diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 8d6ba5f9f3..fe86863893 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -387,6 +387,39 @@ jobs: cd build ctest -L main --verbose + ubuntu-24-cmake-vulkan-deb: + runs-on: ubuntu-24.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: ubuntu-24-cmake-vulkan-deb + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get install -y glslc libvulkan-dev libcurl4-openssl-dev + + - name: Configure + id: cmake_configure + run: | + cmake -B build \ + -DCMAKE_BUILD_TYPE=RelWithDebInfo \ + -DGGML_BACKEND_DL=ON \ + -DGGML_CPU_ALL_VARIANTS=ON \ + -DGGML_VULKAN=ON + + - name: Build + id: cmake_build + run: | + cmake --build build -j $(nproc) + ubuntu-24-cmake-vulkan: runs-on: ubuntu-24.04 diff --git a/common/arg.cpp b/common/arg.cpp index d17645cf2f..33ed7ae857 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1760,7 +1760,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); add_opt(common_arg( {"-t", "--threads"}, "N", - string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads), + string_format("number of CPU threads to use during generation (default: %d)", params.cpuparams.n_threads), [](common_params & params, int value) { params.cpuparams.n_threads = value; if (params.cpuparams.n_threads <= 0) { @@ -3358,7 +3358,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex add_opt(common_arg( {"--chat-template-kwargs"}, "STRING", string_format("sets additional params for the json template parser"), - [](common_params & params, const std::string & value) { + [](common_params & params, const std::string & value) { auto parsed = json::parse(value); for (const auto & item : parsed.items()) { params.default_template_kwargs[item.key()] = item.value().dump(); @@ -3570,21 +3570,23 @@ common_params_context common_params_parser_init(common_params & params, llama_ex common_log_set_file(common_log_main(), value.c_str()); } )); - add_opt(common_arg({ "--log-colors" }, "[on|off|auto]", - "Set colored logging ('on', 'off', or 'auto', default: 'auto')\n" - "'auto' enables colors when output is to a terminal", - [](common_params &, const std::string & value) { - if (is_truthy(value)) { - common_log_set_colors(common_log_main(), LOG_COLORS_ENABLED); - } else if (is_falsey(value)) { - common_log_set_colors(common_log_main(), LOG_COLORS_DISABLED); - } else if (is_autoy(value)) { - common_log_set_colors(common_log_main(), LOG_COLORS_AUTO); - } else { - throw std::invalid_argument( - string_format("error: unkown value for --log-colors: '%s'\n", value.c_str())); - } - }).set_env("LLAMA_LOG_COLORS")); + add_opt(common_arg( + {"--log-colors"}, "[on|off|auto]", + "Set colored logging ('on', 'off', or 'auto', default: 'auto')\n" + "'auto' enables colors when output is to a terminal", + [](common_params &, const std::string & value) { + if (is_truthy(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_ENABLED); + } else if (is_falsey(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_DISABLED); + } else if (is_autoy(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_AUTO); + } else { + throw std::invalid_argument( + string_format("error: unkown value for --log-colors: '%s'\n", value.c_str())); + } + } + ).set_env("LLAMA_LOG_COLORS")); add_opt(common_arg( {"-v", "--verbose", "--log-verbose"}, "Set verbosity level to infinity (i.e. log all messages, useful for debugging)", @@ -3850,7 +3852,87 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_TTS})); - // model-specific + add_opt(common_arg( + {"--diffusion-steps"}, "N", + string_format("number of diffusion steps (default: %d)", params.diffusion.steps), + [](common_params & params, int value) { params.diffusion.steps = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-visual"}, + string_format("enable visual diffusion mode (show progressive generation) (default: %s)", params.diffusion.visual_mode ? "true" : "false"), + [](common_params & params) { params.diffusion.visual_mode = true; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-eps"}, "F", + string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps), + [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-algorithm"}, "N", + string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", params.diffusion.algorithm), + [](common_params & params, int value) { params.diffusion.algorithm = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-alg-temp"}, "F", + string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), + [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-block-length"}, "N", + string_format("llada block length for generation (default: %d)", params.diffusion.block_length), + [](common_params & params, int value) { params.diffusion.block_length = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-cfg-scale"}, "F", + string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale), + [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-add-gumbel-noise"}, "F", + string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"), + [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "-lr", "--learning-rate" }, "ALPHA", + string_format("adamw or sgd optimizer alpha (default: %.2g); note: sgd alpha recommended ~10x (no momentum)", (double) params.lr.lr0), + [](common_params & params, const std::string & value) { params.lr.lr0 = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg({ "-lr-min", "--learning-rate-min" }, "ALPHA", + string_format("(if >0) final learning rate after decay (if -decay-epochs is set, default=%.2g)", + (double) params.lr.lr_min), + [](common_params & params, const std::string & value) { params.lr.lr_min = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-decay-epochs", "--learning-rate-decay-epochs"}, "ALPHA", + string_format("(if >0) decay learning rate to -lr-min after this many epochs (exponential decay, default=%.2g)", (double) params.lr.decay_epochs), + [](common_params & params, const std::string & value) { params.lr.decay_epochs = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-wd", "--weight-decay"}, "WD", + string_format("adamw or sgd optimizer weight decay (0 is off; recommend very small e.g. 1e-9) (default: %.2g).", (double) params.lr.wd), + [](common_params & params, const std::string & value) { params.lr.wd = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-val-split", "--val-split"}, "FRACTION", + string_format("fraction of data to use as validation set for training (default: %.2g).", (double) params.val_split), + [](common_params & params, const std::string & value) { params.val_split = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-epochs", "--epochs"}, "N", + string_format("optimizer max # of epochs (default: %d)", params.lr.epochs), + [](common_params & params, int epochs) { params.lr.epochs = epochs; } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-opt", "--optimizer"}, "sgd|adamw", "adamw or sgd", + [](common_params & params, const std::string & name) { + params.optimizer = common_opt_get_optimizer(name.c_str()); + if (params.optimizer == GGML_OPT_OPTIMIZER_TYPE_COUNT) { + throw std::invalid_argument("invalid --optimizer, valid options: adamw, sgd"); + } + } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + + // presets add_opt(common_arg( {"--tts-oute-default"}, string_format("use default OuteTTS models (note: can download weights from the internet)"), @@ -3863,39 +3945,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_TTS})); add_opt(common_arg( - {"--embd-bge-small-en-default"}, - string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"), + {"--embd-gemma-default"}, + string_format("use default EmbeddingGemma model (note: can download weights from the internet)"), [](common_params & params) { - params.model.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF"; - params.model.hf_file = "bge-small-en-v1.5-q8_0.gguf"; - params.embd_normalize = 2; - params.n_ctx = 512; - params.verbose_prompt = true; - params.embedding = true; - } - ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); - - add_opt(common_arg( - {"--embd-e5-small-en-default"}, - string_format("use default e5-small-v2 model (note: can download weights from the internet)"), - [](common_params & params) { - params.model.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF"; - params.model.hf_file = "e5-small-v2-q8_0.gguf"; - params.embd_normalize = 2; - params.n_ctx = 512; - params.verbose_prompt = true; - params.embedding = true; - } - ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); - - add_opt(common_arg( - {"--embd-gte-small-default"}, - string_format("use default gte-small model (note: can download weights from the internet)"), - [](common_params & params) { - params.model.hf_repo = "ggml-org/gte-small-Q8_0-GGUF"; - params.model.hf_file = "gte-small-q8_0.gguf"; - params.embd_normalize = 2; - params.n_ctx = 512; + params.model.hf_repo = "ggml-org/embeddinggemma-300M-qat-q4_0-GGUF"; + params.model.hf_file = "embeddinggemma-300M-qat-Q4_0.gguf"; + params.port = 8011; + params.n_ubatch = 2048; + params.n_batch = 2048; + params.n_parallel = 32; + params.n_ctx = 2048*params.n_parallel; params.verbose_prompt = true; params.embedding = true; } @@ -3990,96 +4049,65 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( - { "--diffusion-steps" }, "N", - string_format("number of diffusion steps (default: %d)", params.diffusion.steps), - [](common_params & params, int value) { params.diffusion.steps = value; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-visual" }, - string_format("enable visual diffusion mode (show progressive generation) (default: %s)", - params.diffusion.visual_mode ? "true" : "false"), - [](common_params & params) { params.diffusion.visual_mode = true; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + {"--gpt-oss-20b-default"}, + string_format("use gpt-oss-20b (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gpt-oss-20b-GGUF"; + params.model.hf_file = "gpt-oss-20b-mxfp4.gguf"; + params.port = 8013; + params.n_ubatch = 2048; + params.n_batch = 32768; + params.n_parallel = 2; + params.n_ctx = 131072*params.n_parallel; + params.sampling.temp = 1.0f; + params.sampling.top_p = 1.0f; + params.sampling.top_k = 0; + params.sampling.min_p = 0.01f; + params.use_jinja = true; + //params.default_template_kwargs["reasoning_effort"] = "\"high\""; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( - { "--diffusion-eps" }, "F", - string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps), - [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-algorithm" }, "N", - string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", - params.diffusion.algorithm), - [](common_params & params, int value) { params.diffusion.algorithm = value; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-alg-temp" }, "F", - string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), - [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + {"--gpt-oss-120b-default"}, + string_format("use gpt-oss-120b (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gpt-oss-120b-GGUF"; + params.port = 8013; + params.n_ubatch = 2048; + params.n_batch = 32768; + params.n_parallel = 2; + params.n_ctx = 131072*params.n_parallel; + params.sampling.temp = 1.0f; + params.sampling.top_p = 1.0f; + params.sampling.top_k = 0; + params.sampling.min_p = 0.01f; + params.use_jinja = true; + //params.default_template_kwargs["reasoning_effort"] = "\"high\""; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( - { "--diffusion-block-length" }, "N", - string_format("llada block length for generation (default: %d)", params.diffusion.block_length), - [](common_params & params, int value) { params.diffusion.block_length = value; } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-cfg-scale" }, "F", - string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale), - [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); - add_opt(common_arg( - { "--diffusion-add-gumbel-noise" }, "F", - string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"), - [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); } - ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + {"--vision-gemma-4b-default"}, + string_format("use Gemma 3 4B QAT (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gemma-3-4b-it-qat-GGUF"; + params.port = 8014; + params.n_ctx = 0; + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); - - add_opt( - common_arg({ "-lr", "--learning-rate" }, "ALPHA", - string_format( - "adamw or sgd optimizer alpha (default: %.2g); note: sgd alpha recommended ~10x (no momentum)", - (double) params.lr.lr0), - [](common_params & params, const std::string & value) { params.lr.lr0 = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt( - common_arg({ "-lr-min", "--learning-rate-min" }, "ALPHA", - string_format( - "(if >0) final learning rate after decay (if -decay-epochs is set, default=%.2g)", - (double) params.lr.lr_min), - [](common_params & params, const std::string & value) { params.lr.lr_min = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt( - common_arg({ "-decay-epochs", "--learning-rate-decay-epochs" }, "ALPHA", - string_format( - "(if >0) decay learning rate to -lr-min after this many epochs (exponential decay, default=%.2g)", - (double) params.lr.decay_epochs), - [](common_params & params, const std::string & value) { params.lr.decay_epochs = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); add_opt(common_arg( - { "-wd", "--weight-decay" }, "WD", - string_format( - "adamw or sgd optimizer weight decay (0 is off; recommend very small e.g. 1e-9) (default: %.2g).", - (double) params.lr.wd), - [](common_params & params, const std::string & value) { params.lr.wd = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt(common_arg({ "-val-split", "--val-split" }, "FRACTION", - string_format("fraction of data to use as validation set for training (default: %.2g).", - (double) params.val_split), - [](common_params & params, const std::string & value) { params.val_split = std::stof(value); }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt(common_arg({ "-epochs", "--epochs" }, "N", - string_format("optimizer max # of epochs (default: %d)", params.lr.epochs), - [](common_params & params, int epochs) { params.lr.epochs = epochs; }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); - add_opt(common_arg({ "-opt", "--optimizer" }, "sgd|adamw", "adamw or sgd", - [](common_params & params, const std::string & name) { - params.optimizer = common_opt_get_optimizer(name.c_str()); - if (params.optimizer == GGML_OPT_OPTIMIZER_TYPE_COUNT) { - throw std::invalid_argument("invalid --optimizer, valid options: adamw, sgd"); - } - }) - .set_examples({ LLAMA_EXAMPLE_FINETUNE })); + {"--vision-gemma-12b-default"}, + string_format("use Gemma 3 12B QAT (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gemma-3-12b-it-qat-GGUF"; + params.port = 8014; + params.n_ctx = 0; + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); return ctx_arg; } diff --git a/common/chat-parser.cpp b/common/chat-parser.cpp index 7365782e7d..ff83102788 100644 --- a/common/chat-parser.cpp +++ b/common/chat-parser.cpp @@ -432,7 +432,7 @@ std::optional common_chat_msg_parse if (is_arguments_path({})) { // Entire JSON is the arguments and was parsed fully. return consume_json_result { - partial->json.dump(), + partial->json.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true), /* .is_partial = */ false, }; } @@ -444,7 +444,7 @@ std::optional common_chat_msg_parse std::vector path; std::function remove_unsupported_healings_and_dump_args = [&](const json & j) -> json { if (is_arguments_path(path)) { - auto arguments = j.dump(); + auto arguments = j.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true); if (is_partial() && !partial->healing_marker.marker.empty()) { auto idx = arguments.find(partial->healing_marker.json_dump_marker); if (idx != std::string::npos) { diff --git a/common/common.h b/common/common.h index 040a44ebd8..a8cb630ea5 100644 --- a/common/common.h +++ b/common/common.h @@ -426,7 +426,7 @@ struct common_params { int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting int32_t n_ctx_checkpoints = 8; // max number of context checkpoints per slot - int32_t cache_ram_mib = 8192; // 0 = no limit, 1 = 1 MiB, etc. + int32_t cache_ram_mib = 8192; // -1 = no limit, 0 - disable, 1 = 1 MiB, etc. std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT diff --git a/common/json-partial.cpp b/common/json-partial.cpp index d9d9169989..919927dc32 100644 --- a/common/json-partial.cpp +++ b/common/json-partial.cpp @@ -5,6 +5,7 @@ #include #include +#include using json = nlohmann::ordered_json; @@ -168,6 +169,47 @@ bool common_json_parse( } } + // Matches a potentially partial unicode escape sequence, e.g. \u, \uX, \uXX, \uXXX, \uXXXX + static const std::regex partial_unicode_regex(R"(\\u(?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F])?)?)?)?$)"); + + auto is_high_surrogate = [&](const std::string & s) { + // Check if a partial of a high surrogate (U+D800-U+DBFF) + return s.length() >= 4 && + s[0] == '\\' && s[1] == 'u' && + std::tolower(s[2]) == 'd' && + (s[3] == '8' || s[3] == '9' || std::tolower(s[3]) == 'a' || std::tolower(s[3]) == 'b'); + }; + + // Initialize the unicode marker to a low surrogate to handle the edge case + // where a high surrogate (U+D800-U+DBFF) is immediately followed by a + // backslash (\) + std::string unicode_marker_padding = "udc00"; + std::smatch last_unicode_seq; + + if (std::regex_search(str, last_unicode_seq, partial_unicode_regex)) { + std::smatch second_last_seq; + std::string prelude = str.substr(0, last_unicode_seq.position()); + + // Pad the escape sequence with 0s until it forms a complete sequence of 6 characters + unicode_marker_padding = std::string(6 - last_unicode_seq.length(), '0'); + + if (is_high_surrogate(last_unicode_seq.str())) { + // If the sequence is a partial match for a high surrogate, add a low surrogate (U+DC00-U+UDFF) + unicode_marker_padding += "\\udc00"; + } else if (std::regex_search(prelude, second_last_seq, partial_unicode_regex)) { + if (is_high_surrogate(second_last_seq.str())) { + // If this follows a high surrogate, pad it to be a low surrogate + if (last_unicode_seq.length() == 2) { + unicode_marker_padding = "dc00"; + } else if (last_unicode_seq.length() == 3) { + unicode_marker_padding = "c00"; + } else { + // The original unicode_marker_padding is already padded with 0s + } + } + } + } + const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$"; if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) { @@ -186,6 +228,9 @@ bool common_json_parse( } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) { // Was inside an object value string after an escape str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing; + } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) { + // Was inside an object value string after a partial unicode escape + str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing; } else { // find last : auto last_pos = str.find_last_of(':'); @@ -205,6 +250,9 @@ bool common_json_parse( } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) { // Was inside an array value string after an escape str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing; + } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) { + // Was inside an array value string after a partial unicode escape + str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing; } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) { // Had just finished a value str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing; @@ -230,6 +278,9 @@ bool common_json_parse( } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) { // Was inside an object key string after an escape str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing; + } else if (can_parse(str + unicode_marker_padding + "\": 1" + closing)) { + // Was inside an object key string after a partial unicode escape + str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\": 1" + closing; } else { auto last_pos = str.find_last_of(':'); if (last_pos == std::string::npos) { diff --git a/docs/ops.md b/docs/ops.md index 0047ef3fa5..5df72d2501 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -22,6 +22,7 @@ Legend: | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| CEIL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ | @@ -31,7 +32,7 @@ Legend: | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | +| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | | CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | @@ -41,6 +42,7 @@ Legend: | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | +| FLOOR | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | @@ -51,7 +53,7 @@ Legend: | GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | | GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | | GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | @@ -65,11 +67,11 @@ Legend: | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | -| PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | | PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | @@ -82,6 +84,7 @@ Legend: | ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | | ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | +| ROUND | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | @@ -92,9 +95,9 @@ Legend: | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | | SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | -| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | +| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | | SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | | SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ | ❌ | | SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | @@ -102,9 +105,12 @@ Legend: | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | | SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | | SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | | SWIGLU_OAI | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| TOPK_MOE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| TRUNC | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| XIELU | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | diff --git a/docs/ops/CPU.csv b/docs/ops/CPU.csv index 21e0d1b3c9..1820028c9a 100644 --- a/docs/ops/CPU.csv +++ b/docs/ops/CPU.csv @@ -59,6 +59,14 @@ "CPU","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","CPU" "CPU","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","CPU" "CPU","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","CPU" +"CPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" +"CPU","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" +"CPU","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" +"CPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" "CPU","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" "CPU","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" "CPU","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" @@ -119,6 +127,14 @@ "CPU","EXP","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","CPU" "CPU","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","CPU" "CPU","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","CPU" +"CPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" +"CPU","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" +"CPU","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" +"CPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU" +"CPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU" "CPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","CPU" "CPU","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","CPU" "CPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","CPU" diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 5d022ee91a..d7efa43cdf 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -29,6 +29,8 @@ "SYCL0","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" @@ -59,6 +61,8 @@ "SYCL0","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" @@ -89,6 +93,8 @@ "SYCL0","EXP","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","ABS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","SGN","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" @@ -119,6 +125,8 @@ "SYCL0","EXP","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" +"SYCL0","XIELU","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","SYCL" "SYCL0","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","SYCL" "SYCL0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","SYCL" @@ -239,99 +247,117 @@ "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=1,m=8,r=2,b=1,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=1,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=1,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=7,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,b=7,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,b=1,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,b=1,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,b=7,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=f16,n=256,m=5,r=4,b=7,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,b=1,v=0","support","0","no","SYCL" -"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,b=1,v=1","support","0","no","SYCL" -"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,b=7,v=0","support","0","no","SYCL" -"SYCL0","GET_ROWS","type=bf16,n=256,m=5,r=4,b=7,v=1","support","0","no","SYCL" -"SYCL0","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=1,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=1,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=7,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=7,v=1","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=q4_1,n=256,m=5,r=4,b=1,v=0","support","1","yes","SYCL" -"SYCL0","GET_ROWS","type=q4_1,n=256,m=5,r=4,b=1,v=1","support","1","yes","SYCL" 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+"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","0","no","SYCL" +"SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","0","no","SYCL" "SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL" "SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL" "SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL" @@ -2455,6 +4548,264 @@ "SYCL0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","0","no","SYCL" "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","SYCL" "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","0","no","SYCL" +"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL" +"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL" 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+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL" +"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","SYCL" @@ -2573,9 +4924,10 @@ "SYCL0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL" -"SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","SYCL" -"SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","SYCL" +"SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","SYCL" +"SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL" +"SYCL0","ARGMAX","type=f32,ne=[32,513,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[100,10,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[1024,10,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[1024,12,1,1]","support","1","yes","SYCL" @@ -2693,6 +5045,15 @@ "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","SYCL" @@ -2835,6 +5196,8 @@ "SYCL0","CPY","type_src=f16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2879,6 +5242,8 @@ "SYCL0","CPY","type_src=bf16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=bf16,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2923,6 +5288,8 @@ "SYCL0","CPY","type_src=f32,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=mxfp4,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=mxfp4,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2967,6 +5334,8 @@ "SYCL0","CPY","type_src=q5_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=q8_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=mxfp4,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q2_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CPY","type_src=q3_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" @@ -2999,6 +5368,10 @@ "SYCL0","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f16,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=i32,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=f32,type_dst=i32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=f32,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","0","no","SYCL" "SYCL0","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","SYCL" "SYCL0","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","SYCL" "SYCL0","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","SYCL" @@ -3061,6 +5434,10 @@ "SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" +"SYCL0","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" @@ -3165,6 +5542,10 @@ "SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" @@ -3217,6 +5598,10 @@ "SYCL0","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" +"SYCL0","MUL","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" +"SYCL0","SUB","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" +"SYCL0","DIV","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=2","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[1,2,1,1],nf=3","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=4","support","1","yes","SYCL" @@ -3224,44 +5609,69 @@ "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=6","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=7","support","1","yes","SYCL" "SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[2,2,2,2],nf=8","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[16,5,4,3],nr=[1,1,1,1],nf=16","support","1","yes","SYCL" "SYCL0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000","support","1","yes","SYCL" -"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL" +"SYCL0","SCALE","type=f32,ne=[100,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" +"SYCL0","SOFTCAP","type=f32,ne=[10,10,10,10],softcap=50.000000","support","1","yes","SYCL" "SYCL0","SILU_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","SYCL" -"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","0","no","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0","support","1","yes","SYCL" -"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=1,multi_add=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0","support","1","yes","SYCL" +"SYCL0","NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[1,1,1,1],eps=0.000001,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[1,1,1,1],eps=0.000001,broadcast=0,multi_add=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[511,1,1,1],eps=0.000001,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[511,1,1,1],eps=0.000001,broadcast=0,multi_add=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[1025,1,1,1],eps=0.000001,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[1025,1,1,1],eps=0.000001,broadcast=0,multi_add=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[8192,1,1,1],eps=0.000001,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[8192,1,1,1],eps=0.000001,broadcast=0,multi_add=1","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[16896,1,1,1],eps=0.000001,broadcast=0,multi_add=0","support","1","yes","SYCL" +"SYCL0","RMS_NORM_MUL_ADD","type=f32,ne=[16896,1,1,1],eps=0.000001,broadcast=0,multi_add=1","support","1","yes","SYCL" "SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","SYCL" @@ -3296,1243 +5706,1836 @@ "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","SYCL" "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","SYCL" "SYCL0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","SYCL" 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+"SYCL0","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","0","no","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","SYCL" "SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","0","no","SYCL" "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","SYCL" @@ -6006,9 +9325,13 @@ "SYCL0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[16,10,10,10],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[60,10,10,10],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[8,1,1,1],order=1","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[16,10,10,10],order=1","support","1","yes","SYCL" "SYCL0","ARGSORT","type=f32,ne=[60,10,10,10],order=1","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[1024,1,1,1],order=1","support","1","yes","SYCL" +"SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=0","support","1","yes","SYCL" @@ -6019,2115 +9342,6638 @@ "SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=1","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=257","support","0","no","SYCL" "SYCL0","SUM","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SUM_ROWS","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=0","support","0","no","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=0,slice=1","support","0","no","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=1","support","0","no","SYCL" "SYCL0","MEAN","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","SUM","type=f32,ne=[33,1,1,1]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[33,1,1,1],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[33,1,1,1]","support","0","no","SYCL" +"SYCL0","SUM","type=f32,ne=[33,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[33,1024,1,1],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","SUM","type=f32,ne=[33,256,1,1]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[33,256,1,1],permute=0,slice=0","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[33,256,1,1]","support","0","no","SYCL" +"SYCL0","MEAN","type=f32,ne=[32769,1,1,1]","support","0","no","SYCL" "SYCL0","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" "SYCL0","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" +"SYCL0","GROUP_NORM_MUL_ADD","type=f32,ne=[64,64,320,1],num_groups=4,eps=0.000010","support","1","yes","SYCL" +"SYCL0","GROUP_NORM_MUL_ADD","type=f32,ne=[9,9,1280,1],num_groups=4,eps=0.000010","support","1","yes","SYCL" "SYCL0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","SYCL" +"SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","SYCL" "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","SYCL" "SYCL0","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","0","no","SYCL" "SYCL0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" 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+"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","SYCL" "SYCL0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","TOPK_MOE","ne=[8,22,1,1],n_expert_used=4,with_norm=0","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[32,22,1,1],n_expert_used=8,with_norm=0","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[128,1,1,1],n_expert_used=128,with_norm=0","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[8,22,1,1],n_expert_used=4,with_norm=1","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[32,22,1,1],n_expert_used=8,with_norm=1","support","1","yes","SYCL" +"SYCL0","TOPK_MOE","ne=[128,1,1,1],n_expert_used=128,with_norm=1","support","1","yes","SYCL" diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 60c6b63d05..d948b00cc7 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -577,6 +577,10 @@ extern "C" { GGML_UNARY_OP_EXP, GGML_UNARY_OP_GELU_ERF, GGML_UNARY_OP_XIELU, + GGML_UNARY_OP_FLOOR, + GGML_UNARY_OP_CEIL, + GGML_UNARY_OP_ROUND, + GGML_UNARY_OP_TRUNC, GGML_UNARY_OP_COUNT, }; @@ -1151,6 +1155,46 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_floor( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_floor_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_ceil( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_ceil_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_round( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_round_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + + /** + * Truncates the fractional part of each element in the tensor (towards zero). + * For example: trunc(3.7) = 3.0, trunc(-2.9) = -2.0 + * Similar to std::trunc in C/C++. + */ + + GGML_API struct ggml_tensor * ggml_trunc( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_trunc_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + + + // xIELU activation function // x = x * (c_a(alpha_n) + c_b(alpha_p, beta) * sigmoid(beta * x)) + eps * (x > 0) // where c_a = softplus and c_b(a, b) = softplus(a) + b are constraining functions diff --git a/ggml/src/ggml-cann/acl_tensor.cpp b/ggml/src/ggml-cann/acl_tensor.cpp old mode 100755 new mode 100644 index 8ffac31dd6..8958ebcd78 --- a/ggml/src/ggml-cann/acl_tensor.cpp +++ b/ggml/src/ggml-cann/acl_tensor.cpp @@ -51,28 +51,31 @@ aclDataType ggml_cann_type_mapping(ggml_type type) { return ACL_DT_UNDEFINED; } -aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne, - size_t* nb, int64_t dims, aclFormat format, - size_t offset) { +aclTensor * ggml_cann_create_tensor(const ggml_tensor * tensor, + int64_t * ne, + size_t * nb, + int64_t dims, + aclFormat format, + size_t offset) { // If tensor is bcasted, Up to GGML_MAX_DIMS additional dimensions will be // added. int64_t acl_ne[GGML_MAX_DIMS * 2], acl_stride[GGML_MAX_DIMS * 2]; if (ne == nullptr) { for (int i = 0; i < GGML_MAX_DIMS; i++) { - acl_ne[i] = tensor->ne[i]; + acl_ne[i] = tensor->ne[i]; // The step size of acl is in elements. acl_stride[i] = tensor->nb[i] / ggml_element_size(tensor); } } else { // With bcast for (int i = 0; i < dims; i++) { - acl_ne[i] = ne[i]; + acl_ne[i] = ne[i]; acl_stride[i] = nb[i] / ggml_element_size(tensor); } } - int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims); + int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims); int64_t acl_storage_len = 1; for (int i = 0; i < final_dims; i++) { acl_storage_len += (acl_ne[i] - 1) * acl_stride[i]; @@ -84,15 +87,13 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne, std::reverse(acl_ne, acl_ne + final_dims); std::reverse(acl_stride, acl_stride + final_dims); - aclTensor* acl_tensor = aclCreateTensor( - acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride, - elem_offset, format, &acl_storage_len, 1, - tensor->data); + aclTensor * acl_tensor = aclCreateTensor(acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride, + elem_offset, format, &acl_storage_len, 1, tensor->data); return acl_tensor; } -bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) { +bool ggml_cann_need_bcast(const ggml_tensor * t0, const ggml_tensor * t1) { for (int i = 0; i < GGML_MAX_DIMS; i++) { if (t1->ne[i] != t0->ne[i] && t1->ne[i] != 1) { return true; @@ -101,15 +102,16 @@ bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) { return false; } -int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, - const ggml_tensor* src1, - int64_t* bcast_src0_ne, - int64_t* bcast_src1_ne, size_t* bcast_src0_nb, - size_t* bcast_src1_nb) { +int64_t ggml_cann_get_bcast_shape(const ggml_tensor * src0, + const ggml_tensor * src1, + int64_t * bcast_src0_ne, + int64_t * bcast_src1_ne, + size_t * bcast_src0_nb, + size_t * bcast_src1_nb) { GGML_ASSERT(ggml_can_repeat(src1, src0)); int bcast_dim_cnt = 0; for (int i = 0; i < GGML_MAX_DIMS; i++) { - int64_t nr = src0->ne[i] / src1->ne[i]; + int64_t nr = src0->ne[i] / src1->ne[i]; bcast_src0_ne[bcast_dim_cnt] = src0->ne[i] / nr; bcast_src1_ne[bcast_dim_cnt] = src1->ne[i]; bcast_src0_nb[bcast_dim_cnt] = src0->nb[i]; @@ -119,21 +121,26 @@ int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, // Need to add an extra dim. bcast_src0_ne[bcast_dim_cnt] = nr; bcast_src1_ne[bcast_dim_cnt] = 1; - bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] * - bcast_src0_ne[bcast_dim_cnt - 1]; - bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] * - bcast_src1_ne[bcast_dim_cnt - 1]; + bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] * bcast_src0_ne[bcast_dim_cnt - 1]; + bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] * bcast_src1_ne[bcast_dim_cnt - 1]; bcast_dim_cnt++; } } return bcast_dim_cnt; } -int64_t ggml_cann_get_mulmat_bcast_shape( - const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne, - const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb, - int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, - size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb) { +int64_t ggml_cann_get_mulmat_bcast_shape(const int64_t * input_ne, + const int64_t * weight_ne, + const int64_t * dst_ne, + const size_t * input_nb, + const size_t * weight_nb, + const size_t * dst_nb, + int64_t * bcast_input_ne, + int64_t * bcast_weight_ne, + int64_t * bcast_dst_ne, + size_t * bcast_input_nb, + size_t * bcast_weight_nb, + size_t * bcast_dst_nb) { // input and dst shoule in same shape, except first two dims. GGML_ASSERT(input_ne[2] == dst_ne[2]); GGML_ASSERT(input_ne[3] == dst_ne[3]); @@ -148,34 +155,30 @@ int64_t ggml_cann_get_mulmat_bcast_shape( // Do not use bcast in the first two dimensions because we only support // the bcast batch dimension. Just copy them. if (i < 2 || nr == 1) { - bcast_input_ne[bcast_dim_cnt] = input_ne[i]; + bcast_input_ne[bcast_dim_cnt] = input_ne[i]; bcast_weight_ne[bcast_dim_cnt] = weight_ne[i]; - bcast_dst_ne[bcast_dim_cnt] = dst_ne[i]; + bcast_dst_ne[bcast_dim_cnt] = dst_ne[i]; - bcast_input_nb[bcast_dim_cnt] = input_nb[i]; + bcast_input_nb[bcast_dim_cnt] = input_nb[i]; bcast_weight_nb[bcast_dim_cnt] = weight_nb[i]; - bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; + bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; bcast_dim_cnt++; } else { // Need to add an extra dim. - bcast_input_ne[bcast_dim_cnt] = nr; - bcast_dst_ne[bcast_dim_cnt] = nr; + bcast_input_ne[bcast_dim_cnt] = nr; + bcast_dst_ne[bcast_dim_cnt] = nr; bcast_weight_ne[bcast_dim_cnt] = 1; - bcast_input_nb[bcast_dim_cnt] = input_nb[i]; - bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; + bcast_input_nb[bcast_dim_cnt] = input_nb[i]; + bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; bcast_weight_nb[bcast_dim_cnt] = weight_nb[i]; bcast_dim_cnt++; - bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr; - bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr; + bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr; + bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr; bcast_weight_ne[bcast_dim_cnt] = weight_ne[i]; - bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] * - bcast_input_ne[bcast_dim_cnt - 1]; - bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] * - bcast_dst_ne[bcast_dim_cnt - 1]; - bcast_weight_nb[bcast_dim_cnt] = - bcast_weight_nb[bcast_dim_cnt - 1] * - bcast_weight_ne[bcast_dim_cnt - 1]; + bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] * bcast_input_ne[bcast_dim_cnt - 1]; + bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] * bcast_dst_ne[bcast_dim_cnt - 1]; + bcast_weight_nb[bcast_dim_cnt] = bcast_weight_nb[bcast_dim_cnt - 1] * bcast_weight_ne[bcast_dim_cnt - 1]; bcast_dim_cnt++; } } diff --git a/ggml/src/ggml-cann/acl_tensor.h b/ggml/src/ggml-cann/acl_tensor.h old mode 100755 new mode 100644 index 93f09937ef..cb17ebcc1b --- a/ggml/src/ggml-cann/acl_tensor.h +++ b/ggml/src/ggml-cann/acl_tensor.h @@ -62,10 +62,12 @@ aclDataType ggml_cann_type_mapping(ggml_type type); * @param offset Offset in bytes for the ACL tensor data. Defaults to 0. * @return Pointer to the created ACL tensor. */ -aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr, - size_t* nb = nullptr, int64_t dims = 0, - aclFormat format = ACL_FORMAT_ND, - size_t offset = 0); +aclTensor * ggml_cann_create_tensor(const ggml_tensor * tensor, + int64_t * ne = nullptr, + size_t * nb = nullptr, + int64_t dims = 0, + aclFormat format = ACL_FORMAT_ND, + size_t offset = 0); /** * @brief Template for creating an ACL tensor from provided parameters. typename TYPE @@ -87,12 +89,15 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = null * @param offset Offset in bytes for the ACL tensor data. Defaults to 0. * @return Pointer to the created ACL tensor. */ -template -aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, - TYPE type_size, int64_t* ne, TYPE* nb, - int64_t dims, - aclFormat format = ACL_FORMAT_ND, - size_t offset = 0) { +template +aclTensor * ggml_cann_create_tensor(void * data_ptr, + aclDataType dtype, + TYPE type_size, + int64_t * ne, + TYPE * nb, + int64_t dims, + aclFormat format = ACL_FORMAT_ND, + size_t offset = 0) { int64_t tmp_ne[GGML_MAX_DIMS * 2]; int64_t tmp_stride[GGML_MAX_DIMS * 2]; @@ -109,9 +114,8 @@ aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, std::reverse(tmp_ne, tmp_ne + dims); std::reverse(tmp_stride, tmp_stride + dims); - aclTensor* acl_tensor = - aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size, - format, &acl_storage_len, 1, data_ptr); + aclTensor * acl_tensor = + aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size, format, &acl_storage_len, 1, data_ptr); return acl_tensor; } @@ -132,7 +136,7 @@ aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, * to 1. If such a dimension is found, broadcasting is required to align t1 * with t0 for element-wise operations. */ -bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1); +bool ggml_cann_need_bcast(const ggml_tensor * t0, const ggml_tensor * t1); /** * @brief Computes broadcast shapes and strides for two ggml_tensors. @@ -187,19 +191,21 @@ bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1); * dim1 in a inserted dim, should add nb for dim1, * and all other nb moves to next in order. */ -int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1, - int64_t* bcast_ne_src0, int64_t* bcast_ne_src1, - size_t* bcast_nb_src0, size_t* bcast_nb_src1); +int64_t ggml_cann_get_bcast_shape(const ggml_tensor * src0, + const ggml_tensor * src1, + int64_t * bcast_ne_src0, + int64_t * bcast_ne_src1, + size_t * bcast_nb_src0, + size_t * bcast_nb_src1); // Bcast macro to avoid duplicate code. -#define BCAST_SHAPE(src0, src1) \ - int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2]; \ - int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2]; \ - size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2]; \ - size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2]; \ - int64_t bcast_dims = ggml_cann_get_bcast_shape( \ - src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, bcast_##src0##_nb, \ - bcast_##src1##_nb); +#define BCAST_SHAPE(src0, src1) \ + int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2]; \ + int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2]; \ + size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2]; \ + size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2]; \ + int64_t bcast_dims = ggml_cann_get_bcast_shape(src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, \ + bcast_##src0##_nb, bcast_##src1##_nb); #define BCAST_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims @@ -233,26 +239,31 @@ int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* sr * before cast dim. * @sa ggml_cann_get_bcast_shape */ -int64_t ggml_cann_get_mulmat_bcast_shape( - const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne, - const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb, - int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, - size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb); +int64_t ggml_cann_get_mulmat_bcast_shape(const int64_t * input_ne, + const int64_t * weight_ne, + const int64_t * dst_ne, + const size_t * input_nb, + const size_t * weight_nb, + const size_t * dst_nb, + int64_t * bcast_input_ne, + int64_t * bcast_weight_ne, + int64_t * bcast_dst_ne, + size_t * bcast_input_nb, + size_t * bcast_weight_nb, + size_t * bcast_dst_nb); // Bcast macro to avoid duplicate code. -#define BCAST_MUL_MAT_SHAPE(input, weight, dst) \ - int64_t bcast_##input##_ne[GGML_MAX_DIMS * 2]; \ - int64_t bcast_##weight##_ne[GGML_MAX_DIMS * 2]; \ - int64_t bcast_##dst##_ne[GGML_MAX_DIMS * 2]; \ - size_t bcast_##input##_nb[GGML_MAX_DIMS * 2]; \ - size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2]; \ - size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2]; \ - int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape( \ - input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, \ - bcast_##input##_ne, bcast_##weight##_ne, bcast_##dst##_ne, \ - bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb); +#define BCAST_MUL_MAT_SHAPE(input, weight, dst) \ + int64_t bcast_##input##_ne[GGML_MAX_DIMS * 2]; \ + int64_t bcast_##weight##_ne[GGML_MAX_DIMS * 2]; \ + int64_t bcast_##dst##_ne[GGML_MAX_DIMS * 2]; \ + size_t bcast_##input##_nb[GGML_MAX_DIMS * 2]; \ + size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2]; \ + size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2]; \ + int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape( \ + input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, bcast_##input##_ne, bcast_##weight##_ne, \ + bcast_##dst##_ne, bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb); -#define BCAST_MUL_MAT_PARAM(tensor) \ - bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims +#define BCAST_MUL_MAT_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims #endif // CANN_ACL_TENSOR_H diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp old mode 100755 new mode 100644 index 434023dd22..f030ea0136 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -86,9 +86,12 @@ #include "../ggml-common.h" - -void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclTensor ** acl_src0, - aclTensor ** acl_src1, aclTensor ** acl_dst) { +void bcast_shape(ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst, + aclTensor ** acl_src0, + aclTensor ** acl_src1, + aclTensor ** acl_dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_can_repeat(src1, src0)); // Need bcast if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) { @@ -103,40 +106,40 @@ void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclT } } -void ggml_cann_op_unary( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_op_unary(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); unary_op(ctx, acl_src, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst); } -void ggml_cann_op_unary_gated( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; +void ggml_cann_op_unary_gated(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_is_contiguous_1(src0)); GGML_ASSERT(ggml_is_contiguous_1(dst)); const int32_t swapped = ggml_get_op_params_i32(dst, 1); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclTensor *acl_src0 = nullptr, *acl_src1 = nullptr; - if(src1) { + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src0 = nullptr, *acl_src1 = nullptr; + if (src1) { GGML_ASSERT(ggml_is_contiguous_1(src1)); GGML_ASSERT(src0->type == src1->type); acl_src0 = ggml_cann_create_tensor(src0); acl_src1 = ggml_cann_create_tensor(src1); } else { - int64_t ne[] = {src0->ne[0] / 2, src0->ne[1], src0->ne[2], src0->ne[3]}; - size_t nb[] = {src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]}; - acl_src0 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, 0); + int64_t ne[] = { src0->ne[0] / 2, src0->ne[1], src0->ne[2], src0->ne[3] }; + size_t nb[] = { src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3] }; + acl_src0 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, 0); acl_src1 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, ne[0] * ggml_element_size(src0)); if (swapped) { std::swap(acl_src0, acl_src1); @@ -146,9 +149,7 @@ void ggml_cann_op_unary_gated( unary_op(ctx, acl_src0, acl_dst); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_dst, acl_src1); - ggml_cann_release_resources(ctx, acl_src0, acl_dst); - if(src1) - ggml_cann_release_resources(ctx, acl_src1); + ggml_cann_release_resources(ctx, acl_src0, acl_src1, acl_dst); } /** @@ -161,10 +162,12 @@ void ggml_cann_op_unary_gated( * @param repeat_array The array specifying the number of repetitions along each * dimension. */ -static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* repeat_array) { +static void aclnn_repeat(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * repeat_array) { // repeat tensor along each dim with repeat_array - aclIntArray* repeats = aclCreateIntArray(repeat_array, GGML_MAX_DIMS); + aclIntArray * repeats = aclCreateIntArray(repeat_array, GGML_MAX_DIMS); GGML_CANN_CALL_ACLNN_OP(ctx, Repeat, acl_src, repeats, acl_dst); ggml_cann_release_resources(ctx, repeats); @@ -183,61 +186,63 @@ static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param cast_data_type The target data type to which the source tensor will be * casted. */ -static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, aclDataType cast_data_type) { +static void aclnn_cast(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + aclDataType cast_data_type) { GGML_CANN_CALL_ACLNN_OP(ctx, Cast, acl_src, cast_data_type, acl_dst); } -void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_repeat(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(ggml_can_repeat(src, dst)); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - int64_t repeatsArray[] = {dst->ne[3] / src->ne[3], dst->ne[2] / src->ne[2], - dst->ne[1] / src->ne[1], dst->ne[0] / src->ne[0]}; + int64_t repeatsArray[] = { dst->ne[3] / src->ne[3], dst->ne[2] / src->ne[2], dst->ne[1] / src->ne[1], + dst->ne[0] / src->ne[0] }; aclnn_repeat(ctx, acl_src, acl_dst, repeatsArray); ggml_cann_release_resources(ctx, acl_src, acl_dst); } -void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst) { - float alphaValue = 1.0f; - aclScalar* alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - if (acl_dst != nullptr) +void aclnn_add(ggml_backend_cann_context & ctx, aclTensor * acl_src0, aclTensor * acl_src1, aclTensor * acl_dst) { + float alphaValue = 1.0f; + aclScalar * alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Add, acl_src0, acl_src1, alpha, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, acl_src0, acl_src1, alpha); + } ggml_cann_release_resources(ctx, alpha); } -void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst) { - float alphaValue = 1.0f; - aclScalar* alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - if (acl_dst != nullptr) +void aclnn_sub(ggml_backend_cann_context & ctx, aclTensor * acl_src0, aclTensor * acl_src1, aclTensor * acl_dst) { + float alphaValue = 1.0f; + aclScalar * alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Sub, acl_src0, acl_src1, alpha, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSub, acl_src0, acl_src1, alpha); + } ggml_cann_release_resources(ctx, alpha); } -void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst) { - if (acl_dst != nullptr) +void aclnn_mul(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_other, aclTensor * acl_dst) { + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Mul, acl_src, acl_other, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_src, acl_other); + } } -void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst) { - if (acl_dst != nullptr) +void aclnn_div(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_other, aclTensor * acl_dst) { + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src, acl_other, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDiv, acl_src, acl_other); + } } /** @@ -262,9 +267,12 @@ void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param inplace Flag indicating whether to perform the operation in-place on * `acl_src`. */ -static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src, - float scale, aclTensor* acl_dst, bool inplace) { - aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); +static void aclnn_muls(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + float scale, + aclTensor * acl_dst, + bool inplace) { + aclScalar * acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); if (inplace) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_src, acl_scale); } else { @@ -273,19 +281,18 @@ static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src, ggml_cann_release_resources(ctx, acl_scale); } -void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_leaky_relu(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float negative_slope; memcpy(&negative_slope, dst->op_params, sizeof(float)); - aclScalar* acl_negative_slope = - aclCreateScalar(&negative_slope, aclDataType::ACL_FLOAT); + aclScalar * acl_negative_slope = aclCreateScalar(&negative_slope, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, LeakyRelu, acl_src, acl_negative_slope, acl_dst); ggml_cann_release_resources(ctx, acl_negative_slope, acl_src, acl_dst); @@ -301,26 +308,27 @@ void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * stored. * @param concat_dim The dimension along which the tensors will be concatenated. */ -static void aclnn_concat(ggml_backend_cann_context& ctx, - aclTensorList* tensorList, aclTensor* acl_dst, - int64_t concat_dim) { +static void aclnn_concat(ggml_backend_cann_context & ctx, + aclTensorList * tensorList, + aclTensor * acl_dst, + int64_t concat_dim) { GGML_CANN_CALL_ACLNN_OP(ctx, Cat, tensorList, concat_dim, acl_dst); } -void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - aclTensor* acl_src1 = ggml_cann_create_tensor(src1); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); +void ggml_cann_concat(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + aclTensor * acl_src1 = ggml_cann_create_tensor(src1); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); const int32_t dim = ggml_get_op_params_i32(dst, 0); GGML_ASSERT(dim >= 0 && dim < 4); int32_t acl_dim = 3 - dim; - aclTensor* tensors[] = {acl_src0, acl_src1}; - aclTensorList* tensor_list = aclCreateTensorList(tensors, 2); + aclTensor * tensors[] = { acl_src0, acl_src1 }; + aclTensorList * tensor_list = aclCreateTensorList(tensors, 2); aclnn_concat(ctx, tensor_list, acl_dst, acl_dim); ggml_cann_release_resources(ctx, tensor_list, acl_dst); @@ -343,162 +351,157 @@ void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param step The step size between consecutive values. * @param n_elements The number of elements in the destination tensor. */ -static void aclnn_arange(ggml_backend_cann_context& ctx, aclTensor* acl_dst, - float start, float stop, float step, - int64_t n_elements) { - int64_t steps = (int64_t)std::ceil((stop - start) / step); +static void aclnn_arange(ggml_backend_cann_context & ctx, + aclTensor * acl_dst, + float start, + float stop, + float step, + int64_t n_elements) { + int64_t steps = (int64_t) std::ceil((stop - start) / step); GGML_ASSERT(n_elements == steps); - aclScalar* acl_start = aclCreateScalar(&start, aclDataType::ACL_FLOAT); - aclScalar* acl_end = aclCreateScalar(&stop, aclDataType::ACL_FLOAT); - aclScalar* acl_step = aclCreateScalar(&step, aclDataType::ACL_FLOAT); + aclScalar * acl_start = aclCreateScalar(&start, aclDataType::ACL_FLOAT); + aclScalar * acl_end = aclCreateScalar(&stop, aclDataType::ACL_FLOAT); + aclScalar * acl_step = aclCreateScalar(&step, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, Arange, acl_start, acl_end, acl_step, acl_dst); ggml_cann_release_resources(ctx, acl_start, acl_end, acl_step); } -void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +void ggml_cann_arange(ggml_backend_cann_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); int64_t n_elements = ggml_nelements(dst); - float start; - float stop; - float step; - memcpy(&start, (float*)dst->op_params + 0, sizeof(float)); - memcpy(&stop, (float*)dst->op_params + 1, sizeof(float)); - memcpy(&step, (float*)dst->op_params + 2, sizeof(float)); + float start; + float stop; + float step; + memcpy(&start, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&stop, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&step, (float *) dst->op_params + 2, sizeof(float)); aclnn_arange(ctx, acl_dst, start, stop, step, n_elements); ggml_cann_release_resources(ctx, acl_dst); } -void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_clamp(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; float min; float max; memcpy(&min, dst->op_params, sizeof(float)); - memcpy(&max, (float*)dst->op_params + 1, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - aclScalar* acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT); - aclScalar* acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT); + aclScalar * acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT); + aclScalar * acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, Clamp, acl_src, acl_min, acl_max, acl_dst); ggml_cann_release_resources(ctx, acl_min, acl_max, acl_src, acl_dst); } -void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_scale(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; // scale factor float v; memcpy(&v, dst->op_params, sizeof(float)); - aclScalar* scale = aclCreateScalar(&v, aclDataType::ACL_FLOAT); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclScalar * scale = aclCreateScalar(&v, aclDataType::ACL_FLOAT); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); GGML_CANN_CALL_ACLNN_OP(ctx, Muls, acl_src, scale, acl_dst); ggml_cann_release_resources(ctx, scale, acl_src, acl_dst); } -void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - enum ggml_sort_order order = (enum ggml_sort_order)dst->op_params[0]; +void ggml_cann_argsort(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; + enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - ggml_cann_pool_alloc temp_buffer_allocator( - ctx.pool(), ggml_nelements(dst) * sizeof(int64_t)); - void* buffer = temp_buffer_allocator.get(); - aclTensor* tmp_tensor = - ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), - dst->ne, dst->nb, GGML_MAX_DIMS); - GGML_CANN_CALL_ACLNN_OP(ctx, Argsort, acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), - tmp_tensor); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(int64_t)); + void * buffer = temp_buffer_allocator.get(); + aclTensor * tmp_tensor = + ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), dst->ne, dst->nb, GGML_MAX_DIMS); + GGML_CANN_CALL_ACLNN_OP(ctx, Argsort, acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), tmp_tensor); GGML_CANN_CALL_ACLNN_OP(ctx, Cast, tmp_tensor, ggml_cann_type_mapping(dst->type), acl_dst); ggml_cann_release_resources(ctx, acl_src, tmp_tensor, acl_dst); } -void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float eps; memcpy(&eps, dst->op_params, sizeof(float)); - std::vector normData = {dst->ne[0]}; - aclIntArray* norm = aclCreateIntArray(normData.data(), normData.size()); - GGML_CANN_CALL_ACLNN_OP(ctx, LayerNorm, acl_src, norm, nullptr, nullptr, - eps, acl_dst, nullptr, nullptr); + std::vector normData = { dst->ne[0] }; + aclIntArray * norm = aclCreateIntArray(normData.data(), normData.size()); + GGML_CANN_CALL_ACLNN_OP(ctx, LayerNorm, acl_src, norm, nullptr, nullptr, eps, acl_dst, nullptr, nullptr); ggml_cann_release_resources(ctx, norm, acl_src, acl_dst); } -void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); int n_groups = dst->op_params[0]; float eps; memcpy(&eps, dst->op_params + 1, sizeof(float)); - int64_t N = src->ne[3]; - int64_t C = src->ne[2]; + int64_t N = src->ne[3]; + int64_t C = src->ne[2]; int64_t HxW = src->ne[1] * src->ne[0]; - size_t type_size = ggml_type_size(src->type); - int64_t ne[] = {n_groups, N}; - size_t nb[] = {type_size, type_size * n_groups}; - size_t n_bytes = N * n_groups; + size_t type_size = ggml_type_size(src->type); + int64_t ne[] = { n_groups, N }; + size_t nb[] = { type_size, type_size * n_groups }; + size_t n_bytes = N * n_groups; ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes * 2); - void* buffer = temp_buffer_allocator.get(); - aclTensor* acl_mean_out = ggml_cann_create_tensor( - buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); - aclTensor* acl_rstd_out = ggml_cann_create_tensor( - (char*)buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); + void * buffer = temp_buffer_allocator.get(); + aclTensor * acl_mean_out = ggml_cann_create_tensor(buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); + aclTensor * acl_rstd_out = + ggml_cann_create_tensor((char *) buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); - GGML_CANN_CALL_ACLNN_OP(ctx, GroupNorm, acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, - acl_dst, acl_mean_out, acl_rstd_out); + GGML_CANN_CALL_ACLNN_OP(ctx, GroupNorm, acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, acl_dst, acl_mean_out, + acl_rstd_out); ggml_cann_release_resources(ctx, acl_src, acl_dst, acl_mean_out, acl_rstd_out); } -void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; +void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; - size_t nb1 = ((int32_t*)dst->op_params)[0]; - size_t nb2 = ((int32_t*)dst->op_params)[1]; - size_t nb3 = ((int32_t*)dst->op_params)[2]; - size_t offset = ((int32_t*)dst->op_params)[3]; - bool inplace = (bool)((int32_t*)dst->op_params)[4]; + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - size_t param_nb[] = {ggml_element_size(src0), nb1, nb2, nb3}; + size_t param_nb[] = { ggml_element_size(src0), nb1, nb2, nb3 }; - aclTensor* acl_dst = ggml_cann_create_tensor( - dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); - aclTensor* acl_src1 = ggml_cann_create_tensor(src1); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); + aclTensor * acl_src1 = ggml_cann_create_tensor(src1); - aclScalar* alpha = nullptr; - float alphaValue = 1.0f; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + aclScalar * alpha = nullptr; + float alphaValue = 1.0f; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); if (!inplace) { size_t cpy_size = ggml_nbytes(dst); - ggml_cann_async_memcpy(ctx, dst->data, src0->data, cpy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE); - aclTensor* acl_src0 = ggml_cann_create_tensor( - src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); + ggml_cann_async_memcpy(ctx, dst->data, src0->data, cpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE); + aclTensor * acl_src0 = ggml_cann_create_tensor(src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); GGML_CANN_CALL_ACLNN_OP(ctx, Add, acl_src0, acl_src1, alpha, acl_dst); ggml_cann_release_resources(ctx, acl_src0); @@ -518,39 +521,34 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param dim An array of dimension indices. * @param dim_size The number of dimensions. */ -static void aclnn_reduce_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst, - int64_t* dim, size_t dim_size) { +static void aclnn_reduce_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst, int64_t * dim, size_t dim_size) { GGML_ASSERT(dst->ne[0] == 1); - ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclIntArray* reduce_dims = aclCreateIntArray(dim, dim_size); + ggml_tensor * src = dst->src[0]; + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + aclIntArray * reduce_dims = aclCreateIntArray(dim, dim_size); - GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_src, reduce_dims, true, - ggml_cann_type_mapping(dst->type), acl_dst); + GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_src, reduce_dims, true, ggml_cann_type_mapping(dst->type), acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst, reduce_dims); } -void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - int64_t reduce_dims[] = {3}; +void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + int64_t reduce_dims[] = { 3 }; aclnn_reduce_sum(ctx, dst, reduce_dims, 1); } -void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - int64_t reduce_dims[] = {0, 1, 2, 3}; +void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + int64_t reduce_dims[] = { 0, 1, 2, 3 }; aclnn_reduce_sum(ctx, dst, reduce_dims, 4); } -void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = - ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); +void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; + aclTensor * acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - std::vector output_size{dst->ne[1], dst->ne[0]}; - auto output_size_array = aclCreateIntArray(output_size.data(), 2); + std::vector output_size{ dst->ne[1], dst->ne[0] }; + auto output_size_array = aclCreateIntArray(output_size.data(), 2); GGML_CANN_CALL_ACLNN_OP(ctx, UpsampleNearest2d, acl_src, output_size_array, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst, output_size_array); @@ -570,20 +568,22 @@ void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, * The size of the array should be twice the number of dimensions of the tensor. * @param value The value to be used for padding. The default value is 0.0. */ -static void aclnn_pad(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* paddings, - float value = 0.0f) { - aclIntArray* acl_pad = aclCreateIntArray(paddings, GGML_MAX_DIMS * 2); - aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); +static void aclnn_pad(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * paddings, + float value = 0.0f) { + aclIntArray * acl_pad = aclCreateIntArray(paddings, GGML_MAX_DIMS * 2); + aclScalar * acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, ConstantPadNd, acl_src, acl_pad, acl_value, acl_dst); ggml_cann_release_resources(ctx, acl_pad, acl_value); } -void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); +void ggml_cann_pad(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); // padding: value in the array means how much distance will be padding. // the position of elements in the array means which dirction to padding, @@ -598,7 +598,7 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const int32_t lp3 = ggml_get_op_params_i32(dst, 6); const int32_t rp3 = ggml_get_op_params_i32(dst, 7); - int64_t paddings[] = {lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3}; + int64_t paddings[] = { lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3 }; aclnn_pad(ctx, acl_src, acl_dst, paddings); ggml_cann_release_resources(ctx, acl_src, acl_dst); } @@ -615,46 +615,41 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param dst The destination tensor where the result will be stored. The source * tensor is referenced by `dst->src[0]`. */ -static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +static void ggml_cann_avg_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = - ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - const int32_t* opts = (const int32_t*)dst->op_params; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; + const int32_t * opts = (const int32_t *) dst->op_params; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; - std::vector kernel_dims = {k1, k0}; - std::vector stride_dims = {s1, s0}; - std::vector padding_avg_dims = {p1, p0}; // (padH, padW) + std::vector kernel_dims = { k1, k0 }; + std::vector stride_dims = { s1, s0 }; + std::vector padding_avg_dims = { p1, p0 }; // (padH, padW) - auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2); - auto* strides = aclCreateIntArray(stride_dims.data(), 2); - auto* paddings_avg = aclCreateIntArray(padding_avg_dims.data(), 2); + auto * kernel_size = aclCreateIntArray(kernel_dims.data(), 2); + auto * strides = aclCreateIntArray(stride_dims.data(), 2); + auto * paddings_avg = aclCreateIntArray(padding_avg_dims.data(), 2); - bool ceil_mode = false; - bool count_include_pad = true; - int64_t divisor_override = 0; - int8_t cube_math_type = 0; + bool ceil_mode = false; + bool count_include_pad = true; + int64_t divisor_override = 0; + int8_t cube_math_type = 0; #ifdef ASCEND_310P cube_math_type = 1; #endif - GGML_CANN_CALL_ACLNN_OP(ctx, AvgPool2d, acl_src, kernel_size, strides, paddings_avg, - ceil_mode, count_include_pad, divisor_override, - cube_math_type, acl_dst); - ggml_cann_release_resources(ctx, acl_src, acl_dst, kernel_size, strides, - paddings_avg); + GGML_CANN_CALL_ACLNN_OP(ctx, AvgPool2d, acl_src, kernel_size, strides, paddings_avg, ceil_mode, count_include_pad, + divisor_override, cube_math_type, acl_dst); + ggml_cann_release_resources(ctx, acl_src, acl_dst, kernel_size, strides, paddings_avg); } /** @@ -669,68 +664,61 @@ static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx, * @param dst The destination tensor where the result will be stored. The source * tensor is referenced by `dst->src[0]`. */ -static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +static void ggml_cann_max_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = - ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - const int32_t* opts = (const int32_t*)dst->op_params; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; + const int32_t * opts = (const int32_t *) dst->op_params; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; - int64_t temp_ne[] = {src->ne[0] + p0 * 2, src->ne[1] + p1 * 2, src->ne[2], - src->ne[3]}; - size_t temp_nb[GGML_MAX_DIMS]; + int64_t temp_ne[] = { src->ne[0] + p0 * 2, src->ne[1] + p1 * 2, src->ne[2], src->ne[3] }; + size_t temp_nb[GGML_MAX_DIMS]; temp_nb[0] = ggml_element_size(src); for (int i = 1; i < GGML_MAX_DIMS; i++) { temp_nb[i] = temp_nb[i - 1] * temp_ne[i - 1]; } - ggml_cann_pool_alloc temp_buffer_allocator( - ctx.pool(), ggml_nbytes(src) + p0 * 2 + p1 * 2 * src->nb[1]); - void* buffer = temp_buffer_allocator.get(); - aclTensor* tmp_tensor = ggml_cann_create_tensor( - buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, temp_nb, - GGML_MAX_DIMS, ACL_FORMAT_NCHW); + ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), ggml_nbytes(src) + p0 * 2 + p1 * 2 * src->nb[1]); + void * buffer = temp_buffer_allocator.get(); + aclTensor * tmp_tensor = ggml_cann_create_tensor(buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, temp_nb, + GGML_MAX_DIMS, ACL_FORMAT_NCHW); // pad: see padding in ggml_cann_pad() - int64_t paddings[] = {p0, p0, p1, p1, 0, 0, 0, 0}; - float value = -FLT_MAX; + int64_t paddings[] = { p0, p0, p1, p1, 0, 0, 0, 0 }; + float value = -FLT_MAX; aclnn_pad(ctx, acl_src, tmp_tensor, paddings, value); // max_pool - std::vector kernel_dims = {k1, k0}; - std::vector stride_dims = {s1, s0}; + std::vector kernel_dims = { k1, k0 }; + std::vector stride_dims = { s1, s0 }; // padding_max_dims: [dim0_start, dim0_end, dim1_start, dim1_end] - std::vector padding_max_dims = {0, 0, 0, 0}; - std::vector dilation_size = {1, 1}; - auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2); - auto* strides = aclCreateIntArray(stride_dims.data(), 2); - auto* paddings_max = aclCreateIntArray(padding_max_dims.data(), 4); - auto* dilations = aclCreateIntArray(dilation_size.data(), 2); + std::vector padding_max_dims = { 0, 0, 0, 0 }; + std::vector dilation_size = { 1, 1 }; + auto * kernel_size = aclCreateIntArray(kernel_dims.data(), 2); + auto * strides = aclCreateIntArray(stride_dims.data(), 2); + auto * paddings_max = aclCreateIntArray(padding_max_dims.data(), 4); + auto * dilations = aclCreateIntArray(dilation_size.data(), 2); - bool ceil_mode = false; + bool ceil_mode = false; int64_t auto_pads = 0; - GGML_CANN_CALL_ACLNN_OP(ctx, MaxPool, tmp_tensor, kernel_size, strides, auto_pads, - paddings_max, dilations, ceil_mode, acl_dst); - ggml_cann_release_resources(ctx, acl_src, acl_dst, tmp_tensor, kernel_size, - strides, paddings_max, dilations); + GGML_CANN_CALL_ACLNN_OP(ctx, MaxPool, tmp_tensor, kernel_size, strides, auto_pads, paddings_max, dilations, + ceil_mode, acl_dst); + ggml_cann_release_resources(ctx, acl_src, acl_dst, tmp_tensor, kernel_size, strides, paddings_max, dilations); } -void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - const int32_t* opts = (const int32_t*)dst->op_params; - enum ggml_op_pool op = static_cast(opts[0]); +void ggml_cann_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + const int32_t * opts = (const int32_t *) dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); switch (op) { case GGML_OP_POOL_AVG: ggml_cann_avg_pool2d(ctx, dst); @@ -754,17 +742,16 @@ void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param acl_src The source tensor from which data will be copied. * @param acl_dst The destination tensor where the data will be copied to. */ -static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst) { +static void cann_copy(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst, acl_src); } -void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; +void ggml_cann_dup(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; if (ggml_are_same_shape(src0, dst)) { - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); if (dst->type == src0->type) { cann_copy(ctx, acl_src, acl_dst); } else { @@ -772,22 +759,20 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { } ggml_cann_release_resources(ctx, acl_src, acl_dst); } else { - void* src_trans_buffer = src0->data; + void * src_trans_buffer = src0->data; ggml_cann_pool_alloc src_buffer_allocator; if (!ggml_is_contiguous(src0)) { - aclTensor* acl_src = ggml_cann_create_tensor(src0); - src_buffer_allocator.alloc(ctx.pool(), - ggml_nelements(src0) * ggml_type_size(src0->type)); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + src_buffer_allocator.alloc(ctx.pool(), ggml_nelements(src0) * ggml_type_size(src0->type)); src_trans_buffer = src_buffer_allocator.get(); size_t src_trans_nb[GGML_MAX_DIMS]; src_trans_nb[0] = ggml_type_size(src0->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; } - aclTensor* src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, src_trans_nb, - GGML_MAX_DIMS); + aclTensor * src_trans_tensor = + ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), src0->ne, src_trans_nb, GGML_MAX_DIMS); cann_copy(ctx, acl_src, src_trans_tensor); ggml_cann_release_resources(ctx, acl_src, src_trans_tensor); } @@ -798,10 +783,10 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { src_reshape_nb[i] = src_reshape_nb[i - 1] * dst->ne[i - 1]; } - aclTensor* trans_acl_src = ggml_cann_create_tensor(src_trans_buffer, - ggml_cann_type_mapping(src0->type),ggml_type_size(src0->type), - dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * trans_acl_src = + ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); if (dst->type == src0->type) { cann_copy(ctx, trans_acl_src, acl_dst); @@ -829,17 +814,20 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param type_size The size of each element in the tensor data type. * @return An ACL tensor initialized with zeros. */ -static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, - size_t n_bytes, int64_t* ne, int64_t dims, - aclDataType type, size_t type_size) { +static aclTensor * aclnn_zero(ggml_backend_cann_context & ctx, + void * buffer, + size_t n_bytes, + int64_t * ne, + int64_t dims, + aclDataType type, + size_t type_size) { size_t nb[GGML_MAX_DIMS]; nb[0] = type_size; for (int i = 1; i < dims; i++) { nb[i] = nb[i - 1] * ne[i - 1]; } - aclTensor* zero = - ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims); + aclTensor * zero = ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, zero); return zero; GGML_UNUSED(n_bytes); @@ -863,15 +851,18 @@ static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, * is 1.0). * @return An ACL tensor initialized with value. */ -static aclTensor* aclnn_values(ggml_backend_cann_context& ctx, void* buffer, - size_t n_bytes, int64_t* ne, int64_t dims, - aclDataType type, size_t type_size, - float value = 1.0f) { - aclTensor* acl_tensor = - aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size); - float alpha_host = 1.0f; - aclScalar* alpha = aclCreateScalar(&alpha_host, aclDataType::ACL_FLOAT); - aclScalar* other = aclCreateScalar(&value, aclDataType::ACL_FLOAT); +static aclTensor * aclnn_values(ggml_backend_cann_context & ctx, + void * buffer, + size_t n_bytes, + int64_t * ne, + int64_t dims, + aclDataType type, + size_t type_size, + float value = 1.0f) { + aclTensor * acl_tensor = aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size); + float alpha_host = 1.0f; + aclScalar * alpha = aclCreateScalar(&alpha_host, aclDataType::ACL_FLOAT); + aclScalar * other = aclCreateScalar(&value, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_tensor, other, alpha); return acl_tensor; } @@ -886,22 +877,20 @@ static aclTensor* aclnn_values(ggml_backend_cann_context& ctx, void* buffer, * @param scalar The scalar value used to fill the tensor. * @param acl_dst The destination tensor to be filled with the scalar value. */ -static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar, - aclTensor* acl_dst) { +static void aclnn_fill_scalar(ggml_backend_cann_context & ctx, float scalar, aclTensor * acl_dst) { auto acl_scalar = aclCreateScalar(&scalar, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst, acl_scalar); ggml_cann_release_resources(ctx, acl_scalar); } /** - * @brief Get or expand a cached float32 tensor filled with a scalar value. + * @brief Get or expand a cached tensor filled with a scalar value. * - * This function manages cached device memory for float32 tensors. If the current + * This function manages cached device memory for tensors. If the current * cache size is insufficient for the requested tensor shape, the old memory will - * be released and new memory will be allocated. The allocated buffer is then - * initialized either with zeros (when @p value == 0.0f) or with the given scalar - * value using CANN operations. Finally, an aclTensor object is created from the - * cached memory and returned. + * be released and new memory will be allocated. The allocated buffer is + * initialized with the given scalar value using CANN operations. + * Finally, an aclTensor object is created from the cached memory and returned. * * @param ctx The CANN backend context that manages device memory. * @param buffer A pointer to the cached device buffer (will be allocated @@ -910,25 +899,26 @@ static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar, * updated when the cache is expanded. * @param ne The tensor shape array (number of elements in each dimension). * @param nb The stride size for each dimension. + * @param dtype Data type of cached tensor. * @param dims The number of tensor dimensions. * @param value The scalar value used to fill the tensor (supports zero * initialization via memset or arbitrary values via fill_scalar). * @return An aclTensor pointer created from the cached buffer. */ -static aclTensor* get_f32_cache_acl_tensor( - ggml_backend_cann_context& ctx, - void** buffer, - int64_t &cache_element, - int64_t* ne, - size_t* nb, - int64_t dims, - float value) { +static aclTensor * get_cache_acl_tensor(ggml_backend_cann_context & ctx, + void ** buffer, + int64_t & cache_element, + int64_t * ne, + size_t * nb, + ggml_type dtype, + int64_t dims, + float value) { // Calculate total number of elements int64_t n_element = 1; for (int i = 0; i < dims; i++) { n_element *= ne[i]; } - size_t size = n_element * sizeof(float); + size_t size = n_element * ggml_type_size(dtype); // Allocate or expand cache if needed if (cache_element < n_element) { @@ -941,88 +931,77 @@ static aclTensor* get_f32_cache_acl_tensor( cache_element = n_element; // Initialize cache - if (value == 0.0f) { - ACL_CHECK(aclrtMemsetAsync(*buffer, size, 0, size, ctx.stream())); - } else { - int64_t pool_ne[1] = { n_element }; - size_t pool_nb[1] = { sizeof(float) }; - aclTensor* acl_value = ggml_cann_create_tensor( - *buffer, ACL_FLOAT, sizeof(float), pool_ne, pool_nb, 1); - aclnn_fill_scalar(ctx, 1, acl_value); - ggml_cann_release_resources(ctx, acl_value); - } + int64_t pool_ne[1] = { n_element }; + size_t pool_nb[1] = { ggml_type_size(dtype) }; + aclTensor * acl_value = + ggml_cann_create_tensor(*buffer, ggml_cann_type_mapping(dtype), ggml_type_size(dtype), pool_ne, pool_nb, 1); + aclnn_fill_scalar(ctx, value, acl_value); + ggml_cann_release_resources(ctx, acl_value); } - return ggml_cann_create_tensor(*buffer, ACL_FLOAT, sizeof(float), ne, nb, dims); + return ggml_cann_create_tensor(*buffer, ggml_cann_type_mapping(dtype), ggml_type_size(dtype), ne, nb, dims); } -void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_rms_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float eps; memcpy(&eps, dst->op_params, sizeof(float)); - // build gamma, one... + // build gamma. size_t acl_gamma_nb[GGML_MAX_DIMS]; - acl_gamma_nb[0] = sizeof(float); + // gamma's type is the same with dst. + acl_gamma_nb[0] = ggml_type_size(dst->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { acl_gamma_nb[i] = acl_gamma_nb[i - 1] * src->ne[i - 1]; } - aclTensor* acl_gamma = get_f32_cache_acl_tensor( - ctx, - &ctx.rms_norm_one_tensor_cache.cache, - ctx.rms_norm_one_tensor_cache.size, - src->ne, - acl_gamma_nb, - 1, // dims - 1.0f // value + aclTensor * acl_gamma = get_cache_acl_tensor(ctx, &ctx.rms_norm_one_tensor_cache.cache, + ctx.rms_norm_one_tensor_cache.size, src->ne, acl_gamma_nb, dst->type, + 1, // dims + 1.0f // value ); - // build rstd, zero... - int64_t acl_rstd_ne[] = {src->ne[1], src->ne[2], src->ne[3]}; - size_t acl_rstd_nb[GGML_MAX_DIMS - 1]; + // build rstd. + int64_t acl_rstd_ne[] = { src->ne[1], src->ne[2], src->ne[3] }; + size_t acl_rstd_nb[GGML_MAX_DIMS - 1]; + // rstd will always be F32. acl_rstd_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS - 1; i++) { acl_rstd_nb[i] = acl_rstd_nb[i - 1] * acl_rstd_ne[i - 1]; } - aclTensor* acl_rstd = get_f32_cache_acl_tensor( - ctx, - &ctx.rms_norm_zero_tensor_cache.cache, - ctx.rms_norm_zero_tensor_cache.size, - acl_rstd_ne, - acl_rstd_nb, - GGML_MAX_DIMS - 1, - 0.0f // value - ); + aclTensor * acl_rstd = + get_cache_acl_tensor(ctx, &ctx.rms_norm_zero_tensor_cache.cache, ctx.rms_norm_zero_tensor_cache.size, + acl_rstd_ne, acl_rstd_nb, GGML_TYPE_F32, GGML_MAX_DIMS - 1, + 0.0f // value + ); GGML_CANN_CALL_ACLNN_OP(ctx, RmsNorm, acl_src, acl_gamma, eps, acl_dst, acl_rstd); ggml_cann_release_resources(ctx, acl_src, acl_dst, acl_gamma, acl_rstd); } // TODO: performace is low. -void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, - float value) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_diag_mask(ggml_backend_cann_context & ctx, ggml_tensor * dst, float value) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - const int n_past = ((int32_t*)dst->op_params)[0]; + const int n_past = ((int32_t *) dst->op_params)[0]; ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), ggml_nbytes(src)); - void* buffer = one_tensor_allocator.get(); + void * buffer = one_tensor_allocator.get(); - aclTensor* mask_tensor = ggml_cann_create_tensor(buffer, ggml_cann_type_mapping(src->type), - ggml_type_size(src->type), src->ne, src->nb, GGML_MAX_DIMS); + aclTensor * mask_tensor = ggml_cann_create_tensor(buffer, ggml_cann_type_mapping(src->type), + ggml_type_size(src->type), src->ne, src->nb, GGML_MAX_DIMS); aclnn_fill_scalar(ctx, value, mask_tensor); - aclScalar* alpha = nullptr; - float alphaValue = 1.0f; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + aclScalar * alpha = nullptr; + float alphaValue = 1.0f; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceTriu, mask_tensor, n_past + 1); GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src, n_past + 1, acl_dst); @@ -1045,25 +1024,27 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, * tensor. * @param dims The number of dimensions in the tensor. */ -static void aclnn_permute(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* new_dim, uint64_t dims) { - aclIntArray* acl_dims = aclCreateIntArray(new_dim, dims); +static void aclnn_permute(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * new_dim, + uint64_t dims) { + aclIntArray * acl_dims = aclCreateIntArray(new_dim, dims); GGML_CANN_CALL_ACLNN_OP(ctx, Permute, acl_src, acl_dims, acl_dst); ggml_cann_release_resources(ctx, acl_dims); } -static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx, - ggml_tensor* dst, - ggml_tensor* src1, - aclTensor* tmp_cast_tensor, - aclTensor* tmp_im2col_tensor) { +static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context & ctx, + ggml_tensor * dst, + ggml_tensor * src1, + aclTensor * tmp_cast_tensor, + aclTensor * tmp_im2col_tensor) { // Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW] - int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]}; - size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]}; - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1); + int64_t dst_ne[] = { dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3] }; + size_t dst_nb[] = { dst->nb[0], dst->nb[1], dst->nb[3] }; + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1); - int64_t permute_dim[] = {0, 2, 1}; + int64_t permute_dim[] = { 0, 2, 1 }; if (src1->type != dst->type) { aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3); } else { @@ -1073,101 +1054,95 @@ static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx, ggml_cann_release_resources(ctx, acl_dst); } -static void ggml_cann_im2col_1d_post_process( - ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_tensor* src1, - aclTensor* tmp_cast_tensor, aclTensor* tmp_im2col_tensor, - const std::vector& im2col_op_params) { +static void ggml_cann_im2col_1d_post_process(ggml_backend_cann_context & ctx, + ggml_tensor * dst, + ggml_tensor * src1, + aclTensor * tmp_cast_tensor, + aclTensor * tmp_im2col_tensor, + const std::vector & im2col_op_params) { // get params - const int64_t KH = im2col_op_params[0]; - const int64_t KW = im2col_op_params[1]; - const int64_t IW = im2col_op_params[2]; - const int64_t IC = im2col_op_params[3]; - const int64_t N = im2col_op_params[4]; - const int64_t OH = im2col_op_params[5]; - const int64_t OW = im2col_op_params[6]; - const int64_t s0 = im2col_op_params[7]; - const int64_t p0 = im2col_op_params[8]; - const int64_t d0 = im2col_op_params[9]; + const int64_t KH = im2col_op_params[0]; + const int64_t KW = im2col_op_params[1]; + const int64_t IW = im2col_op_params[2]; + const int64_t IC = im2col_op_params[3]; + const int64_t N = im2col_op_params[4]; + const int64_t OH = im2col_op_params[5]; + const int64_t OW = im2col_op_params[6]; + const int64_t s0 = im2col_op_params[7]; + const int64_t p0 = im2col_op_params[8]; + const int64_t d0 = im2col_op_params[9]; const int64_t n_bytes_factor = im2col_op_params[10]; // Permute: [N, IC * KH * KW, OW * OH] -> // [N, OW * OH * n_bytes_factor, IC * KH * KW] ggml_cann_pool_alloc tmp_permute_allocator(ctx.pool()); tmp_permute_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor); - void* tmp_permute_buffer = tmp_permute_allocator.get(); + void * tmp_permute_buffer = tmp_permute_allocator.get(); - int64_t tmp_permute_ne[] = {IC * KH * KW, OW * OH * n_bytes_factor, N}; - size_t tmp_permute_nb[GGML_MAX_DIMS - 1]; + int64_t tmp_permute_ne[] = { IC * KH * KW, OW * OH * n_bytes_factor, N }; + size_t tmp_permute_nb[GGML_MAX_DIMS - 1]; tmp_permute_nb[0] = ggml_type_size(dst->type); for (int i = 1; i < GGML_MAX_DIMS - 1; i++) { tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1]; } - aclTensor* tmp_permute_tensor = ggml_cann_create_tensor( - tmp_permute_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_permute_ne, tmp_permute_nb, - GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + aclTensor * tmp_permute_tensor = + ggml_cann_create_tensor(tmp_permute_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_permute_ne, tmp_permute_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); - int64_t permute_dim[] = {0, 2, 1}; + int64_t permute_dim[] = { 0, 2, 1 }; if (src1->type != dst->type) { aclnn_permute(ctx, tmp_cast_tensor, tmp_permute_tensor, permute_dim, 3); } else { - aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor, permute_dim, - 3); + aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor, permute_dim, 3); } // number of times the kernel moves in W dimension const int n_step_w = (IW + 2 * p0 - d0 * (KW - 1) - 1) / s0 + 1; - size_t offset; - void *cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer; + size_t offset; + void * cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer; // memory copy with offset to restore 1D im2col from 2d if (IC > 1) { - offset = IC * KH * KW * n_step_w * ggml_type_size(dst->type); + offset = IC * KH * KW * n_step_w * ggml_type_size(dst->type); size_t size_cpy = KH * KW * ggml_type_size(dst->type); for (int c = 0; c < IC; c++) { - cur_permute_buffer = (char*)tmp_permute_buffer + offset + - KH * KW * c * ggml_type_size(dst->type); - cur_dst_buffer = (char*)dst->data + - c * KH * KW * n_step_w * ggml_type_size(dst->type); + cur_permute_buffer = (char *) tmp_permute_buffer + offset + KH * KW * c * ggml_type_size(dst->type); + cur_dst_buffer = (char *) dst->data + c * KH * KW * n_step_w * ggml_type_size(dst->type); for (int i = 0; i < n_step_w; i++) { - ggml_cann_async_memcpy(ctx, cur_dst_buffer, cur_permute_buffer, size_cpy, - ACL_MEMCPY_DEVICE_TO_DEVICE); - cur_dst_buffer = - (char*)cur_dst_buffer + KH * KW * ggml_type_size(dst->type); - cur_permute_buffer = (char*)cur_permute_buffer + - KH * KW * IC * ggml_type_size(dst->type); + ggml_cann_async_memcpy(ctx, cur_dst_buffer, cur_permute_buffer, size_cpy, ACL_MEMCPY_DEVICE_TO_DEVICE); + cur_dst_buffer = (char *) cur_dst_buffer + KH * KW * ggml_type_size(dst->type); + cur_permute_buffer = (char *) cur_permute_buffer + KH * KW * IC * ggml_type_size(dst->type); } } } else { - offset = KH * KW * n_step_w * - ggml_type_size(dst->type); // equal to ggml_nbytes(dst) - ggml_cann_async_memcpy(ctx, dst->data, (char*)tmp_permute_buffer + offset, offset, - ACL_MEMCPY_DEVICE_TO_DEVICE); + offset = KH * KW * n_step_w * ggml_type_size(dst->type); // equal to ggml_nbytes(dst) + ggml_cann_async_memcpy(ctx, dst->data, (char *) tmp_permute_buffer + offset, offset, + ACL_MEMCPY_DEVICE_TO_DEVICE); } ggml_cann_release_resources(ctx, tmp_permute_tensor); } -void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // kernel - ggml_tensor* src1 = dst->src[1]; // input +void ggml_cann_im2col(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // kernel + ggml_tensor * src1 = dst->src[1]; // input GGML_TENSOR_BINARY_OP_LOCALS; // aclnnIm2col only works on 2D. set s1, p1, d1 to 1 to perform 2D // im2col and do post-processing to restore it to 1D. - const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1; - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t s1 = is_2D ? ((const int32_t*)(dst->op_params))[1] : 1; - const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; - const int32_t p1 = is_2D ? ((const int32_t*)(dst->op_params))[3] : 1; - const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; - const int32_t d1 = is_2D ? ((const int32_t*)(dst->op_params))[5] : 1; + const bool is_2D = ((const int32_t *) (dst->op_params))[6] == 1; + const int32_t s0 = ((const int32_t *) (dst->op_params))[0]; + const int32_t s1 = is_2D ? ((const int32_t *) (dst->op_params))[1] : 1; + const int32_t p0 = ((const int32_t *) (dst->op_params))[2]; + const int32_t p1 = is_2D ? ((const int32_t *) (dst->op_params))[3] : 1; + const int32_t d0 = ((const int32_t *) (dst->op_params))[4]; + const int32_t d1 = is_2D ? ((const int32_t *) (dst->op_params))[5] : 1; - const int64_t N = ne13; + const int64_t N = ne13; const int64_t IC = ne12; const int64_t KH = ne01; const int64_t KW = ne00; @@ -1180,9 +1155,9 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const int64_t n_bytes_factor = is_2D ? 