama-memory-recurrent.cpp +++ b/src/llama-memory-recurrent.cpp @@ -362,40 +362,42 @@ llama_pos llama_memory_recurrent::seq_pos_max(llama_seq_id seq_id) const { return result; } -llama_memory_state_ptr llama_memory_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) { - auto sbatch = llama_sbatch(batch, hparams.n_embd, false); - +llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) { std::vector ubatches; - while (sbatch.n_tokens > 0) { + while (true) { llama_ubatch ubatch; if (embd_all) { // if all tokens are output, split by sequence - ubatch = sbatch.split_seq(n_ubatch); + ubatch = balloc.split_seq(n_ubatch); } else { - ubatch = sbatch.split_equal(n_ubatch); + ubatch = balloc.split_equal(n_ubatch); } - ubatches.push_back(ubatch); + if (ubatch.n_tokens == 0) { + break; + } + + ubatches.push_back(std::move(ubatch)); // NOLINT } if (!prepare(ubatches)) { - return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); + return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); } - return std::make_unique(this, std::move(sbatch), std::move(ubatches)); + return std::make_unique(this, std::move(ubatches)); } -llama_memory_state_ptr llama_memory_recurrent::init_full() { - return std::make_unique(this); +llama_memory_context_ptr llama_memory_recurrent::init_full() { + return std::make_unique(this); } -llama_memory_state_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) { +llama_memory_context_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) { GGML_UNUSED(lctx); GGML_UNUSED(optimize); - return std::make_unique(LLAMA_MEMORY_STATUS_NO_UPDATE); + return std::make_unique(LLAMA_MEMORY_STATUS_NO_UPDATE); } bool llama_memory_recurrent::prepare(const std::vector & ubatches) { @@ -423,9 +425,8 @@ bool llama_memory_recurrent::prepare(const std::vector & ubatches) } bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { - const uint32_t n_seqs = ubatch.n_seqs; - const uint32_t n_seq_tokens = ubatch.n_seq_tokens; + const uint32_t n_seqs = ubatch.n_seqs; // if we have enough unused cells before the current head -> // better to start searching from the beginning of the cache, hoping to fill it @@ -445,9 +446,11 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { // everything should fit if all seq_ids are smaller than the max for (uint32_t s = 0; s < n_seqs; ++s) { - const uint32_t n_seq_id = ubatch.n_seq_id[s]; + const uint32_t i = s*n_seq_tokens; // first token of sequence set s + const uint32_t n_seq_id = ubatch.n_seq_id[i]; + for (uint32_t j = 0; j < n_seq_id; ++j) { - const llama_seq_id seq_id = ubatch.seq_id[s][j]; + const llama_seq_id seq_id = ubatch.seq_id[i][j]; if (seq_id < 0 || (uint32_t) seq_id >= size) { // too big seq_id @@ -506,7 +509,8 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { // find usable cell range for (uint32_t s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch.seq_id[s][0]; + const uint32_t i = s*n_seq_tokens; + const llama_seq_id seq_id = ubatch.seq_id[i][0]; auto & seq_meta = cells[seq_id]; bool has_cell = false; if (seq_meta.tail >= 0) { @@ -530,7 +534,7 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { seq_meta.tail = next_empty_cell; // find next empty cell if (s + 1 < n_seqs) { - for (uint32_t i = 0; i < size; ++i) { + for (uint32_t j = 0; j < size; ++j) { next_empty_cell += 1; if (next_empty_cell >= size) { next_empty_cell -= size; } auto & cell = cells[next_empty_cell]; @@ -544,8 +548,9 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { // gather and re-order for (uint32_t s = 0; s < n_seqs; ++s) { + const uint32_t i = s*n_seq_tokens; const int32_t dst_id = s + min; - const int32_t src_id = cells[ubatch.seq_id[s][0]].tail; + const int32_t src_id = cells[ubatch.seq_id[i][0]].tail; if (dst_id != src_id) { auto & dst_cell = cells[dst_id]; auto & src_cell = cells[src_id]; @@ -555,8 +560,8 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { std::swap(dst_cell.seq_id, src_cell.seq_id); // swap tails - for (uint32_t i = 0; i < size; ++i) { - int32_t & tail = cells[i].tail; + for (uint32_t j = 0; j < size; ++j) { + int32_t & tail = cells[j].tail; if (tail == src_id) { tail = dst_id; } else if (tail == dst_id) { @@ -568,7 +573,8 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { // update the pos of the used seqs for (uint32_t s = 0; s < n_seqs; ++s) { - const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1]; + const uint32_t i = s*n_seq_tokens; + const llama_pos last_pos = ubatch.pos[i + n_seq_tokens - 1]; const int32_t cell_id = s + min; auto & cell = cells[cell_id]; @@ -576,12 +582,12 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) { // What should happen when the pos backtracks or skips a value? // Clearing the state mid-batch would require special-casing which isn't done. LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n", - __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens); + __func__, last_pos, cell.pos, ubatch.seq_id[i][0], n_seq_tokens); } cell.pos = last_pos; cell.seq_id.clear(); - for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) { - const llama_seq_id seq_id = ubatch.seq_id[s][j]; + for (int32_t j = 0; j < ubatch.n_seq_id[i]; ++j) { + const llama_seq_id seq_id = ubatch.seq_id[i][j]; cell.seq_id.insert(seq_id); cells[seq_id].tail = cell_id; } @@ -827,12 +833,9 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell seq_rm(dest_seq_id, -1, -1); - llama_sbatch sbatch; - llama_ubatch batch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false); + llama_batch_allocr balloc(hparams.n_pos_per_embd()); - batch.n_tokens = cell_count; - batch.n_seq_tokens = cell_count; - batch.n_seqs = 1; + llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1); for (uint32_t i = 0; i < cell_count; ++i) { llama_pos pos; @@ -846,12 +849,12 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell return false; } - batch.pos[i] = pos; + ubatch.pos[i] = pos; } - batch.n_seq_id[0] = 1; - batch.seq_id[0] = &dest_seq_id; + ubatch.n_seq_id[0] = 1; + ubatch.seq_id[0] = &dest_seq_id; - if (!find_slot(batch)) { + if (!find_slot(ubatch)) { LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__); return false; } @@ -859,8 +862,8 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell // DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values) // Assume that this is one contiguous block of cells GGML_ASSERT(head + cell_count <= size); - GGML_ASSERT(cells[head].pos == batch.pos[0]); - GGML_ASSERT(cells[head + cell_count - 1].pos == batch.pos[cell_count - 1]); + GGML_ASSERT(cells[head].pos == ubatch.pos[0]); + GGML_ASSERT(cells[head + cell_count - 1].pos == ubatch.pos[cell_count - 1]); GGML_ASSERT(cells[head].has_seq_id(dest_seq_id)); GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id)); } else { @@ -1037,23 +1040,22 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell } // -// llama_memory_recurrent_state +// llama_memory_recurrent_context // -llama_memory_recurrent_state::llama_memory_recurrent_state(llama_memory_status status) : status(status) {} +llama_memory_recurrent_context::llama_memory_recurrent_context(llama_memory_status status) : status(status) {} -llama_memory_recurrent_state::llama_memory_recurrent_state( +llama_memory_recurrent_context::llama_memory_recurrent_context( llama_memory_recurrent * mem) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), is_full(true) { } -llama_memory_recurrent_state::llama_memory_recurrent_state( +llama_memory_recurrent_context::llama_memory_recurrent_context( llama_memory_recurrent * mem, - llama_sbatch sbatch, - std::vector ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), sbatch(std::move(sbatch)), ubatches(std::move(ubatches)) {} + std::vector ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), ubatches(std::move(ubatches)) {} -llama_memory_recurrent_state::~llama_memory_recurrent_state() = default; +llama_memory_recurrent_context::~llama_memory_recurrent_context() = default; -bool llama_memory_recurrent_state::next() { +bool llama_memory_recurrent_context::next() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); if (++i_next >= ubatches.size()) { @@ -1063,7 +1065,7 @@ bool llama_memory_recurrent_state::next() { return true; } -bool llama_memory_recurrent_state::apply() { +bool llama_memory_recurrent_context::apply() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); mem->find_slot(ubatches[i_next]); @@ -1071,46 +1073,40 @@ bool llama_memory_recurrent_state::apply() { return true; } -std::vector & llama_memory_recurrent_state::out_ids() { - assert(status == LLAMA_MEMORY_STATUS_SUCCESS); - - return sbatch.out_ids; -} - -llama_memory_status llama_memory_recurrent_state::get_status() const { +llama_memory_status llama_memory_recurrent_context::get_status() const { return status; } -const llama_ubatch & llama_memory_recurrent_state::get_ubatch() const { +const llama_ubatch & llama_memory_recurrent_context::get_ubatch() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return ubatches[i_next]; } -uint32_t llama_memory_recurrent_state::get_n_rs() const { +uint32_t llama_memory_recurrent_context::get_n_rs() const { return is_full ? mem->size : mem->n; } -uint32_t llama_memory_recurrent_state::get_head() const { +uint32_t llama_memory_recurrent_context::get_head() const { return is_full ? 