1 : 3; // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH * n_bytes_factor] - aclTensor* acl_src1 = ggml_cann_create_tensor(src1); - int64_t tmp_im2col_ne[] = {OW * OH * n_bytes_factor, IC * KH * KW, N}; - size_t tmp_im2col_nb[GGML_MAX_DIMS - 1]; + aclTensor * acl_src1 = ggml_cann_create_tensor(src1); + int64_t tmp_im2col_ne[] = { OW * OH * n_bytes_factor, IC * KH * KW, N }; + size_t tmp_im2col_nb[GGML_MAX_DIMS - 1]; tmp_im2col_nb[0] = ggml_type_size(src1->type); for (int i = 1; i < GGML_MAX_DIMS - 1; i++) { @@ -1192,31 +1167,27 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { // Calculate im2col. // If dst is f16, tmp_buffer is f32, we need alloc src.typesize * // dst.elemcount. - ggml_cann_pool_alloc im2col_allocator( - ctx.pool(), - ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor); - void* tmp_im2col_buffer = im2col_allocator.get(); + ggml_cann_pool_alloc im2col_allocator(ctx.pool(), ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor); + void * tmp_im2col_buffer = im2col_allocator.get(); - aclTensor* tmp_im2col_tensor = ggml_cann_create_tensor( - tmp_im2col_buffer, ggml_cann_type_mapping(src1->type), - ggml_type_size(src1->type), tmp_im2col_ne, tmp_im2col_nb, - GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + aclTensor * tmp_im2col_tensor = + ggml_cann_create_tensor(tmp_im2col_buffer, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type), + tmp_im2col_ne, tmp_im2col_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); - std::vector kernel_dims = {KH, KW}; - std::vector dilation_size = {d1, d0}; - std::vector padding_dims = {p1, p0}; - std::vector stride_dims = {s1, s0}; - auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2); - auto* dilations = aclCreateIntArray(dilation_size.data(), 2); - auto* paddings = aclCreateIntArray(padding_dims.data(), 2); - auto* strides = aclCreateIntArray(stride_dims.data(), 2); - GGML_CANN_CALL_ACLNN_OP(ctx, Im2col, acl_src1, kernel_size, dilations, - paddings, strides, tmp_im2col_tensor); + std::vector kernel_dims = { KH, KW }; + std::vector dilation_size = { d1, d0 }; + std::vector padding_dims = { p1, p0 }; + std::vector stride_dims = { s1, s0 }; + auto * kernel_size = aclCreateIntArray(kernel_dims.data(), 2); + auto * dilations = aclCreateIntArray(dilation_size.data(), 2); + auto * paddings = aclCreateIntArray(padding_dims.data(), 2); + auto * strides = aclCreateIntArray(stride_dims.data(), 2); + GGML_CANN_CALL_ACLNN_OP(ctx, Im2col, acl_src1, kernel_size, dilations, paddings, strides, tmp_im2col_tensor); // Cast if dst is f16. - aclTensor* tmp_cast_tensor = nullptr; + aclTensor * tmp_cast_tensor = nullptr; ggml_cann_pool_alloc tmp_cast_allocator(ctx.pool()); - void* tmp_cast_buffer = nullptr; + void * tmp_cast_buffer = nullptr; if (src1->type != dst->type) { tmp_cast_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor); tmp_cast_buffer = tmp_cast_allocator.get(); @@ -1226,26 +1197,22 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { temp_cast_nb[i] = temp_cast_nb[i - 1] * tmp_im2col_ne[i - 1]; } - tmp_cast_tensor = ggml_cann_create_tensor( - tmp_cast_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_im2col_ne, temp_cast_nb, - GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + tmp_cast_tensor = + ggml_cann_create_tensor(tmp_cast_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_im2col_ne, temp_cast_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor, ggml_cann_type_mapping(dst->type)); } // post-processing if (is_2D) { - ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor, - tmp_im2col_tensor); + ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor, tmp_im2col_tensor); } else { - std::vector im2col_op_params = { - KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor}; - ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor, - tmp_im2col_tensor, im2col_op_params); + std::vector im2col_op_params = { KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor }; + ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor, tmp_im2col_tensor, im2col_op_params); } - ggml_cann_release_resources(ctx, acl_src1, tmp_im2col_tensor, tmp_cast_tensor, - kernel_size, dilations, paddings, strides); + ggml_cann_release_resources(ctx, acl_src1, tmp_im2col_tensor, tmp_cast_tensor, kernel_size, dilations, paddings, + strides); } /** @@ -1261,136 +1228,123 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param ctx The context for the CANN backend operations. * @param acl_src The tensor on which the exponential function will be applied. */ -static void aclnn_exp(ggml_backend_cann_context& ctx, aclTensor* acl_src) { +static void aclnn_exp(ggml_backend_cann_context & ctx, aclTensor * acl_src) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceExp, acl_src); } -void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst) { - if(acl_dst == nullptr) { +void aclnn_cos(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + if (acl_dst == nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCos, acl_src); } else { GGML_CANN_CALL_ACLNN_OP(ctx, Cos, acl_src, acl_dst); } } -void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst) { - if(acl_dst == nullptr) { +void aclnn_sin(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + if (acl_dst == nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSin, acl_src); } else { GGML_CANN_CALL_ACLNN_OP(ctx, Sin, acl_src, acl_dst); } } -void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - const ggml_tensor* src = dst->src[0]; +void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - const int dim = dst->op_params[0]; + const int dim = dst->op_params[0]; const int max_period = dst->op_params[1]; - int half = dim / 2; + int half = dim / 2; - aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_src = ggml_cann_create_tensor(src); // arange: [0, ..., half) - float start = 0; - float stop = half; - float step = 1; + float start = 0; + float stop = half; + float step = 1; int64_t n_elements_arange = half; - int64_t tmp_arange_ne[] = {half}; - size_t tmp_arange_nb[] = {sizeof(dst->type)}; + int64_t tmp_arange_ne[] = { half }; + size_t tmp_arange_nb[] = { sizeof(dst->type) }; ggml_cann_pool_alloc arange_allocator(ctx.pool(), half * sizeof(dst->type)); - void* tmp_arange_buffer = arange_allocator.get(); - aclTensor* tmp_arange_tensor = ggml_cann_create_tensor( - tmp_arange_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_arange_ne, tmp_arange_nb, - GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + void * tmp_arange_buffer = arange_allocator.get(); + aclTensor * tmp_arange_tensor = + ggml_cann_create_tensor(tmp_arange_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_arange_ne, tmp_arange_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); aclnn_arange(ctx, tmp_arange_tensor, start, stop, step, n_elements_arange); // freq float freq_param = -logf(max_period) / half; - bool inplace = true; + bool inplace = true; aclnn_muls(ctx, tmp_arange_tensor, freq_param, nullptr, inplace); aclnn_exp(ctx, tmp_arange_tensor); // permute: src [0,1,2,3]->[0,1,3,2] - int64_t tmp_permute_ne[] = {src->ne[1], src->ne[0], src->ne[2], src->ne[3]}; - size_t tmp_permute_nb[GGML_MAX_DIMS]; + int64_t tmp_permute_ne[] = { src->ne[1], src->ne[0], src->ne[2], src->ne[3] }; + size_t tmp_permute_nb[GGML_MAX_DIMS]; tmp_permute_nb[0] = ggml_type_size(src->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1]; } ggml_cann_pool_alloc permute_allocator(ctx.pool(), ggml_nbytes(src)); - void* tmp_permute_buffer = permute_allocator.get(); - aclTensor* tmp_permute_tensor = ggml_cann_create_tensor( - tmp_permute_buffer, ggml_cann_type_mapping(src->type), - ggml_type_size(src->type), tmp_permute_ne, tmp_permute_nb, - GGML_MAX_DIMS, ACL_FORMAT_ND); - int64_t permute_dim[] = {0, 1, 3, 2}; - int64_t num_dims = 4; + void * tmp_permute_buffer = permute_allocator.get(); + aclTensor * tmp_permute_tensor = + ggml_cann_create_tensor(tmp_permute_buffer, ggml_cann_type_mapping(src->type), ggml_type_size(src->type), + tmp_permute_ne, tmp_permute_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + int64_t permute_dim[] = { 0, 1, 3, 2 }; + int64_t num_dims = 4; aclnn_permute(ctx, acl_src, tmp_permute_tensor, permute_dim, num_dims); // timestep * freq - int64_t tmp_mul_ne[] = {src->ne[1] * half, src->ne[0], src->ne[2], - src->ne[3]}; - size_t tmp_mul_nb[GGML_MAX_DIMS]; + int64_t tmp_mul_ne[] = { src->ne[1] * half, src->ne[0], src->ne[2], src->ne[3] }; + size_t tmp_mul_nb[GGML_MAX_DIMS]; tmp_mul_nb[0] = ggml_type_size(src->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { tmp_mul_nb[i] = tmp_mul_nb[i - 1] * tmp_mul_ne[i - 1]; } - int mul_nelements = - src->ne[1] * half * src->ne[0] * src->ne[2] * src->ne[3]; + int mul_nelements = src->ne[1] * half * src->ne[0] * src->ne[2] * src->ne[3]; - ggml_cann_pool_alloc mul_allocator( - ctx.pool(), mul_nelements * ggml_type_size(src->type)); - void* tmp_mul_buffer = mul_allocator.get(); - aclTensor* tmp_mul_tensor = ggml_cann_create_tensor( - tmp_mul_buffer, ggml_cann_type_mapping(src->type), - ggml_type_size(src->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, - ACL_FORMAT_ND); + ggml_cann_pool_alloc mul_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type)); + void * tmp_mul_buffer = mul_allocator.get(); + aclTensor * tmp_mul_tensor = + ggml_cann_create_tensor(tmp_mul_buffer, ggml_cann_type_mapping(src->type), ggml_type_size(src->type), + tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_mul(ctx, tmp_permute_tensor, tmp_arange_tensor, tmp_mul_tensor); // cos - ggml_cann_pool_alloc cos_allocator( - ctx.pool(), mul_nelements * ggml_type_size(src->type)); - void* tmp_cos_buffer = cos_allocator.get(); - aclTensor* tmp_cos_tensor = ggml_cann_create_tensor( - tmp_cos_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, - ACL_FORMAT_ND); + ggml_cann_pool_alloc cos_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type)); + void * tmp_cos_buffer = cos_allocator.get(); + aclTensor * tmp_cos_tensor = + ggml_cann_create_tensor(tmp_cos_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, tmp_mul_tensor, tmp_cos_tensor); // sin - ggml_cann_pool_alloc sin_allocator( - ctx.pool(), mul_nelements * ggml_type_size(src->type)); - void* tmp_sin_buffer = sin_allocator.get(); - aclTensor* tmp_sin_tensor = ggml_cann_create_tensor( - tmp_sin_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, - ACL_FORMAT_ND); + ggml_cann_pool_alloc sin_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type)); + void * tmp_sin_buffer = sin_allocator.get(); + aclTensor * tmp_sin_tensor = + ggml_cann_create_tensor(tmp_sin_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_sin(ctx, tmp_mul_tensor, tmp_sin_tensor); // concat - int64_t concat_dim = 3; - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclTensor* tensors[] = {tmp_cos_tensor, tmp_sin_tensor}; - aclTensorList* tensor_list = aclCreateTensorList(tensors, 2); + int64_t concat_dim = 3; + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + aclTensor * tensors[] = { tmp_cos_tensor, tmp_sin_tensor }; + aclTensorList * tensor_list = aclCreateTensorList(tensors, 2); aclnn_concat(ctx, tensor_list, acl_dst, concat_dim); // release // segmentation fault when delete both tensorList and his elements. - ggml_cann_release_resources(ctx, tensor_list, acl_src, tmp_arange_tensor, - tmp_permute_tensor, tmp_mul_tensor, acl_dst); + ggml_cann_release_resources(ctx, tensor_list, acl_src, tmp_arange_tensor, tmp_permute_tensor, tmp_mul_tensor, + acl_dst); } /** @@ -1409,8 +1363,7 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, * @param acl_exp The exponent tensor, each element of which is used to raise * the corresponding element in the destination tensor. */ -static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx, - aclTensor* acl_dst, aclTensor* acl_exp) { +static void aclnn_pow_tensor_tensor(ggml_backend_cann_context & ctx, aclTensor * acl_dst, aclTensor * acl_exp) { GGML_CANN_CALL_ACLNN_OP(ctx, InplacePowTensorTensor, acl_dst, acl_exp); } @@ -1435,25 +1388,29 @@ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx, * @param step Step size for the exponent increment. * @param dtype Data type for slope tensor. */ -static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_buffer, - float m, int64_t size, float start, float stop, float step, ggml_type dtype){ - aclDataType acl_type = ggml_cann_type_mapping(dtype); - size_t type_size = ggml_type_size(dtype); +static void aclnn_get_slope_inner(ggml_backend_cann_context & ctx, + void * slope_buffer, + float m, + int64_t size, + float start, + float stop, + float step, + ggml_type dtype) { + aclDataType acl_type = ggml_cann_type_mapping(dtype); + size_t type_size = ggml_type_size(dtype); - int64_t ne[] = {size}; - size_t nb[] = {type_size}; + int64_t ne[] = { size }; + size_t nb[] = { type_size }; ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * type_size); - void* arange_buffer = arange_allocator.get(); + void * arange_buffer = arange_allocator.get(); - aclTensor* arange_tensor = ggml_cann_create_tensor( - arange_buffer, acl_type, type_size, ne, nb, 1); + aclTensor * arange_tensor = ggml_cann_create_tensor(arange_buffer, acl_type, type_size, ne, nb, 1); aclnn_arange(ctx, arange_tensor, start, stop, step, size); - aclTensor* slope_tensor = ggml_cann_create_tensor( - slope_buffer, acl_type, type_size, ne, nb, 1); + aclTensor * slope_tensor = ggml_cann_create_tensor(slope_buffer, acl_type, type_size, ne, nb, 1); - aclScalar* sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT); + aclScalar * sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, sc, arange_tensor, slope_tensor); ggml_cann_release_resources(ctx, sc, arange_tensor, slope_tensor); @@ -1485,8 +1442,11 @@ static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_bu * @param dtype Data type for slope tensor. * */ -static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head, - void* slope_buffer, float max_bias, ggml_type dtype) { +static void aclnn_get_slope(ggml_backend_cann_context & ctx, + int64_t n_head, + void * slope_buffer, + float max_bias, + ggml_type dtype) { const int n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); float m0 = powf(2.0f, -(max_bias) / n_head_log2); @@ -1510,9 +1470,8 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head, end = 2 * ((n_head - 1) - n_head_log2) + 1; step = 2; count = n_head - n_head_log2; - aclnn_get_slope_inner( - ctx, (char *) slope_buffer + n_head_log2 * sizeof(float), - m1, count, start, end + 1, step, dtype); + aclnn_get_slope_inner(ctx, (char *) slope_buffer + n_head_log2 * sizeof(float), m1, count, start, end + 1, step, + dtype); } } @@ -1537,17 +1496,19 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head, * - Write data into dst_ptr using only the shape information of the dst tensor. * - `GGML_MAX_DIMS + 2` is used to extend tensor dimensions for broadcasting. */ -static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, - ggml_tensor* dst, void* dst_ptr, float max_bias) { - void* slope_buffer = nullptr; - void* bias_buffer = nullptr; +static void aclnn_add_alibi(ggml_backend_cann_context & ctx, + ggml_tensor * mask, + ggml_tensor * dst, + void * dst_ptr, + float max_bias) { + void * slope_buffer = nullptr; + void * bias_buffer = nullptr; if (max_bias > 0.0f) { - int64_t n_heads = dst->ne[2]; + int64_t n_heads = dst->ne[2]; ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(float)); slope_buffer = slope_allocator.get(); - ggml_cann_pool_alloc bias_allocator( - ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst)); + ggml_cann_pool_alloc bias_allocator(ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst)); bias_buffer = bias_allocator.get(); aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias, GGML_TYPE_F32); } @@ -1558,16 +1519,12 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, // broadcast the mask across rows int64_t mask_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], 1, mask->ne[3], 1 }; - size_t mask_nb[] = { - mask_nb[0] = mask->nb[0], mask_nb[1] = mask->nb[1], mask_nb[2] = mask->nb[2], - mask_nb[3] = mask->nb[2], mask_nb[4] = mask->nb[3], mask_nb[5] = mask->nb[3] - }; + size_t mask_nb[] = { mask_nb[0] = mask->nb[0], mask_nb[1] = mask->nb[1], mask_nb[2] = mask->nb[2], + mask_nb[3] = mask->nb[2], mask_nb[4] = mask->nb[3], mask_nb[5] = mask->nb[3] }; int64_t dst_ne[] = { dst->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], nr3 }; - size_t dst_nb[] = { - dst_nb[0] = dst->nb[0], dst_nb[1] = dst->nb[1], dst_nb[2] = dst->nb[2], - dst_nb[3] = dst->nb[2], dst_nb[4] = dst->nb[3], dst_nb[5] = dst->nb[3] - }; + size_t dst_nb[] = { dst_nb[0] = dst->nb[0], dst_nb[1] = dst->nb[1], dst_nb[2] = dst->nb[2], + dst_nb[3] = dst->nb[2], dst_nb[4] = dst->nb[3], dst_nb[5] = dst->nb[3] }; // slope is a 1 dim tensor, slope.ne2 == dst.ne2 int64_t slope_ne[] = { 1, 1, mask->ne[2], nr2, 1, 1 }; @@ -1577,17 +1534,13 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, slope_nb[i] = slope_nb[i - 1] * slope_ne[i - 1]; } - aclTensor* acl_slope = ggml_cann_create_tensor( - slope_buffer, ACL_FLOAT, sizeof(float), - slope_ne, slope_nb, GGML_MAX_DIMS + 2); - aclTensor* acl_mask = ggml_cann_create_tensor( - mask, mask_ne, mask_nb, GGML_MAX_DIMS + 2); + aclTensor * acl_slope = + ggml_cann_create_tensor(slope_buffer, ACL_FLOAT, sizeof(float), slope_ne, slope_nb, GGML_MAX_DIMS + 2); + aclTensor * acl_mask = ggml_cann_create_tensor(mask, mask_ne, mask_nb, GGML_MAX_DIMS + 2); // write data into dst_ptr using only the shape information of the dst tensor. - aclTensor* acl_dst = ggml_cann_create_tensor( - dst_ptr, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), dst_ne, dst_nb, - GGML_MAX_DIMS + 2); + aclTensor * acl_dst = ggml_cann_create_tensor(dst_ptr, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + dst_ne, dst_nb, GGML_MAX_DIMS + 2); if (max_bias > 0.0f) { int64_t bias_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], 1 }; @@ -1596,9 +1549,8 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, for (int i = 1; i < GGML_MAX_DIMS + 2; i++) { bias_nb[i] = bias_nb[i - 1] * bias_ne[i - 1]; } - aclTensor* bias_tensor = ggml_cann_create_tensor( - bias_buffer, ACL_FLOAT, sizeof(float), - bias_ne, bias_nb, GGML_MAX_DIMS + 2); + aclTensor * bias_tensor = + ggml_cann_create_tensor(bias_buffer, ACL_FLOAT, sizeof(float), bias_ne, bias_nb, GGML_MAX_DIMS + 2); aclnn_mul(ctx, acl_slope, acl_mask, bias_tensor); aclnn_add(ctx, acl_dst, bias_tensor); @@ -1627,17 +1579,16 @@ void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst) { * @param acl_dst The destination tensor where the softmax results will be * stored. */ -static void aclnn_softmax(ggml_backend_cann_context & ctx, - aclTensor* acl_src, int64_t dim, aclTensor * acl_dst) { +static void aclnn_softmax(ggml_backend_cann_context & ctx, aclTensor * acl_src, int64_t dim, aclTensor * acl_dst) { GGML_CANN_CALL_ACLNN_OP(ctx, Softmax, acl_src, dim, acl_dst); } void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; // mask + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; // mask - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float scale = 1.0f; float max_bias = 0.0f; @@ -1646,12 +1597,11 @@ void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); // input mul scale - aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); + aclScalar * acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); ggml_cann_pool_alloc src_tensor_allocator(ctx.pool(), ggml_nbytes(src0)); - void* src_tensor_buffer = src_tensor_allocator.get(); - aclTensor* softmax_tensor = ggml_cann_create_tensor( - src_tensor_buffer, ggml_cann_type_mapping(src0->type), - ggml_element_size(src0), src0->ne, src0->nb,GGML_MAX_DIMS); + void * src_tensor_buffer = src_tensor_allocator.get(); + aclTensor * softmax_tensor = ggml_cann_create_tensor(src_tensor_buffer, ggml_cann_type_mapping(src0->type), + ggml_element_size(src0), src0->ne, src0->nb, GGML_MAX_DIMS); aclnn_muls(ctx, acl_src0, scale, softmax_tensor, false); @@ -1683,29 +1633,31 @@ void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { * @param index The index tensor specifying the indices to select from the source tensor. * @param type The data type of the source and destination tensors. */ -static void aclnn_index_select_4d(ggml_backend_cann_context& ctx, - void* src_buffer,int64_t* src_ne, size_t* src_nb, - void* dst_buffer, int64_t* dst_ne, size_t* dst_nb, - ggml_tensor* index, ggml_type type) { +static void aclnn_index_select_4d(ggml_backend_cann_context & ctx, + void * src_buffer, + int64_t * src_ne, + size_t * src_nb, + void * dst_buffer, + int64_t * dst_ne, + size_t * dst_nb, + ggml_tensor * index, + ggml_type type) { for (int64_t i = 0; i < src_ne[3]; i++) { for (int64_t j = 0; j < src_ne[2]; j++) { // src - aclTensor* acl_src_tensor = ggml_cann_create_tensor( - (char*)src_buffer + i * src_nb[3] + j * src_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - src_ne, src_nb, 2); + aclTensor * acl_src_tensor = + ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2); // index - aclTensor* acl_index = ggml_cann_create_tensor( - (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], - ggml_cann_type_mapping(index->type), ggml_element_size(index), - index->ne, index->nb, 1); + aclTensor * acl_index = ggml_cann_create_tensor( + (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], + ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1); // out - aclTensor* acl_out = ggml_cann_create_tensor( - (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - dst_ne, dst_nb, 2); + aclTensor * acl_out = + ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2); GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, acl_src_tensor, 0, acl_index, acl_out); ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out); } @@ -1732,167 +1684,154 @@ static void aclnn_index_select_4d(ggml_backend_cann_context& ctx, * @param index The index tensor specifying target positions in the destination tensor. * @param type The data type of the source and destination tensors. */ -static void aclnn_index_copy_4d(ggml_backend_cann_context& ctx, - void* src_buffer,int64_t* src_ne, size_t* src_nb, - void* dst_buffer, int64_t* dst_ne, size_t* dst_nb, - ggml_tensor* index, ggml_type type) { +static void aclnn_index_copy_4d(ggml_backend_cann_context & ctx, + void * src_buffer, + int64_t * src_ne, + size_t * src_nb, + void * dst_buffer, + int64_t * dst_ne, + size_t * dst_nb, + ggml_tensor * index, + ggml_type type) { for (int64_t i = 0; i < src_ne[3]; i++) { for (int64_t j = 0; j < src_ne[2]; j++) { // src - aclTensor* acl_src_tensor = ggml_cann_create_tensor( - (char*)src_buffer + i * src_nb[3] + j * src_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - src_ne, src_nb, 2); + aclTensor * acl_src_tensor = + ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2); // index - aclTensor* acl_index = ggml_cann_create_tensor( - (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], - ggml_cann_type_mapping(index->type), ggml_element_size(index), - index->ne, index->nb, 1); + aclTensor * acl_index = ggml_cann_create_tensor( + (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], + ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1); // out - aclTensor* acl_out = ggml_cann_create_tensor( - (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - dst_ne, dst_nb, 2); + aclTensor * acl_out = + ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_out, 0, acl_index, acl_src_tensor); ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out); } } } -void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // src - ggml_tensor* src1 = dst->src[1]; // index +void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // src + ggml_tensor * src1 = dst->src[1]; // index + + GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); switch (src0->type) { - case GGML_TYPE_F32: { - aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); + case GGML_TYPE_F16: + case GGML_TYPE_F32: + if (src0->type == dst->type) { + aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1, + dst->type); + } else { + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * ggml_element_size(dst)); + void * src_trans_buffer = src_buffer_allocator.get(); + size_t src_trans_nb[GGML_MAX_DIMS]; + src_trans_nb[0] = dst->nb[0]; + for (int i = 1; i < GGML_MAX_DIMS; i++) { + src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + } + aclTensor * src_trans_tensor = + ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS); + aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type)); + aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1, + dst->type); + ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor); + } break; - } - case GGML_TYPE_F16: { - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - ggml_cann_pool_alloc src_buffer_allocator( - ctx.pool(), ggml_nelements(src0) * sizeof(float)); - void* src_trans_buffer = src_buffer_allocator.get(); - size_t src_trans_nb[GGML_MAX_DIMS]; - src_trans_nb[0] = sizeof(float); - for (int i = 1; i < GGML_MAX_DIMS; i++) { - src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + case GGML_TYPE_Q8_0: + { + // add 1 dim for bcast mul. + size_t weight_nb[GGML_MAX_DIMS + 1], scale_nb[GGML_MAX_DIMS + 1], dequant_nb[GGML_MAX_DIMS + 1]; + int64_t weight_ne[GGML_MAX_DIMS + 1], scale_ne[GGML_MAX_DIMS + 1], *dequant_ne; + int64_t scale_offset = 0; + // [3,4,5,64] -> [3,4,5,2,32] + weight_ne[0] = QK8_0; + weight_ne[1] = src0->ne[0] / QK8_0; + weight_nb[0] = sizeof(int8_t); + weight_nb[1] = weight_nb[0] * weight_ne[0]; + for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { + weight_ne[i] = src0->ne[i - 1]; + weight_nb[i] = weight_nb[i - 1] * weight_ne[i - 1]; + } + // [3,4,5,64] -> [3,4,5,2,1] + scale_ne[0] = 1; + scale_ne[1] = src0->ne[0] / QK8_0; + scale_nb[0] = sizeof(uint16_t); + scale_nb[1] = scale_nb[0] * scale_ne[0]; + for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { + scale_ne[i] = src0->ne[i - 1]; + scale_nb[i] = scale_nb[i - 1] * scale_ne[i - 1]; + } + // [3,4,5,64] -> [3,4,5,2,32] + dequant_ne = weight_ne; + dequant_nb[0] = ggml_type_size(dst->type); + for (int i = 1; i < GGML_MAX_DIMS + 1; i++) { + dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1]; + } + scale_offset = ggml_nelements(src0) * sizeof(int8_t); + ggml_cann_pool_alloc dequant_buffer_allocator(ctx.pool(), + ggml_nelements(src0) * ggml_type_size(dst->type)); + aclTensor * acl_weight_tensor = ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t), weight_ne, + weight_nb, GGML_MAX_DIMS + 1); + aclTensor * acl_scale_tensor = + ggml_cann_create_tensor(src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb, + GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset); + aclTensor * dequant_tensor = + ggml_cann_create_tensor(dequant_buffer_allocator.get(), ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), dequant_ne, dequant_nb, GGML_MAX_DIMS + 1); + aclnn_mul(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor); + dequant_nb[0] = ggml_type_size(dst->type); + dequant_ne = src0->ne; + for (int i = 1; i < GGML_MAX_DIMS; i++) { + dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1]; + } + aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(), dequant_ne, dequant_nb, dst->data, dst->ne, + dst->nb, src1, dst->type); + + ggml_cann_release_resources(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor); + break; } - aclTensor* src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ACL_FLOAT, ggml_type_size(dst->type), - src0->ne, src_trans_nb, GGML_MAX_DIMS); - aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type)); - aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); - ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor); - break; - } - case GGML_TYPE_Q8_0: { - // add 1 dim for bcast mul. - size_t weight_nb[GGML_MAX_DIMS + 1], scale_nb[GGML_MAX_DIMS + 1], - dequant_nb[GGML_MAX_DIMS + 1]; - int64_t weight_ne[GGML_MAX_DIMS + 1], scale_ne[GGML_MAX_DIMS + 1], - *dequant_ne; - int64_t scale_offset = 0; - - // [3,4,5,64] -> [3,4,5,2,32] - weight_ne[0] = QK8_0; - weight_ne[1] = src0->ne[0] / QK8_0; - weight_nb[0] = sizeof(int8_t); - weight_nb[1] = weight_nb[0] * weight_ne[0]; - for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { - weight_ne[i] = src0->ne[i - 1]; - weight_nb[i] = weight_nb[i - 1] * weight_ne[i - 1]; - } - - // [3,4,5,64] -> [3,4,5,2,1] - scale_ne[0] = 1; - scale_ne[1] = src0->ne[0] / QK8_0; - scale_nb[0] = sizeof(uint16_t); - scale_nb[1] = scale_nb[0] * scale_ne[0]; - for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { - scale_ne[i] = src0->ne[i - 1]; - scale_nb[i] = scale_nb[i - 1] * scale_ne[i - 1]; - } - - // [3,4,5,64] -> [3,4,5,2,32] - dequant_ne = weight_ne; - dequant_nb[0] = sizeof(float); - for (int i = 1; i < GGML_MAX_DIMS + 1; i++) { - dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1]; - } - - scale_offset = ggml_nelements(src0) * sizeof(int8_t); - ggml_cann_pool_alloc dequant_buffer_allocator( - ctx.pool(), ggml_nelements(src0) * sizeof(float)); - - aclTensor* acl_weight_tensor = ggml_cann_create_tensor( - src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb, - GGML_MAX_DIMS + 1); - aclTensor* acl_scale_tensor = ggml_cann_create_tensor( - src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb, - GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset); - aclTensor* dequant_tensor = ggml_cann_create_tensor( - dequant_buffer_allocator.get(), ACL_FLOAT, sizeof(float), - dequant_ne, dequant_nb, GGML_MAX_DIMS + 1); - - aclnn_mul(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor); - dequant_nb[0] = sizeof(float); - dequant_ne = src0->ne; - for (int i = 1; i < GGML_MAX_DIMS; i++) { - dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1]; - } - - aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(), - dequant_ne, dequant_nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); - - ggml_cann_release_resources(ctx, dequant_tensor); - break; - } default: GGML_ABORT("Unsupported tensor type for GGML_OP_GET_ROWS"); break; } } -void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // src - ggml_tensor* src1 = dst->src[1]; // index +void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // src + ggml_tensor * src1 = dst->src[1]; // index switch (dst->type) { - case GGML_TYPE_F32: { - aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); - break; - } - case GGML_TYPE_F16: { - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - ggml_cann_pool_alloc src_buffer_allocator( - ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t)); - void* src_trans_buffer = src_buffer_allocator.get(); - size_t src_trans_nb[GGML_MAX_DIMS]; - src_trans_nb[0] = sizeof(uint16_t); - for (int i = 1; i < GGML_MAX_DIMS; i++) { - src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + case GGML_TYPE_F32: + { + aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1, dst->type); + break; + } + case GGML_TYPE_F16: + { + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t)); + void * src_trans_buffer = src_buffer_allocator.get(); + size_t src_trans_nb[GGML_MAX_DIMS]; + src_trans_nb[0] = sizeof(uint16_t); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + } + aclTensor * src_trans_tensor = ggml_cann_create_tensor( + src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS); + aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type)); + aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1, + dst->type); + ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor); + break; } - aclTensor* src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type), - src0->ne, src_trans_nb, GGML_MAX_DIMS); - aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type)); - aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); - ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor); - break; - } default: GGML_ABORT("Unsupported tensor type for GGML_OP_SET_ROWS"); break; @@ -1914,12 +1853,13 @@ void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param repeats The number of times each element will be repeated. * @param output_size The size of the output tensor. */ -static void aclnn_repeat_interleave(ggml_backend_cann_context& ctx, - aclTensor* acl_src, aclTensor* acl_dst, - int64_t dim, int64_t repeats, - int64_t output_size) { - GGML_CANN_CALL_ACLNN_OP(ctx, RepeatInterleaveIntWithDim, acl_src, repeats, dim, - output_size, acl_dst); +static void aclnn_repeat_interleave(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t dim, + int64_t repeats, + int64_t output_size) { + GGML_CANN_CALL_ACLNN_OP(ctx, RepeatInterleaveIntWithDim, acl_src, repeats, dim, output_size, acl_dst); } /** @@ -1934,10 +1874,9 @@ static void aclnn_repeat_interleave(ggml_backend_cann_context& ctx, * @param dst The destination tensor where the result of the matrix * multiplication will be stored. */ -static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* weight = dst->src[0]; // weight - ggml_tensor* input = dst->src[1]; // input +static void ggml_cann_mat_mul_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * weight = dst->src[0]; // weight + ggml_tensor * input = dst->src[1]; // input // when weight ne2 or ne3 is 1, aclnnMatmulGetWorkspaceSize will auto // broadcast, when weight ne2 or ne3 is not 1, weight need repeat. @@ -1952,35 +1891,21 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, } } - aclTensor* acl_input_tensor = - ggml_cann_create_tensor(input, bcast_input_ne, bcast_input_nb, n_dims); - int64_t transpose_ne[] = {bcast_weight_ne[1], bcast_weight_ne[0], - bcast_weight_ne[2], bcast_weight_ne[3], - bcast_weight_ne[4], bcast_weight_ne[5]}; - size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0], - bcast_weight_nb[2], bcast_weight_nb[3], - bcast_weight_nb[4], bcast_weight_nb[5]}; - aclTensor* acl_weight_tensor; + aclTensor * acl_input_tensor = ggml_cann_create_tensor(input, bcast_input_ne, bcast_input_nb, n_dims); + int64_t transpose_ne[] = { bcast_weight_ne[1], bcast_weight_ne[0], bcast_weight_ne[2], + bcast_weight_ne[3], bcast_weight_ne[4], bcast_weight_ne[5] }; + size_t transpose_nb[] = { bcast_weight_nb[1], bcast_weight_nb[0], bcast_weight_nb[2], + bcast_weight_nb[3], bcast_weight_nb[4], bcast_weight_nb[5] }; + aclTensor * acl_weight_tensor; // Only check env once. static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on")); if (weight_to_nz && is_matmul_weight(weight)) { - int64_t acl_stride[2] = {1, transpose_ne[1]}; - - // Reverse ne. - std::reverse(transpose_ne, transpose_ne + n_dims); - - std::vector storageDims = {transpose_ne[0], transpose_ne[1]}; - - acl_weight_tensor = aclCreateTensor( - transpose_ne, n_dims, ggml_cann_type_mapping(weight->type), acl_stride, - 0, ACL_FORMAT_FRACTAL_NZ, storageDims.data(), 2, weight->data); + acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_FRACTAL_NZ); } else { - acl_weight_tensor = - ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_ND); + acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_ND); } - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims); switch (n_dims) { case 2: @@ -2012,11 +1937,9 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, * @param dst The destination tensor where the result of the matrix * multiplication will be stored. */ -static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, - ggml_tensor* dst, - const enum ggml_type type) { - ggml_tensor* src0 = dst->src[0]; // weight - ggml_tensor* src1 = dst->src[1]; // input +static void ggml_cann_mul_mat_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst, const enum ggml_type type) { + ggml_tensor * src0 = dst->src[0]; // weight + ggml_tensor * src1 = dst->src[1]; // input // The shape of the weight is NCHW. // Matrix multiplication uses HW dims. @@ -2030,56 +1953,52 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, } else { GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT"); } - float weight_nb[] = {src0->ne[0] * weight_elem_size, weight_elem_size}; + float weight_nb[] = { src0->ne[0] * weight_elem_size, weight_elem_size }; size_t weight_stride = src0->ne[1] * src0->ne[0] * weight_elem_size; - size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3]; + size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3]; // scale stored at the end of weight. Also need transpose. size_t scale_elem_size = sizeof(uint16_t); - size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size, - scale_elem_size}; - size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; - char* scale_offset = (char*)src0->data + weight_size; + size_t scale_nb[] = { src0->ne[0] / QK8_0 * scale_elem_size, scale_elem_size }; + size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; + char * scale_offset = (char *) src0->data + weight_size; // input - size_t input_elem_size = sizeof(uint16_t); - int64_t input_ne[] = {src1->ne[0], src1->ne[1]}; - size_t input_nb[] = {input_elem_size, input_ne[0] * input_elem_size}; - size_t input_stride = input_ne[0] * input_ne[1] * input_elem_size; + size_t input_elem_size = sizeof(uint16_t); + int64_t input_ne[] = { src1->ne[0], src1->ne[1] }; + size_t input_nb[] = { input_elem_size, input_ne[0] * input_elem_size }; + size_t input_stride = input_ne[0] * input_ne[1] * input_elem_size; ggml_cann_pool_alloc input_alloctor(ctx.pool()); - void* input_buffer = src1->data; + void * input_buffer = src1->data; // case in if (src1->type != GGML_TYPE_F16) { - aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1); - input_buffer = - input_alloctor.alloc(ggml_nelements(src1) * input_elem_size); + aclTensor * acl_src1_tensor = ggml_cann_create_tensor(src1); + input_buffer = input_alloctor.alloc(ggml_nelements(src1) * input_elem_size); - int64_t* input_cast_ne = src1->ne; - size_t input_cast_nb[GGML_MAX_DIMS]; + int64_t * input_cast_ne = src1->ne; + size_t input_cast_nb[GGML_MAX_DIMS]; input_cast_nb[0] = sizeof(uint16_t); for (int i = 1; i < GGML_MAX_DIMS; i++) { input_cast_nb[i] = input_cast_nb[i - 1] * input_cast_ne[i - 1]; } - aclTensor* acl_input_tensor = ggml_cann_create_tensor( - input_buffer, ACL_FLOAT16, input_elem_size, input_cast_ne, - input_cast_nb, GGML_MAX_DIMS); + aclTensor * acl_input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, input_elem_size, + input_cast_ne, input_cast_nb, GGML_MAX_DIMS); aclnn_cast(ctx, acl_src1_tensor, acl_input_tensor, ACL_FLOAT16); ggml_cann_release_resources(ctx, acl_input_tensor, acl_src1_tensor); } // output - size_t output_elem_size = sizeof(uint16_t); - size_t output_nb[] = {output_elem_size, dst->ne[0] * output_elem_size}; + size_t output_elem_size = sizeof(uint16_t); + size_t output_nb[] = { output_elem_size, dst->ne[0] * output_elem_size }; ggml_cann_pool_alloc output_allocator(ctx.pool()); - void* output_buffer = - output_allocator.alloc(ggml_nelements(dst) * output_elem_size); - size_t output_stride = dst->ne[0] * dst->ne[1] * output_elem_size; + void * output_buffer = output_allocator.alloc(ggml_nelements(dst) * output_elem_size); + size_t output_stride = dst->ne[0] * dst->ne[1] * output_elem_size; // aclnn - int64_t max_elem_size = 65535; - int64_t split_size = (src0->ne[1] / max_elem_size) + 1; + int64_t max_elem_size = 65535; + int64_t split_size = (src0->ne[1] / max_elem_size) + 1; ggml_cann_pool_alloc workspace_allocator(ctx.pool()); for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) { for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) { @@ -2089,71 +2008,57 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, int64_t batch1 = (n1 * src1->ne[2]) + c1; int64_t batch0 = (n0 * src0->ne[2]) + c0; - aclTensor* acl_input_tensor = ggml_cann_create_tensor( - (char*)input_buffer + batch1 * input_stride, ACL_FLOAT16, - input_elem_size, input_ne, input_nb, 2); + aclTensor * acl_input_tensor = ggml_cann_create_tensor((char *) input_buffer + batch1 * input_stride, + ACL_FLOAT16, input_elem_size, input_ne, input_nb, 2); // first split int64_t weight_ne_offset = 0; - int64_t weight_ne[2] = { - max_elem_size > src0->ne[1] ? src0->ne[1] : max_elem_size, - src0->ne[0]}; - int64_t scale_ne_offset = 0; - int64_t scale_ne[2] = {weight_ne[0], weight_ne[1] / QK8_0}; + int64_t weight_ne[2] = { max_elem_size > src0->ne[1] ? src0->ne[1] : max_elem_size, src0->ne[0] }; + int64_t scale_ne_offset = 0; + int64_t scale_ne[2] = { weight_ne[0], weight_ne[1] / QK8_0 }; int64_t output_ne_offset = 0; - int64_t output_ne[2] = {weight_ne[0], dst->ne[1]}; + int64_t output_ne[2] = { weight_ne[0], dst->ne[1] }; - aclTensor* acl_weight_tensor = ggml_cann_create_tensor( - (char*)src0->data + batch0 * weight_stride, - ggml_cann_type_mapping(type), weight_elem_size, weight_ne, - weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); - aclTensor* acl_scale_tensor = ggml_cann_create_tensor( - scale_offset + batch0 * scale_stride, ACL_FLOAT16, - scale_elem_size, scale_ne, scale_nb, 2, ACL_FORMAT_ND, - scale_ne_offset); - aclTensor* acl_output_tensor = ggml_cann_create_tensor( - (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, - output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, - output_ne_offset); + aclTensor * acl_weight_tensor = + ggml_cann_create_tensor((char *) src0->data + batch0 * weight_stride, ggml_cann_type_mapping(type), + weight_elem_size, weight_ne, weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); + aclTensor * acl_scale_tensor = + ggml_cann_create_tensor(scale_offset + batch0 * scale_stride, ACL_FLOAT16, scale_elem_size, scale_ne, + scale_nb, 2, ACL_FORMAT_ND, scale_ne_offset); + aclTensor * acl_output_tensor = + ggml_cann_create_tensor((char *) output_buffer + batch1 * output_stride, ACL_FLOAT16, output_elem_size, + output_ne, output_nb, 2, ACL_FORMAT_ND, output_ne_offset); int64_t antiquantGroupSize = 0; if (src0->ne[0] > QK8_0) { antiquantGroupSize = QK8_0; } - GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, - acl_weight_tensor, acl_scale_tensor, nullptr, - nullptr, nullptr, nullptr, antiquantGroupSize, - acl_output_tensor); + GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, acl_weight_tensor, + acl_scale_tensor, nullptr, nullptr, nullptr, nullptr, antiquantGroupSize, + acl_output_tensor); ggml_cann_release_resources(ctx, acl_weight_tensor, acl_scale_tensor, acl_output_tensor); // other splits for (int64_t split = 1; split < split_size; split++) { - weight_ne_offset += - weight_elem_size * weight_ne[0] * weight_ne[1]; - weight_ne[0] = max_elem_size * (split + 1) > src0->ne[1] - ? src0->ne[1] - (max_elem_size * split) - : max_elem_size; + weight_ne_offset += weight_elem_size * weight_ne[0] * weight_ne[1]; + weight_ne[0] = + max_elem_size * (split + 1) > src0->ne[1] ? src0->ne[1] - (max_elem_size * split) : max_elem_size; scale_ne_offset += scale_elem_size * scale_ne[0] * scale_ne[1]; scale_ne[0] = weight_ne[0]; - output_ne_offset += - output_elem_size * output_ne[0] * output_ne[1]; + output_ne_offset += output_elem_size * output_ne[0] * output_ne[1]; output_ne[0] = weight_ne[0]; - acl_weight_tensor = ggml_cann_create_tensor( - (char*)src0->data + batch0 * weight_stride, - ggml_cann_type_mapping(type), weight_elem_size, weight_ne, - weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); - acl_scale_tensor = ggml_cann_create_tensor( - scale_offset + batch0 * scale_stride, ACL_FLOAT16, - scale_elem_size, scale_ne, scale_nb, 2, ACL_FORMAT_ND, - scale_ne_offset); - acl_output_tensor = ggml_cann_create_tensor( - (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, - output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, - output_ne_offset); - GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, - acl_weight_tensor, acl_scale_tensor, nullptr, - nullptr, nullptr, nullptr, antiquantGroupSize, - acl_output_tensor); + acl_weight_tensor = + ggml_cann_create_tensor((char *) src0->data + batch0 * weight_stride, ggml_cann_type_mapping(type), + weight_elem_size, weight_ne, weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); + acl_scale_tensor = + ggml_cann_create_tensor(scale_offset + batch0 * scale_stride, ACL_FLOAT16, scale_elem_size, + scale_ne, scale_nb, 2, ACL_FORMAT_ND, scale_ne_offset); + acl_output_tensor = + ggml_cann_create_tensor((char *) output_buffer + batch1 * output_stride, ACL_FLOAT16, + output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, output_ne_offset); + GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, acl_weight_tensor, + acl_scale_tensor, nullptr, nullptr, nullptr, nullptr, antiquantGroupSize, + acl_output_tensor); ggml_cann_release_resources(ctx, acl_weight_tensor, acl_scale_tensor, acl_output_tensor); } @@ -2163,24 +2068,23 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, // cast out if (dst->type != GGML_TYPE_F16) { - int64_t* output_cast_ne = dst->ne; - size_t output_cast_nb[GGML_MAX_DIMS]; + int64_t * output_cast_ne = dst->ne; + size_t output_cast_nb[GGML_MAX_DIMS]; output_cast_nb[0] = sizeof(uint16_t); for (int i = 1; i < GGML_MAX_DIMS; i++) { output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1]; } - aclTensor* acl_output_tensor = ggml_cann_create_tensor( - output_buffer, ACL_FLOAT16, output_elem_size, output_cast_ne, - output_cast_nb, GGML_MAX_DIMS); - aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); + aclTensor * acl_output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size, + output_cast_ne, output_cast_nb, GGML_MAX_DIMS); + aclTensor * acl_dst_tensor = ggml_cann_create_tensor(dst); aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type)); ggml_cann_release_resources(ctx, acl_output_tensor, acl_dst_tensor); } } -void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +void ggml_cann_mul_mat(ggml_backend_cann_context & ctx, ggml_tensor * dst) { const enum ggml_type type = dst->src[0]->type; switch (type) { case GGML_TYPE_F32: @@ -2213,10 +2117,13 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param dims An array specifying the dimensions along which elements are * shifted. */ -static void aclnn_roll(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* shifts, int64_t* dims) { - aclIntArray* acl_shifts = aclCreateIntArray(shifts, 1); - aclIntArray* acl_dims = aclCreateIntArray(dims, 1); +static void aclnn_roll(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * shifts, + int64_t * dims) { + aclIntArray * acl_shifts = aclCreateIntArray(shifts, 1); + aclIntArray * acl_dims = aclCreateIntArray(dims, 1); GGML_CANN_CALL_ACLNN_OP(ctx, Roll, acl_src, acl_shifts, acl_dims, acl_dst); ggml_cann_release_resources(ctx, acl_shifts, acl_dims); } @@ -2234,12 +2141,14 @@ static void aclnn_roll(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param index_num The number of positions specified in the index array. * @param value The scalar value used to fill the specified positions. */ -static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx, - aclTensor* acl_src, int64_t dim, - int64_t* index, int64_t index_num, - float value) { - aclIntArray* acl_index = aclCreateIntArray(index, index_num); - aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); +static void aclnn_index_fill_tensor(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + int64_t dim, + int64_t * index, + int64_t index_num, + float value) { + aclIntArray * acl_index = aclCreateIntArray(index, index_num); + aclScalar * acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexFillTensor, acl_src, dim, acl_index, acl_value); ggml_cann_release_resources(ctx, acl_index, acl_value); } @@ -2274,85 +2183,82 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx, * @param is_neox Whether to use Neox-style repeat strategy * (dim expansion vs repeat_interleave). */ -static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, - float* corr_dims, float ext_factor, - float theta_scale, float freq_scale, - float attn_factor, bool is_neox) { - ggml_tensor* src0 = dst->src[0]; // input - ggml_tensor* src1 = dst->src[1]; // position - ggml_tensor* src2 = dst->src[2]; // freq_factors +static void aclnn_cache_init(ggml_backend_cann_context & ctx, + ggml_tensor * dst, + float * corr_dims, + float ext_factor, + float theta_scale, + float freq_scale, + float attn_factor, + bool is_neox) { + ggml_tensor * src0 = dst->src[0]; // input + ggml_tensor * src1 = dst->src[1]; // position + ggml_tensor * src2 = dst->src[2]; // freq_factors - if(src2 == nullptr && ctx.rope_cache.cached - && ctx.rope_cache.ext_factor == ext_factor - && ctx.rope_cache.theta_scale == theta_scale - && ctx.rope_cache.freq_scale == freq_scale - && ctx.rope_cache.attn_factor == attn_factor - && ctx.rope_cache.is_neox == is_neox) { + if (src2 == nullptr && ctx.rope_cache.cached && ctx.rope_cache.ext_factor == ext_factor && + ctx.rope_cache.theta_scale == theta_scale && ctx.rope_cache.freq_scale == freq_scale && + ctx.rope_cache.attn_factor == attn_factor && ctx.rope_cache.is_neox == is_neox) { // use cache. return; } int64_t theta_scale_length = src0->ne[0] / 2; - int64_t theta_scale_ne[] = {theta_scale_length, 1, 1, 1}; - size_t theta_scale_nb[] = {sizeof(float), sizeof(float), sizeof(float), - theta_scale_length * sizeof(float)}; + int64_t theta_scale_ne[] = { theta_scale_length, 1, 1, 1 }; + size_t theta_scale_nb[] = { sizeof(float), sizeof(float), sizeof(float), theta_scale_length * sizeof(float) }; GGML_ASSERT(src1->type == GGML_TYPE_I32); int64_t position_length = src1->ne[0]; - int64_t position_ne[] = {1, 1, position_length, 1}; - size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t), sizeof(int32_t), - sizeof(int32_t) * position_length}; + int64_t position_ne[] = { 1, 1, position_length, 1 }; + size_t position_nb[] = { sizeof(int32_t), sizeof(int32_t), sizeof(int32_t), sizeof(int32_t) * position_length }; - int64_t theta_ne[] = {theta_scale_length, 1, position_length, 1}; - size_t theta_nb[GGML_MAX_DIMS]; + int64_t theta_ne[] = { theta_scale_length, 1, position_length, 1 }; + size_t theta_nb[GGML_MAX_DIMS]; theta_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1]; } // theta_scale arange, [0,1,...,ne00/2 - 1] - aclTensor* acl_theta_scale_tensor = nullptr; + aclTensor * acl_theta_scale_tensor = nullptr; // cache theta scale if (ctx.rope_cache.theta_scale_length != theta_scale_length || // theta_scale and freq_scale should not change during the current token inference process, // so we can directly use == here instead of comparing the absolute difference. - ctx.rope_cache.theta_scale != theta_scale || - ctx.rope_cache.freq_scale != freq_scale) { - + ctx.rope_cache.theta_scale != theta_scale || ctx.rope_cache.freq_scale != freq_scale) { ctx.rope_cache.theta_scale_length = theta_scale_length; if (ctx.rope_cache.theta_scale_cache != nullptr) { ACL_CHECK(aclrtFree(ctx.rope_cache.theta_scale_cache)); } - ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); + ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float), + ACL_MEM_MALLOC_HUGE_FIRST)); - acl_theta_scale_tensor = - ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); - float start = 0; - float step = 1; - float stop = theta_scale_length; + float start = 0; + float step = 1; + float stop = theta_scale_length; float n_elements = theta_scale_length; aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements); ggml_cann_pool_alloc yarn_ramp_allocator(ctx.pool()); - aclTensor* acl_yarn_ramp_tensor = nullptr; + aclTensor * acl_yarn_ramp_tensor = nullptr; if (ext_factor != 0) { // -rope_yarn_ramp // const float y = (i0 / 2 - low) / MAX(0.001f, high - low); // return MIN(1, MAX(0, y)) - 1; yarn_ramp_allocator.alloc(theta_scale_length * sizeof(float)); - void* yarn_ramp_buffer = yarn_ramp_allocator.get(); - acl_yarn_ramp_tensor = ggml_cann_create_tensor(yarn_ramp_buffer, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); - float zero_value = 0, one_value = 1; - float denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]); - aclScalar* low = aclCreateScalar(&corr_dims[0], aclDataType::ACL_FLOAT); - aclScalar* zero = aclCreateScalar(&zero_value, aclDataType::ACL_FLOAT); - aclScalar* one = aclCreateScalar(&one_value, aclDataType::ACL_FLOAT); - aclScalar* denom_safe = aclCreateScalar(&denom_safe_value, aclDataType::ACL_FLOAT); - aclScalar* ext_factor_sc = aclCreateScalar(&ext_factor, aclDataType::ACL_FLOAT); + void * yarn_ramp_buffer = yarn_ramp_allocator.get(); + acl_yarn_ramp_tensor = ggml_cann_create_tensor(yarn_ramp_buffer, ACL_FLOAT, sizeof(float), theta_scale_ne, + theta_scale_nb, GGML_MAX_DIMS); + float zero_value = 0, one_value = 1; + float denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]); + aclScalar * low = aclCreateScalar(&corr_dims[0], aclDataType::ACL_FLOAT); + aclScalar * zero = aclCreateScalar(&zero_value, aclDataType::ACL_FLOAT); + aclScalar * one = aclCreateScalar(&one_value, aclDataType::ACL_FLOAT); + aclScalar * denom_safe = aclCreateScalar(&denom_safe_value, aclDataType::ACL_FLOAT); + aclScalar * ext_factor_sc = aclCreateScalar(&ext_factor, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, Subs, acl_theta_scale_tensor, low, one, acl_yarn_ramp_tensor); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDivs, acl_yarn_ramp_tensor, denom_safe); @@ -2369,9 +2275,9 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, // // we cache (freq_scale - freq_scale * ramp_mix + ramp_mix), Considering that the rope_yarn_ramp here is the inverse // cache freq_scale + (freq_scale - 1) * ramp_mix - float freq_scale_1 = freq_scale - 1; - aclScalar* freq_scale_sc = aclCreateScalar(&freq_scale, aclDataType::ACL_FLOAT); - aclScalar* freq_scale_1_sc = aclCreateScalar(&freq_scale_1, aclDataType::ACL_FLOAT); + float freq_scale_1 = freq_scale - 1; + aclScalar * freq_scale_sc = aclCreateScalar(&freq_scale, aclDataType::ACL_FLOAT); + aclScalar * freq_scale_1_sc = aclCreateScalar(&freq_scale_1, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor, freq_scale_1_sc); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_yarn_ramp_tensor, freq_scale_sc, one); @@ -2379,9 +2285,8 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, } // power - aclScalar* acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT); - GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor, - acl_theta_scale_tensor); + aclScalar * acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT); + GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor, acl_theta_scale_tensor); if (ext_factor != 0) { aclnn_mul(ctx, acl_theta_scale_tensor, acl_yarn_ramp_tensor); @@ -2392,22 +2297,20 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_release_resources(ctx, acl_yarn_ramp_tensor, acl_theta_scale); } else { // use cache - acl_theta_scale_tensor = - ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); } ggml_cann_pool_alloc freq_fac_res_allocator(ctx.pool()); // freq_factors if (src2) { freq_fac_res_allocator.alloc(theta_scale_length * sizeof(float)); - void* freq_fac_res_ptr = freq_fac_res_allocator.get(); - aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor( - src2->data, ggml_cann_type_mapping(src2->type), - ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); - aclTensor* acl_freq_fac_res_tensor = ggml_cann_create_tensor( - freq_fac_res_ptr, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + void * freq_fac_res_ptr = freq_fac_res_allocator.get(); + aclTensor * acl_freq_factors_tensor = + ggml_cann_create_tensor(src2->data, ggml_cann_type_mapping(src2->type), ggml_type_size(src2->type), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + aclTensor * acl_freq_fac_res_tensor = ggml_cann_create_tensor(freq_fac_res_ptr, ACL_FLOAT, sizeof(float), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, acl_freq_fac_res_tensor); std::swap(acl_theta_scale_tensor, acl_freq_fac_res_tensor); ggml_cann_release_resources(ctx, acl_freq_factors_tensor, acl_freq_fac_res_tensor); @@ -2423,42 +2326,37 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ACL_CHECK(aclrtFree(ctx.rope_cache.cos_cache)); } int64_t repeat_theta_length = theta_scale_length * position_length * 2; - ACL_CHECK(aclrtMalloc(&ctx.rope_cache.sin_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); - ACL_CHECK(aclrtMalloc(&ctx.rope_cache.cos_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); + ACL_CHECK( + aclrtMalloc(&ctx.rope_cache.sin_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); + ACL_CHECK( + aclrtMalloc(&ctx.rope_cache.cos_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); } // position - aclTensor* acl_position_tensor = ggml_cann_create_tensor( - src1->data, ggml_cann_type_mapping(src1->type), - ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS); + aclTensor * acl_position_tensor = + ggml_cann_create_tensor(src1->data, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type), position_ne, + position_nb, GGML_MAX_DIMS); // power * position - int64_t theta_length = theta_scale_length * position_length; - ggml_cann_pool_alloc theta_allocator(ctx.pool(), - theta_length * sizeof(float)); - void* theta_buffer = theta_allocator.get(); + int64_t theta_length = theta_scale_length * position_length; + ggml_cann_pool_alloc theta_allocator(ctx.pool(), theta_length * sizeof(float)); + void * theta_buffer = theta_allocator.get(); - aclTensor* acl_theta_tensor = - ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float), - theta_ne, theta_nb, GGML_MAX_DIMS); - aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, - acl_theta_tensor); + aclTensor * acl_theta_tensor = + ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, GGML_MAX_DIMS); + aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, acl_theta_tensor); // sin/cos - ggml_cann_pool_alloc sin_allocator(ctx.pool(), - theta_length * sizeof(float)); - void* sin_buffer = sin_allocator.get(); - aclTensor* acl_sin_tensor = ggml_cann_create_tensor( - sin_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, - GGML_MAX_DIMS, ACL_FORMAT_ND); + ggml_cann_pool_alloc sin_allocator(ctx.pool(), theta_length * sizeof(float)); + void * sin_buffer = sin_allocator.get(); + aclTensor * acl_sin_tensor = + ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor); - ggml_cann_pool_alloc cos_allocator(ctx.pool(), - theta_length * sizeof(float)); - void* cos_buffer = cos_allocator.get(); - aclTensor* acl_cos_tensor = ggml_cann_create_tensor( - cos_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, - GGML_MAX_DIMS, ACL_FORMAT_ND); + ggml_cann_pool_alloc cos_allocator(ctx.pool(), theta_length * sizeof(float)); + void * cos_buffer = cos_allocator.get(); + aclTensor * acl_cos_tensor = + ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor); if (ext_factor != 0) { @@ -2471,81 +2369,79 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true); } - int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1}; - size_t sin_reshape_nb[GGML_MAX_DIMS]; + int64_t sin_reshape_ne[4] = { src0->ne[0], 1, src0->ne[2], 1 }; + size_t sin_reshape_nb[GGML_MAX_DIMS]; sin_reshape_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1]; } - aclTensor* acl_sin_repeat_tensor = - ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); - aclTensor* acl_cos_repeat_tensor = - ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_sin_repeat_tensor = ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_cos_repeat_tensor = ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); // repeat if (is_neox) { - int64_t repeatsArray[] = {1, 1, 1, 2}; + int64_t repeatsArray[] = { 1, 1, 1, 2 }; aclnn_repeat(ctx, acl_sin_tensor, acl_sin_repeat_tensor, repeatsArray); aclnn_repeat(ctx, acl_cos_tensor, acl_cos_repeat_tensor, repeatsArray); } else { int64_t num_repeats = 2; - int64_t dim = 3; + int64_t dim = 3; int64_t output_size = theta_scale_length * num_repeats; - aclnn_repeat_interleave(ctx, acl_sin_tensor, acl_sin_repeat_tensor, dim, - num_repeats, output_size); - aclnn_repeat_interleave(ctx, acl_cos_tensor, acl_cos_repeat_tensor, dim, - num_repeats, output_size); + aclnn_repeat_interleave(ctx, acl_sin_tensor, acl_sin_repeat_tensor, dim, num_repeats, output_size); + aclnn_repeat_interleave(ctx, acl_cos_tensor, acl_cos_repeat_tensor, dim, num_repeats, output_size); } // Other layers use cache except first layer. - ctx.rope_cache.cached = true; - ctx.rope_cache.ext_factor = ext_factor; + ctx.rope_cache.cached = true; + ctx.rope_cache.ext_factor = ext_factor; ctx.rope_cache.theta_scale = theta_scale; - ctx.rope_cache.freq_scale = freq_scale; + ctx.rope_cache.freq_scale = freq_scale; ctx.rope_cache.attn_factor = attn_factor; - ctx.rope_cache.is_neox = is_neox; + ctx.rope_cache.is_neox = is_neox; - ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor, - acl_theta_tensor, acl_sin_tensor, acl_sin_repeat_tensor, acl_cos_tensor, - acl_cos_repeat_tensor); + ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor, acl_theta_tensor, acl_sin_tensor, + acl_sin_repeat_tensor, acl_cos_tensor, acl_cos_repeat_tensor); } #ifdef __cplusplus extern "C" { #endif -aclnnStatus aclnnRotaryPositionEmbeddingGetWorkspaceSize( - const aclTensor* x, const aclTensor* cos, const aclTensor* sin, - int64_t mode, const aclTensor* yOut, uint64_t* workspaceSize, - aclOpExecutor** executor); -aclnnStatus aclnnRotaryPositionEmbedding(void* workspace, - uint64_t workspaceSize, - aclOpExecutor* executor, - aclrtStream stream); +aclnnStatus aclnnRotaryPositionEmbeddingGetWorkspaceSize(const aclTensor * x, + const aclTensor * cos, + const aclTensor * sin, + int64_t mode, + const aclTensor * yOut, + uint64_t * workspaceSize, + aclOpExecutor ** executor); +aclnnStatus aclnnRotaryPositionEmbedding(void * workspace, + uint64_t workspaceSize, + aclOpExecutor * executor, + aclrtStream stream); #ifdef __cplusplus } #endif -void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // input +void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // input // param - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; // const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t*)dst->op_params)[1]; - const int mode = ((int32_t*)dst->op_params)[2]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t*)dst->op_params)[4]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; GGML_TENSOR_UNARY_OP_LOCALS - memcpy(&freq_base, (int32_t*)dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t*)dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t*)dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t*)dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float)); + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); // TODO: n_dims <= ne0 GGML_ASSERT(n_dims == ne0); @@ -2554,123 +2450,111 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const float theta_scale = powf(freq_base, -2.0f / n_dims); float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, - beta_slow, corr_dims); + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; // init ctx.rope_cos/rope_sin cache - aclnn_cache_init(ctx, dst, corr_dims, ext_factor, - theta_scale, freq_scale, attn_factor, is_neox); + aclnn_cache_init(ctx, dst, corr_dims, ext_factor, theta_scale, freq_scale, attn_factor, is_neox); - int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1}; - size_t sin_reshape_nb[GGML_MAX_DIMS]; + int64_t sin_reshape_ne[4] = { ne00, 1, ne02, 1 }; + size_t sin_reshape_nb[GGML_MAX_DIMS]; sin_reshape_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1]; } - aclTensor* acl_sin_reshape_tensor = - ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); - aclTensor* acl_cos_reshape_tensor = - ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_sin_reshape_tensor = ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_cos_reshape_tensor = ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); #ifdef ASCEND_310P // Special ROPE operation for 310P // roll input - void* input_roll_buffer; - aclTensor* acl_minus_one_tensor; - void* minus_one_scale_buffer = nullptr; + void * input_roll_buffer; + aclTensor * acl_minus_one_tensor; + void * minus_one_scale_buffer = nullptr; ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0)); - ggml_cann_pool_alloc minus_one_scale_allocator( - ctx.pool(), sizeof(float) * src0->ne[0]); + ggml_cann_pool_alloc minus_one_scale_allocator(ctx.pool(), sizeof(float) * src0->ne[0]); if (!is_neox) { // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...] - input_roll_buffer = roll_allocator.get(); - int64_t input_roll_ne[4] = {2, src0->ne[1] * (src0->ne[0] / 2), - src0->ne[2], src0->ne[3]}; - size_t input_roll_nb[GGML_MAX_DIMS]; + input_roll_buffer = roll_allocator.get(); + int64_t input_roll_ne[4] = { 2, src0->ne[1] * (src0->ne[0] / 2), src0->ne[2], src0->ne[3] }; + size_t input_roll_nb[GGML_MAX_DIMS]; input_roll_nb[0] = ggml_type_size(src0->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { input_roll_nb[i] = input_roll_nb[i - 1] * input_roll_ne[i - 1]; } - aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor( - input_roll_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), input_roll_ne, input_roll_nb, - GGML_MAX_DIMS); - aclTensor* acl_input_tensor = ggml_cann_create_tensor( - src0->data, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), input_roll_ne, input_roll_nb, - GGML_MAX_DIMS); + aclTensor * acl_input_roll_tensor = + ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + input_roll_ne, input_roll_nb, GGML_MAX_DIMS); + aclTensor * acl_input_tensor = + ggml_cann_create_tensor(src0->data, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + input_roll_ne, input_roll_nb, GGML_MAX_DIMS); - int64_t shifts[] = {1}; - int64_t dims[] = {3}; + int64_t shifts[] = { 1 }; + int64_t dims[] = { 3 }; aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); ggml_cann_release_resources(ctx, acl_input_roll_tensor, acl_input_tensor); // init [-1, 1, -1, 1, ...] minus_one_scale_buffer = minus_one_scale_allocator.get(); - int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1}; - size_t minus_one_nb[GGML_MAX_DIMS]; + int64_t minus_one_ne[4] = { src0->ne[0], 1, 1, 1 }; + size_t minus_one_nb[GGML_MAX_DIMS]; minus_one_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; } - acl_minus_one_tensor = aclnn_values( - ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], - minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); - int64_t dim = 3; - int64_t* index = new int64_t[src0->ne[0]]; + acl_minus_one_tensor = aclnn_values(ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], minus_one_ne, + GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); + int64_t dim = 3; + int64_t * index = new int64_t[src0->ne[0]]; for (int i = 0; i < src0->ne[0]; i++) { index[i] = i / 2 * 2; } int64_t index_num = src0->ne[0]; - float value = -1; - aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index, - index_num, value); + float value = -1; + aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index, index_num, value); } else { // roll input: [q0,q1,q2,...] -> // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1] input_roll_buffer = roll_allocator.get(); - aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor( - input_roll_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS); - aclTensor* acl_input_tensor = ggml_cann_create_tensor(src0); + aclTensor * acl_input_roll_tensor = + ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + src0->ne, src0->nb, GGML_MAX_DIMS); + aclTensor * acl_input_tensor = ggml_cann_create_tensor(src0); - int64_t shifts[] = {src0->ne[0] / 2}; - int64_t dims[] = {3}; + int64_t shifts[] = { src0->ne[0] / 2 }; + int64_t dims[] = { 3 }; aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); ggml_cann_release_resources(ctx, acl_input_roll_tensor, acl_input_tensor); // init [-1, -1, -1, 1, 1，1，...] - minus_one_scale_buffer = minus_one_scale_allocator.get(); - int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1}; - size_t minus_one_nb[GGML_MAX_DIMS]; + minus_one_scale_buffer = minus_one_scale_allocator.get(); + int64_t minus_one_ne[4] = { src0->ne[0], 1, 1, 1 }; + size_t minus_one_nb[GGML_MAX_DIMS]; minus_one_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; } - acl_minus_one_tensor = aclnn_values( - ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], - minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); + acl_minus_one_tensor = aclnn_values(ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], minus_one_ne, + GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); // -1 * first half - int64_t first_half_ne[4] = {src0->ne[0] / 2, 1, 1, 1}; - size_t first_half_nb[GGML_MAX_DIMS]; + int64_t first_half_ne[4] = { src0->ne[0] / 2, 1, 1, 1 }; + size_t first_half_nb[GGML_MAX_DIMS]; first_half_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1]; } - aclTensor* acl_first_half_tensor = ggml_cann_create_tensor( - minus_one_scale_buffer, ACL_FLOAT, sizeof(float), first_half_ne, - first_half_nb, GGML_MAX_DIMS); - bool inplace = true; - float scale = -1; + aclTensor * acl_first_half_tensor = ggml_cann_create_tensor(minus_one_scale_buffer, ACL_FLOAT, sizeof(float), + first_half_ne, first_half_nb, GGML_MAX_DIMS); + bool inplace = true; + float scale = -1; aclnn_muls(ctx, acl_first_half_tensor, scale, nullptr, inplace); ggml_cann_release_resources(ctx, acl_first_half_tensor); } @@ -2679,30 +2563,27 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(n_dims == src0->ne[0]); // input * scale - ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(), - ggml_nbytes(src0)); - void* input_roll_mul_scale_buffer = roll_mul_scale_allocator.get(); - size_t input_nb[GGML_MAX_DIMS]; + ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(), ggml_nbytes(src0)); + void * input_roll_mul_scale_buffer = roll_mul_scale_allocator.get(); + size_t input_nb[GGML_MAX_DIMS]; input_nb[0] = ggml_type_size(src0->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { input_nb[i] = input_nb[i - 1] * src0->ne[i - 1]; } - aclTensor* acl_input_roll_mul_scale_tensor = ggml_cann_create_tensor( - input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); - aclTensor* acl_input_roll_reshape_tensor = ggml_cann_create_tensor( - input_roll_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); + aclTensor * acl_input_roll_mul_scale_tensor = + ggml_cann_create_tensor(input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); + aclTensor * acl_input_roll_reshape_tensor = + ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + src0->ne, input_nb, GGML_MAX_DIMS); - aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, - acl_input_roll_mul_scale_tensor); + aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, acl_input_roll_mul_scale_tensor); // output - void* output_fp32_buffer; + void * output_fp32_buffer; if (src0->type == GGML_TYPE_F32) { aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor); - aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, - acl_sin_reshape_tensor); + aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor); aclnn_add(ctx, acl_src, acl_input_roll_mul_scale_tensor, acl_dst); // TODO: ne0 != n_dims in mode2 } else if (src0->type == GGML_TYPE_F16) { @@ -2711,36 +2592,27 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1]; } - ggml_cann_pool_alloc fp32_allocator1( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - void* input_fp32_buffer1 = fp32_allocator1.get(); - aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor( - input_fp32_buffer1, ACL_FLOAT, sizeof(float), dst->ne, - input_fp32_nb, GGML_MAX_DIMS); - ggml_cann_pool_alloc fp32_allocator2( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - void* input_fp32_buffer2 = fp32_allocator2.get(); - aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor( - input_fp32_buffer2, ACL_FLOAT, sizeof(float), dst->ne, - input_fp32_nb, GGML_MAX_DIMS); + ggml_cann_pool_alloc fp32_allocator1(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + void * input_fp32_buffer1 = fp32_allocator1.get(); + aclTensor * input_fp32_tensor1 = ggml_cann_create_tensor(input_fp32_buffer1, ACL_FLOAT, sizeof(float), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + ggml_cann_pool_alloc fp32_allocator2(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + void * input_fp32_buffer2 = fp32_allocator2.get(); + aclTensor * input_fp32_tensor2 = ggml_cann_create_tensor(input_fp32_buffer2, ACL_FLOAT, sizeof(float), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); - ggml_cann_pool_alloc fp32_allocator( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - output_fp32_buffer = fp32_allocator.get(); - aclTensor* output_fp32_tensor = ggml_cann_create_tensor( - output_fp32_buffer, ACL_FLOAT, sizeof(float), dst->ne, - input_fp32_nb, GGML_MAX_DIMS); + ggml_cann_pool_alloc fp32_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + output_fp32_buffer = fp32_allocator.get(); + aclTensor * output_fp32_tensor = ggml_cann_create_tensor(output_fp32_buffer, ACL_FLOAT, sizeof(float), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor, input_fp32_tensor1); - aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, - input_fp32_tensor2); - aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, - output_fp32_tensor); + aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, input_fp32_tensor2); + aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, output_fp32_tensor); aclnn_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16); - ggml_cann_release_resources(ctx, input_fp32_tensor1, input_fp32_tensor2, - output_fp32_tensor, acl_sin_reshape_tensor, - acl_minus_one_tensor, acl_input_roll_mul_scale_tensor, - acl_input_roll_reshape_tensor, acl_src); + ggml_cann_release_resources(ctx, input_fp32_tensor1, input_fp32_tensor2, output_fp32_tensor, + acl_sin_reshape_tensor, acl_minus_one_tensor, acl_input_roll_mul_scale_tensor, + acl_input_roll_reshape_tensor, acl_src); } return; #endif @@ -2749,155 +2621,146 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { int64_t acl_mode = mode == 0 ? 1 : mode; switch (src0->type) { - case GGML_TYPE_F32: { - GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src, - acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, acl_dst); - break; - } - case GGML_TYPE_F16: { - ggml_cann_pool_alloc src_trans_allocator( - ctx.pool(), ggml_nelements(src0) * sizeof(float)); - void* src_trans_buffer = src_trans_allocator.get(); - ggml_cann_pool_alloc dst_trans_allocator( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - void* dst_trans_buffer = dst_trans_allocator.get(); - - size_t src_trans_nb[GGML_MAX_DIMS]; - src_trans_nb[0] = sizeof(float); - for (int i = 1; i < GGML_MAX_DIMS; i++) { - src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + case GGML_TYPE_F32: + { + GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src, acl_cos_reshape_tensor, + acl_sin_reshape_tensor, acl_mode, acl_dst); + break; } + case GGML_TYPE_F16: + { + ggml_cann_pool_alloc src_trans_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(float)); + void * src_trans_buffer = src_trans_allocator.get(); + ggml_cann_pool_alloc dst_trans_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + void * dst_trans_buffer = dst_trans_allocator.get(); - aclTensor* acl_src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne, src_trans_nb, - GGML_MAX_DIMS); - aclTensor* acl_dst_trans_tensor = ggml_cann_create_tensor( - dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne, src_trans_nb, - GGML_MAX_DIMS); + size_t src_trans_nb[GGML_MAX_DIMS]; + src_trans_nb[0] = sizeof(float); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + } - aclnn_cast(ctx, acl_src, acl_src_trans_tensor, ACL_FLOAT); + aclTensor * acl_src_trans_tensor = ggml_cann_create_tensor(src_trans_buffer, ACL_FLOAT, sizeof(float), + src0->ne, src_trans_nb, GGML_MAX_DIMS); + aclTensor * acl_dst_trans_tensor = ggml_cann_create_tensor(dst_trans_buffer, ACL_FLOAT, sizeof(float), + dst->ne, src_trans_nb, GGML_MAX_DIMS); - GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor, - acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, - acl_dst_trans_tensor); + aclnn_cast(ctx, acl_src, acl_src_trans_tensor, ACL_FLOAT); - aclnn_cast(ctx, acl_dst_trans_tensor, acl_dst, ACL_FLOAT16); + GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor, acl_cos_reshape_tensor, + acl_sin_reshape_tensor, acl_mode, acl_dst_trans_tensor); - ggml_cann_release_resources(ctx, acl_src_trans_tensor, - acl_dst_trans_tensor); - break; - } + aclnn_cast(ctx, acl_dst_trans_tensor, acl_dst, ACL_FLOAT16); + + ggml_cann_release_resources(ctx, acl_src_trans_tensor, acl_dst_trans_tensor); + break; + } default: GGML_ABORT("Unsupported tensor type for GGML_OP_ROPE"); break; } - ggml_cann_release_resources(ctx, acl_cos_reshape_tensor, - acl_sin_reshape_tensor, acl_src, acl_dst); + ggml_cann_release_resources(ctx, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_src, acl_dst); } - - void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_argmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3); GGML_CANN_CALL_ACLNN_OP(ctx, ArgMax, acl_src, 3, false, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst); } -void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; ggml_tensor * src1 = dst->src[1]; // stride - int64_t s0 = ((const int32_t*)(dst->op_params))[0]; + int64_t s0 = ((const int32_t *) (dst->op_params))[0]; - aclTensor* acl_input = ggml_cann_create_tensor(src1, src1->ne, src1->nb, 3, ACL_FORMAT_NCL); - aclTensor* acl_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL); - aclTensor* acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3, ACL_FORMAT_NCL); + aclTensor * acl_input = ggml_cann_create_tensor(src1, src1->ne, src1->nb, 3, ACL_FORMAT_NCL); + aclTensor * acl_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3, ACL_FORMAT_NCL); int64_t strideVal[1]; - strideVal[0] = s0; - aclIntArray *stride = aclCreateIntArray(strideVal, 1); - int64_t paddingVal[] = {0}; - aclIntArray *padding = aclCreateIntArray(paddingVal, 1); - int64_t dilationVal[] = {1}; - aclIntArray *dilation = aclCreateIntArray(dilationVal, 1); - int8_t cubeMathType = 0; + strideVal[0] = s0; + aclIntArray * stride = aclCreateIntArray(strideVal, 1); + int64_t paddingVal[] = { 0 }; + aclIntArray * padding = aclCreateIntArray(paddingVal, 1); + int64_t dilationVal[] = { 1 }; + aclIntArray * dilation = aclCreateIntArray(dilationVal, 1); + int8_t cubeMathType = 0; #ifdef ASCEND_310P cubeMathType = 1; #endif - GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input, acl_weight, nullptr, stride, - padding, dilation, true, padding, 1, acl_dst, cubeMathType); + GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input, acl_weight, nullptr, stride, padding, dilation, true, padding, + 1, acl_dst, cubeMathType); ggml_cann_release_resources(ctx, acl_weight, acl_dst, stride, padding, dilation); } -void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_elu(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_input = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_input = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - float alphaValue = 1.0f; - aclScalar* alpha = nullptr; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + float alphaValue = 1.0f; + aclScalar * alpha = nullptr; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - GGML_CANN_CALL_ACLNN_OP(ctx, Elu, acl_input, alpha, alpha, alpha, - acl_dst); + GGML_CANN_CALL_ACLNN_OP(ctx, Elu, acl_input, alpha, alpha, alpha, acl_dst); ggml_cann_release_resources(ctx, acl_input, acl_dst, alpha); } -void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_mean(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - int64_t reduceDimValue[] = {3}; - aclIntArray* reduceDim = aclCreateIntArray(reduceDimValue, 1); - bool keepDim = true; + int64_t reduceDimValue[] = { 3 }; + aclIntArray * reduceDim = aclCreateIntArray(reduceDimValue, 1); + bool keepDim = true; GGML_CANN_CALL_ACLNN_OP(ctx, Mean, acl_src, reduceDim, keepDim, ACL_FLOAT, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst, reduceDim); } -void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst){ - ggml_tensor * src0 = dst->src[0]; - int32_t *opts = (int32_t *) dst->op_params; - int64_t paddingsArray[2] = {opts[0], opts[1]}; - aclIntArray* paddings = aclCreateIntArray(paddingsArray, 2); +void ggml_cann_pad_reflect_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + int32_t * opts = (int32_t *) dst->op_params; + int64_t paddingsArray[2] = { opts[0], opts[1] }; + aclIntArray * paddings = aclCreateIntArray(paddingsArray, 2); for (int64_t i = 0; i < src0->ne[3]; i++) { - aclTensor* acl_src = ggml_cann_create_tensor( - (char*)src0->data + i * src0->ne[3], - ggml_cann_type_mapping(src0->type), ggml_element_size(src0), - src0->ne, src0->nb, 3); + aclTensor * acl_src = + ggml_cann_create_tensor((char *) src0->data + i * src0->ne[3], ggml_cann_type_mapping(src0->type), + ggml_element_size(src0), src0->ne, src0->nb, 3); - aclTensor* acl_dst = ggml_cann_create_tensor( - (char*)dst->data + i * src0->ne[3], - ggml_cann_type_mapping(dst->type), ggml_element_size(dst), - dst->ne, dst->nb, 3); + aclTensor * acl_dst = + ggml_cann_create_tensor((char *) dst->data + i * src0->ne[3], ggml_cann_type_mapping(dst->type), + ggml_element_size(dst), dst->ne, dst->nb, 3); - GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src, paddings, acl_dst); + GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src, paddings, acl_dst); - ggml_cann_release_resources(ctx, acl_src, acl_dst); + ggml_cann_release_resources(ctx, acl_src, acl_dst); } ggml_cann_release_resources(ctx, paddings); } -void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; ggml_tensor * src1 = dst->src[1]; - aclTensor* acl_self = ggml_cann_create_tensor(src0); - aclTensor* acl_other = ggml_cann_create_tensor(src1); + aclTensor * acl_self = ggml_cann_create_tensor(src0); + aclTensor * acl_other = ggml_cann_create_tensor(src1); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceEqTensor, acl_self, acl_other); @@ -2906,15 +2769,15 @@ void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst){ ggml_cann_release_resources(ctx, acl_self, acl_other); } -void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - float alphaValue = 0.0f; - aclScalar* alpha = nullptr; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + float alphaValue = 0.0f; + aclScalar * alpha = nullptr; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, GtScalar, acl_src, alpha, acl_dst); @@ -2939,7 +2802,7 @@ void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){ * @note This function assumes floating-point data types and is designed for * MoE architectures, possibly involving sparse expert routing. */ -static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) { //dst [M, K, N, 1] ggml_tensor * src0 = dst->src[0]; //src0 [D, M, A, 1] -> [D, M, K, 1] ggml_tensor * src1 = dst->src[1]; //src1 [D, B, N, 1], B = K or B = 1 -> [D, 1, K, 1] @@ -2953,36 +2816,42 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* GGML_ASSERT(batch == ids->ne[1]); ggml_cann_pool_alloc export_allocator(ctx.pool(), src0->ne[0] * src0->ne[1] * ids->ne[0] * ggml_element_size(src0)); - void* export_ptr = export_allocator.get(); + void * export_ptr = export_allocator.get(); for (int64_t i = 0; i < batch; i++) { - aclTensor *select_index = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, i * ids->nb[1]); - aclTensor *export_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3); + aclTensor * select_index = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, i * ids->nb[1]); + aclTensor * export_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3); - int64_t select_export_ne[] = {src0->ne[0], src0->ne[1], ids->ne[0]}; - size_t select_export_nb[3]; + int64_t select_export_ne[] = { src0->ne[0], src0->ne[1], ids->ne[0] }; + size_t select_export_nb[3]; select_export_nb[0] = src0->nb[0]; - for (int k = 1;k < 3; k++) { - select_export_nb[k] = select_export_nb[k-1] * select_export_ne[k-1]; + for (int k = 1; k < 3; k++) { + select_export_nb[k] = select_export_nb[k - 1] * select_export_ne[k - 1]; } - aclTensor *select_export = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_export_ne, select_export_nb, 3); + aclTensor * select_export = + ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), + select_export_ne, select_export_nb, 3); GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, export_weight, 0, select_index, select_export); - int64_t select_transpose_ne[] = {select_export_ne[1], select_export_ne[0], select_export_ne[2]}; - size_t select_transpose_nb[] = {select_export_nb[1], select_export_nb[0], select_export_nb[2]}; - aclTensor *select_export_transpose = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_transpose_ne, select_transpose_nb, 3); + int64_t select_transpose_ne[] = { select_export_ne[1], select_export_ne[0], select_export_ne[2] }; + size_t select_transpose_nb[] = { select_export_nb[1], select_export_nb[0], select_export_nb[2] }; + aclTensor * select_export_transpose = + ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), + select_transpose_ne, select_transpose_nb, 3); - int64_t active_tensor_ne[] = {src1->ne[0], 1, src1->ne[1]}; - size_t active_tensor_nb[] = {src1->nb[0], src1->nb[1], src1->nb[1]}; - aclTensor *active_tensor = ggml_cann_create_tensor(src1, active_tensor_ne, active_tensor_nb, 3, ACL_FORMAT_ND, i * src1->nb[2]); + int64_t active_tensor_ne[] = { src1->ne[0], 1, src1->ne[1] }; + size_t active_tensor_nb[] = { src1->nb[0], src1->nb[1], src1->nb[1] }; + aclTensor * active_tensor = + ggml_cann_create_tensor(src1, active_tensor_ne, active_tensor_nb, 3, ACL_FORMAT_ND, i * src1->nb[2]); - int64_t dst_ne[] = {dst->ne[0], 1, dst->ne[1]}; - size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[1]}; - aclTensor *acl_dst = ggml_cann_create_tensor(dst, dst_ne,dst_nb, 3, ACL_FORMAT_ND, i * dst->nb[2]); + int64_t dst_ne[] = { dst->ne[0], 1, dst->ne[1] }; + size_t dst_nb[] = { dst->nb[0], dst->nb[1], dst->nb[1] }; + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst_ne, dst_nb, 3, ACL_FORMAT_ND, i * dst->nb[2]); GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, active_tensor, select_export_transpose, acl_dst, 2); - ggml_cann_release_resources(ctx, select_index, export_weight, select_export, active_tensor, acl_dst, select_export_transpose); + ggml_cann_release_resources(ctx, select_index, export_weight, select_export, active_tensor, acl_dst, + select_export_transpose); } } @@ -3009,7 +2878,7 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* * @note This function assumes quantized data types and is designed for * MoE architectures with potential sparse expert routing. */ -static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst) { // TODO: Use aclnnGroupedMatMul //dst [M, K, N, 1] ggml_tensor * src0 = dst->src[0]; //src0 [D, M, A, 1] @@ -3019,24 +2888,23 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens GGML_TENSOR_BINARY_OP_LOCALS // copy index from npu to cpu - int64_t n_as = ne02; // A - int64_t n_ids = ids->ne[0]; // K + int64_t n_as = ne02; // A + int64_t n_ids = ids->ne[0]; // K std::vector ids_host(ggml_nbytes(ids)); - ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids), - ACL_MEMCPY_DEVICE_TO_HOST); + ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids), ACL_MEMCPY_DEVICE_TO_HOST); ACL_CHECK(aclrtSynchronizeStream(ctx.stream())); char * src0_original = (char *) src0->data; char * src1_original = (char *) src1->data; - char * dst_original = (char *) dst->data; + char * dst_original = (char *) dst->data; ggml_tensor src0_row = *src0; ggml_tensor src1_row = *src1; - ggml_tensor dst_row = *dst; + ggml_tensor dst_row = *dst; const enum ggml_type type = dst->src[0]->type; - float weight_elem_size; + float weight_elem_size; if (type == GGML_TYPE_Q4_0) { weight_elem_size = float(sizeof(uint8_t)) / 2; } else if (type == GGML_TYPE_Q8_0) { @@ -3046,18 +2914,18 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens } // src0_row [D, M, 1, 1] weight without permute - src0_row.ne[2] = 1; - src0_row.ne[3] = 1; - src0_row.nb[0] = weight_elem_size; - src0_row.nb[1] = weight_elem_size * ne00; - src0_row.nb[2] = weight_elem_size * ne00; - src0_row.nb[3] = weight_elem_size * ne00; + src0_row.ne[2] = 1; + src0_row.ne[3] = 1; + src0_row.nb[0] = weight_elem_size; + src0_row.nb[1] = weight_elem_size * ne00; + src0_row.nb[2] = weight_elem_size * ne00; + src0_row.nb[3] = weight_elem_size * ne00; size_t weight_stride = ne00 * ne01 * weight_elem_size; - size_t weight_size = weight_stride * ne02 * ne03; + size_t weight_size = weight_stride * ne02 * ne03; // scale [D, M, 1, 1] -> scale && permute size_t scale_elem_size = sizeof(uint16_t); - size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; + size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; // src1_row [D, 1, 1, 1] -> input src1_row.ne[1] = 1; @@ -3075,11 +2943,11 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens //create weight for one row ggml_cann_pool_alloc weight_allocator(ctx.pool()); - void* weight_buffer = weight_allocator.alloc(nb02); + void * weight_buffer = weight_allocator.alloc(nb02); for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { for (int64_t id = 0; id < n_ids; id++) { // expert index - int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); + int32_t i02 = *(int32_t *) (ids_host.data() + iid1 * ids->nb[1] + id * ids->nb[0]); GGML_ASSERT(i02 >= 0 && i02 < n_as); // If B = 1 (broadcast), always use 0; otherwise, use id. @@ -3089,21 +2957,19 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens int64_t i1 = id; int64_t i2 = i12; - void* src0_tmp_ptr = src0_original + i02*weight_stride; - void* scale_tmp_ptr = src0_original + weight_size + i02*scale_stride; - void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12; - void* dst_tmp_ptr = dst_original + i1*nb1 + i2*nb2; + void * src0_tmp_ptr = src0_original + i02 * weight_stride; + void * scale_tmp_ptr = src0_original + weight_size + i02 * scale_stride; + void * src1_tmp_ptr = src1_original + i11 * nb11 + i12 * nb12; + void * dst_tmp_ptr = dst_original + i1 * nb1 + i2 * nb2; // mem cpy - ggml_cann_async_memcpy(ctx, weight_buffer, src0_tmp_ptr, weight_stride, - ACL_MEMCPY_DEVICE_TO_DEVICE); - void* scale_buffer = (char*)weight_buffer + weight_stride; - ggml_cann_async_memcpy(ctx, scale_buffer, scale_tmp_ptr, scale_stride, - ACL_MEMCPY_DEVICE_TO_DEVICE); + ggml_cann_async_memcpy(ctx, weight_buffer, src0_tmp_ptr, weight_stride, ACL_MEMCPY_DEVICE_TO_DEVICE); + void * scale_buffer = (char *) weight_buffer + weight_stride; + ggml_cann_async_memcpy(ctx, scale_buffer, scale_tmp_ptr, scale_stride, ACL_MEMCPY_DEVICE_TO_DEVICE); - src0_row.data = weight_buffer; - src1_row.data = src1_tmp_ptr; - dst_row.data = dst_tmp_ptr; + src0_row.data = weight_buffer; + src1_row.data = src1_tmp_ptr; + dst_row.data = dst_tmp_ptr; dst_row.src[0] = &src0_row; dst_row.src[1] = &src1_row; @@ -3113,7 +2979,7 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens return; } -void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst) { const enum ggml_type type = dst->src[0]->type; switch (type) { case GGML_TYPE_F32: @@ -3130,12 +2996,11 @@ void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst) { } } -void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){ - - ggml_tensor* src0 = dst->src[0]; // q, fp32 | B, N, S, D (uncont) -> B, S, N, D (cont) - ggml_tensor* src1 = dst->src[1]; // k, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) - ggml_tensor* src2 = dst->src[2]; // v, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) - ggml_tensor* src3 = dst->src[3]; // mask, fp16 +void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // q, fp32 | B, N, S, D (uncont) -> B, S, N, D (cont) + ggml_tensor * src1 = dst->src[1]; // k, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) + ggml_tensor * src2 = dst->src[2]; // v, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) + ggml_tensor * src3 = dst->src[3]; // mask, fp16 // B, N, S, D (uncont) -> B, S, N, D (cont) int64_t src0_bsnd_ne[GGML_MAX_DIMS]; @@ -3151,124 +3016,96 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){ size_t src2_bsnd_nb[GGML_MAX_DIMS]; memcpy(src2_bsnd_nb, src2->nb, GGML_MAX_DIMS * sizeof(size_t)); - auto transpose12 = [](int64_t* ne, size_t* nb) { + auto transpose12 = [](int64_t * ne, size_t * nb) { int64_t ne_tmp = ne[1]; size_t nb_tmp = nb[1]; - ne[1] = ne[2]; - nb[1] = nb[2]; - ne[2] = ne_tmp; - nb[2] = nb_tmp; + ne[1] = ne[2]; + nb[1] = nb[2]; + ne[2] = ne_tmp; + nb[2] = nb_tmp; }; transpose12(src0_bsnd_ne, src0_bsnd_nb); transpose12(src1_bsnd_ne, src1_bsnd_nb); transpose12(src2_bsnd_ne, src2_bsnd_nb); - float maxBias = 0.0f; - float scaleValue = 1.0f; + float maxBias = 0.0f; + float scaleValue = 1.0f; float logitSoftcap = 0.0f; - memcpy(&scaleValue, (float*)dst->op_params + 0, sizeof(float)); - memcpy(&maxBias, (float*)dst->op_params + 1, sizeof(float)); - memcpy(&logitSoftcap, (float*)dst->op_params + 2, sizeof(float)); + memcpy(&scaleValue, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&maxBias, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&logitSoftcap, (float *) dst->op_params + 2, sizeof(float)); - if(logitSoftcap == 0.0f){ + if (logitSoftcap == 0.0f) { size_t faElemSize = sizeof(uint16_t); - auto faDataType = ACL_FLOAT16; //ACL_BF16; + auto faDataType = ACL_FLOAT16; //ACL_BF16; - aclTensor* acl_src0_f16_tensor = nullptr; - aclTensor* acl_src1_f16_tensor = nullptr; - aclTensor* acl_src2_f16_tensor = nullptr; - aclTensor* acl_dst_f16_tensor = nullptr; + aclTensor * acl_src0_f16_tensor = nullptr; + aclTensor * acl_src1_f16_tensor = nullptr; + aclTensor * acl_src2_f16_tensor = nullptr; // Step 1: cast the src0 (Query) to fp16 if needed ggml_cann_pool_alloc src0_f16_allocator(ctx.pool()); - void* src0_f16_buffer = nullptr; + void * src0_f16_buffer = nullptr; - if(ggml_cann_type_mapping(src0->type) != faDataType){ - aclTensor* acl_src0_f32_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, - src0_bsnd_nb, GGML_MAX_DIMS); - src0_f16_buffer = src0_f16_allocator.alloc( - ggml_nelements(src0) * faElemSize); + if (ggml_cann_type_mapping(src0->type) != faDataType) { + aclTensor * acl_src0_f32_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, src0_bsnd_nb, GGML_MAX_DIMS); + src0_f16_buffer = src0_f16_allocator.alloc(ggml_nelements(src0) * faElemSize); - int64_t* src0_f16_ne = src0_bsnd_ne; - size_t src0_f16_nb[GGML_MAX_DIMS]; + int64_t * src0_f16_ne = src0_bsnd_ne; + size_t src0_f16_nb[GGML_MAX_DIMS]; src0_f16_nb[0] = sizeof(uint16_t); - for(int i = 1; i < GGML_MAX_DIMS; ++i){ + for (int i = 1; i < GGML_MAX_DIMS; ++i) { src0_f16_nb[i] = src0_f16_nb[i - 1] * src0_f16_ne[i - 1]; } - acl_src0_f16_tensor = ggml_cann_create_tensor( - src0_f16_buffer, faDataType, faElemSize, - src0_f16_ne, src0_f16_nb, GGML_MAX_DIMS - ); + acl_src0_f16_tensor = ggml_cann_create_tensor(src0_f16_buffer, faDataType, faElemSize, src0_f16_ne, + src0_f16_nb, GGML_MAX_DIMS); aclnn_cast(ctx, acl_src0_f32_tensor, acl_src0_f16_tensor, faDataType); ggml_cann_release_resources(ctx, acl_src0_f32_tensor); - }else{ - acl_src0_f16_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, - src0_bsnd_nb, GGML_MAX_DIMS); + } else { + acl_src0_f16_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, src0_bsnd_nb, GGML_MAX_DIMS); } // Step 2: create the acl tensors for src1 (Key), src2 (Value), // and the direct output from FusedInferAttention - acl_src1_f16_tensor = ggml_cann_create_tensor(src1, src1_bsnd_ne, - src1_bsnd_nb, GGML_MAX_DIMS); - acl_src2_f16_tensor = ggml_cann_create_tensor(src2, src2_bsnd_ne, - src2_bsnd_nb, GGML_MAX_DIMS); - - ggml_cann_pool_alloc out_f16_allocator(ctx.pool()); - void* out_f16_buffer = out_f16_allocator.alloc( - ggml_nelements(dst) * faElemSize); - - int64_t* out_f16_ne = src0_bsnd_ne; - size_t out_f16_nb[GGML_MAX_DIMS]; - out_f16_nb[0] = faElemSize; - for(int i = 1; i < GGML_MAX_DIMS; ++i){ - out_f16_nb[i] = out_f16_nb[i - 1] * out_f16_ne[i - 1]; - } - - acl_dst_f16_tensor = ggml_cann_create_tensor( - out_f16_buffer, faDataType, faElemSize, - out_f16_ne, out_f16_nb, GGML_MAX_DIMS - ); + acl_src1_f16_tensor = ggml_cann_create_tensor(src1, src1_bsnd_ne, src1_bsnd_nb, GGML_MAX_DIMS); + acl_src2_f16_tensor = ggml_cann_create_tensor(src2, src2_bsnd_ne, src2_bsnd_nb, GGML_MAX_DIMS); // Step 3: create the PSEShift tensor if needed // this tensor is considered as mask (f16) in the llama.cpp - aclTensor* bcast_pse_tensor = nullptr; + aclTensor * bcast_pse_tensor = nullptr; ggml_cann_pool_alloc bcast_pse_allocator(ctx.pool()); - if(src3 != nullptr){ + if (src3 != nullptr) { // Construct the truncated pse tensor (common for prefill/decode) int64_t trunc_pse_ne[GGML_MAX_DIMS] = { - src3->ne[0], // D - src0->ne[1], // S (number of Q tokens) - src3->ne[2], // mask N - src3->ne[3] // B + src3->ne[0], // D + src0->ne[1], // S (number of Q tokens) + src3->ne[2], // mask N + src3->ne[3] // B }; - size_t* trunc_pse_nb = src3->nb; + size_t * trunc_pse_nb = src3->nb; - aclTensor* acl_mask_f16_trunc_tensor = ggml_cann_create_tensor( - src3->data, ACL_FLOAT16, sizeof(uint16_t), - trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS - ); + aclTensor * acl_mask_f16_trunc_tensor = ggml_cann_create_tensor(src3->data, ACL_FLOAT16, sizeof(uint16_t), + trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS); int64_t bcast_pse_ne[GGML_MAX_DIMS]; - size_t bcast_pse_nb[GGML_MAX_DIMS]; - bcast_pse_ne[0] = src3->ne[0]; // D - bcast_pse_ne[1] = src0->ne[1]; // S - bcast_pse_ne[2] = src0->ne[2]; // N (num_heads) - bcast_pse_ne[3] = src3->ne[3]; // B + size_t bcast_pse_nb[GGML_MAX_DIMS]; + bcast_pse_ne[0] = src3->ne[0]; // D + bcast_pse_ne[1] = src0->ne[1]; // S + bcast_pse_ne[2] = src0->ne[2]; // N (num_heads) + bcast_pse_ne[3] = src3->ne[3]; // B if (maxBias == 0.0f) { // When maxBias == 0.0f, use nb = 0 reduce once repeat (Qwen2) // Construct the bcast tensor (simulate repeat on the head dimension using stride=0) bcast_pse_nb[0] = sizeof(uint16_t); bcast_pse_nb[1] = bcast_pse_nb[0] * bcast_pse_ne[0]; - bcast_pse_nb[2] = 0; // <---- the head dimension shares the same data + bcast_pse_nb[2] = 0; // <---- the head dimension shares the same data bcast_pse_nb[3] = src3->nb[3]; - bcast_pse_tensor = ggml_cann_create_tensor( - src3->data, ACL_FLOAT16, sizeof(uint16_t), - bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS - ); + bcast_pse_tensor = ggml_cann_create_tensor(src3->data, ACL_FLOAT16, sizeof(uint16_t), bcast_pse_ne, + bcast_pse_nb, GGML_MAX_DIMS); ggml_cann_release_resources(ctx, acl_mask_f16_trunc_tensor); } else { @@ -3277,35 +3114,31 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){ bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1]; } - void* bcast_pse_buffer = bcast_pse_allocator.alloc( - ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t) - ); + void * bcast_pse_buffer = + bcast_pse_allocator.alloc(ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t)); - bcast_pse_tensor = ggml_cann_create_tensor( - bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t), - bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS - ); + bcast_pse_tensor = ggml_cann_create_tensor(bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t), + bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS); - int64_t repeats[] = {1, src0->ne[2], 1, 1}; + int64_t repeats[] = { 1, src0->ne[2], 1, 1 }; aclnn_repeat(ctx, acl_mask_f16_trunc_tensor, bcast_pse_tensor, repeats); // alibi // Compute the slope if needed. Derived from ggml_cann_softmax(). - const int64_t n_heads = src0->ne[2]; + const int64_t n_heads = src0->ne[2]; ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(uint16_t)); - void* slope_buffer = slope_allocator.get(); + void * slope_buffer = slope_allocator.get(); aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias, GGML_TYPE_F16); - int64_t slope_ne[] = {1, 1, n_heads, 1}; - size_t slope_nb[GGML_MAX_DIMS]; + int64_t slope_ne[] = { 1, 1, n_heads, 1 }; + size_t slope_nb[GGML_MAX_DIMS]; slope_nb[0] = sizeof(uint16_t); - for(int i = 1;ine[2]; // N - int64_t numKeyValueHeads = src1->ne[2]; + int64_t numHeads = src0->ne[2]; // N + int64_t numKeyValueHeads = src1->ne[2]; // double scaleValue = 1 / sqrt(src0->ne[0]); // 1/sqrt(d) - int64_t preTokens = 65535; - int64_t nextTokens = 65535; - char layout[5] = {'B', 'S', 'N', 'D', 0}; - int64_t sparseMode = 0; - int64_t innerPrecise = (src0->ne[1] == 1) ? 0 : 2; - int64_t blockSize = 0; - int64_t antiquantMode = 0; - bool softmaxLseFlag = false; - int64_t keyAntiquantMode = 0; + int64_t preTokens = 65535; + int64_t nextTokens = 65535; + char layout[5] = { 'B', 'S', 'N', 'D', 0 }; + int64_t sparseMode = 0; + int64_t innerPrecise = (src0->ne[1] == 1) ? 0 : 2; + int64_t blockSize = 0; + int64_t antiquantMode = 0; + bool softmaxLseFlag = false; + int64_t keyAntiquantMode = 0; int64_t valueAntiquantMode = 0; - // Step 5: launch the FusedInferAttentionScoreV2 kernel. - // Refer to https://gitee.com/ascend/cann-ops-adv/blob/master/docs/FusedInferAttentionScoreV2.md + GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + aclTensor * fa_dst_tensor = nullptr; + aclTensor * acl_dst_tensor = nullptr; + ggml_cann_pool_alloc out_f16_allocator(ctx.pool()); + if (dst->type == GGML_TYPE_F32) { + void * out_f16_buffer = out_f16_allocator.alloc(ggml_nelements(dst) * faElemSize); - GGML_CANN_CALL_ACLNN_OP(ctx, FusedInferAttentionScoreV2, - acl_q_tensor, acl_k_tensor_list, acl_v_tensor_list, // q, k, v - bcast_pse_tensor, nullptr, // pse, mask - nullptr, nullptr, // actSeqLen, actSeqLenkv - nullptr, nullptr, // deqScale1, quantScale1 - nullptr, nullptr, nullptr, // deqScale2, quantScale2, quantOffset2 - nullptr, nullptr, // antiquantScale, antiquantOffset - nullptr, // blockTable - nullptr, nullptr, // qPadSize, kvPadSize - nullptr, nullptr, // kAntiquantScale, kAntiQuantOffset - nullptr, nullptr, // vAntiquantScale, vAntiQuantOffset - nullptr, nullptr, nullptr, // kSharedPrefix, vSharedPrefix, actSharedLen - numHeads, scaleValue, // heads, scaleValue - preTokens, nextTokens, // preTokens, nextTokens - layout, // inputLayout - numKeyValueHeads, // numKVHeads - sparseMode, innerPrecise, // sparseMode, innerPrecise - blockSize, antiquantMode, // blockSize, antiquantMode - softmaxLseFlag, // softmaxLseFlag - keyAntiquantMode, valueAntiquantMode, // keyAntiqMode, valueAntiqMode - acl_dst_f16_tensor, // attentionOut - nullptr // softmaxLse + int64_t * out_f16_ne = src0_bsnd_ne; + size_t out_f16_nb[GGML_MAX_DIMS]; + out_f16_nb[0] = faElemSize; + for (int i = 1; i < GGML_MAX_DIMS; ++i) { + out_f16_nb[i] = out_f16_nb[i - 1] * out_f16_ne[i - 1]; + } + + fa_dst_tensor = + ggml_cann_create_tensor(out_f16_buffer, faDataType, faElemSize, out_f16_ne, out_f16_nb, GGML_MAX_DIMS); + } else { + fa_dst_tensor = ggml_cann_create_tensor(dst); + } + + GGML_CANN_CALL_ACLNN_OP(ctx, FusedInferAttentionScoreV2, acl_q_tensor, acl_k_tensor_list, + acl_v_tensor_list, // q, k, v + bcast_pse_tensor, nullptr, // pse, mask + nullptr, nullptr, // actSeqLen, actSeqLenkv + nullptr, nullptr, // deqScale1, quantScale1 + nullptr, nullptr, nullptr, // deqScale2, quantScale2, quantOffset2 + nullptr, nullptr, // antiquantScale, antiquantOffset + nullptr, // blockTable + nullptr, nullptr, // qPadSize, kvPadSize + nullptr, nullptr, // kAntiquantScale, kAntiQuantOffset + nullptr, nullptr, // vAntiquantScale, vAntiQuantOffset + nullptr, nullptr, nullptr, // kSharedPrefix, vSharedPrefix, actSharedLen + numHeads, scaleValue, // heads, scaleValue + preTokens, nextTokens, // preTokens, nextTokens + layout, // inputLayout + numKeyValueHeads, // numKVHeads + sparseMode, innerPrecise, // sparseMode, innerPrecise + blockSize, antiquantMode, // blockSize, antiquantMode + softmaxLseFlag, // softmaxLseFlag + keyAntiquantMode, valueAntiquantMode, // keyAntiqMode, valueAntiqMode + fa_dst_tensor, // attentionOut + nullptr // softmaxLse ); - // Step 6: post-processing, permute and cast to f32 - aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); - // TODO: when dst is fp16, don't need cast - aclnn_cast(ctx, acl_dst_f16_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type)); - ggml_cann_release_resources(ctx, acl_src0_f16_tensor, - acl_src1_f16_tensor, - acl_src2_f16_tensor, - acl_dst_f16_tensor, - acl_dst_tensor); - if(src3 != nullptr){ - ggml_cann_release_resources(ctx, bcast_pse_tensor); + if (dst->type == GGML_TYPE_F32) { + // Step 6: post-processing, permute and cast to f32 + aclTensor * acl_dst_tensor = ggml_cann_create_tensor(dst); + aclnn_cast(ctx, fa_dst_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type)); } - }else{ + + ggml_cann_release_resources(ctx, acl_src0_f16_tensor, acl_k_tensor_list, acl_v_tensor_list, fa_dst_tensor, + acl_dst_tensor, bcast_pse_tensor); + + } else { GGML_ABORT("Function is not implemented."); } } diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h old mode 100755 new mode 100644 index 5c510cc993..ec7455af88 --- a/ggml/src/ggml-cann/aclnn_ops.h +++ b/ggml/src/ggml-cann/aclnn_ops.h @@ -62,7 +62,7 @@ * @param dst The ggml tensor representing the destination, which op is * GGML_OP_REPEAT and specifies the desired dimensions. */ -void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_repeat(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies the Leaky ReLU activation function to a tensor using the CANN @@ -82,7 +82,7 @@ void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result of the Leaky ReLU * activation is stored, which op is `GGML_OP_LEAKY_RELU` */ -void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_leaky_relu(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Concatenates multiple tensors along a specified dimension using the @@ -97,7 +97,7 @@ void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @attention tensorList length should be 2 and the dimension using for concat * default to 1. */ -void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_concat(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Generates a sequence of evenly spaced values within a specified @@ -113,7 +113,7 @@ void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst); * `start`, 'stop' and 'step' are in dst->op_params and dst->op is * `GGML_OP_ARANGE`. */ -void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_arange(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies a clamp operation to the elements of a ggml tensor using the @@ -131,7 +131,7 @@ void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the clamped values will be stored. * dst->op is `GGML_OP_CLAMP`, `min` and `max` value is in dst->params. */ -void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_clamp(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Scales the elements of a ggml tensor by a constant factor using the @@ -148,7 +148,7 @@ void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the scaled values will be stored. * dst->op is `GGML_OP_SCALE` and `scale` value is in dst->params. */ -void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_scale(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Sorts the elements of a ggml tensor and returns the indices that @@ -163,7 +163,7 @@ void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the sorted indices will be stored. * dst->op is `GGML_OP_ARGSORT`. */ -void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_argsort(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the Layer Normalization for a ggml tensor using the CANN @@ -185,7 +185,7 @@ void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the normalized values will be stored. * @attention `Var` defaults to dst->ne[0]. */ -void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the Group Normalization for a ggml tensor using the CANN @@ -209,7 +209,7 @@ void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); * * @attention eps defaults to 1e-6f. */ -void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the accumulation of tensors using the CANN backend. @@ -228,7 +228,7 @@ void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the accumulated values will be stored. * `inplace` is in dst->params, and dst->op is `GGML_OP_ACC`. */ -void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the sum of elements along the last dimension of a ggml tensor @@ -244,7 +244,7 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst); * * @attention `reduce_dims` defaults to 3, which means the last dimension. */ -void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the sum of elements in a ggml tensor. @@ -258,7 +258,7 @@ void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); * */ -void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Upsamples a ggml tensor using nearest neighbor interpolation using @@ -274,8 +274,7 @@ void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the upsampled values will be stored. * dst->op is `GGML_OP_UPSCALE`. */ -void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst); +void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Pads a ggml tensor to match the dimensions of the destination tensor @@ -290,7 +289,7 @@ void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, * @param dst The destination tensor, which specifies the target dimensions for * padding. dst->op is `GGML_OP_PAD`. */ -void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_pad(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Executes a 2D pooling operation on a ggml tensor using the CANN @@ -307,7 +306,7 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor on which the pooling operation is to be * performed. dst->op is `GGML_OP_POOL_2D`. */ -void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Duplicates a ggml tensor using the CANN backend. @@ -326,7 +325,7 @@ void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst); * different shape and dst is no-contiguous. * @note: This func need to simplify. */ -void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_dup(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the Root Mean Square (RMS) normalization of a ggml tensor @@ -348,7 +347,7 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the normalized values will be stored. * dst->op is `GGML_OP_RMS_NORM`. */ -void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_rms_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies a diagonal mask to the tensor with a specified value. @@ -363,7 +362,7 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); * `GGML_OP_DIAG_MASK` * @param value The value to use for masking. */ -void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float value); +void ggml_cann_diag_mask(ggml_backend_cann_context & ctx, ggml_tensor * dst, float value); /** * @brief Performs an image-to-column transformation on the input tensor. @@ -378,7 +377,7 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float * @param dst The destination tensor that stores the result of the operation. * dst->op is `GGML_OP_IM2COL`. */ -void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_im2col(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes time step embeddings using sine and cosine functions. @@ -392,10 +391,10 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result of the embedding operation * will be stored. dst->op is `GGML_OP_TIMESTEP_EMBEDDING`. */ -void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor * dst); // @see ggml_cann_dup. -void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the softmax activation with optional masking. @@ -417,7 +416,7 @@ void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. dst->op is * `GGML_OP_SOFTMAX`. */ -void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Extracts specific rows from a tensor based on indices. @@ -429,7 +428,7 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param ctx The backend CANN context for executing operations. * @param dst The destination tensor where the extracted rows will be stored. */ -void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Writes specific rows into a tensor at positions specified by indices. @@ -441,7 +440,7 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param ctx The backend CANN context for executing operations. * @param dst The destination tensor where the specified rows will be updated. */ -void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Executes matrix multiplication for the given tensor. @@ -454,7 +453,7 @@ void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor for storing the result of the matrix * multiplication. dst->op is `GGML_OP_MUL_MAT`. */ -void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_mul_mat(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies Rotary Positional Embedding (RoPE) to the input tensor. @@ -477,7 +476,7 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @note The function currently does not support cases where the freq_scale is * not equal 1. */ -void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the index of the maximum value along the specified dimension @@ -492,7 +491,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the indices of the maximum values will * be stored. dst->op is `GGML_OP_ARGMAX`. */ -void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_argmax(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Adds two tensors element-wise and stores the result in a destination @@ -509,8 +508,10 @@ void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param acl_src1 The second source tensor. * @param acl_dst The destination tensor where the result will be stored. */ -void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst = nullptr); +void aclnn_add(ggml_backend_cann_context & ctx, + aclTensor * acl_src0, + aclTensor * acl_src1, + aclTensor * acl_dst = nullptr); /** * @brief Sub two tensors element-wise and stores the result in a destination @@ -527,8 +528,10 @@ void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, * @param acl_src1 The second source tensor. * @param acl_dst The destination tensor where the result will be stored. */ -void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst = nullptr); +void aclnn_sub(ggml_backend_cann_context & ctx, + aclTensor * acl_src0, + aclTensor * acl_src1, + aclTensor * acl_dst = nullptr); /** * @brief Performs element-wise multiplication of two tensors and stores the @@ -546,8 +549,10 @@ void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, * @param acl_other The second tensor for element-wise multiplication. * @param acl_dst The destination tensor where the result will be stored. */ -void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst = nullptr); +void aclnn_mul(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_other, + aclTensor * acl_dst = nullptr); /** * @brief Matrix division, optionally in-place. @@ -567,8 +572,10 @@ void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param inplace Flag indicating whether to perform the operation in-place on * `acl_src`. */ -void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst = nullptr); +void aclnn_div(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_other, + aclTensor * acl_dst = nullptr); /** * @brief Applies element-wise cosine function to the elements of a tensor. @@ -584,8 +591,7 @@ void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param acl_dst The destination tensor where the cosine results will be * stored. */ -void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst); +void aclnn_cos(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst); /** * @brief Applies element-wise sine function to the elements of a tensor. @@ -602,8 +608,7 @@ void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param acl_src The source tensor on which the sine function will be applied. * @param acl_dst The destination tensor where the sine results will be stored. */ -void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst); +void aclnn_sin(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst); /** * @brief Prepares broadcast-compatible ACL tensors for two input tensors and one @@ -621,8 +626,12 @@ void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param acl_src1 Output pointer to the created ACL tensor corresponding to src1. * @param acl_dst Output pointer to the created ACL tensor corresponding to dst. */ -void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, - aclTensor ** acl_src0, aclTensor ** acl_src1, aclTensor ** acl_dst); +void bcast_shape(ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst, + aclTensor ** acl_src0, + aclTensor ** acl_src1, + aclTensor ** acl_dst); /** * @brief Computes the 1D transposed convolution (deconvolution) of a ggml @@ -637,7 +646,7 @@ void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, * @param dst The destination tensor where the transposed convolution result * will be stored. dst->op is `GGML_OP_CONV_TRANSPOSE_1D`. */ -void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies the ELU (Exponential Linear Unit) activation to a ggml tensor @@ -662,7 +671,7 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds * @param dst The destination tensor where the ELU-activated result will be stored. * dst->op is expected to be `GGML_OP_ELU`. */ -void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_elu(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the mean of a ggml tensor element-wise using the CANN backend. @@ -677,7 +686,7 @@ void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the mean result will be stored. * dst->op is expected to be `GGML_OP_MEAN`. */ -void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_mean(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies 1D reflect padding to a ggml tensor using the CANN backend. @@ -692,7 +701,7 @@ void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the padded result will be stored. * dst->op is expected to be `GGML_OP_PAD_REFLECT_1D`. */ -void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_pad_reflect_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Counts the number of equal elements in two ggml tensors using the CANN backend. @@ -708,7 +717,7 @@ void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. * dst->op is expected to be `GGML_OP_COUNT_EQUAL`. */ -void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies the Step activation function to a ggml tensor using the CANN backend. @@ -723,7 +732,7 @@ void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. * dst->op is expected to be `GGML_OP_STEP`. */ -void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Performs the Flash Attention extended operator using the CANN backend. @@ -738,59 +747,46 @@ void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. * dst->op is expected to be `GGML_OP_FLASH_ATTN_EXT`. */ -void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst); /* * @brief A generic wrapper for ACL resources with custom deleter support. */ -using any_acl_resource = std::unique_ptr>; +using any_acl_resource = std::unique_ptr>; /** * @brief Trait structure used to define how to destroy a given ACL resource type. * * @tparam T ACL resource type. */ -template -struct acl_resource_traits; +template struct acl_resource_traits; /** * @brief Specialization for aclTensor, defines how to destroy an aclTensor resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyTensor(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyTensor(static_cast(p))); } }; /** * @brief Specialization for aclIntArray, defines how to destroy an aclIntArray resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyIntArray(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyIntArray(static_cast(p))); } }; /** * @brief Specialization for aclScalar, defines how to destroy an aclScalar resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyScalar(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyScalar(static_cast(p))); } }; /** * @brief Specialization for aclTensorList, defines how to destroy an aclTensorList resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyTensorList(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyTensorList(static_cast(p))); } }; /** @@ -800,14 +796,8 @@ struct acl_resource_traits { * @param ptr Raw pointer to ACL resource. * @return any_acl_resource Smart pointer that handles destruction. */ -template -any_acl_resource make_acl_resource(T* ptr) { - return any_acl_resource( - static_cast(ptr), - [](void* p) { - acl_resource_traits::destroy(p); - } - ); +template any_acl_resource make_acl_resource(T * ptr) { + return any_acl_resource(static_cast(ptr), [](void * p) { acl_resource_traits::destroy(p); }); } /** @@ -817,8 +807,7 @@ any_acl_resource make_acl_resource(T* ptr) { * @param vec Target vector to hold ACL resources. * @param args Raw pointers to ACL resources. */ -template -void register_acl_resources(std::vector& vec, Args*... args) { +template void register_acl_resources(std::vector & vec, Args *... args) { (vec.emplace_back(make_acl_resource(args)), ...); } @@ -826,39 +815,36 @@ void register_acl_resources(std::vector& vec, Args*... args) { * @brief Task class that wraps the execution of an aclnn function call. */ class aclnn_task : public cann_task { - public: - aclnn_task(aclnn_func_t aclnn_func, void * workspace_addr, - uint64_t workspace_size, aclOpExecutor * executor, - aclrtStream stream) : - aclnn_func_(aclnn_func), - workspace_addr_(workspace_addr), - workspace_size_(workspace_size), - executor_(executor), - stream_(stream) {} - virtual void run_task() override { - ACL_CHECK(aclnn_func_(workspace_addr_, workspace_size_, executor_, stream_)); - } - private: - aclnn_func_t aclnn_func_; - void * workspace_addr_; - uint64_t workspace_size_; - aclOpExecutor * executor_; - aclrtStream stream_; + public: + aclnn_task(aclnn_func_t aclnn_func, + void * workspace_addr, + uint64_t workspace_size, + aclOpExecutor * executor, + aclrtStream stream) : + aclnn_func_(aclnn_func), + workspace_addr_(workspace_addr), + workspace_size_(workspace_size), + executor_(executor), + stream_(stream) {} + + virtual void run_task() override { ACL_CHECK(aclnn_func_(workspace_addr_, workspace_size_, executor_, stream_)); } + private: + aclnn_func_t aclnn_func_; + void * workspace_addr_; + uint64_t workspace_size_; + aclOpExecutor * executor_; + aclrtStream stream_; }; /** * @brief Task class that releases ACL resources after usage. */ class release_resource_task : public cann_task { -public: - release_resource_task(std::vector&& resources){ - resource_ = std::move(resources); - } + public: + release_resource_task(std::vector && resources) { resource_ = std::move(resources); } - virtual void run_task() override { - resource_.clear(); - } -private: + virtual void run_task() override { resource_.clear(); } + private: std::vector resource_; }; @@ -866,38 +852,40 @@ private: * @brief Task class for performing asynchronous memory copy operations. */ class async_memcpy_task : public cann_task { -public: - async_memcpy_task(void* dst, const void* src, size_t size, - aclrtMemcpyKind kind, aclrtStream stream) - : dst_(dst), src_(src), size_(size), kind_(kind), stream_(stream) {} + public: + async_memcpy_task(void * dst, const void * src, size_t size, aclrtMemcpyKind kind, aclrtStream stream) : + dst_(dst), + src_(src), + size_(size), + kind_(kind), + stream_(stream) {} - virtual void run_task() override { - ACL_CHECK(aclrtMemcpyAsync(dst_, size_, src_, size_, kind_, stream_)); - } -private: - void* dst_; - const void* src_; - size_t size_; + virtual void run_task() override { ACL_CHECK(aclrtMemcpyAsync(dst_, size_, src_, size_, kind_, stream_)); } + private: + void * dst_; + const void * src_; + size_t size_; aclrtMemcpyKind kind_; - aclrtStream stream_; + aclrtStream stream_; }; /** * @brief Task class for performing asynchronous memory set operations. */ class async_memset_task : public cann_task { - public: - async_memset_task(void* buffer, size_t size, int32_t value, aclrtStream stream) - : buffer_(buffer), size_(size), value_(value), stream_(stream) {} + public: + async_memset_task(void * buffer, size_t size, int32_t value, aclrtStream stream) : + buffer_(buffer), + size_(size), + value_(value), + stream_(stream) {} - virtual void run_task() override { - ACL_CHECK(aclrtMemsetAsync(buffer_, size_, value_, size_, stream_)); - } - private: - void* buffer_; - size_t size_; - int32_t value_; - aclrtStream stream_; + virtual void run_task() override { ACL_CHECK(aclrtMemsetAsync(buffer_, size_, value_, size_, stream_)); } + private: + void * buffer_; + size_t size_; + int32_t value_; + aclrtStream stream_; }; /** @@ -918,25 +906,24 @@ class async_memset_task : public cann_task { * same stream are executed in queue order. */ -#define GGML_CANN_CALL_ACLNN_OP(CTX, OP_NAME, ...) \ - do { \ - uint64_t workspaceSize = 0; \ - aclOpExecutor * executor; \ - void * workspaceAddr = nullptr; \ - ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor));\ - /* workspace should alloced in main thread to keep malloc order when using vmm. */ \ - if (workspaceSize > 0) { \ - ggml_cann_pool_alloc workspace_allocator(CTX.pool(), workspaceSize); \ - workspaceAddr = workspace_allocator.get(); \ - } \ - if (CTX.async_mode) { \ - auto task = \ - std::make_unique(aclnn##OP_NAME, workspaceAddr, workspaceSize, \ - executor, CTX.stream()); \ - CTX.task_queue.submit_task(std::move(task)); \ - } else { \ - ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, CTX.stream()));\ - } \ +#define GGML_CANN_CALL_ACLNN_OP(CTX, OP_NAME, ...) \ + do { \ + uint64_t workspaceSize = 0; \ + aclOpExecutor * executor; \ + void * workspaceAddr = nullptr; \ + ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor)); \ + /* workspace should alloced in main thread to keep malloc order when using vmm. */ \ + if (workspaceSize > 0) { \ + ggml_cann_pool_alloc workspace_allocator(CTX.pool(), workspaceSize); \ + workspaceAddr = workspace_allocator.get(); \ + } \ + if (CTX.async_mode) { \ + auto task = \ + std::make_unique(aclnn##OP_NAME, workspaceAddr, workspaceSize, executor, CTX.stream()); \ + CTX.task_queue.submit_task(std::move(task)); \ + } else { \ + ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, CTX.stream())); \ + } \ } while (0) /** @@ -947,11 +934,10 @@ class async_memset_task : public cann_task { * @param ctx Backend context which manages task submission and async mode. * @param args Pointers to ACL resources to be released. */ -template -void ggml_cann_release_resources(ggml_backend_cann_context & ctx, Args &&... args) { +template void ggml_cann_release_resources(ggml_backend_cann_context & ctx, Args &&... args) { std::vector resources; register_acl_resources(resources, std::forward(args)...); - if(ctx.async_mode) { + if (ctx.async_mode) { auto task = std::make_unique(std::move(resources)); ctx.task_queue.submit_task(std::move(task)); } @@ -966,8 +952,11 @@ void ggml_cann_release_resources(ggml_backend_cann_context & ctx, Args &&... arg * @param len Size of memory to copy (in bytes). * @param kind Type of memory copy (host-to-device, device-to-host, etc). */ -inline void ggml_cann_async_memcpy(ggml_backend_cann_context & ctx, void * dst, - const void * src, size_t len, aclrtMemcpyKind kind) { +inline void ggml_cann_async_memcpy(ggml_backend_cann_context & ctx, + void * dst, + const void * src, + size_t len, + aclrtMemcpyKind kind) { if (ctx.async_mode) { auto task = std::make_unique(dst, const_cast(src), len, kind, ctx.stream()); ctx.task_queue.submit_task(std::move(task)); @@ -976,8 +965,11 @@ inline void ggml_cann_async_memcpy(ggml_backend_cann_context & ctx, void * dst, } } -inline void ggml_cann_async_memcpy(ggml_backend_cann_context * ctx, void * dst, - const void * src, size_t len, aclrtMemcpyKind kind) { +inline void ggml_cann_async_memcpy(ggml_backend_cann_context * ctx, + void * dst, + const void * src, + size_t len, + aclrtMemcpyKind kind) { if (ctx->async_mode) { auto task = std::make_unique(dst, const_cast(src), len, kind, ctx->stream()); ctx->task_queue.submit_task(std::move(task)); @@ -994,8 +986,7 @@ inline void ggml_cann_async_memcpy(ggml_backend_cann_context * ctx, void * dst, * @param size Size of the memory buffer (in bytes). * @param value Value to set in the buffer. */ -inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffer, - size_t size, int value) { +inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffer, size_t size, int value) { if (ctx.async_mode) { auto task = std::make_unique(buffer, size, value, ctx.stream()); ctx.task_queue.submit_task(std::move(task)); @@ -1029,7 +1020,7 @@ inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffe * @param dst The destination tensor where the expert-weighted token outputs are stored. * Expected to be of shape [M, K, N, 1]. */ -void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Check whether a tensor is a weight tensor for matrix multiplication. @@ -1041,20 +1032,14 @@ void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst); * * @param tensor Pointer to the target ggml_tensor object (const-qualified). */ -static bool is_matmul_weight(const ggml_tensor* tensor) { - std::string name = ggml_get_name(tensor); - static const std::unordered_set weight_suffixes{ - "output.weight", - "attn_q.weight", - "attn_k.weight", - "attn_v.weight", - "attn_output.weight", - "ffn_gate.weight", - "ffn_up.weight", - "ffn_down.weight" - }; +static bool is_matmul_weight(const ggml_tensor * tensor) { + std::string name = ggml_get_name(tensor); + static const std::unordered_set weight_suffixes{ "output.weight", "attn_q.weight", + "attn_k.weight", "attn_v.weight", + "attn_output.weight", "ffn_gate.weight", + "ffn_up.weight", "ffn_down.weight" }; - for (const auto& suffix : weight_suffixes) { + for (const auto & suffix : weight_suffixes) { if (name.find(suffix) != std::string::npos) { return true; } @@ -1078,14 +1063,13 @@ static bool is_matmul_weight(const ggml_tensor* tensor) { * @param ctx The CANN backend context used to manage execution and resources. * @param dst The destination tensor. */ -template -void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; +template void ggml_cann_binary_op(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; - aclTensor* acl_src0; - aclTensor* acl_src1; - aclTensor* acl_dst; + aclTensor * acl_src0; + aclTensor * acl_src1; + aclTensor * acl_dst; // Need bcast bcast_shape(src0, src1, dst, &acl_src0, &acl_src1, &acl_dst); @@ -1094,7 +1078,6 @@ void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_release_resources(ctx, acl_src0, acl_src1, acl_dst); } - /** * @brief Applies a unary operation to an input tensor using the CANN backend. * @@ -1107,12 +1090,12 @@ void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param ctx The CANN backend context for managing resources and execution. * @param dst The destination tensor. Its src[0] is treated as the input tensor. */ -template - void ggml_cann_op_unary(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +template +void ggml_cann_op_unary(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); unary_op(ctx, acl_src, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst); @@ -1138,9 +1121,9 @@ template * * @see GGML_CANN_CALL_OP_UNARY */ -void ggml_cann_op_unary( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_op_unary(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst); /** * @brief Applies a gated (GLU-style) unary operation using the CANN backend. @@ -1172,9 +1155,9 @@ void ggml_cann_op_unary( * * @see GGML_CANN_CALL_OP_UNARY_GATED */ -void ggml_cann_op_unary_gated( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_op_unary_gated(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst); /** * @brief Helper macro to call a unary ACL operator via ggml_cann_op_unary. @@ -1197,16 +1180,13 @@ void ggml_cann_op_unary_gated( * @see ggml_cann_op_unary * @see GGML_CANN_CALL_ACLNN_OP */ -#define GGML_CANN_CALL_OP_UNARY(OP_NAME) \ - do { \ - auto lambda = [](ggml_backend_cann_context& ctx, \ - aclTensor* acl_src, \ - aclTensor* acl_dst) { \ - GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ - }; \ - ggml_cann_op_unary(lambda, ctx, dst); \ - } \ - while (0) +#define GGML_CANN_CALL_OP_UNARY(OP_NAME) \ + do { \ + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \ + GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ + }; \ + ggml_cann_op_unary(lambda, ctx, dst); \ + } while (0) /** * @brief Helper macro to call a gated unary ACL operator via ggml_cann_op_unary_gated. @@ -1229,15 +1209,12 @@ void ggml_cann_op_unary_gated( * @see ggml_cann_op_unary_gated * @see GGML_CANN_CALL_ACLNN_OP */ -#define GGML_CANN_CALL_OP_UNARY_GATED(OP_NAME) \ - do { \ - auto lambda = [](ggml_backend_cann_context& ctx, \ - aclTensor* acl_src, \ - aclTensor* acl_dst) { \ - GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ - }; \ - ggml_cann_op_unary_gated(lambda, ctx, dst); \ - } \ - while (0) +#define GGML_CANN_CALL_OP_UNARY_GATED(OP_NAME) \ + do { \ + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \ + GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ + }; \ + ggml_cann_op_unary_gated(lambda, ctx, dst); \ + } while (0) #endif // CANN_ACLNN_OPS diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h old mode 100755 new mode 100644 index debbcadc1e..e87dbcf329 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -44,7 +44,7 @@ #include "../include/ggml.h" #include "../ggml-impl.h" -#define MATRIX_ROW_PADDING 512 +#define MATRIX_ROW_PADDING 512 #define GGML_CANN_MAX_STREAMS 8 /** @@ -56,8 +56,7 @@ * @param line The line number at which the error occurred. * @param msg The error message. */ -[[noreturn]] void ggml_cann_error(const char* stmt, const char* func, - const char* file, int line, const char* msg); +[[noreturn]] void ggml_cann_error(const char * stmt, const char * func, const char * file, int line, const char * msg); /** * @brief Checks the result of a CANN function call and invokes the error @@ -89,25 +88,24 @@ struct ggml_cann_device_info { * @brief Information about a single CANN device. */ struct cann_device_info { - int cc; /**< Compute capability. */ + int cc; /**< Compute capability. */ size_t smpb; /**< Maximum shared memory per block. */ - bool vmm; /**< Virtual memory support. */ + bool vmm; /**< Virtual memory support. */ size_t vmm_granularity; /**< Granularity of virtual memory. */ size_t total_vram; /**< Total video RAM available on the device. */ }; - cann_device_info devices[GGML_CANN_MAX_DEVICES] = - {}; /**< Array of CANN device information. */ + cann_device_info devices[GGML_CANN_MAX_DEVICES] = {}; /**< Array of CANN device information. */ }; -const ggml_cann_device_info& ggml_cann_info(); +const ggml_cann_device_info & ggml_cann_info(); -void ggml_cann_set_device(int32_t device); +void ggml_cann_set_device(int32_t device); int32_t ggml_cann_get_device(); -std::optional get_env(const std::string& name); -bool parse_bool(const std::string& value); -int parse_integer(const std::string& value); +std::optional get_env(const std::string & name); +bool parse_bool(const std::string & value); +int parse_integer(const std::string & value); /** * @brief Abstract base class for memory pools used by CANN. @@ -126,7 +124,7 @@ struct ggml_cann_pool { * will be stored. * @return Pointer to the allocated memory block. */ - virtual void* alloc(size_t size, size_t* actual_size) = 0; + virtual void * alloc(size_t size, size_t * actual_size) = 0; /** * @brief Frees a previously allocated memory block. @@ -136,16 +134,16 @@ struct ggml_cann_pool { * @note Note that all CANN opertors are running async. Make sure memory is * still avaiable before this operator finished. */ - virtual void free(void* ptr, size_t size) = 0; + virtual void free(void * ptr, size_t size) = 0; }; /** * @brief RAII wrapper for managing memory allocations from a CANN memory pool. */ struct ggml_cann_pool_alloc { - ggml_cann_pool* pool = nullptr; /**< Pointer to the memory pool. */ - void* ptr = nullptr; /**< Pointer to the allocated memory block. */ - size_t actual_size = 0; /**< Actual size of the allocated memory block. */ + ggml_cann_pool * pool = nullptr; /**< Pointer to the memory pool. */ + void * ptr = nullptr; /**< Pointer to the allocated memory block. */ + size_t actual_size = 0; /**< Actual size of the allocated memory block. */ /** * @brief Default constructor. @@ -156,16 +154,14 @@ struct ggml_cann_pool_alloc { * @brief Constructor that initializes the memory pool. * @param pool Reference to the memory pool. */ - explicit ggml_cann_pool_alloc(ggml_cann_pool& pool) : pool(&pool) {} + explicit ggml_cann_pool_alloc(ggml_cann_pool & pool) : pool(&pool) {} /** * @brief Constructor that initializes the memory pool and allocates memory. * @param pool Reference to the memory pool. * @param size Size of the memory block to allocate. */ - ggml_cann_pool_alloc(ggml_cann_pool& pool, size_t size) : pool(&pool) { - alloc(size); - } + ggml_cann_pool_alloc(ggml_cann_pool & pool, size_t size) : pool(&pool) { alloc(size); } /** * @brief Destructor that frees the allocated memory block. @@ -181,7 +177,7 @@ struct ggml_cann_pool_alloc { * @param size Size of the memory block to allocate. * @return Pointer to the allocated memory block. */ - void* alloc(size_t size) { + void * alloc(size_t size) { GGML_ASSERT(pool != nullptr); GGML_ASSERT(ptr == nullptr); ptr = pool->alloc(size, &this->actual_size); @@ -194,7 +190,7 @@ struct ggml_cann_pool_alloc { * @param size Size of the memory block to allocate. * @return Pointer to the allocated memory block. */ - void* alloc(ggml_cann_pool& pool, size_t size) { + void * alloc(ggml_cann_pool & pool, size_t size) { this->pool = &pool; return alloc(size); } @@ -203,25 +199,25 @@ struct ggml_cann_pool_alloc { * @brief Gets the pointer to the allocated memory block. * @return Pointer to the allocated memory block. */ - void* get() { return ptr; } + void * get() { return ptr; } // Deleted copy constructor - ggml_cann_pool_alloc(const ggml_cann_pool_alloc&) = delete; + ggml_cann_pool_alloc(const ggml_cann_pool_alloc &) = delete; // Deleted move constructor - ggml_cann_pool_alloc(ggml_cann_pool_alloc&&) = delete; + ggml_cann_pool_alloc(ggml_cann_pool_alloc &&) = delete; // Deleted copy assignment operator - ggml_cann_pool_alloc& operator=(const ggml_cann_pool_alloc&) = delete; + ggml_cann_pool_alloc & operator=(const ggml_cann_pool_alloc &) = delete; // Deleted move assignment operator - ggml_cann_pool_alloc& operator=(ggml_cann_pool_alloc&&) = delete; + ggml_cann_pool_alloc & operator=(ggml_cann_pool_alloc &&) = delete; }; /** * @brief Function pointer type for ACLNN operator calls. */ -using aclnn_func_t = aclnnStatus (*)(void*, uint64_t, aclOpExecutor*, aclrtStream); +using aclnn_func_t = aclnnStatus (*)(void *, uint64_t, aclOpExecutor *, aclrtStream); /** * @brief Base class for all CANN tasks to be submitted to the task queue. @@ -229,7 +225,7 @@ using aclnn_func_t = aclnnStatus (*)(void*, uint64_t, aclOpExecutor*, aclrtStrea * Users should override the run_task() method with actual task logic. */ class cann_task { -public: + public: virtual void run_task() {} }; @@ -237,16 +233,20 @@ public: * @brief A lock-free ring-buffer based task queue for asynchronously executing cann_task instances. */ class cann_task_queue { -public: + public: /** * @brief Constructs a task queue with a fixed power-of-two capacity for a specific device. * * @param capacity Queue capacity. Must be a power of 2. * @param device Target device ID (used for context setting). */ - explicit cann_task_queue(size_t capacity, int32_t device) - : buffer_(capacity), capacity_(capacity), head_(0), tail_(0), - running_(false), device_(device) { + explicit cann_task_queue(size_t capacity, int32_t device) : + buffer_(capacity), + capacity_(capacity), + head_(0), + tail_(0), + running_(false), + device_(device) { GGML_ASSERT((capacity & (capacity - 1)) == 0 && "capacity must be power of 2"); mask_ = capacity_ - 1; } @@ -257,7 +257,7 @@ public: * @param item Unique pointer to the task. * @return true if the task was successfully enqueued, false if the queue was full. */ - bool enqueue(std::unique_ptr&& item) { + bool enqueue(std::unique_ptr && item) { size_t next_tail = (tail_ + 1) & mask_; if (next_tail == head_) { @@ -276,17 +276,16 @@ public: * * @param task Task to be submitted. */ - void submit_task(std::unique_ptr&& task) { - while(!enqueue(std::move(task))) { + void submit_task(std::unique_ptr && task) { + while (!enqueue(std::move(task))) { std::this_thread::yield(); continue; } if (!running_) { running_ = true; - thread_ = std::thread(&cann_task_queue::execute, this); + thread_ = std::thread(&cann_task_queue::execute, this); } - } /** @@ -309,7 +308,7 @@ public: } } -private: + private: /** * @brief Worker thread function that continuously dequeues and executes tasks. */ @@ -317,7 +316,7 @@ private: ggml_cann_set_device(device_); while (running_) { - if(head_ == tail_) { + if (head_ == tail_) { std::this_thread::yield(); continue; } @@ -330,24 +329,24 @@ private: } std::vector> buffer_; - const size_t capacity_; - size_t mask_; - size_t head_; - size_t tail_; - bool running_; - std::thread thread_; - int32_t device_; + const size_t capacity_; + size_t mask_; + size_t head_; + size_t tail_; + bool running_; + std::thread thread_; + int32_t device_; }; #ifdef USE_ACL_GRAPH struct ggml_graph_node_properties { // dst tensor - void * node_address; + void * node_address; int64_t ne[GGML_MAX_DIMS]; - size_t nb[GGML_MAX_DIMS]; + size_t nb[GGML_MAX_DIMS]; // src tensor - void * src_address[GGML_MAX_SRC]; + void * src_address[GGML_MAX_SRC]; int64_t src_ne[GGML_MAX_SRC][GGML_MAX_DIMS]; size_t src_nb[GGML_MAX_SRC][GGML_MAX_DIMS]; @@ -376,13 +375,11 @@ struct ggml_cann_graph { * move existing graphs to the front (most recently used), and clear the cache. */ struct ggml_cann_graph_lru_cache { - size_t capacity; /**< Maximum number of graphs in the cache. */ + size_t capacity; /**< Maximum number of graphs in the cache. */ - std::list cache_list; /**< List storing cached graphs as raw pointers. */ + std::list cache_list; /**< List storing cached graphs as raw pointers. */ - ggml_cann_graph_lru_cache() { - capacity = parse_integer(get_env("GGML_CANN_GRAPH_CACHE_CAPACITY").value_or("12")); - } + ggml_cann_graph_lru_cache() { capacity = parse_integer(get_env("GGML_CANN_GRAPH_CACHE_CAPACITY").value_or("12")); } /** * @brief Push a new graph to the front of the cache. @@ -390,11 +387,11 @@ struct ggml_cann_graph_lru_cache { * @param new_node Pointer to the new ggml_cann_graph to cache. * Ownership is transferred to the cache (cache will delete it). */ - void push(ggml_cann_graph* new_node) { + void push(ggml_cann_graph * new_node) { if (cache_list.size() >= capacity) { - ggml_cann_graph* old = cache_list.back(); + ggml_cann_graph * old = cache_list.back(); cache_list.pop_back(); - delete old; // free the old graph + delete old; // free the old graph } cache_list.push_front(new_node); } @@ -403,7 +400,7 @@ struct ggml_cann_graph_lru_cache { * @brief Move an existing graph to the front of the cache. * @param node Pointer to the ggml_cann_graph to move. */ - void move_to_front(ggml_cann_graph* node) { + void move_to_front(ggml_cann_graph * node) { cache_list.remove(node); cache_list.push_front(node); } @@ -421,92 +418,89 @@ struct ggml_cann_graph_lru_cache { /** * @brief Destructor that clears the cache and frees all cached graphs. */ - ~ggml_cann_graph_lru_cache() { - clear(); - } + ~ggml_cann_graph_lru_cache() { clear(); } }; #endif // USE_ACL_GRAPH struct ggml_cann_rope_cache { ~ggml_cann_rope_cache() { - if(theta_scale_cache != nullptr) { + if (theta_scale_cache != nullptr) { ACL_CHECK(aclrtFree(theta_scale_cache)); } - if(sin_cache != nullptr) { + if (sin_cache != nullptr) { ACL_CHECK(aclrtFree(sin_cache)); } - if(cos_cache != nullptr) { + if (cos_cache != nullptr) { ACL_CHECK(aclrtFree(cos_cache)); } } - void* theta_scale_cache = nullptr; + void * theta_scale_cache = nullptr; int64_t theta_scale_length = 0; // sin/cos cache, used only to accelerate first layer on each device - void* sin_cache = nullptr; - void* cos_cache = nullptr; - int64_t position_length = 0; + void * sin_cache = nullptr; + void * cos_cache = nullptr; + int64_t position_length = 0; // Properties to check before reusing the sincos cache - bool cached = false; - float