0 : mem->head; } -int32_t llama_memory_recurrent_state::get_rs_z() const { +int32_t llama_memory_recurrent_context::get_rs_z() const { return is_full ? 0 : mem->rs_z; } -uint32_t llama_memory_recurrent_state::get_size() const { +uint32_t llama_memory_recurrent_context::get_size() const { return mem->size; } -ggml_tensor * llama_memory_recurrent_state::get_r_l(int32_t il) const { +ggml_tensor * llama_memory_recurrent_context::get_r_l(int32_t il) const { return mem->r_l[il]; } -ggml_tensor * llama_memory_recurrent_state::get_s_l(int32_t il) const { +ggml_tensor * llama_memory_recurrent_context::get_s_l(int32_t il) const { return mem->s_l[il]; } -int32_t llama_memory_recurrent_state::s_copy(int i) const { +int32_t llama_memory_recurrent_context::s_copy(int i) const { return mem->cells[i + mem->head].src0; } diff --git a/src/llama-memory-recurrent.h b/src/llama-memory-recurrent.h index 290cc84ab..4d094f9a0 100644 --- a/src/llama-memory-recurrent.h +++ b/src/llama-memory-recurrent.h @@ -11,8 +11,8 @@ // llama_memory_recurrent // -// TODO: extract the cache state used for graph computation into llama_memory_recurrent_state_i -// see the implementation of llama_kv_cache_unified_state_i for an example how to do it +// TODO: extract the cache state used for graph computation into llama_memory_recurrent_context_i +// see the implementation of llama_kv_cache_unified_context_i for an example how to do it class llama_memory_recurrent : public llama_memory_i { public: @@ -34,14 +34,14 @@ public: // llama_memory_i // - llama_memory_state_ptr init_batch( - const llama_batch & batch, + llama_memory_context_ptr init_batch( + llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) override; - llama_memory_state_ptr init_full() override; + llama_memory_context_ptr init_full() override; - llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override; + llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override; void clear(bool data) override; @@ -125,37 +125,34 @@ private: bool state_read_data(llama_io_read_i & io, uint32_t cell_count); }; -class llama_memory_recurrent_state : public llama_memory_state_i { +class llama_memory_recurrent_context : public llama_memory_context_i { public: // used for errors - llama_memory_recurrent_state(llama_memory_status status); + llama_memory_recurrent_context(llama_memory_status status); - // used to create a full-cache state - llama_memory_recurrent_state( + // used to create a full-cache or update context + llama_memory_recurrent_context( llama_memory_recurrent * mem); - // used to create a state from a batch - llama_memory_recurrent_state( + // used to create a batch processing context from a batch + llama_memory_recurrent_context( llama_memory_recurrent * mem, - llama_sbatch sbatch, std::vector ubatches); - virtual ~llama_memory_recurrent_state(); + virtual ~llama_memory_recurrent_context(); // - // llama_memory_state_i + // llama_memory_context_i // bool next() override; bool apply() override; - std::vector & out_ids() override; - llama_memory_status get_status() const override; const llama_ubatch & get_ubatch() const override; // - // llama_memory_recurrent_state specific API + // llama_memory_recurrent_context specific API // uint32_t get_n_rs() const; @@ -173,8 +170,6 @@ private: llama_memory_recurrent * mem; - llama_sbatch sbatch; - size_t i_next = 0; std::vector ubatches; diff --git a/src/llama-memory.h b/src/llama-memory.h index 24668f861..16b7e5ee2 100644 --- a/src/llama-memory.h +++ b/src/llama-memory.h @@ -3,10 +3,11 @@ #include "llama.h" #include -#include struct llama_ubatch; +class llama_batch_allocr; + class llama_io_write_i; class llama_io_read_i; @@ -26,23 +27,21 @@ enum llama_memory_status { LLAMA_MEMORY_STATUS_FAILED_COMPUTE, }; -// helper function for combining the status of two memory states +// helper function for combining the status of two memory contexts // useful for implementing hybrid memory types (e.g. iSWA) llama_memory_status llama_memory_status_combine(llama_memory_status s0, llama_memory_status s1); -// the interface for managing the memory state during batch processing +// the interface for managing the memory context during batch processing // this interface is implemented per memory type. see: -// - llama_kv_cache_unified_state -// - llama_kv_cache_unified_iswa_state +// - llama_kv_cache_unified_context +// - llama_kv_cache_unified_iswa_context // ... // -// the only method that can mutate the memory and the memory state is llama_memory_i::apply() -// -// TODO: rename to llama_memory_context_i ? -struct llama_memory_state_i { - virtual ~llama_memory_state_i() = default; +// the only method that should mutate the memory and the memory context is llama_memory_i::apply() +struct llama_memory_context_i { + virtual ~llama_memory_context_i() = default; - // consume the current ubatch from the state and proceed to the next one + // consume the current ubatch from the context and proceed to the next one // return false if we are done virtual bool next() = 0; @@ -50,17 +49,14 @@ struct llama_memory_state_i { // return false on failure virtual bool apply() = 0; - // TODO: this might get reworked in the future when refactoring llama_batch - virtual std::vector & out_ids() = 0; - // get the current ubatch virtual const llama_ubatch & get_ubatch() const = 0; - // get the status of the memory state - used for error handling and checking if any updates would be applied + // get the status of the memory context - used for error handling and checking if any updates would be applied virtual llama_memory_status get_status() const = 0; }; -using llama_memory_state_ptr = std::unique_ptr; +using llama_memory_context_ptr = std::unique_ptr; // general concept of LLM memory // the KV cache is a type of LLM memory, but there can be other types @@ -68,19 +64,19 @@ struct llama_memory_i { virtual ~llama_memory_i() = default; // split the input batch into a set of ubatches and verify that they can fit into the cache - // return a state object containing the ubatches and KV cache state required to process them - // check the llama_memory_state_i::get_status() for the result - virtual llama_memory_state_ptr init_batch( - const llama_batch & batch, + // return a context object containing the ubatches and memory state required to process them + // check the llama_memory_context_i::get_status() for the result + virtual llama_memory_context_ptr init_batch( + llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) = 0; // simulate full cache, used for allocating worst-case compute buffers - virtual llama_memory_state_ptr init_full() = 0; + virtual llama_memory_context_ptr init_full() = 0; // prepare for any pending memory updates, such as shifts, defrags, etc. // status == LLAMA_MEMORY_STATUS_NO_UPDATE if there is nothing to update - virtual llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) = 0; + virtual llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) = 0; // getters virtual bool get_can_shift() const = 0; diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index a70b98923..563823dc3 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -228,6 +228,7 @@ void llama_model_saver::add_kv_from_model() { // add_kv(LLM_KV_TOKENIZER_MASK_ID, ???); add_kv(LLM_KV_TOKENIZER_ADD_BOS, vocab.get_add_bos()); add_kv(LLM_KV_TOKENIZER_ADD_EOS, vocab.get_add_eos()); + add_kv(LLM_KV_TOKENIZER_ADD_SEP, vocab.get_add_sep()); add_kv(LLM_KV_TOKENIZER_ADD_PREFIX, vocab.get_add_space_prefix()); add_kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.get_remove_extra_whitespaces()); add_kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, vocab.get_precompiled_charsmap()); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 0bd099494..3c3b3981e 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -4795,6 +4795,8 @@ struct llm_build_llama : public llm_graph_context { const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -4857,9 +4859,7 @@ struct llm_build_llama : public llm_graph_context { cb(cur, "attn_out", il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -4955,6 +4955,8 @@ struct llm_build_llama_iswa : public llm_graph_context { const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5031,9 +5033,7 @@ struct llm_build_llama_iswa : public llm_graph_context { cb(cur, "attn_out", il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -5133,6 +5133,9 @@ struct llm_build_deci : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; const int64_t n_head_kv = hparams.n_head_kv(il); @@ -5206,9 +5209,7 @@ struct llm_build_deci : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -5287,6 +5288,8 @@ struct llm_build_baichuan : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5338,9 +5341,7 @@ struct llm_build_baichuan : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -5409,6 +5410,8 @@ struct llm_build_xverse : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5453,9 +5456,7 @@ struct llm_build_xverse : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -5523,6 +5524,8 @@ struct llm_build_falcon : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * attn_norm; @@ -5578,9 +5581,7 @@ struct llm_build_falcon : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids); @@ -5649,6 +5650,8 @@ struct llm_build_grok : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5708,9 +5711,7 @@ struct llm_build_grok : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -5809,6 +5810,8 @@ struct llm_build_dbrx : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -5859,9 +5862,7 @@ struct llm_build_dbrx : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -5941,6 +5942,8 @@ struct llm_build_starcoder : public llm_graph_context { inpL = ggml_add(ctx0, inpL, pos); cb(inpL, "inpL", -1); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, model.layers[il].attn_norm, @@ -5973,9 +5976,7 @@ struct llm_build_starcoder : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -6040,6 +6041,8 @@ struct llm_build_refact : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -6072,9 +6075,7 @@ struct llm_build_refact : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -6160,78 +6161,79 @@ struct llm_build_bert : public llm_graph_context { auto * inp_attn = build_attn_inp_no_cache(); - // iterate layers + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * cur = inpL; - ggml_tensor * Qcur; - ggml_tensor * Kcur; - ggml_tensor * Vcur; + { + ggml_tensor * Qcur; + ggml_tensor * Kcur; + ggml_tensor * Vcur; - // self-attention - if (model.layers[il].wqkv) { - cur = build_lora_mm(model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); + // self-attention + if (model.layers[il].wqkv) { + cur = build_lora_mm(model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); - if (model.layers[il].bqkv) { - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); + if (model.layers[il].bqkv) { + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + } + + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + } else { + Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq); + Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk); + Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv); } - Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - } else { - Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq); - Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk); - Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv); + if (model.layers[il].attn_q_norm) { + Qcur = build_norm(Qcur, + model.layers[il].attn_q_norm, + model.layers[il].attn_q_norm_b, + LLM_NORM, il); + } + + if (model.layers[il].attn_k_norm) { + Kcur = build_norm(Kcur, + model.layers[il].attn_k_norm, + model.layers[il].attn_k_norm_b, + LLM_NORM, il); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + // RoPE + if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) { + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + } + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, gf, + model.layers[il].wo, model.layers[il].bo, + Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + cb(cur, "kqv_out", il); } - if (model.layers[il].attn_q_norm) { - Qcur = build_norm(Qcur, - model.layers[il].attn_q_norm, - model.layers[il].attn_q_norm_b, - LLM_NORM, il); - } - - if (model.layers[il].attn_k_norm) { - Kcur = build_norm(Kcur, - model.layers[il].attn_k_norm, - model.layers[il].attn_k_norm_b, - LLM_NORM, il); - } - - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); - - // RoPE - if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) { - Qcur = ggml_rope_ext( - ctx0, Qcur, inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - - Kcur = ggml_rope_ext( - ctx0, Kcur, inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - } - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - cur = build_attn(inp_attn, gf, - model.layers[il].wo, model.layers[il].bo, - Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); - cb(cur, "kqv_out", il); - - if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -6328,56 +6330,57 @@ struct llm_build_neo_bert : public llm_graph_context { auto * inp_attn = build_attn_inp_no_cache(); - // iterate layers + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * cur = inpL; - ggml_tensor * Qcur; - ggml_tensor * Kcur; - ggml_tensor * Vcur; - // pre-norm cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); - // self-attention - cur = build_lora_mm(model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); + { + ggml_tensor * Qcur; + ggml_tensor * Kcur; + ggml_tensor * Vcur; - Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); - Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); - Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + // self-attention + cur = build_lora_mm(model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); - // RoPE - Qcur = ggml_rope_ext( - ctx0, Qcur, inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); - Kcur = ggml_rope_ext( - ctx0, Kcur, inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); + // RoPE + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); - cur = build_attn(inp_attn, gf, - model.layers[il].wo, nullptr, - Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); - cb(cur, "kqv_out", il); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); - if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = build_attn(inp_attn, gf, + model.layers[il].wo, nullptr, + Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + cb(cur, "kqv_out", il); + } + + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -6442,6 +6445,8 @@ struct llm_build_bloom : public llm_graph_context { LLM_NORM, -1); cb(inpL, "inp_norm", -1); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, model.layers[il].attn_norm, @@ -6474,9 +6479,7 @@ struct llm_build_bloom : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -6553,6 +6556,8 @@ struct llm_build_mpt : public llm_graph_context { cb(inpL, "inpL", -1); } + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * attn_norm; @@ -6615,9 +6620,7 @@ struct llm_build_mpt : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -6686,6 +6689,8 @@ struct llm_build_stablelm : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { // norm cur = build_norm(inpL, @@ -6761,9 +6766,7 @@ struct llm_build_stablelm : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); @@ -6838,6 +6841,8 @@ struct llm_build_qwen : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -6884,9 +6889,7 @@ struct llm_build_qwen : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -6955,6 +6958,8 @@ struct llm_build_qwen2 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -7004,9 +7009,7 @@ struct llm_build_qwen2 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -7076,6 +7079,8 @@ struct llm_build_qwen2vl : public llm_graph_context { int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -7125,9 +7130,7 @@ struct llm_build_qwen2vl : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -7194,6 +7197,8 @@ struct llm_build_qwen2moe : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -7252,9 +7257,7 @@ struct llm_build_qwen2moe : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -7353,6 +7356,8 @@ struct llm_build_qwen3 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -7405,9 +7410,7 @@ struct llm_build_qwen3 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -7474,6 +7477,8 @@ struct llm_build_qwen3moe : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -7526,9 +7531,7 @@ struct llm_build_qwen3moe : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -7604,6 +7607,8 @@ struct llm_build_phi2 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { attn_norm_output = build_norm(inpL, model.layers[il].attn_norm, @@ -7666,9 +7671,7 @@ struct llm_build_phi2 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids); @@ -7740,6 +7743,8 @@ struct llm_build_phi3 : public llm_graph_context { inp_attn = build_attn_inp_kv_unified(); } + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { auto * residual = inpL; @@ -7803,9 +7808,7 @@ struct llm_build_phi3 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor* inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); residual = ggml_get_rows(ctx0, residual, inp_out_ids); } @@ -7891,15 +7894,16 @@ struct llm_build_plamo : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); - for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { // norm cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); - ggml_tensor * attention_norm = cur; + ggml_tensor * sa_inp = cur; // self-attention { @@ -7937,18 +7941,17 @@ struct llm_build_plamo : public llm_graph_context { model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - ggml_tensor * sa_out = cur; - cur = attention_norm; - - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); - sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids); + sa_inp = ggml_get_rows(ctx0, sa_inp, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } + ggml_tensor * sa_out = cur; + + cur = sa_inp; + // feed-forward network { cur = build_ffn(cur, @@ -8013,6 +8016,8 @@ struct llm_build_gpt2 : public llm_graph_context { inpL = ggml_add(ctx0, inpL, pos); cb(inpL, "inpL", -1); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, model.layers[il].attn_norm, @@ -8045,9 +8050,7 @@ struct llm_build_gpt2 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -8117,6 +8120,8 @@ struct llm_build_codeshell : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, model.layers[il].attn_norm, @@ -8161,9 +8166,7 @@ struct llm_build_codeshell : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -8217,128 +8220,128 @@ struct llm_build_codeshell : public llm_graph_context { struct