ext_factor = 0.0f; - float theta_scale = 0.0f; - float freq_scale = 0.0f; - float attn_factor = 0.0f; - bool is_neox = false; + bool cached = false; + float ext_factor = 0.0f; + float theta_scale = 0.0f; + float freq_scale = 0.0f; + float attn_factor = 0.0f; + bool is_neox = false; }; struct ggml_cann_tensor_cache { ~ggml_cann_tensor_cache() { - if(cache != nullptr) { + if (cache != nullptr) { ACL_CHECK(aclrtFree(cache)); } } - void* cache = nullptr; - int64_t size = 0; + void * cache = nullptr; + int64_t size = 0; }; /** * @brief Context for managing CANN backend operations. */ struct ggml_backend_cann_context { - int32_t device; /**< Device ID. */ - std::string name; /**< Name of the device. */ - std::string description; /**< Description of the device. */ - aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */ + int32_t device; /**< Device ID. */ + std::string name; /**< Name of the device. */ + std::string description; /**< Description of the device. */ + aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */ #ifdef USE_ACL_GRAPH /// Cached CANN ACL graph used for executing the current ggml computation graph. ggml_cann_graph_lru_cache graph_lru_cache; - bool acl_graph_mode = true; + bool acl_graph_mode = true; #endif - cann_task_queue task_queue; - bool async_mode; + cann_task_queue task_queue; + bool async_mode; // Rope Cache - ggml_cann_rope_cache rope_cache; + ggml_cann_rope_cache rope_cache; // Constant Pool ggml_cann_tensor_cache rms_norm_one_tensor_cache; ggml_cann_tensor_cache rms_norm_zero_tensor_cache; - aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */ + aclrtStream streams[GGML_CANN_MAX_STREAMS] = { nullptr }; /**< Array of streams for the device. */ /** * @brief Constructor for initializing the context with a given device. * @param device Device ID. */ - explicit ggml_backend_cann_context(int device) - : device(device), name("CANN" + std::to_string(device)), task_queue(1024, device) { + explicit ggml_backend_cann_context(int device) : + device(device), + name("CANN" + std::to_string(device)), + task_queue(1024, device) { ggml_cann_set_device(device); description = aclrtGetSocName(); async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or("")); - GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__, - device, async_mode ? "ON" : "OFF"); + GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__, device, async_mode ? "ON" : "OFF"); #ifdef USE_ACL_GRAPH acl_graph_mode = parse_bool(get_env("GGML_CANN_ACL_GRAPH").value_or("on")); - GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n", - __func__, device, - acl_graph_mode ? "GRAPH" : "EAGER", - acl_graph_mode ? "acl graph enabled" : "acl graph disabled"); + GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n", __func__, device, acl_graph_mode ? "GRAPH" : "EAGER", + acl_graph_mode ? "acl graph enabled" : "acl graph disabled"); #endif } @@ -549,8 +543,7 @@ struct ggml_backend_cann_context { aclrtStream stream() { return stream(0); } // TODO: each stream should have a memory pool. - std::unique_ptr - mem_pool; /**< Memory pool for the device. */ + std::unique_ptr mem_pool; /**< Memory pool for the device. */ /** * @brief Create a new memory pool for a given device. @@ -563,7 +556,7 @@ struct ggml_backend_cann_context { * @brief Get or create the memory pool for the context. * @return Reference to the memory pool. */ - ggml_cann_pool& pool() { + ggml_cann_pool & pool() { if (mem_pool == nullptr) { mem_pool = new_pool_for_device(device); } diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp old mode 100755 new mode 100644 index ad1adba6b3..8bd5449f1f --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -56,14 +56,12 @@ * @param line The line number where the error occurred. * @param msg The error message. */ -[[noreturn]] void ggml_cann_error(const char* stmt, const char* func, - const char* file, int line, const char* msg) { +[[noreturn]] void ggml_cann_error(const char * stmt, const char * func, const char * file, int line, const char * msg) { int32_t id = -1; aclrtGetDevice(&id); GGML_LOG_ERROR("CANN error: %s\n", msg); - GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, - file, line); + GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line); GGML_LOG_ERROR(" %s\n", stmt); // abort with GGML_ASSERT to get a stack trace GGML_ABORT("CANN error"); @@ -79,7 +77,7 @@ void ggml_cann_set_device(const int32_t device) { aclrtGetDevice(¤t_device); if (device == current_device) { - return; + return; } ACL_CHECK(aclrtSetDevice(device)); } @@ -99,9 +97,11 @@ int32_t ggml_cann_get_device() { * @brief Get the value of the specified environment variable (name). * if not empty, return a std::string object */ -std::optional get_env(const std::string& name) { - const char* val = std::getenv(name.c_str()); - if (!val) return std::nullopt; +std::optional get_env(const std::string & name) { + const char * val = std::getenv(name.c_str()); + if (!val) { + return std::nullopt; + } std::string res = std::string(val); std::transform(res.begin(), res.end(), res.begin(), ::tolower); return res; @@ -110,8 +110,8 @@ std::optional get_env(const std::string& name) { /** * @brief Verify whether the environment variable is a valid value. */ -bool parse_bool(const std::string& value) { - std::unordered_set valid_values = {"on", "1", "yes", "y", "enable", "true"}; +bool parse_bool(const std::string & value) { + std::unordered_set valid_values = { "on", "1", "yes", "y", "enable", "true" }; return valid_values.find(value) != valid_values.end(); } @@ -125,7 +125,7 @@ bool parse_bool(const std::string& value) { * @param value The string to parse. * @return The parsed integer, or 0 if conversion fails. */ -int parse_integer(const std::string& value) { +int parse_integer(const std::string & value) { try { return std::stoi(value); } catch (...) { @@ -144,11 +144,10 @@ int parse_integer(const std::string& value) { static ggml_cann_device_info ggml_cann_init() { ggml_cann_device_info info = {}; - aclError err = aclrtGetDeviceCount((uint32_t*)&info.device_count); + aclError err = aclrtGetDeviceCount((uint32_t *) &info.device_count); if (err != ACL_SUCCESS) { - GGML_LOG_ERROR("%s: failed to initialize CANN: %s\n", - __func__, aclGetRecentErrMsg()); + GGML_LOG_ERROR("%s: failed to initialize CANN: %s\n", __func__, aclGetRecentErrMsg()); return info; } @@ -156,16 +155,15 @@ static ggml_cann_device_info ggml_cann_init() { for (int id = 0; id < info.device_count; ++id) { aclrtPhysicalMemProp prop = {}; - prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; - prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; - prop.memAttr = ACL_HBM_MEM_HUGE; - prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = id; - prop.reserve = 0; - err = aclrtMemGetAllocationGranularity( - &prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED, - &info.devices[id].vmm_granularity); - info.devices[id].vmm = err == ACL_SUCCESS; + prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; + prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; + prop.memAttr = ACL_HBM_MEM_HUGE; + prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; + prop.location.id = id; + prop.reserve = 0; + err = aclrtMemGetAllocationGranularity(&prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED, + &info.devices[id].vmm_granularity); + info.devices[id].vmm = err == ACL_SUCCESS; size_t free, total; ggml_backend_cann_get_device_memory(id, &free, &total); @@ -185,7 +183,7 @@ static ggml_cann_device_info ggml_cann_init() { * * @return A reference to the structure containing the device information. */ -const ggml_cann_device_info& ggml_cann_info() { +const ggml_cann_device_info & ggml_cann_info() { static ggml_cann_device_info info = ggml_cann_init(); return info; } @@ -205,7 +203,7 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { /** * @brief The minimum free margin for a buffer. */ - static const size_t min_free_margin = 1ull << 20; // 1MB + static const size_t min_free_margin = 1ull << 20; // 1MB /** * @brief The alignment for buffer allocation. @@ -226,22 +224,18 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { * @brief Structure representing a CANN buffer. */ struct ggml_cann_buffer { - void* ptr = nullptr; ///< Pointer to the buffer. - size_t size = 0; ///< Size of the buffer. - std::chrono::steady_clock::time_point last_used; ///< Last used time. + void * ptr = nullptr; ///< Pointer to the buffer. + size_t size = 0; ///< Size of the buffer. + std::chrono::steady_clock::time_point last_used; ///< Last used time. - bool operator>(const ggml_cann_buffer& other) const { - return size > other.size; - } + bool operator>(const ggml_cann_buffer & other) const { return size > other.size; } }; /** * @brief Array of CANN buffers in the pool. */ - std::unordered_map buffer_pool; - std::priority_queue, - std::greater<>> free_buffers ; + std::unordered_map buffer_pool; + std::priority_queue, std::greater<>> free_buffers; /** * @brief Total size of all buffers in the pool. @@ -262,7 +256,7 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { */ ~ggml_cann_pool_buf_prio() { ggml_cann_set_device(device); - for (auto& [b_ptr, b_size] : buffer_pool) { + for (auto & [b_ptr, b_size] : buffer_pool) { aclrtFree(b_ptr); pool_size -= b_size; } @@ -278,14 +272,14 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { * the allocated buffer. * @return A pointer to the allocated buffer. */ - void* alloc(size_t size, size_t* actual_size) override { + void * alloc(size_t size, size_t * actual_size) override { size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void* ptr = nullptr; - auto now = std::chrono::steady_clock::now(); + void * ptr = nullptr; + auto now = std::chrono::steady_clock::now(); std::vector free_buffers_rest; free_buffers_rest.reserve(free_buffers.size()); @@ -298,24 +292,22 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { const size_t margin = b.size - size; if (margin <= max_reuse_margin) { *actual_size = b.size; - ptr = b.ptr; + ptr = b.ptr; #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: reused %p, " "pool_size = %5u MB, " "size = %5u MB, " "margin = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(margin, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(margin, 1048576) / 1048576)); #endif break; } } - bool should_clean = !disable_clean && - b.size > min_free_margin && + bool should_clean = !disable_clean && b.size > min_free_margin && std::chrono::duration_cast(now - b.last_used).count() > 100; if (should_clean) { // free the buffer if the size is needed to be freed @@ -327,20 +319,20 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { "cann pool[%d]: clean %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(b.size, 1048576) / 1048576)); #endif continue; } free_buffers_rest.push_back(b); } - for (ggml_cann_buffer &b : free_buffers_rest) { + for (ggml_cann_buffer & b : free_buffers_rest) { free_buffers.push(std::move(b)); } #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d] free pool_size = %5u MB\n\n", device, (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576)); + GGML_LOG_INFO("cann pool[%d] free pool_size = %5u MB\n\n", device, + (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576)); #endif if (ptr != nullptr) { return ptr; @@ -356,8 +348,8 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { "cann pool[%d]: allocate %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, ptr, (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(size, 1048576) / 1048576)); + device, ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(size, 1048576) / 1048576)); #endif buffer_pool.emplace(ptr, size); return ptr; @@ -369,7 +361,7 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { * @param ptr Pointer to the buffer to free. * @param size Size of the buffer to free. */ - void free(void* ptr, size_t size) override { + void free(void * ptr, size_t size) override { GGML_UNUSED(size); auto it = buffer_pool.find(ptr); if (it == buffer_pool.end()) { @@ -377,13 +369,12 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { } auto now = std::chrono::steady_clock::now(); - free_buffers.emplace(ggml_cann_buffer{ptr, it->second, now}); + free_buffers.emplace(ggml_cann_buffer{ ptr, it->second, now }); #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: return %p, " "pool_size = %5u MB\n", - device, ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576)); + device, ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576)); #endif } }; @@ -402,7 +393,7 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { /** * @brief The minimum free margin for a buffer. */ - static const size_t min_free_margin = 1ull << 20; // 1MB + static const size_t min_free_margin = 1ull << 20; // 1MB /** * @brief The alignment for buffer allocation. @@ -428,10 +419,10 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { * @brief Structure representing a CANN buffer. */ struct ggml_cann_buffer { - void* ptr = nullptr; ///< Pointer to the buffer memory. - size_t size = 0; ///< Size of the buffer. - bool used = false; ///< Whether the buffer is currently in use. - std::chrono::steady_clock::time_point last_used; ///< Last used time. + void * ptr = nullptr; ///< Pointer to the buffer memory. + size_t size = 0; ///< Size of the buffer. + bool used = false; ///< Whether the buffer is currently in use. + std::chrono::steady_clock::time_point last_used; ///< Last used time. }; /** @@ -459,7 +450,7 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { ~ggml_cann_pool_buf() { ggml_cann_set_device(device); for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; if (b.ptr != nullptr) { aclrtFree(b.ptr); pool_size -= b.size; @@ -476,18 +467,18 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { * the allocated buffer. * @return A pointer to the allocated buffer. */ - void* alloc(size_t size, size_t* actual_size) override { + void * alloc(size_t size, size_t * actual_size) override { size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void* ptr = nullptr; - auto now = std::chrono::steady_clock::now(); + void * ptr = nullptr; + auto now = std::chrono::steady_clock::now(); int i = 0; for (; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; if (b.ptr == nullptr) { break; } @@ -499,25 +490,23 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { const size_t margin = b.size - size; if (margin <= max_reuse_margin) { *actual_size = b.size; - b.used = true; - ptr = b.ptr; + b.used = true; + ptr = b.ptr; #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: reused %p, " "pool_size = %5u MB, " "size = %5u MB, " "margin = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(margin, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(margin, 1048576) / 1048576)); #endif break; } } - bool should_clean = !disable_clean && - b.size > min_free_margin && + bool should_clean = !disable_clean && b.size > min_free_margin && std::chrono::duration_cast(now - b.last_used).count() > 100; if (should_clean) { // free the buffer if the size is needed to be freed @@ -528,9 +517,8 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { "cann pool[%d]: clean %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(b.size, 1048576) / 1048576)); #endif b.ptr = nullptr; } @@ -541,13 +529,13 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { if (i < MAX_BUFFERS) { // allocate a new buffer if no buffer can be reused - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; ggml_cann_set_device(device); ACL_CHECK(aclrtMalloc(&b.ptr, size, ACL_MEM_MALLOC_HUGE_FIRST)); pool_size += size; *actual_size = size; - b.size = size; - b.used = true; + b.size = size; + b.used = true; if (i >= MAX_BUFFERS - 8) { GGML_LOG_WARN("cann pool[%d]: slots almost full\n", device); } @@ -556,9 +544,8 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { "cann pool[%d]: allocate %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(b.size, 1048576) / 1048576)); #endif return b.ptr; } @@ -572,21 +559,20 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { * @param ptr Pointer to the buffer to free. * @param size Size of the buffer to free. */ - void free(void* ptr, size_t size) override { + void free(void * ptr, size_t size) override { GGML_UNUSED(size); for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; if (b.ptr != ptr) { continue; } - b.used = false; + b.used = false; b.last_used = std::chrono::steady_clock::now(); #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: return %p, " "pool_size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576)); #endif return; } @@ -614,7 +600,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { /** * @brief Pointer to the start of the virtual memory pool. */ - void* pool_addr = 0; + void * pool_addr = 0; /** * @brief Amount of virtual memory used in the pool. @@ -639,7 +625,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { /** * @brief Offsets for the mapped memory regions. */ - std::vector map_offsets; + std::vector map_offsets; /** * @brief Constructor to initialize the buffer pool with virtual memory for @@ -647,11 +633,10 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * * @param device The device ID to associate with this buffer pool. */ - explicit ggml_cann_pool_vmm(int device) - : device(device) { - auto dev = ggml_cann_info().devices[device]; + explicit ggml_cann_pool_vmm(int device) : device(device) { + auto dev = ggml_cann_info().devices[device]; granularity = dev.vmm_granularity; - max_size = dev.total_vram; + max_size = dev.total_vram; } /** @@ -659,10 +644,10 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { */ ~ggml_cann_pool_vmm() { if (pool_addr != 0) { - for (auto& offset : map_offsets) { + for (auto & offset : map_offsets) { ACL_CHECK(aclrtUnmapMem(offset)); } - for (auto& handle : handles) { + for (auto & handle : handles) { ACL_CHECK(aclrtFreePhysical(handle)); } ACL_CHECK(aclrtReleaseMemAddress(pool_addr)); @@ -677,11 +662,11 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * the allocated buffer. * @return A pointer to the allocated buffer. */ - void* alloc(size_t size, size_t* actual_size) override { + void * alloc(size_t size, size_t * actual_size) override { // round up the allocation size to the alignment to ensure that all // allocations are aligned for all data types const size_t alignment = 128; - size = GGML_PAD(size, alignment); + size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } @@ -691,53 +676,51 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { if (size > avail) { // round up to the next multiple of the granularity size_t reserve_size = size - avail; - reserve_size = GGML_PAD(reserve_size, granularity); + reserve_size = GGML_PAD(reserve_size, granularity); GGML_ASSERT(pool_size + reserve_size <= max_size); // allocate more physical memory aclrtPhysicalMemProp prop = {}; - prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; - prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; - prop.memAttr = ACL_HBM_MEM_HUGE; - prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = device; - prop.reserve = 0; + prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; + prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; + prop.memAttr = ACL_HBM_MEM_HUGE; + prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; + prop.location.id = device; + prop.reserve = 0; aclrtDrvMemHandle handle; ACL_CHECK(aclrtMallocPhysical(&handle, reserve_size, &prop, 0)); // reserve virtual address space (if not already reserved) if (pool_addr == 0) { - ACL_CHECK(aclrtReserveMemAddress( - &pool_addr, max_size, 0, NULL, 1)); + ACL_CHECK(aclrtReserveMemAddress(&pool_addr, max_size, 0, NULL, 1)); } // map at the end of the pool - ACL_CHECK(aclrtMapMem((char*)pool_addr + pool_size, reserve_size, 0, - handle, 0)); + ACL_CHECK(aclrtMapMem((char *) pool_addr + pool_size, reserve_size, 0, handle, 0)); handles.push_back(handle); - map_offsets.push_back((char*)pool_addr + pool_size); + map_offsets.push_back((char *) pool_addr + pool_size); // add to the pool pool_size += reserve_size; #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB (reserved %llu MB)\n", - device, (unsigned long long) (pool_size/1024/1024), - (unsigned long long) (reserve_size/1024/1024)); + GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB (reserved %llu MB)\n", device, + (unsigned long long) (pool_size / 1024 / 1024), + (unsigned long long) (reserve_size / 1024 / 1024)); #endif } GGML_ASSERT(pool_addr != 0); - void* ptr = (void*)((char*)pool_addr + pool_used); + void * ptr = (void *) ((char *) pool_addr + pool_used); *actual_size = size; pool_used += size; #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device, - (unsigned long long)size, (unsigned long long)ptr); + GGML_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, + (unsigned long long) ptr); #endif return ptr; } @@ -748,16 +731,16 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * @param ptr Pointer to the buffer to free. * @param size Size of the buffer to free. */ - void free(void* ptr, size_t size) override { + void free(void * ptr, size_t size) override { #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device, - (unsigned long long)size, (unsigned long long)ptr); + GGML_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, + (unsigned long long) ptr); #endif pool_used -= size; // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void*)((char*)pool_addr + pool_used)); + GGML_ASSERT(ptr == (void *) ((char *) pool_addr + pool_used)); } }; @@ -769,8 +752,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * @param device The device ID for which to create the pool. * @return A unique pointer to the created CANN pool. */ -std::unique_ptr ggml_backend_cann_context::new_pool_for_device( - int device) { +std::unique_ptr ggml_backend_cann_context::new_pool_for_device(int device) { std::string mem_pool_type = get_env("GGML_CANN_MEM_POOL").value_or(""); if (mem_pool_type == "prio") { @@ -795,9 +777,8 @@ std::unique_ptr ggml_backend_cann_context::new_pool_for_device( * ID, device pointer, and a name derived from GGML_CANN_NAME and the device ID. */ struct ggml_backend_cann_buffer_context { - int32_t device; ///< The device ID associated with this buffer context. - void* dev_ptr = - nullptr; ///< Pointer to the device memory allocated for the buffer. + int32_t device; ///< The device ID associated with this buffer context. + void * dev_ptr = nullptr; ///< Pointer to the device memory allocated for the buffer. /** * @brief Constructor to initialize the CANN buffer context. @@ -805,9 +786,7 @@ struct ggml_backend_cann_buffer_context { * @param device The device ID associated with this buffer context. * @param dev_ptr Pointer to the device memory allocated for the buffer. */ - ggml_backend_cann_buffer_context(int32_t device, void* dev_ptr) - : device(device), - dev_ptr(dev_ptr) {} + ggml_backend_cann_buffer_context(int32_t device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} /** * @brief Destructor to free the device memory allocated for the buffer. @@ -825,8 +804,8 @@ struct ggml_backend_cann_buffer_context { * @return true if the buffer is a CANN buffer, false otherwise. */ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft); -static bool ggml_backend_buffer_is_cann( - ggml_backend_buffer_t buffer) { + +static bool ggml_backend_buffer_is_cann(ggml_backend_buffer_t buffer) { return ggml_backend_buft_is_cann(buffer->buft); } @@ -838,10 +817,8 @@ static bool ggml_backend_buffer_is_cann( * * @param buffer The CANN buffer to free. */ -static void ggml_backend_cann_buffer_free_buffer( - ggml_backend_buffer_t buffer) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void ggml_backend_cann_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; delete ctx; } @@ -854,10 +831,8 @@ static void ggml_backend_cann_buffer_free_buffer( * @param buffer The CANN buffer whose base pointer is to be retrieved. * @return A pointer to the base of the device memory allocated for the buffer. */ -static void* ggml_backend_cann_buffer_get_base( - ggml_backend_buffer_t buffer) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void * ggml_backend_cann_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; return ctx->dev_ptr; } @@ -874,21 +849,17 @@ static void* ggml_backend_cann_buffer_get_base( * @param dst Pointer to the destination buffer where transformed data will be * stored. */ -static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor, - const void* src, - void* dst) { +static void ggml_backend_cann_transform_q4_0(ggml_tensor * tensor, const void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK4_0; + size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK4_0; - size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; - - uint8_t* quant_offset = (uint8_t*)dst; - uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes); + uint8_t * quant_offset = (uint8_t *) dst; + uint16_t * scale_offset = (uint16_t *) ((char *) dst + quant_bytes); for (int i = 0; i < groups; i++) { - const block_q4_0* group = - (const block_q4_0*)((const char*)src + i * sizeof(block_q4_0)); - *scale_offset = group->d; + const block_q4_0 * group = (const block_q4_0 *) ((const char *) src + i * sizeof(block_q4_0)); + *scale_offset = group->d; scale_offset++; // 0-15 @@ -907,8 +878,7 @@ static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor, } // put (uint4b_t -8) into int4b_t - for (quant_offset = (uint8_t*)dst; - quant_offset < (uint8_t*)dst + quant_bytes; quant_offset++) { + for (quant_offset = (uint8_t *) dst; quant_offset < (uint8_t *) dst + quant_bytes; quant_offset++) { (*quant_offset) ^= 0x88; } } @@ -926,29 +896,27 @@ static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor, * @param dst Pointer to the destination buffer where the Q4.0 formatted data * will be stored. */ -static void ggml_backend_cann_transform_back_q4_0( - const ggml_tensor* tensor, void* src, void* dst) { +static void ggml_backend_cann_transform_back_q4_0(const ggml_tensor * tensor, void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK4_0; + size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK4_0; - size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; + uint8_t * quant_offset = (uint8_t *) src; + uint16_t * scale_offset = (uint16_t *) ((char *) src + quant_bytes); - uint8_t* quant_offset = (uint8_t*)src; - uint16_t* scale_offset = (uint16_t*)((char*)src + quant_bytes); - - for (; quant_offset < (uint8_t*)src + quant_bytes; quant_offset++) { + for (; quant_offset < (uint8_t *) src + quant_bytes; quant_offset++) { (*quant_offset) ^= 0x88; } - quant_offset = (uint8_t*)src; + quant_offset = (uint8_t *) src; for (int i = 0; i < groups; i++) { - block_q4_0* group = (block_q4_0*)((char*)dst + i * sizeof(block_q4_0)); - group->d = *scale_offset; + block_q4_0 * group = (block_q4_0 *) ((char *) dst + i * sizeof(block_q4_0)); + group->d = *scale_offset; scale_offset++; // 0-15 for (int j = 0; j < QK4_0 / 2; j += 2) { - group->qs[j] = ((*quant_offset) & 0x0F); + group->qs[j] = ((*quant_offset) & 0x0F); group->qs[j + 1] = ((*quant_offset) >> 4); quant_offset++; } @@ -975,20 +943,17 @@ static void ggml_backend_cann_transform_back_q4_0( * @param dst Pointer to the destination buffer where transformed data will be * stored. */ -static void ggml_backend_cann_transform_q8_0(ggml_tensor* tensor, - const void* src, - void* dst) { - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK8_0; - size_t quant_bytes = n_elems * sizeof(uint8_t); +static void ggml_backend_cann_transform_q8_0(ggml_tensor * tensor, const void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK8_0; + size_t quant_bytes = n_elems * sizeof(uint8_t); - uint8_t* quant_offset = (uint8_t*)dst; - uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes); + uint8_t * quant_offset = (uint8_t *) dst; + uint16_t * scale_offset = (uint16_t *) ((char *) dst + quant_bytes); for (int i = 0; i < groups; i++) { - const block_q8_0* group = - (const block_q8_0*)((const char*)src + i * sizeof(block_q8_0)); - *scale_offset = group->d; + const block_q8_0 * group = (const block_q8_0 *) ((const char *) src + i * sizeof(block_q8_0)); + *scale_offset = group->d; scale_offset++; size_t group_quant_size = QK8_0 * sizeof(uint8_t); memcpy(quant_offset, group->qs, group_quant_size); @@ -1009,19 +974,17 @@ static void ggml_backend_cann_transform_q8_0(ggml_tensor* tensor, * @param dst Pointer to the destination buffer where the Q8.0 formatted data * will be stored. */ -static void ggml_backend_cann_transform_back_q8_0( - const ggml_tensor* tensor, const void* src, void* dst) { - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK8_0; - size_t quant_bytes = n_elems * sizeof(uint8_t); +static void ggml_backend_cann_transform_back_q8_0(const ggml_tensor * tensor, const void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK8_0; + size_t quant_bytes = n_elems * sizeof(uint8_t); - const uint8_t* quant_offset = (const uint8_t*)src; - const uint16_t* scale_offset = - (const uint16_t*)((const char*)src + quant_bytes); + const uint8_t * quant_offset = (const uint8_t *) src; + const uint16_t * scale_offset = (const uint16_t *) ((const char *) src + quant_bytes); for (int i = 0; i < groups; i++) { - block_q8_0* group = (block_q8_0*)((char*)dst + i * sizeof(block_q8_0)); - group->d = *scale_offset; + block_q8_0 * group = (block_q8_0 *) ((char *) dst + i * sizeof(block_q8_0)); + group->d = *scale_offset; scale_offset++; size_t group_quant_size = QK8_0 * sizeof(uint8_t); memcpy(group->qs, quant_offset, group_quant_size); @@ -1041,8 +1004,7 @@ static void ggml_backend_cann_transform_back_q8_0( * @param dst Pointer to the destination buffer where transformed data will be * stored. */ -static void ggml_backend_cann_transform(ggml_tensor* tensor, - const void* src, void* dst) { +static void ggml_backend_cann_transform(ggml_tensor * tensor, const void * src, void * dst) { switch (tensor->type) { case GGML_TYPE_Q4_0: ggml_backend_cann_transform_q4_0(tensor, src, dst); @@ -1067,8 +1029,7 @@ static void ggml_backend_cann_transform(ggml_tensor* tensor, * @param dst Pointer to the destination buffer where transformed tensor data * will be stored. */ -static void ggml_backend_cann_transform_back( - const ggml_tensor* tensor, void* src, void* dst) { +static void ggml_backend_cann_transform_back(const ggml_tensor * tensor, void * src, void * dst) { switch (tensor->type) { case GGML_TYPE_Q4_0: ggml_backend_cann_transform_back_q4_0(tensor, src, dst); @@ -1109,8 +1070,7 @@ static bool need_transform(ggml_type type) { * @param buffer The CANN buffer from which to initialize the tensor. * @param tensor Pointer to the tensor to be initialized. */ -static enum ggml_status ggml_backend_cann_buffer_init_tensor( - ggml_backend_buffer_t buffer, ggml_tensor* tensor) { +static enum ggml_status ggml_backend_cann_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { if (tensor->view_src != NULL && tensor->view_offs == 0) { GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); return GGML_STATUS_SUCCESS; @@ -1121,13 +1081,11 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor( if (ggml_is_quantized(tensor->type)) { // Initialize padding to 0 to avoid possible NaN values size_t original_size = ggml_nbytes(tensor); - size_t padded_size = - ggml_backend_buft_get_alloc_size(buffer->buft, tensor); + size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { size_t memset_size = padded_size - original_size; - ACL_CHECK(aclrtMemset((char*)tensor->data + original_size, - memset_size, 0, memset_size)); + ACL_CHECK(aclrtMemset((char *) tensor->data + original_size, memset_size, 0, memset_size)); } } return GGML_STATUS_SUCCESS; @@ -1141,8 +1099,8 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor( * designed to be used with a global array, one per device. */ struct ggml_cann_nz_workspace { - void* ptr; // Pointer to allocated device buffer - size_t allocated; // Size of currently allocated buffer in bytes + void * ptr; // Pointer to allocated device buffer + size_t allocated; // Size of currently allocated buffer in bytes /** * @brief Constructor. Initializes the workspace with no allocated memory. @@ -1158,7 +1116,7 @@ struct ggml_cann_nz_workspace { void clear() { if (ptr) { ACL_CHECK(aclrtFree(ptr)); - ptr = nullptr; + ptr = nullptr; allocated = 0; } } @@ -1185,7 +1143,7 @@ struct ggml_cann_nz_workspace { * * @return Pointer to the allocated buffer, or nullptr if not allocated. */ - void* get() const { return ptr; } + void * get() const { return ptr; } }; /** @@ -1207,19 +1165,17 @@ static ggml_cann_nz_workspace g_nz_workspaces[GGML_CANN_MAX_DEVICES]; * @note The workspace buffer used in this function is managed globally and reused * across calls. This reduces overhead from repeated memory allocation and deallocation. */ -static void weight_format_to_nz(ggml_tensor *tensor, size_t offset, int device) { - aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne, - tensor->nb, 2, ACL_FORMAT_ND, offset); - uint64_t workspaceSize = 0; - aclOpExecutor *executor; +static void weight_format_to_nz(ggml_tensor * tensor, size_t offset, int device) { + aclTensor * weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne, tensor->nb, 2, ACL_FORMAT_ND, offset); + uint64_t workspaceSize = 0; + aclOpExecutor * executor; // TransMatmulWeight - ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed, - &workspaceSize, &executor)); + ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed, &workspaceSize, &executor)); // Avoid frequent malloc/free of the workspace. g_nz_workspaces[device].realloc(workspaceSize); - void* g_nz_workspace = g_nz_workspaces[device].get(); + void * g_nz_workspace = g_nz_workspaces[device].get(); ACL_CHECK(aclnnTransMatmulWeight(g_nz_workspace, workspaceSize, executor, nullptr)); ACL_CHECK(aclDestroyTensor(weightTransposed)); @@ -1238,11 +1194,12 @@ static void weight_format_to_nz(ggml_tensor *tensor, size_t offset, int device) * @param offset Offset in the source data from where to start copying. * @param size Size of the data to be copied, in bytes. */ -static void ggml_backend_cann_buffer_set_tensor( - ggml_backend_buffer_t buffer, ggml_tensor *tensor, const void *data, - size_t offset, size_t size) { - ggml_backend_cann_buffer_context *ctx = - (ggml_backend_cann_buffer_context *)buffer->context; +static void ggml_backend_cann_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; ggml_cann_set_device(ctx->device); // TODO: refer to cann(#6017), it use thread's default stream. @@ -1252,20 +1209,17 @@ static void ggml_backend_cann_buffer_set_tensor( // Only check env once. static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on")); if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size, - ACL_MEMCPY_HOST_TO_DEVICE)); - if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) { + ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE)); + if (weight_to_nz && is_matmul_weight((const ggml_tensor *) tensor)) { GGML_ASSERT(tensor->ne[2] == 1); GGML_ASSERT(tensor->ne[3] == 1); weight_format_to_nz(tensor, offset, ctx->device); } } else { - void *transform_buffer = malloc(size); + void * transform_buffer = malloc(size); ggml_backend_cann_transform(tensor, data, transform_buffer); - ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, - transform_buffer, size, - ACL_MEMCPY_HOST_TO_DEVICE)); + ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE)); free(transform_buffer); } } @@ -1283,22 +1237,20 @@ static void ggml_backend_cann_buffer_set_tensor( * @param offset Offset in the destination buffer where to start copying. * @param size Size of the data to be copied, in bytes. */ -static void ggml_backend_cann_buffer_get_tensor( - ggml_backend_buffer_t buffer, const ggml_tensor* tensor, void* data, - size_t offset, size_t size) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void ggml_backend_cann_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; ggml_cann_set_device(ctx->device); if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpy(data, size, (char*)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST)); + ACL_CHECK(aclrtMemcpy(data, size, (char *) tensor->data + offset, size, ACL_MEMCPY_DEVICE_TO_HOST)); } else { - void* transform_buffer = malloc(size); - ACL_CHECK(aclrtMemcpy(transform_buffer, size, - (char*)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST)); + void * transform_buffer = malloc(size); + ACL_CHECK(aclrtMemcpy(transform_buffer, size, (char *) tensor->data + offset, size, ACL_MEMCPY_DEVICE_TO_HOST)); ggml_backend_cann_transform_back(tensor, transform_buffer, data); free(transform_buffer); } @@ -1317,19 +1269,17 @@ static void ggml_backend_cann_buffer_get_tensor( * @param dst Pointer to the destination tensor where the data will be copied. * @return true if the copy operation succeeded, false otherwise. */ -static bool ggml_backend_cann_buffer_cpy_tensor( - ggml_backend_buffer_t buffer, const ggml_tensor* src, ggml_tensor* dst) { +static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * src, + ggml_tensor * dst) { if (ggml_backend_buffer_is_cann(src->buffer)) { - ggml_backend_cann_buffer_context* src_ctx = - (ggml_backend_cann_buffer_context*)src->buffer->context; - ggml_backend_cann_buffer_context* dst_ctx = - (ggml_backend_cann_buffer_context*)buffer->context; + ggml_backend_cann_buffer_context * src_ctx = (ggml_backend_cann_buffer_context *) src->buffer->context; + ggml_backend_cann_buffer_context * dst_ctx = (ggml_backend_cann_buffer_context *) buffer->context; size_t memcpy_size = ggml_nbytes(src); // Same device. if (src_ctx->device == dst_ctx->device) { - ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size, - (const char*)src->data, memcpy_size, + ACL_CHECK(aclrtMemcpy((char *) dst->data, memcpy_size, (const char *) src->data, memcpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE)); return true; } else { @@ -1339,13 +1289,11 @@ static bool ggml_backend_cann_buffer_cpy_tensor( #endif // Different device but can access by peer. int32_t canAccessPeer = 0; - ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device, - dst_ctx->device)); + ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device, dst_ctx->device)); if (canAccessPeer) { ggml_cann_set_device(src_ctx->device); ACL_CHECK(aclrtDeviceEnablePeerAccess(dst_ctx->device, 0)); - ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size, - (const char*)src->data, memcpy_size, + ACL_CHECK(aclrtMemcpy((char *) dst->data, memcpy_size, (const char *) src->data, memcpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE)); return true; } @@ -1363,10 +1311,8 @@ static bool ggml_backend_cann_buffer_cpy_tensor( * @param buffer The CANN buffer to be cleared. * @param value The value to which each byte in the buffer will be set. */ -static void ggml_backend_cann_buffer_clear( - ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void ggml_backend_cann_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; ggml_cann_set_device(ctx->device); ACL_CHECK(aclrtMemset(ctx->dev_ptr, buffer->size, value, buffer->size)); @@ -1396,9 +1342,8 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { * buffer type. */ struct ggml_backend_cann_buffer_type_context { - int32_t - device; /**< Device identifier associated with the buffer context. */ - std::string name; /**< Name associated with the buffer context. */ + int32_t device; /**< Device identifier associated with the buffer context. */ + std::string name; /**< Name associated with the buffer context. */ }; /** @@ -1410,10 +1355,8 @@ struct ggml_backend_cann_buffer_type_context { * @param buft Pointer to the buffer type context. * @return Const pointer to the C-style string containing the name. */ -static const char* ggml_backend_cann_buffer_type_name( - ggml_backend_buffer_type_t buft) { - ggml_backend_cann_buffer_type_context* buft_ctx = - (ggml_backend_cann_buffer_type_context*)buft->context; +static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context; return buft_ctx->name.c_str(); } @@ -1428,34 +1371,27 @@ static const char* ggml_backend_cann_buffer_type_name( * @param size Size in bytes of the buffer to allocate. * @return Pointer to the allocated buffer, or nullptr if allocation fails. */ -static ggml_backend_buffer_t -ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, - size_t size) { - ggml_backend_cann_buffer_type_context* buft_ctx = - (ggml_backend_cann_buffer_type_context*)buft->context; +static ggml_backend_buffer_t ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context; ggml_cann_set_device(buft_ctx->device); const size_t alignment = 128; - size = GGML_PAD(size, alignment); + size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void* dev_ptr; + void * dev_ptr; aclError err = aclrtMalloc(&dev_ptr, size, ACL_MEM_MALLOC_HUGE_FIRST); if (err != ACL_SUCCESS) { - GGML_LOG_ERROR( - "%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n", - __func__, size / 1024.0 / 1024.0, buft_ctx->device, - aclGetRecentErrMsg()); + GGML_LOG_ERROR("%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n", __func__, + size / 1024.0 / 1024.0, buft_ctx->device, aclGetRecentErrMsg()); return nullptr; } - ggml_backend_cann_buffer_context* ctx = - new ggml_backend_cann_buffer_context(buft_ctx->device, dev_ptr); + ggml_backend_cann_buffer_context * ctx = new ggml_backend_cann_buffer_context(buft_ctx->device, dev_ptr); - return ggml_backend_buffer_init(buft, ggml_backend_cann_buffer_interface, - ctx, size); + return ggml_backend_buffer_init(buft, ggml_backend_cann_buffer_interface, ctx, size); } /** @@ -1470,8 +1406,7 @@ ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, * @return The alignment requirement in bytes (fixed at 128 bytes for CANN * buffers). */ -static size_t ggml_backend_cann_buffer_type_get_alignment( - ggml_backend_buffer_type_t buft) { +static size_t ggml_backend_cann_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; GGML_UNUSED(buft); @@ -1491,10 +1426,10 @@ static size_t ggml_backend_cann_buffer_type_get_alignment( * @return The total allocation size in bytes required for the tensor in the * CANN buffer. */ -static size_t ggml_backend_cann_buffer_type_get_alloc_size( - ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; +static size_t ggml_backend_cann_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, + const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); + int64_t ne0 = tensor->ne[0]; // Only check env once. static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on")); @@ -1507,19 +1442,17 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size( // size += (line_size_align_32 - line_size); if (ggml_is_quantized(tensor->type)) { if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size( - tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); } - } else if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) { + } else if (weight_to_nz && is_matmul_weight((const ggml_tensor *) tensor)) { // NZ format weight are not support quantized yet. // If ND tensor transform to NZ, size may changed. - int64_t shape[] = {tensor->ne[1], tensor->ne[0]}; + int64_t shape[] = { tensor->ne[1], tensor->ne[0] }; GGML_ASSERT(tensor->ne[2] == 1); GGML_ASSERT(tensor->ne[3] == 1); - const aclIntArray *acl_shape = aclCreateIntArray(shape, 2); - size_t new_size; - ACL_CHECK(aclnnCalculateMatmulWeightSizeV2(acl_shape, - ggml_cann_type_mapping(tensor->type), &new_size)); + const aclIntArray * acl_shape = aclCreateIntArray(shape, 2); + size_t new_size; + ACL_CHECK(aclnnCalculateMatmulWeightSizeV2(acl_shape, ggml_cann_type_mapping(tensor->type), &new_size)); ACL_CHECK(aclDestroyIntArray(acl_shape)); size = std::max(size, new_size); } @@ -1560,17 +1493,15 @@ static const ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface * @return A pointer to the buffer type interface for the specified device, or * nullptr if the device index is out of range. */ -ggml_backend_buffer_type_t -ggml_backend_cann_buffer_type(int32_t device) { - static std::mutex mutex; +ggml_backend_buffer_type_t ggml_backend_cann_buffer_type(int32_t device) { + static std::mutex mutex; std::lock_guard lock(mutex); if (device >= ggml_backend_cann_get_device_count()) { return nullptr; } - static ggml_backend_buffer_type - ggml_backend_cann_buffer_types[GGML_CANN_MAX_DEVICES]; + static ggml_backend_buffer_type ggml_backend_cann_buffer_types[GGML_CANN_MAX_DEVICES]; static bool ggml_backend_cann_buffer_type_initialized = false; @@ -1580,8 +1511,7 @@ ggml_backend_cann_buffer_type(int32_t device) { /* .iface = */ ggml_backend_cann_buffer_type_interface, /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), i), /* .context = */ - new ggml_backend_cann_buffer_type_context{ - i, "CANN" + std::to_string(i)}, + new ggml_backend_cann_buffer_type_context{ i, "CANN" + std::to_string(i) }, }; } ggml_backend_cann_buffer_type_initialized = true; @@ -1645,16 +1575,16 @@ static void * ggml_cann_host_malloc(size_t size) { } const size_t alignment = 128; - size = GGML_PAD(size, alignment); + size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void * hostPtr = nullptr; - aclError err = aclrtMallocHost((void **) &hostPtr, size); + void * hostPtr = nullptr; + aclError err = aclrtMallocHost((void **) &hostPtr, size); if (err != ACL_SUCCESS) { - GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size / 1024.0 / 1024.0, aclGetRecentErrMsg()); + GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, size / 1024.0 / 1024.0, + aclGetRecentErrMsg()); return nullptr; } return hostPtr; @@ -1667,7 +1597,8 @@ static void * ggml_cann_host_malloc(size_t size) { * @param size Size in bytes of the host buffer to allocate. * @return Pointer to the allocated host buffer, or CPU buffer pointer if allocation fails. */ -static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) { void * hostPtr = ggml_cann_host_malloc(size); if (hostPtr == nullptr) { @@ -1676,8 +1607,8 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm } ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(hostPtr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free; + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free; return buffer; } @@ -1691,14 +1622,15 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() { static struct ggml_backend_buffer_type ggml_backend_cann_buffer_type_host = { /* .iface = */ { - /* .get_name = */ ggml_backend_cann_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cann_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_name = */ ggml_backend_cann_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_cann_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0), + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .device = */ + ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0), /* .context = */ nullptr, }; @@ -1718,8 +1650,7 @@ ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() { * stored. * @return true if the computation was successful; false otherwise. */ -static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, - struct ggml_tensor* dst) { +static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct ggml_tensor * dst) { switch (dst->op) { case GGML_OP_REPEAT: ggml_cann_repeat(ctx, dst); @@ -1765,14 +1696,14 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, case GGML_UNARY_OP_SILU: GGML_CANN_CALL_OP_UNARY(Silu); break; - case GGML_UNARY_OP_GELU_QUICK: { - auto lambda = [](ggml_backend_cann_context& ctx, - aclTensor* acl_src, - aclTensor* acl_dst) { - GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); - }; - ggml_cann_op_unary(lambda, ctx, dst); - } break; + case GGML_UNARY_OP_GELU_QUICK: + { + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); + }; + ggml_cann_op_unary(lambda, ctx, dst); + } + break; case GGML_UNARY_OP_TANH: GGML_CANN_CALL_OP_UNARY(Tanh); break; @@ -1817,14 +1748,14 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, case GGML_GLU_OP_SWIGLU: GGML_CANN_CALL_OP_UNARY_GATED(Silu); break; - case GGML_GLU_OP_GEGLU_QUICK: { - auto lambda = [](ggml_backend_cann_context& ctx, - aclTensor* acl_src, - aclTensor* acl_dst) { - GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); - }; - ggml_cann_op_unary_gated(lambda, ctx, dst); - } break; + case GGML_GLU_OP_GEGLU_QUICK: + { + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); + }; + ggml_cann_op_unary_gated(lambda, ctx, dst); + } + break; default: return false; } @@ -1956,9 +1887,8 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, * @param backend Pointer to the CANN backend structure. * @return A pointer to a constant string representing the backend name. */ -static const char* ggml_backend_cann_name(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; +static const char * ggml_backend_cann_name(ggml_backend_t backend) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; return cann_ctx->name.c_str(); } @@ -1972,8 +1902,7 @@ static const char* ggml_backend_cann_name(ggml_backend_t backend) { * @param backend Pointer to the CANN backend structure to be freed. */ static void ggml_backend_cann_free(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; ACL_CHECK(aclrtSynchronizeDevice()); ACL_CHECK(aclrtResetDevice(cann_ctx->device)); @@ -1981,7 +1910,6 @@ static void ggml_backend_cann_free(ggml_backend_t backend) { delete backend; } - /** * @brief Sets tensor data asynchronously in the CANN backend. * @@ -1994,21 +1922,17 @@ static void ggml_backend_cann_free(ggml_backend_t backend) { * @param size Size of the data to copy in bytes. */ static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend, - ggml_tensor *tensor, - const void *data, - size_t offset, - size_t size) { - ggml_backend_cann_context *cann_ctx = - (ggml_backend_cann_context *)backend->context; - ggml_backend_buffer_t buf = - tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && - "unsupported buffer type"); + GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && "unsupported buffer type"); GGML_ASSERT(!ggml_is_quantized(tensor->type)); - ggml_cann_async_memcpy(cann_ctx, (char *)tensor->data + offset, data, size, - ACL_MEMCPY_HOST_TO_DEVICE); + ggml_cann_async_memcpy(cann_ctx, (char *) tensor->data + offset, data, size, ACL_MEMCPY_HOST_TO_DEVICE); } /** @@ -2022,21 +1946,18 @@ static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend, * @param offset Offset in bytes within the host data. * @param size Size of the data to copy in bytes. */ -static void ggml_backend_cann_get_tensor_async( - ggml_backend_t backend, const ggml_tensor *tensor, void *data, - size_t offset, size_t size) { - ggml_backend_cann_context *cann_ctx = - (ggml_backend_cann_context *)backend->context; - ggml_backend_buffer_t buf = - tensor->view_src ? tensor->view_src->buffer : tensor->buffer; +static void ggml_backend_cann_get_tensor_async(ggml_backend_t backend, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && - "unsupported buffer type"); + GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && "unsupported buffer type"); GGML_ASSERT(!ggml_is_quantized(tensor->type)); - ggml_cann_async_memcpy(cann_ctx, data, (char *)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST); - + ggml_cann_async_memcpy(cann_ctx, data, (char *) tensor->data + offset, size, ACL_MEMCPY_DEVICE_TO_HOST); } /** @@ -2052,28 +1973,23 @@ static void ggml_backend_cann_get_tensor_async( * @param dst Pointer to the destination tensor to copy data to. * @return true if the copy operation succeeds, false otherwise. */ -static bool ggml_backend_cann_cpy_tensor_async( - ggml_backend_t backend_src, ggml_backend_t backend_dst, - const ggml_tensor* src, ggml_tensor* dst) { - GGML_ASSERT(ggml_backend_is_cann(backend_src) || - ggml_backend_is_cann(backend_dst)); +static bool ggml_backend_cann_cpy_tensor_async(ggml_backend_t backend_src, + ggml_backend_t backend_dst, + const ggml_tensor * src, + ggml_tensor * dst) { + GGML_ASSERT(ggml_backend_is_cann(backend_src) || ggml_backend_is_cann(backend_dst)); - GGML_ASSERT(!is_matmul_weight((const ggml_tensor*)src)); + GGML_ASSERT(!is_matmul_weight((const ggml_tensor *) src)); - if (!ggml_backend_buffer_is_cann(src->buffer) || - !ggml_backend_buffer_is_cann(dst->buffer)) { + if (!ggml_backend_buffer_is_cann(src->buffer) || !ggml_backend_buffer_is_cann(dst->buffer)) { return false; } - ggml_backend_buffer_t buf_src = - src->view_src ? src->view_src->buffer : src->buffer; - ggml_backend_buffer_t buf_dst = - dst->view_src ? dst->view_src->buffer : dst->buffer; + ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer; + ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer; - ggml_backend_cann_context* cann_ctx_src = - (ggml_backend_cann_context*)backend_src->context; - ggml_backend_cann_context* cann_ctx_dst = - (ggml_backend_cann_context*)backend_dst->context; + ggml_backend_cann_context * cann_ctx_src = (ggml_backend_cann_context *) backend_src->context; + ggml_backend_cann_context * cann_ctx_dst = (ggml_backend_cann_context *) backend_dst->context; size_t copy_size = ggml_nbytes(dst); if (copy_size == 0) { @@ -2084,17 +2000,14 @@ static bool ggml_backend_cann_cpy_tensor_async( // TODO: Support 310p P2P copy return false; #endif - ggml_backend_cann_buffer_context* buf_ctx_src = - (ggml_backend_cann_buffer_context*)buf_src->context; - ggml_backend_cann_buffer_context* buf_ctx_dst = - (ggml_backend_cann_buffer_context*)buf_dst->context; + ggml_backend_cann_buffer_context * buf_ctx_src = (ggml_backend_cann_buffer_context *) buf_src->context; + ggml_backend_cann_buffer_context * buf_ctx_dst = (ggml_backend_cann_buffer_context *) buf_dst->context; GGML_ASSERT(cann_ctx_src->device == buf_ctx_src->device); GGML_ASSERT(cann_ctx_dst->device == buf_ctx_dst->device); int32_t canAccessPeer = 0; - ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, cann_ctx_src->device, - cann_ctx_dst->device)); + ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, cann_ctx_src->device, cann_ctx_dst->device)); if (!canAccessPeer) { return false; } @@ -2106,8 +2019,7 @@ static bool ggml_backend_cann_cpy_tensor_async( // wait for task_queue empty to keep task order. cann_ctx_src->task_queue.wait(); - ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE, + ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, cann_ctx_src->stream())); // record event on src stream after the copy // TODO: this event is not effective with acl graph mode, change to use aclrtSynchronizeStream @@ -2122,8 +2034,7 @@ static bool ggml_backend_cann_cpy_tensor_async( ACL_CHECK(aclrtSynchronizeStream(cann_ctx_src->stream())); } else { // src and dst are on the same backend - ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE, + ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, cann_ctx_dst->stream())); } @@ -2139,8 +2050,7 @@ static bool ggml_backend_cann_cpy_tensor_async( * @param backend Pointer to the CANN backend structure to synchronize. */ static void ggml_backend_cann_synchronize(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; cann_ctx->task_queue.wait(); ggml_cann_set_device(cann_ctx->device); ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream())); @@ -2168,16 +2078,14 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) { * @param cann_ctx The CANN backend context containing the graph cache. * @param cgraph The current ggml computation graph. */ -static void add_lru_matched_graph_node_properties( - ggml_backend_cann_context * cann_ctx, - ggml_cgraph * cgraph) { +static void add_lru_matched_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) { // Create a new ggml_cann_graph object on the heap (its lifetime is managed by the cache). ggml_cann_graph * new_graph = new ggml_cann_graph(); new_graph->ggml_graph_properties.resize(cgraph->n_nodes); for (int node_idx = 0; node_idx < cgraph->n_nodes; ++node_idx) { ggml_tensor * node = cgraph->nodes[node_idx]; - auto & prop = new_graph->ggml_graph_properties[node_idx]; + auto & prop = new_graph->ggml_graph_properties[node_idx]; prop.node_address = node->data; prop.node_op = node->op; @@ -2214,11 +2122,9 @@ static void add_lru_matched_graph_node_properties( * @param graph_node_properties The stored properties of a CANN graph node. * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise. */ -static bool ggml_graph_node_has_matching_properties( - ggml_tensor * node, - ggml_graph_node_properties * graph_node_properties) { - if (node->data != graph_node_properties->node_address && - node->op != GGML_OP_VIEW) { +static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, + ggml_graph_node_properties * graph_node_properties) { + if (node->data != graph_node_properties->node_address && node->op != GGML_OP_VIEW) { return false; } @@ -2237,8 +2143,7 @@ static bool ggml_graph_node_has_matching_properties( for (int i = 0; i < GGML_MAX_SRC; i++) { if (node->src[i]) { - if (node->src[i]->data != graph_node_properties->src_address[i] && - node->op != GGML_OP_VIEW) { + if (node->src[i]->data != graph_node_properties->src_address[i] && node->op != GGML_OP_VIEW) { return false; } @@ -2280,8 +2185,8 @@ static bool ggml_graph_node_has_matching_properties( * @return true if a matching cached graph exists; false otherwise. */ static bool is_matched_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) { - ggml_cann_graph_lru_cache &lru_cache = cann_ctx->graph_lru_cache; - for (auto &graph_ptr : lru_cache.cache_list) { + ggml_cann_graph_lru_cache & lru_cache = cann_ctx->graph_lru_cache; + for (auto & graph_ptr : lru_cache.cache_list) { // Skip graphs with a different number of nodes. if (graph_ptr->ggml_graph_properties.size() != static_cast(cgraph->n_nodes)) { continue; @@ -2320,21 +2225,24 @@ static bool is_matched_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * * @param use_cann_graph Whether to use CANN graph execution. * @param cann_graph_update_required Whether graph capture is needed due to graph changes. */ -static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph, - bool & use_cann_graph, bool & cann_graph_update_required) { +static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, + ggml_cgraph * cgraph, + bool & use_cann_graph, + bool & cann_graph_update_required) { #ifdef USE_ACL_GRAPH - ggml_cann_graph* matched_graph = cann_ctx->graph_lru_cache.cache_list.front(); + ggml_cann_graph * matched_graph = cann_ctx->graph_lru_cache.cache_list.front(); if (use_cann_graph && cann_graph_update_required) { ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL)); } -#endif // USE_ACL_GRAPH +#endif // USE_ACL_GRAPH // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph. // With the use of CANN graphs, the execution will be performed by the graph launch. if (!use_cann_graph || cann_graph_update_required) { for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || + node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; } @@ -2347,7 +2255,7 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx } #ifdef USE_ACL_GRAPH - if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture + if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &matched_graph->graph)); } @@ -2355,10 +2263,9 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx // Execute graph ACL_CHECK(aclmdlRIExecuteAsync(matched_graph->graph, cann_ctx->stream())); } -#endif // USE_ACL_GRAPH +#endif // USE_ACL_GRAPH } - /** * @brief Computes a computational graph using a CANN backend. * @@ -2371,10 +2278,8 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx * @return enum ggml_status Returns GGML_STATUS_SUCCESS if computation * completes successfully, otherwise an appropriate error status. */ -static enum ggml_status ggml_backend_cann_graph_compute( - ggml_backend_t backend, ggml_cgraph* cgraph) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; +static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; ggml_cann_set_device(cann_ctx->device); g_nz_workspaces[cann_ctx->device].clear(); @@ -2382,7 +2287,7 @@ static enum ggml_status ggml_backend_cann_graph_compute( cann_ctx->rope_cache.cached = false; #ifdef USE_ACL_GRAPH - bool use_cann_graph = true; + bool use_cann_graph = true; bool cann_graph_update_required = false; static bool prefill_use_graph = parse_bool(get_env("GGML_CANN_PREFILL_USE_GRAPH").value_or("")); @@ -2413,15 +2318,10 @@ static enum ggml_status ggml_backend_cann_graph_compute( } } #else - bool use_cann_graph = false; + bool use_cann_graph = false; bool cann_graph_update_required = false; #endif // USE_ACL_GRAPH - evaluate_and_capture_cann_graph( - cann_ctx, - cgraph, - use_cann_graph, - cann_graph_update_required - ); + evaluate_and_capture_cann_graph(cann_ctx, cgraph, use_cann_graph, cann_graph_update_required); return GGML_STATUS_SUCCESS; } @@ -2438,8 +2338,7 @@ static enum ggml_status ggml_backend_cann_graph_compute( * @return bool Returns true if the operation is supported by the backend, * otherwise false. */ -static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, - const ggml_tensor* op) { +static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -2474,24 +2373,24 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, return false; } break; - case GGML_OP_MUL_MAT: { - switch (op->src[0]->type) { - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return true; - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q4_0: + case GGML_OP_MUL_MAT: + { + switch (op->src[0]->type) { + case GGML_TYPE_F16: + case GGML_TYPE_F32: + return true; + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q4_0: #ifdef ASCEND_310P - // Q4 && Q8 per group is not support on 310p device - return false; + // Q4 && Q8 per group is not support on 310p device + return false; #endif - // only support contiguous for quantized types. - return ggml_is_contiguous(op->src[0]) && - ggml_is_contiguous(op->src[1]); - default: - return false; + // only support contiguous for quantized types. + return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]); + default: + return false; + } } - } case GGML_OP_MUL_MAT_ID: switch (op->src[0]->type) { case GGML_TYPE_F16: @@ -2504,99 +2403,107 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, return false; #endif // only support contiguous for quantized types. - return ggml_is_contiguous(op->src[0]) && - ggml_is_contiguous(op->src[1]); + return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]); default: return false; } // embedding - case GGML_OP_GET_ROWS: { - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q8_0: - return true; - default: + case GGML_OP_GET_ROWS: + { + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_Q8_0: + return true; + default: + return false; + } + } + break; + case GGML_OP_SET_ROWS: + { + switch (op->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + return true; + default: + return false; + } + } + break; + case GGML_OP_CPY: + { + ggml_tensor * src = op->src[0]; + if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) || + (src->type != GGML_TYPE_F32 && src->type != GGML_TYPE_F16)) { + // only support F32 and F16. return false; + } + return true; } - } break; - case GGML_OP_SET_ROWS: { - switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - return true; - default: + break; + case GGML_OP_CONT: + { + // TODO: support GGML_TYPE_BF16 + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + return true; + default: + return false; + } + } + case GGML_OP_ROPE: + { + // TODO: with ops-test v == 1 + // TODO: n_dims <= ne0 + if (op->src[0]->ne[0] != op->op_params[1]) { return false; - } - } break; - case GGML_OP_CPY: { - ggml_tensor *src = op->src[0]; - if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) || - (src->type != GGML_TYPE_F32 && - src->type != GGML_TYPE_F16)) { - // only support F32 and F16. - return false; - } - return true; - } break; - case GGML_OP_CONT: { - // TODO: support GGML_TYPE_BF16 - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - return true; - default: - return false; - } - } - case GGML_OP_ROPE: { - // TODO: with ops-test v == 1 - // TODO: n_dims <= ne0 - if (op->src[0]->ne[0] != op->op_params[1]) { - return false; - } + } - const int mode = ((const int32_t *) op->op_params)[2]; - if (mode & GGML_ROPE_TYPE_MROPE) { - return false; - } - if (mode & GGML_ROPE_TYPE_VISION) { - return false; - } + const int mode = ((const int32_t *) op->op_params)[2]; + if (mode & GGML_ROPE_TYPE_MROPE) { + return false; + } + if (mode & GGML_ROPE_TYPE_VISION) { + return false; + } #ifdef ASCEND_310P - if(!ggml_is_contiguous(op->src[0])){ - return false; - } + if (!ggml_is_contiguous(op->src[0])) { + return false; + } #endif - return true; - } - case GGML_OP_UPSCALE: { - // aclnnUpsampleNearest2dGetWorkspaceSize not support - // selfDimN[2]/outDimN[2] or selfDimC[3]/outDimC[3] not equal - if (op->src[0]->ne[2] * op->ne[3] != op->src[0]->ne[3] * op->ne[2]) { - return false; + return true; } - if (op->op_params[0] != GGML_SCALE_MODE_NEAREST) { - return false; + case GGML_OP_UPSCALE: + { + // aclnnUpsampleNearest2dGetWorkspaceSize not support + // selfDimN[2]/outDimN[2] or selfDimC[3]/outDimC[3] not equal + if (op->src[0]->ne[2] * op->ne[3] != op->src[0]->ne[3] * op->ne[2]) { + return false; + } + if (op->op_params[0] != GGML_SCALE_MODE_NEAREST) { + return false; + } + return true; } - return true; - } - case GGML_OP_POOL_2D: { - const int32_t * opts = (const int32_t *) op->op_params; + case GGML_OP_POOL_2D: + { + const int32_t * opts = (const int32_t *) op->op_params; #ifdef ASCEND_310P - enum ggml_op_pool opt = static_cast(opts[0]); - if(opt == GGML_OP_POOL_MAX){ - return false; - } + enum ggml_op_pool opt = static_cast(opts[0]); + if (opt == GGML_OP_POOL_MAX) { + return false; + } #endif - const int k0 = opts[1]; - const int k1 = opts[2]; - const int p0 = opts[5]; - const int p1 = opts[6]; - // value of paddingH should be at most half of kernelH - // value of paddingW should be at most half of kernelW - return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2)); - } + const int k0 = opts[1]; + const int k1 = opts[2]; + const int p0 = opts[5]; + const int p1 = opts[6]; + // value of paddingH should be at most half of kernelH + // value of paddingW should be at most half of kernelW + return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2)); + } case GGML_OP_DUP: case GGML_OP_SUM: case GGML_OP_IM2COL: @@ -2639,48 +2546,50 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, return (op->src[0]->ne[0] - 1) <= 255; case GGML_OP_SCALE: float bias; - memcpy(&bias, (const float *)(op->op_params) + 1, sizeof(float)); - return bias == 0.0f; // TODO: support bias != 0.0f + memcpy(&bias, (const float *) (op->op_params) + 1, sizeof(float)); + return bias == 0.0f; // TODO: support bias != 0.0f case GGML_OP_SOFT_MAX: // TODO: support attention sinks [TAG_ATTN_SINKS] if (op->src[2]) { return false; } return true; - case GGML_OP_FLASH_ATTN_EXT:{ + case GGML_OP_FLASH_ATTN_EXT: + { #ifdef ASCEND_310P - // FA not support on 310p device - return false; + // FA not support on 310p device + return false; #endif - // derived from [ggml-cuda.cu] - if(op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16){ - return false; + // derived from [ggml-cuda.cu] + if (op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16) { + return false; + } + if (op->src[1]->type != GGML_TYPE_F16 && op->src[1]->type != GGML_TYPE_F32 && + op->src[1]->type != GGML_TYPE_BF16) { + return false; + } + if (op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16) { + return false; + } + // TODO: support attention sinks [TAG_ATTN_SINKS] + if (op->src[4]) { + return false; + } + if (op->src[1]->ne[0] != op->src[2]->ne[0]) { + // different head sizes of K and V are not supported yet + return false; + } + if (op->src[0]->ne[0] % 16 != 0) { + // TODO: padding to support + return false; + } + float logitSoftcap = 0.0f; + memcpy(&logitSoftcap, (const float *) (op->op_params) + 2, sizeof(float)); + if (logitSoftcap != 0.0f) { + return false; + } + return true; } - if(op->src[1]->type != GGML_TYPE_F16 && op->src[1]->type != GGML_TYPE_F32 && op->src[1]->type != GGML_TYPE_BF16){ - return false; - } - if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){ - return false; - } - // TODO: support attention sinks [TAG_ATTN_SINKS] - if (op->src[4]) { - return false; - } - if (op->src[1]->ne[0] != op->src[2]->ne[0]) { - // different head sizes of K and V are not supported yet - return false; - } - if (op->src[0]->ne[0] % 16 != 0) { - // TODO: padding to support - return false; - } - float logitSoftcap = 0.0f; - memcpy(&logitSoftcap, (const float *)(op->op_params) + 2, sizeof(float)); - if(logitSoftcap != 0.0f) { - return false; - } - return true; - } default: return false; } @@ -2717,8 +2626,7 @@ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) { * @return bool Returns true if the operation should be offloaded, otherwise * false. */ -static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, - const ggml_tensor* op) { +static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { const int min_batch_size = 32; GGML_UNUSED(dev); @@ -2734,9 +2642,8 @@ static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, * @param event Pointer to the event structure to be recorded. */ static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_event_t event) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream())); + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; + ACL_CHECK(aclrtRecordEvent((aclrtEvent) event->context, cann_ctx->stream())); } /** @@ -2749,13 +2656,10 @@ static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_ * @param event Pointer to the event structure that the backend needs to wait * for. */ -static void ggml_backend_cann_event_wait(ggml_backend_t backend, - ggml_backend_event_t event) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; +static void ggml_backend_cann_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; if (ggml_backend_is_cann(backend)) { - ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(), - (aclrtEvent)event->context)); + ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(), (aclrtEvent) event->context)); } else { GGML_ABORT("fatal error"); } @@ -2794,30 +2698,30 @@ static const ggml_backend_i ggml_backend_cann_interface = { * @return A pointer to the static GUID. */ static ggml_guid_t ggml_backend_cann_guid() { - static ggml_guid guid = {0xa1, 0x94, 0xaf, 0xac, 0xbd, 0x4f, 0x47, 0x34, - 0xbe, 0x1a, 0x9e, 0x71, 0x1f, 0x9e, 0xed, 0x64}; + static ggml_guid guid = { 0xa1, 0x94, 0xaf, 0xac, 0xbd, 0x4f, 0x47, 0x34, + 0xbe, 0x1a, 0x9e, 0x71, 0x1f, 0x9e, 0xed, 0x64 }; return &guid; } // backend device struct ggml_backend_cann_device_context { - int device; + int device; std::string name; std::string description; }; static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ctx->name.c_str(); } -static const char* ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; +static const char * ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ctx->description.c_str(); } static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; ggml_backend_cann_get_device_memory(ctx->device, free, total); } @@ -2844,7 +2748,7 @@ static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_back static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, const char * params) { GGML_UNUSED(params); - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ggml_backend_cann_init(ctx->device); } @@ -2861,19 +2765,17 @@ static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, cons * @return bool Returns true if the CANN backend supports the buffer type, * otherwise false. */ -static bool ggml_backend_cann_supports_buft( - ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { +static bool ggml_backend_cann_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { if (ggml_backend_buft_is_cann(buft)) { - ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; - ggml_backend_cann_buffer_type_context * buft_ctx = - (ggml_backend_cann_buffer_type_context *)buft->context; + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *) dev->context; + ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context; return buft_ctx->device == dev_ctx->device; } return false; } static ggml_backend_buffer_type_t ggml_backend_cann_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ggml_backend_cann_buffer_type(ctx->device); } @@ -2892,9 +2794,8 @@ static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type( * @param backend Pointer to the CANN backend. * @return ggml_backend_event_t Returns a pointer to the new event structure. */ -static ggml_backend_event_t ggml_backend_cann_device_event_new( - ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; +static ggml_backend_event_t ggml_backend_cann_device_event_new(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *) dev->context; ggml_cann_set_device(dev_ctx->device); @@ -2916,7 +2817,7 @@ static ggml_backend_event_t ggml_backend_cann_device_event_new( * @param event Pointer to the event structure to be freed. */ static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { - ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context)); + ACL_CHECK(aclrtDestroyEvent((aclrtEvent) event->context)); delete event; GGML_UNUSED(dev); @@ -2930,7 +2831,7 @@ static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_bac * @param event Pointer to the event structure to be synchronized. */ static void ggml_backend_cann_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { - ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context)); + ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent) event->context)); GGML_UNUSED(dev); } @@ -2941,10 +2842,10 @@ static const ggml_backend_device_i ggml_backend_cann_device_interface = { /* .get_memory = */ ggml_backend_cann_device_get_memory, /* .get_type = */ ggml_backend_cann_device_get_type, /* .get_props = */ ggml_backend_cann_device_get_props, - /* .init_backend = */ ggml_backend_cann_device_init, // called for every card + /* .init_backend = */ ggml_backend_cann_device_init, // called for every card /* .get_buffer_type = */ ggml_backend_cann_device_get_buffer_type, /* .get_host_buffer_type = */ ggml_backend_cann_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ NULL, // not supported for CANN + /* .buffer_from_host_ptr = */ NULL, // not supported for CANN /* .supports_op = */ ggml_backend_cann_supports_op, /* .supports_buft = */ ggml_backend_cann_supports_buft, /* .offload_op = */ ggml_backend_cann_offload_op, @@ -2953,7 +2854,6 @@ static const ggml_backend_device_i ggml_backend_cann_device_interface = { /* .event_synchronize = */ ggml_backend_cann_device_event_synchronize, }; - // backend reg struct ggml_backend_cann_reg_context { std::vector devices; @@ -2965,12 +2865,12 @@ static const char * ggml_backend_cann_reg_get_name(ggml_backend_reg_t reg) { } static size_t ggml_backend_cann_reg_get_device_count(ggml_backend_reg_t reg) { - ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *) reg->context; return ctx->devices.size(); } static ggml_backend_dev_t ggml_backend_cann_reg_get_device(ggml_backend_reg_t reg, size_t index) { - ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *) reg->context; GGML_ASSERT(index < ctx->devices.size()); return ctx->devices[index]; } @@ -2992,34 +2892,30 @@ static const ggml_backend_reg_i ggml_backend_cann_reg_interface = { // backend registry, called only once for cann backend ggml_backend_reg_t ggml_backend_cann_reg() { static ggml_backend_reg reg; - static bool initialized = false; + static bool initialized = false; { - static std::mutex mutex; + static std::mutex mutex; std::lock_guard lock(mutex); if (!initialized) { aclInit(nullptr); ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context; for (int i = 0; i < ggml_cann_info().device_count; i++) { - ggml_backend_cann_device_context* dev_ctx = new ggml_backend_cann_device_context(); - dev_ctx->description = aclrtGetSocName(); - dev_ctx->device = i; - dev_ctx->name = GGML_CANN_NAME + std::to_string(i); + ggml_backend_cann_device_context * dev_ctx = new ggml_backend_cann_device_context(); + dev_ctx->description = aclrtGetSocName(); + dev_ctx->device = i; + dev_ctx->name = GGML_CANN_NAME + std::to_string(i); ggml_cann_set_device(i); - ggml_backend_dev_t dev = new ggml_backend_device { - /* .iface = */ ggml_backend_cann_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx - }; + ggml_backend_dev_t dev = new ggml_backend_device{ /* .iface = */ ggml_backend_cann_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx }; ctx->devices.push_back(dev); } - reg = ggml_backend_reg { - /* .api_version = */ GGML_BACKEND_API_VERSION, - /* .iface = */ ggml_backend_cann_reg_interface, - /* .context = */ ctx - }; + reg = ggml_backend_reg{ /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_cann_reg_interface, + /* .context = */ ctx }; } initialized = true; @@ -3035,39 +2931,36 @@ ggml_backend_t ggml_backend_cann_init(int32_t device) { return nullptr; } - ggml_backend_cann_context* ctx = new ggml_backend_cann_context(device); + ggml_backend_cann_context * ctx = new ggml_backend_cann_context(device); if (ctx == nullptr) { GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); return nullptr; } ggml_cann_set_device(ctx->device); ggml_backend_t cann_backend = - new ggml_backend{/* .guid = */ ggml_backend_cann_guid(), - /* .interface = */ ggml_backend_cann_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), - /* .context = */ ctx}; + new ggml_backend{ /* .guid = */ ggml_backend_cann_guid(), + /* .interface = */ ggml_backend_cann_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), + /* .context = */ ctx }; return cann_backend; } bool ggml_backend_is_cann(ggml_backend_t backend) { - return backend != NULL && - ggml_guid_matches(backend->guid, ggml_backend_cann_guid()); + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cann_guid()); } int32_t ggml_backend_cann_get_device_count() { return ggml_cann_info().device_count; } -void ggml_backend_cann_get_device_description( - int32_t device, char* description, size_t description_size) { +void ggml_backend_cann_get_device_description(int32_t device, char * description, size_t description_size) { ggml_cann_set_device(device); - const char* soc_name = aclrtGetSocName(); + const char * soc_name = aclrtGetSocName(); snprintf(description, description_size, "%s", soc_name); } -void ggml_backend_cann_get_device_memory(int32_t device, size_t* free, - size_t* total) { +void ggml_backend_cann_get_device_memory(int32_t device, size_t * free, size_t * total) { ggml_cann_set_device(device); ACL_CHECK(aclrtGetMemInfo(ACL_HBM_MEM, free, total)); } diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h index 799e2b1187..713bf85e5a 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -68,7 +68,7 @@ struct ggml_compute_params { #endif // __VXE2__ #endif // __s390x__ && __VEC__ -#if defined(__ARM_FEATURE_SVE) +#if defined(__ARM_FEATURE_SVE) && defined(__linux__) #include #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index eded6eb77e..9ec485cfa2 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -689,8 +689,13 @@ bool ggml_is_numa(void) { #endif static void ggml_init_arm_arch_features(void) { -#if defined(__linux__) && defined(__aarch64__) && defined(__ARM_FEATURE_SVE) +#if defined(__aarch64__) && defined(__ARM_FEATURE_SVE) +#if defined(__linux__) ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); +#else + // TODO: add support of SVE for non-linux systems +#error "TODO: SVE is not supported on this platform. To use SVE, sve_cnt needs to be initialized here." +#endif #endif } @@ -2179,6 +2184,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_FLOOR: + case GGML_UNARY_OP_CEIL: + case GGML_UNARY_OP_ROUND: + case GGML_UNARY_OP_TRUNC: { n_tasks = 1; } break; @@ -3558,13 +3567,17 @@ void ggml_cpu_init(void) { #ifdef GGML_USE_OPENMP //if (!getenv("OMP_WAIT_POLICY")) { // // set the wait policy to active, so that OpenMP threads don't sleep - // putenv("OMP_WAIT_POLICY=active"); + // setenv("OMP_WAIT_POLICY", "active", 0) //} if (!getenv("KMP_BLOCKTIME")) { // set the time to wait before sleeping a thread // this is less aggressive than setting the wait policy to active, but should achieve similar results in most cases - putenv("KMP_BLOCKTIME=200"); // 200ms +#ifdef _WIN32 + _putenv_s("KMP_BLOCKTIME", "200"); // 200ms +#else + setenv("KMP_BLOCKTIME", "200", 0); // 200ms +#endif } #endif } diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 1c43865ff6..b52f0f8472 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -8993,6 +8993,22 @@ void ggml_compute_forward_unary( { ggml_compute_forward_exp(params, dst); } break; + case GGML_UNARY_OP_FLOOR: + { + ggml_compute_forward_floor(params, dst); + } break; + case GGML_UNARY_OP_CEIL: + { + ggml_compute_forward_ceil(params, dst); + } break; + case GGML_UNARY_OP_ROUND: + { + ggml_compute_forward_round(params, dst); + } break; + case GGML_UNARY_OP_TRUNC: + { + ggml_compute_forward_trunc(params, dst); + } break; case GGML_UNARY_OP_XIELU: { ggml_compute_forward_xielu(params, dst); diff --git a/ggml/src/ggml-cpu/unary-ops.cpp b/ggml/src/ggml-cpu/unary-ops.cpp index cf1a4615d0..a047537b34 100644 --- a/ggml/src/ggml-cpu/unary-ops.cpp +++ b/ggml/src/ggml-cpu/unary-ops.cpp @@ -73,6 +73,22 @@ static inline float op_log(float x) { return logf(x); } +static inline float op_floor(float x) { + return floorf(x); +} + +static inline float op_ceil(float x) { + return ceilf(x); +} + +static inline float op_round(float x) { + return roundf(x); +} + +static inline float op_trunc(float x) { + return truncf(x); +} + template static inline void vec_unary_op(int64_t n, dst_t * y, const src0_t * x) { constexpr auto src0_to_f32 = type_conversion_table::to_f32; @@ -274,6 +290,22 @@ void ggml_compute_forward_log(const ggml_compute_params * params, ggml_tensor * unary_op(params, dst); } +void ggml_compute_forward_floor(const ggml_compute_params * params, ggml_tensor * dst) { + unary_op(params, dst); +} + +void ggml_compute_forward_ceil(const ggml_compute_params * params, ggml_tensor * dst) { + unary_op(params, dst); +} + +void ggml_compute_forward_round(const ggml_compute_params * params, ggml_tensor * dst) { + unary_op(params, dst); +} + +void ggml_compute_forward_trunc(const ggml_compute_params * params, ggml_tensor * dst) { + unary_op(params, dst); +} + void ggml_compute_forward_xielu(const ggml_compute_params * params, ggml_tensor * dst) { const float alpha_n = ggml_get_op_params_f32(dst, 1); const float alpha_p = ggml_get_op_params_f32(dst, 2); diff --git a/ggml/src/ggml-cpu/unary-ops.h b/ggml/src/ggml-cpu/unary-ops.h index 697c1e0da0..fa45d9f0e6 100644 --- a/ggml/src/ggml-cpu/unary-ops.h +++ b/ggml/src/ggml-cpu/unary-ops.h @@ -22,6 +22,10 @@ void ggml_compute_forward_sqrt(const struct ggml_compute_params * params, struct void ggml_compute_forward_sin(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_cos(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_log(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_floor(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_ceil(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_round(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_trunc(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_xielu(const struct ggml_compute_params * params, struct ggml_tensor * dst); #ifdef __cplusplus diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp index b8e37052d3..43dc7537c3 100644 --- a/ggml/src/ggml-cpu/vec.cpp +++ b/ggml/src/ggml-cpu/vec.cpp @@ -463,9 +463,9 @@ ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const floa #endif for (; i < n; ++i) { float val = x[i] - mean; + y[i] = val; val *= val; sum += (ggml_float)val; - y[i] = val; } return sum/n; } diff --git a/ggml/src/ggml-cpu/vec.h b/ggml/src/ggml-cpu/vec.h index 2751359ce4..65c7dfb6b9 100644 --- a/ggml/src/ggml-cpu/vec.h +++ b/ggml/src/ggml-cpu/vec.h @@ -144,14 +144,14 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG for (int i = 0; i < np; i += ggml_f16_step) { ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0); // 8 elements - ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elemnst + ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elements sum_00 = GGML_F16x_VEC_FMA(sum_00, ax1, ay1); // sum_00 = sum_00+ax1*ay1 ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 0*ggml_f16_epr, 0); // 8 elements sum_10 = GGML_F16x_VEC_FMA(sum_10, ax1, ay1); ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1); // next 8 elements - ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 ekements + ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 elements sum_01 = GGML_F16x_VEC_FMA(sum_01, ax2, ay2); ax2 = GGML_F16x_VEC_LOAD(x[1] + i + 1*ggml_f16_epr, 1); sum_11 = GGML_F16x_VEC_FMA(sum_11, ax2, ay2); @@ -160,7 +160,7 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG ax3 = GGML_F16x_VEC_LOAD(x[0] + i + 2*ggml_f16_epr, 2); sum_02 = GGML_F16x_VEC_FMA(sum_02, ax3, ay3); - ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2); + ax3 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2); sum_12 = GGML_F16x_VEC_FMA(sum_12, ax3, ay3); ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3); @@ -820,7 +820,8 @@ inline static void ggml_vec_tanh_f16 (const int n, ggml_fp16_t * y, const ggml_f inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } inline static void ggml_vec_elu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { for (int i = 0; i < n; ++i) { - y[i] = GGML_CPU_FP32_TO_FP16(expm1f(GGML_CPU_FP16_TO_FP32(x[i]))); + const float v = GGML_CPU_FP16_TO_FP32(x[i]); + y[i] = GGML_CPU_FP32_TO_FP16((v > 0.f) ? v : expm1f(v)); } } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt index bdcefe7b7e..3024775135 100644 --- a/ggml/src/ggml-cuda/CMakeLists.txt +++ b/ggml/src/ggml-cuda/CMakeLists.txt @@ -44,6 +44,8 @@ if (CUDAToolkit_FOUND) list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h") file(GLOB GGML_SOURCES_CUDA "*.cu") + file(GLOB SRCS "template-instances/fattn-tile*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) file(GLOB SRCS "template-instances/fattn-mma*.cu") list(APPEND GGML_SOURCES_CUDA ${SRCS}) file(GLOB SRCS "template-instances/mmq*.cu") diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index d51abbeafa..41ff89c4d6 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -245,7 +245,8 @@ static bool fp16_available(const int cc) { } static bool fast_fp16_available(const int cc) { - return (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && cc != 610) || GGML_CUDA_CC_IS_AMD(cc); + return GGML_CUDA_CC_IS_AMD(cc) || + (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && ggml_cuda_highest_compiled_arch(cc) != 610); } // To be used for feature selection of external libraries, e.g. cuBLAS. @@ -571,6 +572,10 @@ static __device__ __forceinline__ void ggml_cuda_mad(half2 & acc, const half2 v, } // Aligned memory transfers of 8/16 bytes can be faster than 2 transfers with 4 bytes, especially on AMD. +// Important: do not use this function if dst and src both point at registers. +// Due to the strict aliasing rule the compiler can do incorrect optimizations if src and dst have different types. +// The function is intended for copies between registers and SRAM/VRAM to make the compiler emit the right instructions. +// If dst and src point at different address spaces then they are guaranteed to not be aliased. template static __device__ __forceinline__ void ggml_cuda_memcpy_1(void * __restrict__ dst, const void * __restrict__ src) { if constexpr (alignment != 0) { @@ -939,13 +944,6 @@ struct ggml_cuda_graph { bool disable_due_to_failed_graph_capture = false; int number_consecutive_updates = 0; std::vector ggml_graph_properties; - bool use_cpy_indirection = false; - std::vector cpy_dest_ptrs; - char ** dest_ptrs_d; - int dest_ptrs_size = 0; - // Index to allow each cpy kernel to be aware of it's position within the graph - // relative to other cpy nodes. - int graph_cpynode_index = -1; #endif }; diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index 746f43966b..12d5bf7763 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -8,18 +8,16 @@ typedef void (*cpy_kernel_t)(const char * cx, char * cdst); template -static __global__ void cpy_flt(const char * cx, char * cdst_direct, const int ne, +static __global__ void cpy_flt(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13, char ** cdst_indirect, int graph_cpynode_index) { + const int nb12, const int nb13) { const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= ne) { return; } - char * cdst = (cdst_indirect != nullptr) ? cdst_indirect[graph_cpynode_index]: cdst_direct; - // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets const int64_t i03 = i/(ne00 * ne01 * ne02); @@ -63,18 +61,16 @@ static __device__ void cpy_blck_q_f32(const char * cxi, char * cdsti) { } template -static __global__ void cpy_f32_q(const char * cx, char * cdst_direct, const int ne, +static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13, char ** cdst_indirect, int graph_cpynode_index) { + const int nb12, const int nb13) { const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk; if (i >= ne) { return; } - char * cdst = (cdst_indirect != nullptr) ? cdst_indirect[graph_cpynode_index]: cdst_direct; - const int i03 = i/(ne00 * ne01 * ne02); const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; @@ -91,18 +87,16 @@ static __global__ void cpy_f32_q(const char * cx, char * cdst_direct, const int } template -static __global__ void cpy_q_f32(const char * cx, char * cdst_direct, const int ne, +static __global__ void cpy_q_f32(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13, char ** cdst_indirect, int graph_cpynode_index) { + const int nb12, const int nb13) { const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk; if (i >= ne) { return; } - char * cdst = (cdst_indirect != nullptr) ? cdst_indirect[graph_cpynode_index]: cdst_direct; - const int i03 = i/(ne00 * ne01 * ne02); const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; @@ -118,67 +112,47 @@ static __global__ void cpy_q_f32(const char * cx, char * cdst_direct, const int cpy_blck(cx + x_offset, cdst + dst_offset); } -// Copy destination pointers to GPU to be available when pointer indirection is in use - -void ggml_cuda_cpy_dest_ptrs_copy(ggml_cuda_graph * cuda_graph, char ** host_dest_ptrs, const int host_dest_ptrs_size, cudaStream_t stream) { -#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS) || defined(GGML_MUSA_GRAPHS) - if (cuda_graph->dest_ptrs_size < host_dest_ptrs_size) { // (re-)allocate GPU memory for destination pointers - CUDA_CHECK(cudaStreamSynchronize(stream)); - if (cuda_graph->dest_ptrs_d != nullptr) { - CUDA_CHECK(cudaFree(cuda_graph->dest_ptrs_d)); - } - CUDA_CHECK(cudaMalloc(&cuda_graph->dest_ptrs_d, host_dest_ptrs_size*sizeof(char *))); - cuda_graph->dest_ptrs_size = host_dest_ptrs_size; - } - // copy destination pointers to GPU - CUDA_CHECK(cudaMemcpyAsync(cuda_graph->dest_ptrs_d, host_dest_ptrs, host_dest_ptrs_size*sizeof(char *), cudaMemcpyHostToDevice, stream)); - cuda_graph->graph_cpynode_index = 0; // reset index -#else - GGML_UNUSED_VARS(cuda_graph, host_dest_ptrs, host_dest_ptrs_size, stream); -#endif -} - template static void ggml_cpy_flt_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; cpy_flt><<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q8_0_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK8_0 == 0); const int num_blocks = ne / QK8_0; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_q8_0_f32_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { const int num_blocks = ne; cpy_q_f32<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q4_0_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK4_0 == 0); const int num_blocks = ne / QK4_0; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_q4_0_f32_cuda( @@ -187,22 +161,22 @@ static void ggml_cpy_q4_0_f32_cuda( const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + cudaStream_t stream) { const int num_blocks = ne; cpy_q_f32, QK4_0><<>>( cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q4_1_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK4_1 == 0); const int num_blocks = ne / QK4_1; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_q4_1_f32_cuda( @@ -211,22 +185,22 @@ static void ggml_cpy_q4_1_f32_cuda( const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + cudaStream_t stream) { const int num_blocks = ne; cpy_q_f32, QK4_1><<>>( cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q5_0_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK5_0 == 0); const int num_blocks = ne / QK5_0; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_q5_0_f32_cuda( @@ -235,22 +209,22 @@ static void ggml_cpy_q5_0_f32_cuda( const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + cudaStream_t stream) { const int num_blocks = ne; cpy_q_f32, QK5_0><<>>( cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q5_1_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK5_1 == 0); const int num_blocks = ne / QK5_1; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_q5_1_f32_cuda( @@ -259,25 +233,25 @@ static void ggml_cpy_q5_1_f32_cuda( const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + cudaStream_t stream) { const int num_blocks = ne; cpy_q_f32, QK5_1><<>>( cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, - ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_iq4_nl_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream, char ** cdst_indirect, int & graph_cpynode_index) { + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK4_NL == 0); const int num_blocks = ne / QK4_NL; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, cdst_indirect, graph_cpynode_index++); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } -void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1, bool disable_indirection_for_this_node) { +void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1) { const int64_t ne = ggml_nelements(src0); GGML_ASSERT(ne == ggml_nelements(src1)); @@ -311,16 +285,6 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg char * src0_ddc = (char *) src0->data; char * src1_ddc = (char *) src1->data; - char ** dest_ptrs_d = nullptr; - int graph_cpynode_index = -1; -#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS) || defined(GGML_MUSA_GRAPHS) - if(ctx.cuda_graph->use_cpy_indirection && !disable_indirection_for_this_node) { - dest_ptrs_d = ctx.cuda_graph->dest_ptrs_d; - graph_cpynode_index = ctx.cuda_graph->graph_cpynode_index; - } -#else - GGML_UNUSED(disable_indirection_for_this_node); -#endif if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1)); #if defined(GGML_USE_MUSA) && defined(GGML_MUSA_MUDNN_COPY) @@ -329,134 +293,62 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg } else #endif // GGML_USE_MUSA && GGML_MUSA_MUDNN_COPY { - if (src0->type == GGML_TYPE_F32) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); - } else { - CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream)); - } + CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream)); } } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) { - ggml_cpy_q8_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_q8_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q4_0 && src1->type == GGML_TYPE_F32) { ggml_cpy_q4_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, - nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q4_1 && src1->type == GGML_TYPE_F32) { ggml_cpy_q4_1_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, - nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) { - ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q5_0 && src1->type == GGML_TYPE_F32) { ggml_cpy_q5_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, - nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) { - ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) { - ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q5_1 && src1->type == GGML_TYPE_F32) { - ggml_cpy_q5_1_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_q5_1_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_BF16) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index); + ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); } -#if defined(GGML_CUDA_USE_GRAPHS) || defined(GGML_HIP_GRAPHS) || defined(GGML_MUSA_GRAPHS) - if(ctx.cuda_graph->use_cpy_indirection && !disable_indirection_for_this_node) { - ctx.cuda_graph->graph_cpynode_index = graph_cpynode_index; - } -#else - GGML_UNUSED(disable_indirection_for_this_node); -#endif - } void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; - bool disable_indirection = true; - ggml_cuda_cpy(ctx, src0, dst, disable_indirection); -} - -void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) { - if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { - // Prioritize CUDA graph compatibility over direct memory copy optimization. - // Using copy kernels here maintains graph indirection support, preventing performance regression from disabled CUDA graphs. - if (src0->type == GGML_TYPE_F32) { - return (void*) cpy_flt>; - } else { - return nullptr; - } - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - return (void*) cpy_f32_q; - } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_q_f32; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - return (void*) cpy_f32_q; - } else if (src0->type == GGML_TYPE_Q4_0 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_q_f32, QK4_0>; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - return (void*) cpy_f32_q; - } else if (src0->type == GGML_TYPE_Q4_1 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_q_f32, QK4_1>; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) { - return (void*) cpy_f32_q; - } else if (src0->type == GGML_TYPE_Q5_0 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_q_f32, QK5_0>; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) { - return (void*) cpy_f32_q; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) { - return (void*) cpy_f32_q; - } else if (src0->type == GGML_TYPE_Q5_1 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_q_f32, QK5_1>; - } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_BF16) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32) { - return (void*) cpy_flt>; - } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_F32) { - return (void*) cpy_flt>; - } else { - GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__, - ggml_type_name(src0->type), ggml_type_name(src1->type)); - } + ggml_cuda_cpy(ctx, src0, dst); } diff --git a/ggml/src/ggml-cuda/cpy.cuh b/ggml/src/ggml-cuda/cpy.cuh index 0bd3c0c6f8..a7a87d8fcf 100644 --- a/ggml/src/ggml-cuda/cpy.cuh +++ b/ggml/src/ggml-cuda/cpy.cuh @@ -2,10 +2,6 @@ #define CUDA_CPY_BLOCK_SIZE 64 -void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1, bool disable_indirection = false); +void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1); void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst); - -void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1); - -void ggml_cuda_cpy_dest_ptrs_copy(ggml_cuda_graph * cuda_graph, char ** host_dest_ptrs, const int host_dest_ptrs_size, cudaStream_t stream); diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 33d2f0f49e..bc0c2523cc 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -793,8 +793,6 @@ void launch_fattn( GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); - GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); - ggml_cuda_pool & pool = ctx.pool(); cudaStream_t main_stream = ctx.stream(); const int id = ggml_cuda_get_device(); @@ -878,7 +876,7 @@ void launch_fattn( // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped. // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or // multiple sequences of possibly different lengths. - if (mask && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) { + if (mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) { const int s31 = mask->nb[1] / sizeof(half2); const int s33 = mask->nb[3] / sizeof(half2); @@ -916,8 +914,7 @@ void launch_fattn( dst_tmp_meta.alloc(blocks_num.x*ncols * (2*2 + DV) * sizeof(float)); } else { - GGML_ASSERT(K->ne[1] % KQ_row_granularity == 0); - const int ntiles_KQ = K->ne[1] / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size. + const int ntiles_KQ = (K->ne[1] + KQ_row_granularity - 1) / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size. // parallel_blocks must not be larger than what the tensor size allows: parallel_blocks = std::min(parallel_blocks, ntiles_KQ); @@ -946,7 +943,7 @@ void launch_fattn( blocks_num.x = ntiles_x; blocks_num.y = parallel_blocks; - blocks_num.z = Q->ne[2]*Q->ne[3]; + blocks_num.z = (Q->ne[2]/ncols2)*Q->ne[3]; if (parallel_blocks > 1) { dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); diff --git a/ggml/src/ggml-cuda/fattn-tile.cu b/ggml/src/ggml-cuda/fattn-tile.cu index 68de623d80..3a5806d909 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cu +++ b/ggml/src/ggml-cuda/fattn-tile.cu @@ -1,756 +1,45 @@ #include "common.cuh" -#include "fattn-common.cuh" #include "fattn-tile.cuh" #include "fattn-wmma-f16.cuh" -// kq_stride == number of KQ rows to process per iteration -// kq_nbatch == number of K columns to load in parallel for KQ calculation - -static int fattn_tile_get_kq_stride_host(const int D, const int ncols, const int cc, const int warp_size) { - if (GGML_CUDA_CC_IS_AMD(cc)) { - if (GGML_CUDA_CC_IS_RDNA(cc)) { - switch (D) { - case 64: - return 128; - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - GGML_ABORT("fatal error"); - return -1; - } - } - switch (D) { - case 64: - return ncols == 32 ? 