llm_build_orion : public llm_graph_context { llm_build_orion(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + 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); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); - ggml_tensor * cur; - ggml_tensor * inpL; + ggml_tensor * cur; + ggml_tensor * inpL; - inpL = build_inp_embd(model.tok_embd); + inpL = build_inp_embd(model.tok_embd); - // inp_pos - contains the positions - ggml_tensor * inp_pos = build_inp_pos(); + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); - auto * inp_attn = build_attn_inp_kv_unified(); + auto * inp_attn = build_attn_inp_kv_unified(); - for (int il = 0; il < n_layer; ++il) { - ggml_tensor * inpSA = inpL; + ggml_tensor * inp_out_ids = build_inp_out_ids(); - // norm - cur = build_norm(inpL, - model.layers[il].attn_norm, model.layers[il].attn_norm_b, - LLM_NORM, il); - cb(cur, "attn_norm", il); + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; - // self-attention - { - // compute Q and K and RoPE them - ggml_tensor * Qcur = build_lora_mm(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); - // } + // norm + cur = build_norm(inpL, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, il); + cb(cur, "attn_norm", il); - ggml_tensor * Kcur = build_lora_mm(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); - // } + // self-attention + { + // compute Q and K and RoPE them + ggml_tensor * Qcur = build_lora_mm(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); + // } - ggml_tensor * Vcur = build_lora_mm(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); - // } + ggml_tensor * Kcur = build_lora_mm(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); + // } - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + ggml_tensor * Vcur = build_lora_mm(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, Qcur, inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); - Kcur = ggml_rope_ext( - ctx0, Kcur, inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); - cur = build_attn(inp_attn, gf, - model.layers[il].wo, NULL, - Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, gf, + model.layers[il].wo, NULL, + Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); - } + cur = inpL; - ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); + cur = build_norm(cur, + model.output_norm, model.output_norm_b, + LLM_NORM, -1); - // feed-forward network - cur = build_norm(ffn_inp, - model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, - LLM_NORM, il); - cb(cur, "ffn_norm", il); + cb(cur, "result_norm", -1); + res->t_embd = cur; - cur = build_ffn(cur, - model.layers[il].ffn_up, NULL, NULL, - model.layers[il].ffn_gate, NULL, NULL, - model.layers[il].ffn_down, NULL, NULL, - NULL, - LLM_FFN_SILU, LLM_FFN_PAR, il); - cb(cur, "ffn_out", il); + // lm_head + cur = build_lora_mm(model.output, cur); - cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "result_output", -1); + res->t_logits = cur; - cur = build_cvec(cur, il); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - cur = build_norm(cur, - model.output_norm, model.output_norm_b, - LLM_NORM, -1); - - cb(cur, "result_norm", -1); - res->t_embd = cur; - - // lm_head - cur = build_lora_mm(model.output, cur); - - cb(cur, "result_output", -1); - res->t_logits = cur; - - ggml_build_forward_expand(gf, cur); + ggml_build_forward_expand(gf, cur); } }; @@ -8359,6 +8362,8 @@ struct llm_build_internlm2 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -8417,9 +8422,7 @@ struct llm_build_internlm2 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -8495,6 +8498,8 @@ struct llm_build_minicpm3 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -8614,15 +8619,13 @@ struct llm_build_minicpm3 : public llm_graph_context { q_states, k_states, v_states, nullptr, nullptr, kq_scale, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } // scale_res - scale the hidden states for residual connection - const float scale_res = scale_depth/sqrtf(float(n_layer)); + const float scale_res = scale_depth/sqrtf(float(n_layer)); // TODO: is this correct? cur = ggml_scale(ctx0, cur, scale_res); cb(cur, "hidden_scaled", il); @@ -8699,6 +8702,8 @@ struct llm_build_gemma : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { // norm cur = build_norm(inpL, @@ -8744,9 +8749,7 @@ struct llm_build_gemma : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -8815,6 +8818,8 @@ struct llm_build_gemma2_iswa : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified_iswa(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { // norm cur = build_norm(inpL, @@ -8859,18 +8864,16 @@ struct llm_build_gemma2_iswa : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_post_norm", il); - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); - } - ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL); cb(sa_out, "sa_out", il); @@ -8949,6 +8952,8 @@ struct llm_build_gemma3_iswa : public llm_graph_context { // TODO: is causal == true correct? might need some changes auto * inp_attn = build_attn_inp_kv_unified_iswa(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { const float freq_base_l = model.get_rope_freq_base (cparams, il); const float freq_scale_l = model.get_rope_freq_scale(cparams, il); @@ -9001,18 +9006,16 @@ struct llm_build_gemma3_iswa : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f, il); } + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_post_norm", il); - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); - } - ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL); cb(sa_out, "sa_out", il); @@ -9083,6 +9086,8 @@ struct llm_build_starcoder2 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -9141,9 +9146,7 @@ struct llm_build_starcoder2 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -9206,6 +9209,8 @@ struct llm_build_mamba : public llm_graph_context { auto * rs_inp = build_rs_inp(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { // norm cur = build_norm(inpL, @@ -9219,9 +9224,7 @@ struct llm_build_mamba : public llm_graph_context { cur = build_mamba_layer(rs_inp, gf, cur, ubatch, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -9259,9 +9262,9 @@ struct llm_build_mamba : public llm_graph_context { ggml_tensor * cur, const llama_ubatch & ubatch, int il) const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); - const auto kv_head = kv_state->get_head(); + const auto kv_head = mctx_cur->get_head(); const int64_t d_conv = hparams.ssm_d_conv; const int64_t d_inner = hparams.ssm_d_inner; @@ -9281,8 +9284,8 @@ struct llm_build_mamba : public llm_graph_context { GGML_ASSERT(ubatch.equal_seqs); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); - ggml_tensor * conv_states_all = kv_state->get_r_l(il); - ggml_tensor * ssm_states_all = kv_state->get_s_l(il); + ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); + ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs); conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner, n_seqs); @@ -9357,7 +9360,7 @@ struct llm_build_mamba : public llm_graph_context { // (this is necessary in order to properly use the states before they are overwritten, // while avoiding to make unnecessary copies of the states) auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) { - ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, kv_state->get_size()); + ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, mctx_cur->get_size()); // Custom operator to optimize the parallel associative scan // as described in the Annex D of the Mamba paper. @@ -9397,9 +9400,9 @@ struct llm_build_mamba : public llm_graph_context { ggml_tensor * cur, const llama_ubatch & ubatch, int il) const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); - const auto kv_head = kv_state->get_head(); + const auto kv_head = mctx_cur->get_head(); const int64_t d_conv = hparams.ssm_d_conv; const int64_t d_inner = hparams.ssm_d_inner; @@ -9415,8 +9418,8 @@ struct llm_build_mamba : public llm_graph_context { GGML_ASSERT(ubatch.equal_seqs); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); - ggml_tensor * conv_states_all = kv_state->get_r_l(il); - ggml_tensor * ssm_states_all = kv_state->get_s_l(il); + ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); + ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs); conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs); @@ -9481,7 +9484,7 @@ struct llm_build_mamba : public llm_graph_context { // (this is necessary in order to properly use the states before they are overwritten, // while avoiding to make unnecessary copies of the states) auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) { - ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, kv_state->get_size()); + ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, mctx_cur->get_size()); // TODO: use semistructured matrices to implement state-space duality // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs} @@ -9538,13 +9541,15 @@ struct llm_build_command_r : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); - for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { // norm cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM, il); cb(cur, "attn_norm", il); + ggml_tensor * ffn_inp = cur; // self-attention @@ -9612,9 +9617,7 @@ struct llm_build_command_r : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids); @@ -9685,6 +9688,8 @@ struct llm_build_cohere2_iswa : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified_iswa(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { const bool is_swa = hparams.is_swa(il); @@ -9747,9 +9752,7 @@ struct llm_build_cohere2_iswa : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids); @@ -9820,6 +9823,8 @@ struct llm_build_olmo : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -9878,9 +9883,7 @@ struct llm_build_olmo : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -9948,6 +9951,8 @@ struct llm_build_olmo2 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -9998,18 +10003,16 @@ struct llm_build_olmo2 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_post_norm", il); - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); - } - ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); @@ -10077,6 +10080,8 @@ struct llm_build_olmoe : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -10131,9 +10136,7 @@ struct llm_build_olmoe : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -10203,6 +10206,8 @@ struct llm_build_openelm : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { const int64_t n_head = hparams.n_head(il); const int64_t n_head_kv = hparams.n_head_kv(il); @@ -10264,11 +10269,9 @@ struct llm_build_openelm : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { residual = ggml_get_rows(ctx0, residual, inp_out_ids); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); } ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); @@ -10334,6 +10337,8 @@ struct llm_build_gptneox : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, model.layers[il].attn_norm, @@ -10378,9 +10383,7 @@ struct llm_build_gptneox : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -10482,6 +10485,8 @@ struct llm_build_arctic : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -10528,9 +10533,7 @@ struct llm_build_arctic : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -10622,6 +10625,8 @@ struct llm_build_deepseek : public llm_graph_context { const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -10683,14 +10688,11 @@ struct llm_build_deepseek : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } - ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); @@ -10798,6 +10800,8 @@ struct llm_build_deepseek2 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -10947,9 +10951,7 @@ struct llm_build_deepseek2 : public llm_graph_context { } } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -11045,6 +11047,8 @@ struct llm_build_bitnet : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -11127,9 +11131,7 @@ struct llm_build_bitnet : public llm_graph_context { cb(cur, "attn_o_out", il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -11204,6 +11206,8 @@ struct llm_build_t5_enc : public llm_graph_context { auto * inp_attn = build_attn_inp_no_cache(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -11237,9 +11241,7 @@ struct llm_build_t5_enc : public llm_graph_context { cb(cur, "kqv_out", il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -11310,6 +11312,8 @@ struct llm_build_t5_dec : public llm_graph_context { auto * inp_attn_self = build_attn_inp_kv_unified(); auto * inp_attn_cross = build_attn_inp_cross(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -11401,11 +11405,8 @@ struct llm_build_t5_dec : public llm_graph_context { //cb(cur, "kqv_out", il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids); } @@ -11475,6 +11476,8 @@ struct llm_build_jais : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { cur = build_norm(inpL, model.layers[il].attn_norm, @@ -11507,9 +11510,7 @@ struct llm_build_jais : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/float(n_embd_head), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } @@ -11573,6 +11574,8 @@ struct llm_build_chatglm : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -11639,9 +11642,7 @@ struct llm_build_chatglm : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -11706,6 +11707,8 @@ struct llm_build_glm4 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -11772,9 +11775,7 @@ struct llm_build_glm4 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -11857,6 +11858,8 @@ struct llm_build_nemotron : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -11916,9 +11919,7 @@ struct llm_build_nemotron : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -11986,6 +11987,8 @@ struct llm_build_exaone : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -12047,9 +12050,7 @@ struct llm_build_exaone : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -12142,7 +12143,7 @@ struct llm_build_rwkv6_base : public llm_graph_context { ggml_tensor * x_prev, const llama_ubatch & ubatch, int il) const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); const auto n_tokens = ubatch.n_tokens; const auto n_seqs = ubatch.n_seqs; @@ -12152,7 +12153,7 @@ struct llm_build_rwkv6_base : public llm_graph_context { const auto n_head = n_embd / head_size; const auto n_head_kv = hparams.n_head_kv(il); - const auto kv_head = kv_state->get_head(); + const auto kv_head = mctx_cur->get_head(); const auto & layer = model.layers[il]; @@ -12264,7 +12265,7 @@ struct llm_build_rwkv6_base : public llm_graph_context { } ggml_tensor * wkv_state = build_rs( - inp, gf, kv_state->get_s_l(il), + inp, gf, mctx_cur->get_s_l(il), hparams.n_embd_s(), n_seqs); ggml_tensor * wkv_output; @@ -12283,9 +12284,9 @@ struct llm_build_rwkv6_base : public llm_graph_context { wkv_state, ggml_view_1d( ctx0, - kv_state->get_s_l(il), + mctx_cur->get_s_l(il), hparams.n_embd_s() * n_seqs, - hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il)) + hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il)) ) ) ); @@ -12325,6 +12326,8 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base { const auto n_seq_tokens = ubatch.n_seq_tokens; const auto n_seqs = ubatch.n_seqs; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); @@ -12366,13 +12369,16 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base { ); ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il)); - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - ffn_inp = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens), inp_out_ids); - ffn_norm = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens), inp_out_ids); - x_prev = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens), inp_out_ids); - cur = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, cur, n_embd, n_tokens), inp_out_ids); + ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens); + ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens); + x_prev = ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens); + cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens); + + if (il == n_layer - 1 && inp_out_ids) { + ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids); + ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids); + x_prev = ggml_get_rows(ctx0, x_prev, inp_out_ids); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); } cur = build_rwkv6_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV6); @@ -12420,6 +12426,8 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base { const auto n_seq_tokens = ubatch.n_seq_tokens; const auto n_seqs = ubatch.n_seqs; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); @@ -12444,11 +12452,12 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base { ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); cb(ffn_inp, "ffn_inp", il); - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, cur, n_embd, n_tokens), inp_out_ids); - ffn_inp = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens), inp_out_ids); + cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens); + ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens); + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids); } // feed-forward network @@ -12531,7 +12540,7 @@ struct llm_build_rwkv7_base : public llm_graph_context { ggml_tensor *& first_layer_value, const llama_ubatch & ubatch, int il) const { - const auto * kv_state = static_cast(mstate); + const auto * mctx_cur = static_cast(mctx); const auto n_tokens = ubatch.n_tokens; const auto n_seqs = ubatch.n_seqs; @@ -12540,7 +12549,7 @@ struct llm_build_rwkv7_base : public llm_graph_context { const auto head_count = n_embd / head_size; const auto n_seq_tokens = ubatch.n_seq_tokens; - const auto kv_head = kv_state->get_head(); + const auto kv_head = mctx_cur->get_head(); const auto & layer = model.layers[il]; @@ -12611,7 +12620,7 @@ struct llm_build_rwkv7_base : public llm_graph_context { a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens); ggml_tensor * wkv_state = build_rs( - inp, gf, kv_state->get_s_l(il), + inp, gf, mctx_cur->get_s_l(il), hparams.n_embd_s(), n_seqs); ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state); @@ -12625,9 +12634,9 @@ struct llm_build_rwkv7_base : public llm_graph_context { wkv_state, ggml_view_1d( ctx0, - kv_state->get_s_l(il), + mctx_cur->get_s_l(il), hparams.n_embd_s() * n_seqs, - hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il)) + hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il)) ) ) ); @@ -12674,6 +12683,8 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base { const auto n_seq_tokens = ubatch.n_seq_tokens; const auto n_seqs = ubatch.n_seqs; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); @@ -12715,12 +12726,14 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base { ); ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il)); - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - ffn_inp = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens), inp_out_ids); - ffn_norm = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens), inp_out_ids); - x_prev = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens), inp_out_ids); + ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens); + ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens); + x_prev = ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens); + + if (il == n_layer - 1 && inp_out_ids) { + ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids); + ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids); + x_prev = ggml_get_rows(ctx0, x_prev, inp_out_ids); } cur = build_rwkv7_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV7); @@ -12765,6 +12778,8 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base { const auto n_seq_tokens = ubatch.n_seq_tokens; const auto n_seqs = ubatch.n_seqs; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { const llama_layer * layer = &model.layers[il]; inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs); @@ -12789,11 +12804,12 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base { ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); cb(ffn_inp, "ffn_inp", il); - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, cur, n_embd, n_tokens), inp_out_ids); - ffn_inp = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens), inp_out_ids); + cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens); + ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens); + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids); } // feed-forward network @@ -12862,6 +12878,9 @@ struct llm_build_granite : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -12924,9 +12943,7 @@ struct llm_build_granite : public llm_graph_context { cb(cur, "attn_out", il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -13045,6 +13062,8 @@ struct llm_build_chameleon : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -13121,21 +13140,19 @@ struct llm_build_chameleon : public llm_graph_context { cur = build_attn(inp_attn, gf, model.layers[il].wo, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); - - if (hparams.swin_norm) { - cur = build_norm(cur, - model.layers[il].attn_norm, NULL, - LLM_NORM_RMS, il); - } } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } + if (hparams.swin_norm) { + cur = build_norm(cur, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + } + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); @@ -13376,6 +13393,8 @@ struct llm_build_plm : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -13479,9 +13498,7 @@ struct llm_build_plm : public llm_graph_context { q_states, k_states, v_states, nullptr, nullptr, kq_scale, il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -13541,6 +13558,8 @@ struct llm_build_bailingmoe : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -13602,9 +13621,7 @@ struct llm_build_bailingmoe : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_rot)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -13690,6 +13707,8 @@ struct llm_build_dots1 : public llm_graph_context { auto * inp_attn = build_attn_inp_kv_unified(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -13742,9 +13761,7 @@ struct llm_build_dots1 : public llm_graph_context { Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -13842,6 +13859,8 @@ struct llm_build_arcee : public llm_graph_context { const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -13904,9 +13923,7 @@ struct llm_build_arcee : public llm_graph_context { cb(cur, "attn_out", il); } - if (il == n_layer - 1) { - // skip computing output for unused tokens - ggml_tensor * inp_out_ids = build_inp_out_ids(); + if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 8cf45732f..43229e193 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -1,5 +1,4 @@ #include "llama-quant.h" - #include "llama-impl.h" #include "llama-model.h" #include "llama-model-loader.h" @@ -27,6 +26,56 @@ static void zeros(std::ofstream & file, size_t n) { } } +static std::string remap_layer(const std::string & orig_name, const std::vector & prune, std::map & mapped, int & next_id) { + if (prune.empty()) { + return orig_name; + } + + static const std::regex pattern(R"(blk\.(\d+)\.)"); + if (std::smatch match; std::regex_search(orig_name, match, pattern)) { + const int blk = std::stoi(match[1]); + std::string new_name = orig_name; + + if (mapped.count(blk)) { + // Already mapped, do nothing + } else if (std::find(prune.begin(), prune.end(), blk) != prune.end()) { + mapped[blk] = ""; + } else if (blk < prune.front()) { + mapped[blk] = std::to_string(blk); + next_id = blk + 1; + } else { + mapped[blk] = std::to_string(next_id); + ++next_id; + } + + return mapped[blk].empty() ? mapped[blk] : new_name.replace(match.position(1), match.length(1), mapped[blk]); + } + + return orig_name; +} + +static std::string remap_imatrix (const std::string & orig_name, const std::map & mapped) { + if (mapped.empty()) { + return orig_name; + } + + static const std::regex pattern(R"(blk\.(\d+)\.)"); + if (std::smatch match; std::regex_search(orig_name, match, pattern)) { + const std::string blk(match[1]); + std::string new_name = orig_name; + + for (const auto & p : mapped) { + if (p.second == blk) { + LLAMA_LOG_DEBUG("(blk.%d imatrix) ", p.first); + return new_name.replace(match.position(1), match.length(1), std::to_string(p.first)); + } + } + GGML_ABORT("\n%s: imatrix mapping error for %s\n", __func__, orig_name.c_str()); + } + + return orig_name; +} + struct quantize_state_impl { const llama_model & model; const llama_model_quantize_params * params; @@ -568,6 +617,11 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: const size_t align = GGUF_DEFAULT_ALIGNMENT; gguf_context_ptr ctx_out { gguf_init_empty() }; + std::vector prune_list = {}; + if (params->prune_layers) { + prune_list = *static_cast *>(params->prune_layers); + } + // copy the KV pairs from the input file gguf_set_kv (ctx_out.get(), ml.meta.get()); gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV @@ -597,12 +651,32 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } } + std::map mapped; + int blk_id = 0; + int pruned_attention_w = 0; + // make a list of weights std::vector tensors; tensors.reserve(ml.weights_map.size()); for (const auto & it : ml.weights_map) { + const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id)); + if (remapped_name.empty()) { + if (it.first.find("attn_v.weight") != std::string::npos || + it.first.find("attn_qkv.weight") != std::string::npos || + it.first.find("attn_kv_b.weight") != std::string::npos) { + pruned_attention_w++; + } + LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str()); + continue; + } else if (remapped_name != it.first) { + ggml_set_name(it.second.tensor, remapped_name.c_str()); + LLAMA_LOG_DEBUG("%s: tensor %s remapped to %s\n", __func__, it.first.c_str(), ggml_get_name(it.second.tensor)); + } tensors.push_back(&it.second); } + if (!prune_list.empty()) { + gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_BLOCK_COUNT).c_str(), blk_id); + } // keep_split requires that the weights are sorted by split index if (params->keep_split) { @@ -640,7 +714,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: if (llama_model_has_encoder(&model)) { n_attn_layer *= 3; } - GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected"); + GGML_ASSERT((qs.n_attention_wv == n_attn_layer - pruned_attention_w) && "n_attention_wv is unexpected"); } size_t total_size_org = 0; @@ -681,7 +755,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: for (size_t i = 0; i < ctx_outs.size(); ++i) { gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i); gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split); - gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors); + gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), (int32_t)tensors.size()); } } @@ -832,7 +906,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: const float * imatrix = nullptr; if (imatrix_data) { - auto it = imatrix_data->find(tensor->name); + auto it = imatrix_data->find(remap_imatrix(tensor->name, mapped)); if (it == imatrix_data->end()) { LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name); } else { @@ -947,6 +1021,7 @@ llama_model_quantize_params llama_model_quantize_default_params() { /*.imatrix =*/ nullptr, /*.kv_overrides =*/ nullptr, /*.tensor_type =*/ nullptr, + /*.prune_layers =*/ nullptr }; return result; diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index dd2251ef3..5c9eb8756 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1269,6 +1269,7 @@ struct llama_vocab::impl { bool add_space_prefix = false; bool add_bos = false; bool add_eos = false; + bool add_sep = false; bool ignore_merges = false; bool clean_spaces = false; // clean_up_tokenization_spaces bool remove_extra_whitespaces = false; @@ -1421,6 +1422,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { special_sep_id = 102; special_pad_id = 0; special_mask_id = 103; + + add_sep = true; } else if (tokenizer_model == "gpt2") { type = LLAMA_VOCAB_TYPE_BPE; @@ -1550,12 +1553,15 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "jina-es" || tokenizer_pre == "jina-de" || tokenizer_pre == "gigachat" || - tokenizer_pre == "jina-v1-en" || tokenizer_pre == "jina-v2-es" || - tokenizer_pre == "jina-v2-de" || + tokenizer_pre == "jina-v2-de") { + pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2; + } else if ( + tokenizer_pre == "jina-v1-en" || tokenizer_pre == "jina-v2-code" || tokenizer_pre == "roberta-bpe") { pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2; + add_sep = true; } else if ( tokenizer_pre == "refact") { pre_type = LLAMA_VOCAB_PRE_TYPE_REFACT; @@ -1665,6 +1671,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { clean_spaces = true; add_bos = true; add_eos = false; + add_sep = true; } else if (type == LLAMA_VOCAB_TYPE_UGM) { pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT; add_bos = false; @@ -1801,7 +1808,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { } } - // Handle add_bos and add_eos + // Handle add_bos, add_eos and add_sep { bool temp = true; @@ -1811,6 +1818,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) { add_eos = temp; } + if (ml.get_key(LLM_KV_TOKENIZER_ADD_SEP, temp, false)) { + add_sep = temp; + } } // auto-detect special tokens by text @@ -2060,9 +2070,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { //NOTE: Per token attributes are missing from the GGUF file. //TODO: Extract attributes from GGUF file. { - auto _contains_any = [] (const std::string & str, const std::vector & substrs) -> bool { + auto _contains_any = [] (const std::string & str, const std::vector & substrs) -> bool { for (const auto & substr : substrs) { - if (str.find(substr) < std::string::npos) { + if (str.find(substr) != std::string::npos) { return true; } } @@ -3000,6 +3010,10 @@ bool llama_vocab::get_add_eos() const { return pimpl->add_eos; } +bool llama_vocab::get_add_sep() const { + return pimpl->add_sep; +} + bool llama_vocab::get_ignore_merges() const { return pimpl->ignore_merges; } @@ -3060,6 +3074,11 @@ int32_t llama_vocab::tokenize( bool add_special, bool parse_special) const { auto res = tokenize(std::string(text, text_len), add_special, parse_special); + if (res.size() >= static_cast(std::numeric_limits::max())) { + LLAMA_LOG_ERROR("%s: tokenization result size %zu exceeds int32_t limit\n", __func__, res.size()); + return std::numeric_limits::min(); + } + if (n_tokens_max < (int) res.size()) { // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); return -((int) res.size()); @@ -3191,6 +3210,10 @@ bool llama_vocab_get_add_eos(const struct llama_vocab * vocab) { return vocab->get_add_eos(); } +bool llama_vocab_get_add_sep(const struct llama_vocab * vocab) { + return vocab->get_add_sep(); +} + llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab) { return vocab->token_fim_pre(); } diff --git a/src/llama-vocab.h b/src/llama-vocab.h index daa6cf308..40e4d1c05 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -74,6 +74,7 @@ struct llama_vocab { bool get_add_space_prefix () const; bool get_add_bos () const; bool get_add_eos () const; + bool get_add_sep () const; bool get_ignore_merges () const; bool get_clean_spaces () const; bool get_remove_extra_whitespaces () const; diff --git a/src/unicode.cpp b/src/unicode.cpp index e63bb4ab0..43a4581b9 100644 --- a/src/unicode.cpp +++ b/src/unicode.cpp @@ -204,12 +204,17 @@ static inline std::wstring unicode_wstring_from_utf8(const std::string & s) { // disable C++17 deprecation warning for std::codecvt_utf8 # pragma clang diagnostic push # pragma clang diagnostic ignored "-Wdeprecated-declarations" +#elif defined(__GNUC__) +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Wdeprecated-declarations" #endif std::wstring_convert> conv; #if defined(__clang__) # pragma clang diagnostic pop +#elif defined(__GNUC__) +# pragma GCC diagnostic pop #endif return conv.from_bytes(s); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 3957325ec..89e388ad3 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2755,6 +2755,35 @@ struct test_conv_transpose_1d : public test_case { } }; +// GGML_OP_CONV_TRANSPOSE_2D +struct test_conv_transpose_2d : public test_case { + const std::array ne_input; + const std::array ne_kernel; + const int stride; + + std::string vars() override { + return VARS_TO_STR3(ne_input, ne_kernel, stride); + } + + test_conv_transpose_2d(std::array ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1] + std::array ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1] + int stride = 1) + : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride){} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data()); + ggml_set_name(input, "input"); + + ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne_kernel.data()); + ggml_set_name(kernel, "kernel"); + + ggml_tensor * out = ggml_conv_transpose_2d_p0(ctx, kernel, input, stride); + ggml_set_name(out, "out"); + + return out; + } +}; + // GGML_OP_IM2COL struct test_im2col : public test_case { const ggml_type type_input; @@ -4080,6 +4109,9 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1)); test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1)); + test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1)); + test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2)); + test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 500, 1, 1})); test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1})); @@ -4649,6 +4681,10 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false)); test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true)); + test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1)); + + test_cases.emplace_back(new test_mean(GGML_TYPE_F32, {256, 256, 3, 1})); + return test_cases; } diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index e59d61f19..b80e984d0 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -267,6 +267,7 @@ struct cmd_params { int delay; bool verbose; bool progress; + bool no_warmup; output_formats output_format; output_formats output_format_stderr; }; @@ -303,6 +304,7 @@ static const cmd_params cmd_params_defaults = { /* delay */ 0, /* verbose */ false, /* progress */ false, + /* no_warmup */ false, /* output_format */ MARKDOWN, /* output_format_stderr */ NONE, }; @@ -325,6 +327,7 @@ static void print_usage(int /* argc */, char ** argv) { output_format_str(cmd_params_defaults.output_format_stderr)); printf(" -v, --verbose verbose output\n"); printf(" --progress print test progress indicators\n"); + printf(" --no-warmup skip warmup runs before benchmarking\n"); printf("\n"); printf("test parameters:\n"); printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); @@ -425,6 +428,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { params.prio = cmd_params_defaults.prio; params.delay = cmd_params_defaults.delay; params.progress = cmd_params_defaults.progress; + params.no_warmup = cmd_params_defaults.no_warmup; for (int i = 1; i < argc; i++) { arg = argv[i]; @@ -798,6 +802,8 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { params.verbose = true; } else if (arg == "--progress") { params.progress = true; + } else if (arg == "--no-warmup") { + params.no_warmup = true; } else { invalid_param = true; break; @@ -1925,25 +1931,27 @@ int main(int argc, char ** argv) { llama_attach_threadpool(ctx, threadpool, NULL); // warmup run - if (t.n_prompt > 0) { - if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count); + if (!params.no_warmup) { + if (t.n_prompt > 0) { + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count); + } + //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); + bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); + if (!res) { + fprintf(stderr, "%s: error: failed to run prompt warmup\n", __func__); + exit(1); + } } - //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); - bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); - if (!res) { - fprintf(stderr, "%s: error: failed to run prompt warmup\n", __func__); - exit(1); - } - } - if (t.n_gen > 0) { - if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count); - } - bool res = test_gen(ctx, 1, t.n_threads); - if (!res) { - fprintf(stderr, "%s: error: failed to run gen warmup\n", __func__); - exit(1); + if (t.n_gen > 0) { + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count); + } + bool res = test_gen(ctx, 1, t.n_threads); + if (!res) { + fprintf(stderr, "%s: error: failed to run gen warmup\n", __func__); + exit(1); + } } } diff --git a/tools/main/main.cpp b/tools/main/main.cpp index 19b247b0d..154b37cdb 100644 --- a/tools/main/main.cpp +++ b/tools/main/main.cpp @@ -292,6 +292,7 @@ int main(int argc, char ** argv) { if (!params.system_prompt.empty() || !params.prompt.empty()) { common_chat_templates_inputs inputs; + inputs.use_jinja = g_params->use_jinja; inputs.messages = chat_msgs; inputs.add_generation_prompt = !params.prompt.empty(); diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 30283d6f1..a990520ed 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -2211,6 +2211,9 @@ struct clip_model_loader { { hparams.rope_theta = 10000.0f; hparams.warmup_image_size = hparams.patch_size * 8; + // Mistral Small 2506 needs 1024x1024 image size cap to prevent OOM + // ref: https://github.com/ggml-org/llama.cpp/issues/14310 + hparams.image_size = 1024; get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false); } break; case PROJECTOR_TYPE_GEMMA3: diff --git a/tools/quantize/quantize.cpp b/tools/quantize/quantize.cpp index 3f54af7c5..8acc76517 100644 --- a/tools/quantize/quantize.cpp +++ b/tools/quantize/quantize.cpp @@ -107,13 +107,11 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp return false; } -// usage: -// ./llama-quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] -// [[noreturn]] static void usage(const char * executable) { - printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type]\n", executable); - printf(" [--token-embedding-type] [--tensor-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n"); + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]\n", executable); + printf(" [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--prune-layers] [--keep-split] [--override-kv]\n"); + printf(" model-f32.gguf [model-quant.gguf] type [nthreads]\n\n"); printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n"); @@ -124,6 +122,8 @@ static void usage(const char * executable) { printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n"); printf(" --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n"); printf(" Advanced option to selectively quantize tensors. May be specified multiple times.\n"); + printf(" --prune-layers L0,L1,L2...comma-separated list of layer numbers to prune from the model\n"); + printf(" Advanced option to remove all tensors from the given layers\n"); printf(" --keep-split: will generate quantized model in the same shards as input\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n"); @@ -286,6 +286,32 @@ static bool parse_tensor_type(const char * data, std::vector & prune_layers) { + if (!data) { + printf("\n%s: no layer pruning ids provided\n\n", __func__); + return false; + } + + const auto block_ids = string_split(data, ','); + for (const auto & block_id : block_ids) { + int id; + try { + id = std::stoi(block_id); + } catch (...) { + id = -1; + } + if (id < 0) { + printf("\n%s: invalid layer id '%s'\n\n", __func__, block_id.c_str()); + return false; + } + prune_layers.emplace_back(id); + } + + sort(prune_layers.begin(), prune_layers.end()); + prune_layers.erase(std::unique(prune_layers.begin(), prune_layers.end()), prune_layers.end()); + return true; +} + int main(int argc, char ** argv) { if (argc < 3) { usage(argv[0]); @@ -298,6 +324,7 @@ int main(int argc, char ** argv) { std::vector included_weights, excluded_weights; std::vector kv_overrides; std::vector tensor_types; + std::vector prune_layers; for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) { @@ -324,6 +351,10 @@ int main(int argc, char ** argv) { if (arg_idx == argc-1 || !parse_tensor_type(argv[++arg_idx], tensor_types)) { usage(argv[0]); } + } else if (strcmp(argv[arg_idx], "--prune-layers") == 0) { + if (arg_idx == argc-1 || !parse_layer_prune(argv[++arg_idx], prune_layers)) { + usage(argv[0]); + } } else if (strcmp(argv[arg_idx], "--override-kv") == 0) { if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) { usage(argv[0]); @@ -411,6 +442,9 @@ int main(int argc, char ** argv) { if (!tensor_types.empty()) { params.tensor_types = &tensor_types; } + if (!prune_layers.empty()) { + params.prune_layers = &prune_layers; + } llama_backend_init(); diff --git a/tools/run/run.cpp b/tools/run/run.cpp index c65afd61e..6fe728c68 100644 --- a/tools/run/run.cpp +++ b/tools/run/run.cpp @@ -9,6 +9,9 @@ #include #if defined(_WIN32) +# ifndef NOMINMAX +# define NOMINMAX +# endif # include # include #else @@ -939,17 +942,30 @@ static int apply_chat_template(const struct common_chat_templates * tmpls, Llama // Function to tokenize the prompt static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt, std::vector & prompt_tokens, const LlamaData & llama_data) { - const bool is_first = llama_memory_seq_pos_max(llama_get_memory(llama_data.context.get()), 0) == 0; - - const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true); - prompt_tokens.resize(n_prompt_tokens); - if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), is_first, - true) < 0) { - printe("failed to tokenize the prompt\n"); + const bool is_first = llama_memory_seq_pos_max(llama_get_memory(llama_data.context.get()), 0) == -1; + int n_tokens = prompt.size() + 2 * is_first; + prompt_tokens.resize(n_tokens); + n_tokens = llama_tokenize(vocab, prompt.c_str(), prompt.size(), + prompt_tokens.data(), prompt_tokens.size(), + is_first, /*parse_special =*/true); + if (n_tokens == std::numeric_limits::min()) { + printe("tokenization failed: input too large\n"); return -1; } - - return n_prompt_tokens; + if (n_tokens < 0) { + prompt_tokens.resize(-n_tokens); + int check = llama_tokenize(vocab, prompt.c_str(), prompt.size(), + prompt_tokens.data(), prompt_tokens.size(), + is_first, /*parse_special =*/true); + if (check != -n_tokens) { + printe("failed to tokenize the prompt (size mismatch)\n"); + return -1; + } + n_tokens = check; + } else { + prompt_tokens.resize(n_tokens); + } + return n_tokens; } // Check if we have enough space in the context to evaluate this batch diff --git a/tools/server/README.md b/tools/server/README.md index 06533c172..43aa65d50 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -187,6 +187,8 @@ The project is under active development, and we are [looking for feedback and co | `-devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | | `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_MODEL_DRAFT) | +| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for speculative decoding model
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) | +| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for speculative decoding model
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) | | `-mv, --model-vocoder FNAME` | vocoder model for audio generation (default: unused) | | `--tts-use-guide-tokens` | Use guide tokens to improve TTS word recall | | `--embd-bge-small-en-default` | use default bge-small-en-v1.5 model (note: can download weights from the internet) | diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 721d09182..852352383 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -1969,10 +1969,8 @@ struct server_context { params_dft.n_ctx = params_base.speculative.n_ctx == 0 ? params_base.n_ctx / params_base.n_parallel : params_base.speculative.n_ctx; params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers; params_dft.n_parallel = 1; - - // force F16 KV cache for the draft model for extra performance - params_dft.cache_type_k = GGML_TYPE_F16; - params_dft.cache_type_v = GGML_TYPE_F16; + params_dft.cache_type_k = params_base.speculative.cache_type_k; + params_dft.cache_type_v = params_base.speculative.cache_type_v; llama_init_dft = common_init_from_params(params_dft); @@ -3387,38 +3385,6 @@ struct server_context { llama_set_embeddings(ctx, slot_batched->need_embd()); } - // pad the batch so that batch.n_tokens >= n_slots - // TODO: temporary workaround for https://github.com/ggml-org/llama.cpp/issues/13689 - if (slot_batched->need_embd()) { - const int n_slots = slots.size(); - - if (batch.n_tokens < n_slots) { - std::set seq_ids; - for (int j = 0; j < batch.n_tokens; ++j) { - seq_ids.insert(batch.seq_id[j][0]); - } - - // find unused sequence id - llama_seq_id seq_id = -1; - for (int i = 0; i < n_slots; ++i) { - if (seq_ids.find(i) == seq_ids.end()) { - seq_id = i; - } - } - - const int n_add = n_slots - batch.n_tokens; - - SRV_WRN("adding %d dummy tokens to the batch, seq_id = %d\n", n_add, seq_id); - - for (int j = 0; j < n_add; ++j) { - common_batch_add(batch, 0, j, { seq_id }, true); - } - - slots[seq_id].cache_tokens.clear(); - llama_memory_seq_rm(llama_get_memory(ctx), seq_id, -1, -1); - } - } - int32_t i_next = 0; // process the created batch of tokens @@ -3452,9 +3418,12 @@ struct server_context { } if (ret < -1) { + // TODO: update slot state based on llama_memory_seq_pos_min() and llama_memory_seq_pos_max() err = "Compute error."; } + // TODO: handle ret == 2 (abort) when we start aborting + if (!err.empty()) { SRV_ERR("%s, i = %d, n_batch = %d, ret = %d\n", err.c_str(), i, n_batch, ret); for (auto & slot : slots) { diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp index f3e0392a4..f8fab2c86 100644 --- a/tools/server/utils.hpp +++ b/tools/server/utils.hpp @@ -271,12 +271,20 @@ static llama_tokens format_rerank(const struct llama_vocab * vocab, const llama_ } result.reserve(doc.size() + query.size() + 4); - result.push_back(llama_vocab_bos(vocab)); + if (llama_vocab_get_add_bos(vocab)) { + result.push_back(llama_vocab_bos(vocab)); + } result.insert(result.end(), query.begin(), query.end()); - result.push_back(eos_token); - result.push_back(llama_vocab_sep(vocab)); + if (llama_vocab_get_add_eos(vocab)) { + result.push_back(eos_token); + } + if (llama_vocab_get_add_sep(vocab)) { + result.push_back(llama_vocab_sep(vocab)); + } result.insert(result.end(), doc.begin(), doc.end()); - result.push_back(eos_token); + if (llama_vocab_get_add_eos(vocab)) { + result.push_back(eos_token); + } return result; }