128 : 64; - case 128: - return ncols == 32 ? 64 : 32; - case 256: - return 32; - default: - GGML_ABORT("fatal error"); - return -1; - } - } - if (fast_fp16_available(cc)) { - switch (D) { - case 64: - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - GGML_ABORT("fatal error"); - return -1; - } - } - switch (D) { - case 64: - return ncols <= 16 ? 128 : 64; - case 128: - return ncols <= 16 ? 64 : 32; - case 256: - return 32; - default: - GGML_ABORT("fatal error"); - return -1; - } - GGML_UNUSED(warp_size); -} - -static constexpr __device__ int fattn_tile_get_kq_stride_device(int D, int ncols, int warp_size) { -#ifdef GGML_USE_HIP -#ifdef RDNA - switch (D) { - case 64: - return 128; - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - return -1; - } -#else - switch (D) { - case 64: - return ncols == 32 ? 128 : 64; - case 128: - return ncols == 32 ? 64 : 32; - case 256: - return 32; - default: - return -1; - } -#endif // RDNA -#else -#ifdef FAST_FP16_AVAILABLE - switch (D) { - case 64: - case 128: - case 256: - return ncols <= 16 ? 128 : 64; - default: - return -1; - } -#else - switch (D) { - case 64: - return ncols <= 16 ? 128 : 64; - case 128: - return ncols <= 16 ? 64 : 32; - case 256: - return 32; - default: - return -1; - } -#endif // FAST_FP16_AVAILABLE -#endif // GGML_USE_HIP - GGML_UNUSED_VARS(ncols, warp_size); -} - -static constexpr __device__ int fattn_tile_get_kq_nbatch_device(int D, int ncols, int warp_size) { -#ifdef GGML_USE_HIP - switch (D) { - case 64: - return 64; - case 128: - case 256: - return 128; - default: - return -1; - } -#else -#ifdef FAST_FP16_AVAILABLE - switch (D) { - case 64: - return 64; - case 128: - case 256: - return 128; - default: - return -1; - } -#else - switch (D) { - case 64: - return 64; - case 128: - return 128; - case 256: - return ncols <= 16 ? 128 : 64; - default: - return -1; - } -#endif // FAST_FP16_AVAILABLE -#endif // GGML_USE_HIP - GGML_UNUSED_VARS(ncols, warp_size); -} - -static int fattn_tile_get_nthreads_host(const int cc, const int ncols) { - return 256; - GGML_UNUSED_VARS(cc, ncols); -} - -static constexpr __device__ int fattn_tile_get_nthreads_device(int ncols) { - return 256; - GGML_UNUSED(ncols); -} - -static constexpr __device__ int fattn_tile_get_occupancy_device(int ncols) { -#ifdef RDNA - return 3; -#else - return ncols <= 16 ? 3 : 2; -#endif // RDNA - GGML_UNUSED(ncols); -} - -template // D == head size -__launch_bounds__(fattn_tile_get_nthreads_device(ncols), fattn_tile_get_occupancy_device(ncols)) -static __global__ void flash_attn_tile( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - const char * __restrict__ sinks, - const int * __restrict__ KV_max, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const float logit_softcap, - const int32_t ne00, const int32_t ne01, const int32_t ne02, const int32_t ne03, - const int32_t nb01, const int32_t nb02, const int32_t nb03, - const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13, - const int32_t nb11, const int32_t nb12, const int64_t nb13, - const int32_t nb21, const int32_t nb22, const int64_t nb23, - const int32_t ne31, const int32_t ne32, const int32_t ne33, - const int32_t nb31, const int32_t nb32, const int64_t nb33) { -#ifdef FLASH_ATTN_AVAILABLE - - // Skip unused kernel variants for faster compilation: -#ifdef GGML_USE_WMMA_FATTN - NO_DEVICE_CODE; - return; -#endif // GGML_USE_WMMA_FATTN - - if (use_logit_softcap && !(D == 128 || D == 256)) { - GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, - max_bias, m0, m1, n_head_log2, logit_softcap, - ne00, ne01, ne02, ne03, - nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb11, nb12, nb13, - nb21, nb22, nb23, - ne31, ne32, ne33, - nb31, nb32, nb33); - NO_DEVICE_CODE; - return; - } - - constexpr int warp_size = 32; - constexpr int nwarps = fattn_tile_get_nthreads_device(ncols) / warp_size; - constexpr int kq_stride = fattn_tile_get_kq_stride_device(D, ncols, warp_size); - static_assert(kq_stride % warp_size == 0, "kq_stride not divisable by warp_size."); - constexpr int kq_nbatch = fattn_tile_get_kq_nbatch_device(D, ncols, warp_size); - static_assert(kq_nbatch % (2*warp_size) == 0, "bad kq_nbatch"); - - // In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = blockIdx.x * ncols; // Index of the Q/QKV column to work on. - - const int sequence = blockIdx.z / ne02; - const int head = blockIdx.z - sequence*ne02; - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float * Q_f = (const float *) (Q + nb03* sequence + nb02* head + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb13* sequence + nb12*(head / gqa_ratio)); - const half2 * V_h2 = (const half2 *) (V + nb13* sequence + nb12*(head / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0); - const float * sinksf = (const float *) (sinks); - - const int stride_KV2 = nb11 / sizeof(half2); - - const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1); - - constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); - constexpr int cpy_ne = cpy_nb / 4; - - constexpr int cpw = ncols/nwarps; // cols per warp - - // softmax_iter_j == number of KQ columns for which to calculate softmax in parallel. - // KQ is originall 2D but uses a Z-shaped memory pattern for larger reads/writes. -#ifdef FAST_FP16_AVAILABLE - constexpr int softmax_iter_j = cpw < 2*cpy_ne ? cpw : 2*cpy_ne; - - __shared__ half KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j]; - __shared__ half2 Q_tmp[ncols][D/2]; - __shared__ half2 KV_tmp[kq_stride * (kq_nbatch/2 + cpy_ne)]; // Padded to avoid memory bank conflicts. - half2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}}; -#else - constexpr int softmax_iter_j = cpw < 1*cpy_ne ? cpw : 1*cpy_ne; - - __shared__ float KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j]; - __shared__ float Q_tmp[ncols][D]; - __shared__ float KV_tmp[kq_stride * (kq_nbatch + cpy_ne)]; // Padded to avoid memory bank conflicts. - float2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}}; -#endif // FAST_FP16_AVAILABLE - static_assert(cpw % softmax_iter_j == 0, "bad softmax_iter_j"); - - float KQ_max[cpw]; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - KQ_max[j0/nwarps] = -FLT_MAX/2.0f; - } - float KQ_sum[cpw] = {0.0f}; - - // Load Q data, convert to FP16 if fast. -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - const int j = j0 + threadIdx.y*cpw; - - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; - -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - float tmp_f[cpy_ne_D] = {0.0f}; - if (ic0 + j < ne01) { - ggml_cuda_memcpy_1(tmp_f, &Q_f[j*(nb01/sizeof(float)) + i0 + threadIdx.x*cpy_ne_D]); - } - -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D; ++i1) { - tmp_f[i1] *= scale; - } - -#ifdef FAST_FP16_AVAILABLE - half2 tmp_h2[cpy_ne_D/2]; -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) { - tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]); - } - ggml_cuda_memcpy_1(&Q_tmp[j][i0/2 + threadIdx.x*(cpy_ne_D/2)], tmp_h2); -#else - ggml_cuda_memcpy_1 (&Q_tmp[j][i0 + threadIdx.x* cpy_ne_D], tmp_f); -#endif // FAST_FP16_AVAILABLE - } - } - - __syncthreads(); - - // Main loop over KV cache: - const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11; - for (int k_VKQ_0 = blockIdx.y*kq_stride; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*kq_stride) { - // Calculate KQ tile and keep track of new maximum KQ values: - - float KQ_max_new[cpw]; -#pragma unroll - for (int j = 0; j < cpw; ++j) { - KQ_max_new[j] = KQ_max[j]; - } - - float KQ_acc[kq_stride/warp_size][cpw] = {{0.0f}}; // Accumulators for KQ matrix multiplication. - - // KQ = K @ Q matrix multiplication: -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += kq_nbatch) { -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - -#ifdef FAST_FP16_AVAILABLE - constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/(2*warp_size) ? cpy_ne : kq_nbatch/(2*warp_size); -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += warp_size*cpy_ne_kqnb) { - ggml_cuda_memcpy_1( - &KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb], - &K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1 + threadIdx.x*cpy_ne_kqnb]); - } -#else - constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/warp_size ? cpy_ne : kq_nbatch/warp_size; -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += warp_size*cpy_ne_kqnb) { - half2 tmp_h2[cpy_ne_kqnb/2]; - ggml_cuda_memcpy_1( - tmp_h2, &K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1/2 + threadIdx.x*(cpy_ne_kqnb/2)]); - - float2 tmp_f2[cpy_ne_kqnb/2]; -#pragma unroll - for (int k_KQ_2 = 0; k_KQ_2 < cpy_ne_kqnb/2; ++k_KQ_2) { - tmp_f2[k_KQ_2] = __half22float2(tmp_h2[k_KQ_2]); - } - ggml_cuda_memcpy_1( - &KV_tmp[i_KQ*(kq_nbatch + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb], tmp_f2); - } -#endif // FAST_FP16_AVAILABLE - } - - __syncthreads(); - -#ifdef FAST_FP16_AVAILABLE -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += cpy_ne) { - half2 K_k[kq_stride/warp_size][cpy_ne]; - half2 Q_k[cpw][cpy_ne]; -#else -#pragma unroll - for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += cpy_ne) { - float K_k[kq_stride/warp_size][cpy_ne]; - float Q_k[cpw][cpy_ne]; -#endif // FAST_FP16_AVAILABLE - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) { - const int i_KQ = i_KQ_0 + threadIdx.x; - -#ifdef FAST_FP16_AVAILABLE - ggml_cuda_memcpy_1(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1]); -#else - ggml_cuda_memcpy_1(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch + cpy_ne) + k_KQ_1]); -#endif // FAST_FP16_AVAILABLE - } -#pragma unroll - for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) { - const int j_KQ = j_KQ_0 + threadIdx.y*cpw; - -#ifdef FAST_FP16_AVAILABLE - ggml_cuda_memcpy_1(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0/2 + k_KQ_1]); -#else - ggml_cuda_memcpy_1(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0 + k_KQ_1]); -#endif // FAST_FP16_AVAILABLE - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) { -#pragma unroll - for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) { -#pragma unroll - for (int k = 0; k < cpy_ne; ++k) { - ggml_cuda_mad(KQ_acc[i_KQ_0/warp_size][j_KQ_0], K_k[i_KQ_0/warp_size][k], Q_k[j_KQ_0][k]); - } - } - } - } - - if (k_KQ_0 + kq_nbatch < D) { - __syncthreads(); // Sync not needed on last iteration. - } - } - - // Apply logit softcap, mask, update KQ_max: -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) { - const int i_KQ = i_KQ_0 + threadIdx.x; - -#pragma unroll - for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) { - const int j_KQ = j_KQ_0 + threadIdx.y*cpw; - - if (use_logit_softcap) { - KQ_acc[i_KQ_0/warp_size][j_KQ_0] = logit_softcap * tanhf(KQ_acc[i_KQ_0/warp_size][j_KQ_0]); - } - - KQ_acc[i_KQ_0/warp_size][j_KQ_0] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; - - KQ_max_new[j_KQ_0] = fmaxf(KQ_max_new[j_KQ_0], KQ_acc[i_KQ_0/warp_size][j_KQ_0]); - } - } - - __syncthreads(); - - // Calculate KQ softmax, write to shared KQ buffer, re-scale VKQ accumulators: -#pragma unroll - for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) { -#ifdef FAST_FP16_AVAILABLE - half tmp[kq_stride/warp_size][softmax_iter_j]; -#else - float tmp[kq_stride/warp_size][softmax_iter_j]; -#endif // FAST_FP16_AVAILABLE - -#pragma unroll - for (int j1 = 0; j1 < softmax_iter_j; ++j1) { - KQ_max_new[j0+j1] = warp_reduce_max(KQ_max_new[j0+j1]); - const float KQ_max_scale = expf(KQ_max[j0+j1] - KQ_max_new[j0+j1]); - KQ_max[j0+j1] = KQ_max_new[j0+j1]; - - float KQ_sum_add = 0.0f; -#pragma unroll - for (int i0 = 0; i0 < kq_stride; i0 += warp_size) { - const float val = expf(KQ_acc[i0/warp_size][j0+j1] - KQ_max[j0+j1]); - KQ_sum_add += val; - tmp[i0/warp_size][j1] = val; - } - KQ_sum[j0+j1] = KQ_sum[j0+j1]*KQ_max_scale + KQ_sum_add; - -#ifdef FAST_FP16_AVAILABLE - const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0+j1][i0/warp_size] *= KQ_max_scale_h2; - } -#else -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0+j1][i0/warp_size].x *= KQ_max_scale; - VKQ[j0+j1][i0/warp_size].y *= KQ_max_scale; - } -#endif // FAST_FP16_AVAILABLE - } - -#pragma unroll - for (int i0 = 0; i0 < kq_stride; i0 += warp_size) { - const int i = i0 + threadIdx.x; - - ggml_cuda_memcpy_1( - KQ[j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j)][i], tmp[i0/warp_size]); - } - } - - // VKQ = V @ KQ matrix multiplication: - constexpr int V_cols_per_iter = kq_stride*kq_nbatch / D; // Number of V columns that fit in SRAM for K. - static_assert(kq_stride % V_cols_per_iter == 0, "bad V_cols_per_iter"); -#pragma unroll - for (int k0 = 0; k0 < kq_stride; k0 += V_cols_per_iter) { -#pragma unroll - for (int k1 = 0; k1 < V_cols_per_iter; k1 += nwarps) { - const int k_tile = k1 + threadIdx.y; - -#ifdef FAST_FP16_AVAILABLE - constexpr int cpy_ne_D = cpy_ne < D/(2*warp_size) ? cpy_ne : D/(2*warp_size); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1( - &KV_tmp[k_tile*(D/2) + i0 + threadIdx.x*cpy_ne_D], - &V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0 + threadIdx.x*cpy_ne_D]); - } -#else - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - half2 tmp_h2[cpy_ne_D/2]; - ggml_cuda_memcpy_1( - tmp_h2, &V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0/2 + threadIdx.x*(cpy_ne_D/2)]); - - float2 tmp_f2[cpy_ne_D/2]; -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D/2; ++i1) { - tmp_f2[i1] = __half22float2(tmp_h2[i1]); - } - ggml_cuda_memcpy_1( - &KV_tmp[k_tile*D + i0 + threadIdx.x*cpy_ne_D], tmp_f2); - } -#endif // FAST_FP16_AVAILABLE - } - - __syncthreads(); - -#ifdef FAST_FP16_AVAILABLE -#pragma unroll - for (int k1 = 0; k1 < V_cols_per_iter; ++k1) { - half2 V_k[(D/2)/warp_size]; - half2 KQ_k[cpw]; - - constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1(&V_k[i0/warp_size], &KV_tmp[k1*(D/2) + i0 + threadIdx.x*cpy_ne_D]); - } -#pragma unroll - for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) { - const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j); - - half tmp[softmax_iter_j]; - ggml_cuda_memcpy_1( - &tmp, KQ[j][k0 + k1]); -#pragma unroll - for (int j1 = 0; j1 < softmax_iter_j; ++j1) { - KQ_k[j0+j1] = __half2half2(tmp[j1]); - } - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - VKQ[j0][i0/warp_size] += V_k[i0/warp_size]*KQ_k[j0]; - } - } - } -#else -#pragma unroll - for (int k1 = 0; k1 < V_cols_per_iter; ++k1) { - float2 V_k[(D/2)/warp_size]; - float KQ_k[cpw]; - - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1(&V_k[i0/(2*warp_size)], &KV_tmp[k1*D + i0 + threadIdx.x*cpy_ne_D]); - } -#pragma unroll - for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) { - const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j); - - ggml_cuda_memcpy_1( - &KQ_k[j0], KQ[j][k0 + k1]); - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - VKQ[j0][i0/warp_size].x += V_k[i0/warp_size].x*KQ_k[j0]; - VKQ[j0][i0/warp_size].y += V_k[i0/warp_size].y*KQ_k[j0]; - } - } - } -#endif // FAST_FP16_AVAILABLE - - __syncthreads(); - } - } - - - // Attention sink: adjust running max and sum once per head - if (sinksf && blockIdx.y == 0) { - const float sink = sinksf[head]; - -#pragma unroll - for (int j0 = 0; j0 < cpw; ++j0) { - float KQ_max_new_j = fmaxf(KQ_max[j0], sink); - KQ_max_new_j = warp_reduce_max(KQ_max_new_j); - - const float KQ_max_scale = expf(KQ_max[j0] - KQ_max_new_j); - KQ_max[j0] = KQ_max_new_j; - - const float val = expf(sink - KQ_max[j0]); - KQ_sum[j0] = KQ_sum[j0] * KQ_max_scale; - if (threadIdx.x == 0) { - KQ_sum[j0] += val; - } - -#ifdef FAST_FP16_AVAILABLE - const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0][i0/warp_size] *= KQ_max_scale_h2; - } -#else -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - VKQ[j0][i0/warp_size].x *= KQ_max_scale; - VKQ[j0][i0/warp_size].y *= KQ_max_scale; - } -#endif // FAST_FP16_AVAILABLE - } - } - -#pragma unroll - for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) { - KQ_sum[j_VKQ_0] = warp_reduce_sum(KQ_sum[j_VKQ_0]); - } - if (gridDim.y == 1) { -#pragma unroll - for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) { -#ifdef FAST_FP16_AVAILABLE - const half2 KQ_sum_j_inv = make_half2(1.0f/KQ_sum[j_VKQ_0], 1.0f/KQ_sum[j_VKQ_0]); -#pragma unroll - for (int i = 0; i < (D/2)/warp_size; ++i) { - VKQ[j_VKQ_0][i] *= KQ_sum_j_inv; - } -#else - const float KQ_sum_j_inv = 1.0f/KQ_sum[j_VKQ_0]; -#pragma unroll - for (int i = 0; i < (D/2)/warp_size; ++i) { - VKQ[j_VKQ_0][i].x *= KQ_sum_j_inv; - VKQ[j_VKQ_0][i].y *= KQ_sum_j_inv; - } -#endif // FAST_FP16_AVAILABLE - } - } - - // Write back results: -#pragma unroll - for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) { - const int j_VKQ = j_VKQ_0 + threadIdx.y*cpw; - - if (ic0 + j_VKQ >= ne01) { - return; - } - - const int j_dst_unrolled = ((sequence*ne01 + ic0 + j_VKQ)*ne02 + head)*gridDim.y + blockIdx.y; - -#ifdef FAST_FP16_AVAILABLE - constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) { - float2 tmp[cpy_ne_D]; -#pragma unroll - for (int i1 = 0; i1 < cpy_ne_D; ++i1) { - tmp[i1] = __half22float2(VKQ[j_VKQ_0][i0/warp_size + i1]); - } - ggml_cuda_memcpy_1(&dst[j_dst_unrolled*D + 2*i0 + threadIdx.x*(2*cpy_ne_D)], tmp); - } -#else - constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) { - ggml_cuda_memcpy_1( - &dst[j_dst_unrolled*D + i0 + threadIdx.x*cpy_ne_D], &VKQ[j_VKQ_0][i0/(2*warp_size)]); - } -#endif // FAST_FP16_AVAILABLE - - if (gridDim.y != 1 && threadIdx.x == 0) { - dst_meta[j_dst_unrolled] = make_float2(KQ_max[j_VKQ_0], KQ_sum[j_VKQ_0]); - } - } -#else - GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, - max_bias, m0, m1, n_head_log2, logit_softcap, - ne00, ne01, ne02, ne03, - nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb11, nb12, nb13, - nb21, nb22, nb23, - ne31, ne32, ne33, - nb31, nb32, nb33); - NO_DEVICE_CODE; -#endif // FLASH_ATTN_AVAILABLE -} - -template -static void launch_fattn_tile_switch_ncols(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - - const int id = ggml_cuda_get_device(); - const int cc = ggml_cuda_info().devices[id].cc; - const int warp_size = 32; - - constexpr size_t nbytes_shared = 0; - -#ifdef GGML_USE_HIP - if constexpr (D <= 128) { - if (Q->ne[1] > 32) { - constexpr int cols_per_block = 64; - const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size; - fattn_kernel_t fattn_kernel = flash_attn_tile; - const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size); - launch_fattn - (ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size); - return; - } - } -#endif // GGML_USE_HIP - - if (Q->ne[1] > 16) { - constexpr int cols_per_block = 32; - const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size; - fattn_kernel_t fattn_kernel = flash_attn_tile; - const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size); - launch_fattn - (ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size); - return; - } - - constexpr int cols_per_block = 16; - const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size; - fattn_kernel_t fattn_kernel = flash_attn_tile; - const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size); - launch_fattn - (ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size); -} - -template -static void launch_fattn_tile_switch_head_size(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { +void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + switch (K->ne[0]) { + case 40: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 40, 40>(ctx, dst); + } break; case 64: { - launch_fattn_tile_switch_ncols< 64, use_logit_softcap>(ctx, dst); + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 64, 64>(ctx, dst); + } break; + case 80: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 80, 80>(ctx, dst); + } break; + case 96: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case< 96, 96>(ctx, dst); + } break; + case 112: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case<112, 112>(ctx, dst); } break; case 128: { - launch_fattn_tile_switch_ncols<128, use_logit_softcap>(ctx, dst); + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case<128, 128>(ctx, dst); } break; case 256: { - launch_fattn_tile_switch_ncols<256, use_logit_softcap>(ctx, dst); + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_cuda_flash_attn_ext_tile_case<256, 256>(ctx, dst); + } break; + case 576: { + GGML_ASSERT(V->ne[0] == 512); + ggml_cuda_flash_attn_ext_tile_case<576, 512>(ctx, dst); } break; default: { GGML_ABORT("Unsupported head size"); } break; } } - -void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - - float logit_softcap; - memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); - - if (logit_softcap == 0.0f) { - constexpr bool use_logit_softcap = false; - launch_fattn_tile_switch_head_size(ctx, dst); - } else { - constexpr bool use_logit_softcap = true; - launch_fattn_tile_switch_head_size(ctx, dst); - } -} diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh index 10dc22d1bf..2b60b3bb13 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cuh +++ b/ggml/src/ggml-cuda/fattn-tile.cuh @@ -1,3 +1,1206 @@ #include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-wmma-f16.cuh" + +// nbatch_fa == number of KQ rows to process per iteration +// nbatch_K == number of K columns to load in parallel for KQ calculation + +// TODO optimize kernel parameters for FP16 NVIDIA (P100) +// TODO optimize kernel parameters for head sizes 40, 80, 96, 112 + +// The ROCm compiler cannot handle templating in __launch_bounds__. +// As a workaround, define a macro to package the kernel parameters as uint32_t: +#define GGML_CUDA_FATTN_TILE_CONFIG_CASE(DKQ_, DV_, ncols_, nthreads, occupancy, nbatch_fa, nbatch_K) \ + if (DKQ == (DKQ_) && DV == (DV_) && ncols == (ncols_)) { \ + static_assert((nthreads) <= 512, "bad nthreads"); \ + static_assert((occupancy) <= 8, "bad occupancy"); \ + static_assert((nbatch_fa) <= 256, "bad nbatch_fa"); \ + static_assert((nbatch_K) <= 256, "bad nbatch_K"); \ + return ((nthreads) << 0) | ((occupancy) << 10) | ((nbatch_fa) << 14) | ((nbatch_K) << 23); \ + } \ + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nvidia_fp16(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 64, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 64, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 64, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 64, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 64, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 64, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 64, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 64, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 64, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 64, 64) + + return 0; +} + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nvidia_fp32(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 3, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 128, 3, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 128, 3, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 32, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 32, 64) + + return 0; +} + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_amd(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 3, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 3, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 2, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 256, 2, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 64, 256, 2, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 64, 256, 2, 32, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 64, 256, 2, 32, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 256, 2, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 2, 64, 32) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 256, 2, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 64, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 32, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 32, 128) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 32, 512, 1, 128, 64) + + return 0; +} + +static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_amd_rdna(const int DKQ, const int DV, const int ncols) { + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 40, 40, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 5, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 128, 5, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 128, 4, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 64, 64, 64, 128, 5, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 80, 80, 64, 256, 2, 32, 40) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + GGML_CUDA_FATTN_TILE_CONFIG_CASE( 96, 96, 64, 256, 2, 32, 48) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(112, 112, 64, 256, 2, 32, 56) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 2, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 8, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 8, 128, 8, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 3, 128, 128) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 3, 128, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 3, 64, 64) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 2, 64, 8, 32, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 6, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 8, 128, 6, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 5, 32, 256) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 3, 64, 128) + + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 4, 64, 64) + GGML_CUDA_FATTN_TILE_CONFIG_CASE(576, 512, 32, 256, 2, 128, 64) + + return 0; +} + +static __host__ uint32_t ggml_cuda_fattn_tile_get_config(const int DKQ, const int DV, const int ncols, const int cc) { + if (GGML_CUDA_CC_IS_AMD(cc)) { + if (GGML_CUDA_CC_IS_RDNA(cc)) { + return ggml_cuda_fattn_tile_get_config_amd_rdna(DKQ, DV, ncols); + } + return ggml_cuda_fattn_tile_get_config_amd(DKQ, DV, ncols); + } + if (fast_fp16_available(cc)) { + return ggml_cuda_fattn_tile_get_config_nvidia_fp16(DKQ, DV, ncols); + } + return ggml_cuda_fattn_tile_get_config_nvidia_fp32(DKQ, DV, ncols); +} + +static constexpr __device__ uint32_t ggml_cuda_fattn_tile_get_config(const int DKQ, const int DV, const int ncols) { +#ifdef GGML_USE_HIP +#ifdef RDNA + return ggml_cuda_fattn_tile_get_config_amd_rdna(DKQ, DV, ncols); +#else + return ggml_cuda_fattn_tile_get_config_amd(DKQ, DV, ncols); +#endif // RDNA +#else +#ifdef FAST_FP16_AVAILABLE + return ggml_cuda_fattn_tile_get_config_nvidia_fp16(DKQ, DV, ncols); +#else + return ggml_cuda_fattn_tile_get_config_nvidia_fp32(DKQ, DV, ncols); +#endif // FAST_FP16_AVAILABLE +#endif // GGML_USE_HIP +} + +static __host__ int ggml_cuda_fattn_tile_get_nthreads(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 0) & ((1 << 10) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_nthreads(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 0) & ((1 << 10) - 1); +} + +static __host__ int ggml_cuda_fattn_tile_get_occupancy(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 10) & ((1 << 4) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_occupancy(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 10) & ((1 << 4) - 1); +} + +static __host__ int ggml_cuda_fattn_tile_get_nbatch_fa(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 14) & ((1 << 9) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_nbatch_fa(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 14) & ((1 << 9) - 1); +} + +static __host__ int ggml_cuda_fattn_tile_get_nbatch_K(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 23) & ((1 << 9) - 1); +} + +static constexpr __device__ int ggml_cuda_fattn_tile_get_nbatch_K(const int DKQ, const int DV, const int ncols) { + return (ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols) >> 23) & ((1 << 9) - 1); +} + +// TODO: deduplicate with mma-f16 +template +static __device__ __forceinline__ void flash_attn_tile_load_tile( + const half2 * const __restrict__ KV, half2 * const __restrict__ tile_KV, const int stride_KV, const int i_sup) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + auto load = [&] __device__ (const int n) { + const int stride_j = warp_size >> n; + + if (stride_j == 0) { + return; + } + + const int j0_start = stride_j == warp_size ? 0 : ((J/2)/cpy_ne) - ((J/2)/cpy_ne) % (2*stride_j); + const int j0_stop = ((J/2)/cpy_ne) - ((J/2)/cpy_ne) % (1*stride_j); + const int stride_i = warp_size / stride_j; + + if (j0_start == j0_stop) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nwarps*stride_i) { + const int i = i0 + threadIdx.y*stride_i + (stride_j == warp_size ? 0 : threadIdx.x / stride_j); + + if (i0 + nwarps*stride_i <= I || i < I) { +#pragma unroll + for (int j0 = j0_start; j0 < j0_stop; j0 += stride_j) { + const int j = j0*cpy_ne + (stride_j == warp_size ? threadIdx.x : threadIdx.x % stride_j)*cpy_ne; + + const half2 zero[cpy_ne] = {{0.0f, 0.0f}}; + ggml_cuda_memcpy_1( + tile_KV + i*(J/2 + J_padding) + j, + !oob_check || i < i_sup ? KV + i*stride_KV + j : zero); + } + } + } + }; + // 1: max 64*16=512 bytes, 512 half + // 2: max 32*16=512 bytes, 256 half + // 3: max 16*16=256 bytes, 128 half + // 4: max 8*16=128 bytes, 64 half + // 5: max 4*16= 64 bytes, 32 half + // 6: max 2*16= 32 bytes, 16 half + // 7: max 1*16= 16 bytes, 8 half + static_assert(J % 8 == 0, "bad J"); + static_assert((J/2) % cpy_ne == 0, "bad J"); + ggml_cuda_unroll<7>{}(load); +} + +template +static __device__ __forceinline__ void flash_attn_tile_load_tile( + const half2 * const __restrict__ KV, float * const __restrict__ tile_KV, const int stride_KV, const int i_sup) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + auto load = [&] __device__ (const int n) { + const int stride_j = warp_size >> n; + + if (stride_j == 0) { + return; + } + + const int j0_start = stride_j == warp_size ? 0 : (J/cpy_ne) - (J/cpy_ne) % (2*stride_j); + const int j0_stop = (J/cpy_ne) - (J/cpy_ne) % (1*stride_j); + const int stride_i = warp_size / stride_j; + + if (j0_start == j0_stop) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nwarps*stride_i) { + const int i = i0 + threadIdx.y*stride_i + (stride_j == warp_size ? 0 : threadIdx.x / stride_j); + + if (i0 + nwarps*stride_i <= I || i < I) { +#pragma unroll + for (int j0 = j0_start; j0 < j0_stop; j0 += stride_j) { + const int j = j0*(cpy_ne/2) + (stride_j == warp_size ? threadIdx.x : threadIdx.x % stride_j)*(cpy_ne/2); + + const half2 zero[cpy_ne/2] = {{0.0f, 0.0f}}; + half2 tmp_h2[cpy_ne/2]; + ggml_cuda_memcpy_1( + tmp_h2, !oob_check || i < i_sup ? KV + i*stride_KV + j : zero); + + float2 tmp_f2[cpy_ne/2]; +#pragma unroll + for (int l = 0; l < cpy_ne/2; ++l) { + tmp_f2[l] = __half22float2(tmp_h2[l]); + } + ggml_cuda_memcpy_1(tile_KV + i*(J + J_padding) + 2*j, tmp_f2); + } + } + } + }; + // 1: max 32*16=512 bytes, 128 float + // 2: max 16*16=256 bytes, 64 float + // 3: max 8*16=128 bytes, 32 float + // 4: max 4*16= 64 bytes, 16 float + // 5: max 2*16= 32 bytes, 8 float + static_assert(J % 8 == 0, "bad J"); + static_assert(J % cpy_ne == 0, "bad J"); + ggml_cuda_unroll<5>{}(load); +} + +// Function that performs a single iteration in for the KQ matrix multiplication: +template +static __device__ __forceinline__ void flash_attn_tile_iter_KQ( + T_vec_dot * const Q_tmp, + const half2 * const __restrict__ K_h2, + T_vec_dot * const KV_tmp, + const int stride_K2, + const int k_VKQ_0, + const int k_VKQ_sup, + const int k_KQ_0, + float * KQ_acc) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int ncols = ncols1*ncols2; + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // number of parallel warps per Q column + + flash_attn_tile_load_tile + (K_h2 + int64_t(k_VKQ_0)*stride_K2 + k_KQ_0/2, KV_tmp, stride_K2, k_VKQ_sup); + __syncthreads(); + +#ifdef FAST_FP16_AVAILABLE + static_assert((nbatch_K/2) % cpy_ne == 0, "bad nbatch_K"); +#pragma unroll + for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K/2; k_KQ_1 += cpy_ne) { + half2 K_k[nbatch_fa/(np*warp_size)][cpy_ne]; + half2 Q_k[cpw][cpy_ne]; +#else + static_assert(nbatch_K % cpy_ne == 0, "bad nbatch_K"); +#pragma unroll + for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K; k_KQ_1 += cpy_ne) { + float K_k[nbatch_fa/(np*warp_size)][cpy_ne]; + float Q_k[cpw][cpy_ne]; +#endif // FAST_FP16_AVAILABLE + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { + const int i_KQ = i_KQ_0 + (threadIdx.y % np)*warp_size + threadIdx.x; + +#ifdef FAST_FP16_AVAILABLE + ggml_cuda_memcpy_1(&K_k[i_KQ_0/(np*warp_size)], &KV_tmp[i_KQ*(nbatch_K/2 + cpy_ne) + k_KQ_1]); +#else + ggml_cuda_memcpy_1(&K_k[i_KQ_0/(np*warp_size)], &KV_tmp[i_KQ*(nbatch_K + cpy_ne) + k_KQ_1]); +#endif // FAST_FP16_AVAILABLE + } +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y / np)*cpw; + +#ifdef FAST_FP16_AVAILABLE + ggml_cuda_memcpy_1(&Q_k[jc0], &Q_tmp[jc*(DKQ/2) + k_KQ_0/2 + k_KQ_1]); +#else + ggml_cuda_memcpy_1(&Q_k[jc0], &Q_tmp[jc* DKQ + k_KQ_0 + k_KQ_1]); +#endif // FAST_FP16_AVAILABLE + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { +#pragma unroll + for (int k = 0; k < cpy_ne; ++k) { + ggml_cuda_mad(KQ_acc[i_KQ_0/(np*warp_size)*cpw + jc0], K_k[i_KQ_0/(np*warp_size)][k], Q_k[jc0][k]); + } + } + } + } + + if (k_KQ_0 + nbatch_K < DKQ) { + __syncthreads(); // Sync not needed on last iteration. + } +} + +// Function that performs a single iteration of the main loop over up to nbatch_fa tokens. +template +static __device__ __forceinline__ void flash_attn_tile_iter( + T_vec_dot * const Q_tmp, + const half2 * const __restrict__ K_h2, + const half2 * const __restrict__ V_h2, + const half * const __restrict__ mask, + const float logit_softcap, + const float slope, + T_KQ * const KQ, + T_vec_dot * const KV_tmp, + const int stride_K2, + const int stride_V2, + const int stride_mask, + float * const KQ_max, + float * const KQ_sum, + T_acc * const VKQ, + const int k_VKQ_0, + const int k_VKQ_max) { + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int ncols = ncols1*ncols2; + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // number of parallel warps per Q column + + constexpr int DVp = (DV + 2*warp_size - 1) & ~(2*warp_size - 1); // DV padded to multiple of 2*warp_size. + + // KQ_cs == KQ chunk size, number of KQ values in j direction to store as one contiguous chunk in memory. + // KQ is originally 2D but uses a Z-shaped 3D memory pattern like KQ[ncols/KQ_cs][DVp][KQ_cs]. +#ifdef FAST_FP16_AVAILABLE + constexpr int KQ_cs = cpw < 2*cpy_ne ? cpw : 2*cpy_ne; +#else + constexpr int KQ_cs = cpw < 1*cpy_ne ? cpw : 1*cpy_ne; +#endif // FAST_FP16_AVAILABLE + static_assert(cpw % KQ_cs == 0, "bad KQ_cs"); + const int k_VKQ_sup = k_VKQ_max - k_VKQ_0; // k supremum, only smaller k values have valid KV data + + float KQ_max_new[cpw]; +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + KQ_max_new[jc0] = KQ_max[jc0]; + } + + float KQ_acc[nbatch_fa/(np*warp_size) * cpw] = {0.0f}; // Accumulators for KQ matrix multiplication. + + // KQ = K @ Q matrix multiplication: + constexpr int nbatch_K_last = DKQ % nbatch_K; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < DKQ - nbatch_K_last; k_KQ_0 += nbatch_K) { + flash_attn_tile_iter_KQ( + Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc); + } + if (nbatch_K_last > 0) { + constexpr int k_KQ_0 = DKQ - nbatch_K_last; + flash_attn_tile_iter_KQ( + Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc); + } + + // Apply logit softcap + mask, update KQ_max: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int j = (jc0 + (threadIdx.y / np)*cpw)/ncols2; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { + const int i_KQ = i_KQ_0 + (threadIdx.y % np)*warp_size + threadIdx.x; + + if (use_logit_softcap) { + KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0] = logit_softcap * tanhf(KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0]); + } + + if (!oob_check || i_KQ < k_VKQ_sup) { + KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0] += (ncols2 > 1 || mask) ? + slope*__half2float(mask[j*stride_mask + k_VKQ_0 + i_KQ]) : 0.0f; + + KQ_max_new[jc0] = fmaxf(KQ_max_new[jc0], KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0]); + } + } + + KQ_max_new[jc0] = warp_reduce_max(KQ_max_new[jc0]); + } + + if constexpr (np == 1) { + __syncthreads(); + } else { + static_assert(cpw == 1, "bad cpw"); + __shared__ float KQ_max_new_shared[nwarps]; + if (threadIdx.x == 0) { + KQ_max_new_shared[threadIdx.y] = KQ_max_new[0]; + } + __syncthreads(); + KQ_max_new[0] = KQ_max_new_shared[(threadIdx.y & ~(np-1)) + threadIdx.x % np]; + KQ_max_new[0] = warp_reduce_max(KQ_max_new[0]); + } + + // Calculate KQ softmax, write to shared KQ buffer, re-scale VKQ accumulators: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; jc0 += KQ_cs) { +#ifdef FAST_FP16_AVAILABLE + half tmp[nbatch_fa/(np*warp_size)][KQ_cs]; +#else + float tmp[nbatch_fa/(np*warp_size)][KQ_cs]; +#endif // FAST_FP16_AVAILABLE + +#pragma unroll + for (int jc1 = 0; jc1 < KQ_cs; ++jc1) { + const int jc = jc0 + jc1; + + const float KQ_max_scale = expf(KQ_max[jc] - KQ_max_new[jc]); + KQ_max[jc] = KQ_max_new[jc]; + + float KQ_sum_add = 0.0f; +#pragma unroll + for (int i0 = 0; i0 < nbatch_fa; i0 += np*warp_size) { + const float val = !oob_check || i0 + (threadIdx.y % np)*warp_size + threadIdx.x < k_VKQ_sup ? + expf(KQ_acc[(i0/(np*warp_size))*cpw + jc] - KQ_max[jc]) : 0.0f; + KQ_sum_add += val; + tmp[i0/(np*warp_size)][jc1] = val; + } + KQ_sum[jc] = KQ_sum[jc]*KQ_max_scale + KQ_sum_add; + +#ifdef FAST_FP16_AVAILABLE + const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size] *= KQ_max_scale_h2; + } +#else +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].x *= KQ_max_scale; + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].y *= KQ_max_scale; + } +#endif // FAST_FP16_AVAILABLE + } + +#pragma unroll + for (int i0 = 0; i0 < nbatch_fa; i0 += np*warp_size) { + const int i = i0 + (threadIdx.y % np)*warp_size + threadIdx.x; + + ggml_cuda_memcpy_1( + KQ + (jc0/KQ_cs + (threadIdx.y / np)*(cpw/KQ_cs))*(nbatch_fa*KQ_cs) + i*KQ_cs, + tmp[i0/(np*warp_size)]); + } + } + + // VKQ = V @ KQ matrix multiplication: + static_assert(DV <= DKQ, "bad DV"); + static_assert(DV % nbatch_K == 0 || (nbatch_K % 3 == 0 && DV % (nbatch_K*2/3) == 0), "bad nbatch_K"); + constexpr int nbatch_V = (DV % nbatch_K == 0 ? nbatch_K : nbatch_K*2/3) * nbatch_fa / DV; // Number of V columns that fit in SRAM for K. + static_assert(nbatch_fa % nbatch_V == 0, "bad nbatch_V"); + static_assert(nbatch_V % np == 0, "bad nbatch_V"); +#pragma unroll + for (int k0 = 0; k0 < nbatch_fa; k0 += nbatch_V) { + flash_attn_tile_load_tile + (V_h2 + int64_t(k_VKQ_0 + k0)*stride_V2, KV_tmp, stride_V2, k_VKQ_sup - k0); + __syncthreads(); + +#ifdef FAST_FP16_AVAILABLE +#pragma unroll + for (int k1 = 0; k1 < nbatch_V; k1 += np) { + half2 V_k[(DVp/2)/warp_size]; + half2 KQ_k[cpw]; + + constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1(&V_k[i0/warp_size], &KV_tmp[(k1 + threadIdx.y % np)*(DV/2) + i0 + threadIdx.x*cpy_ne_D]); + } +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; jc_VKQ_0 += KQ_cs) { + const int jc_KQ = jc_VKQ_0/KQ_cs + (threadIdx.y / np)*(cpw/KQ_cs); + + half tmp[KQ_cs]; + ggml_cuda_memcpy_1( + &tmp, KQ + jc_KQ*(nbatch_fa*KQ_cs) + (k0 + k1 + threadIdx.y % np)*KQ_cs); +#pragma unroll + for (int jc_VKQ_1 = 0; jc_VKQ_1 < KQ_cs; ++jc_VKQ_1) { + KQ_k[jc_VKQ_0+jc_VKQ_1] = __half2half2(tmp[jc_VKQ_1]); + } + } + +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; ++jc_VKQ_0) { + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size] += V_k[i0/warp_size]*KQ_k[jc_VKQ_0]; + } + } + } +#else +#pragma unroll + for (int k1 = 0; k1 < nbatch_V; k1 += np) { + float2 V_k[(DVp/2)/warp_size]; + float KQ_k[cpw]; + + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1(&V_k[i0/(2*warp_size)], &KV_tmp[(k1 + threadIdx.y % np)*DV + i0 + threadIdx.x*cpy_ne_D]); + } +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; jc_VKQ_0 += KQ_cs) { + const int jc_KQ = jc_VKQ_0/KQ_cs + (threadIdx.y / np)*(cpw/KQ_cs); + + ggml_cuda_memcpy_1( + &KQ_k[jc_VKQ_0], KQ + jc_KQ*(nbatch_fa*KQ_cs) + (k0 + k1 + threadIdx.y % np)*KQ_cs); + } + +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; ++jc_VKQ_0) { + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].x += V_k[i0/warp_size].x*KQ_k[jc_VKQ_0]; + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].y += V_k[i0/warp_size].y*KQ_k[jc_VKQ_0]; + } + } + } +#endif // FAST_FP16_AVAILABLE + + __syncthreads(); + } +} + +template // D == head size +__launch_bounds__(ggml_cuda_fattn_tile_get_nthreads(DKQ, DV, ncols1*ncols2), ggml_cuda_fattn_tile_get_occupancy(DKQ, DV, ncols1*ncols2)) +static __global__ void flash_attn_tile( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + const char * __restrict__ sinks, + const int * __restrict__ KV_max, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const float logit_softcap, + const int32_t ne00, const int32_t ne01, const int32_t ne02, const int32_t ne03, + const int32_t nb01, const int32_t nb02, const int32_t nb03, + const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13, + const int32_t nb11, const int32_t nb12, const int64_t nb13, + const int32_t nb21, const int32_t nb22, const int64_t nb23, + const int32_t ne31, const int32_t ne32, const int32_t ne33, + const int32_t nb31, const int32_t nb32, const int64_t nb33) { +#ifdef FLASH_ATTN_AVAILABLE + + // Skip unused kernel variants for faster compilation: + + if ( +#ifdef GGML_USE_WMMA_FATTN + (ncols2 != 1 && DV != 40 && DV != 512) || +#endif // GGML_USE_WMMA_FATTN + (use_logit_softcap && !(DV == 128 || DV == 256)) + ) { + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); + NO_DEVICE_CODE; + return; + } + + static_assert(ggml_cuda_fattn_tile_get_config(DKQ, DV, ncols1*ncols2) != 0, "kernel config not defined"); + + constexpr int ncols = ncols1*ncols2; + constexpr int warp_size = 32; + constexpr int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, ncols1*ncols2) / warp_size; + constexpr int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, ncols1*ncols2); + constexpr int nbatch_K = ggml_cuda_fattn_tile_get_nbatch_K (DKQ, DV, ncols1*ncols2); + + // In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int col_Q_0 = blockIdx.x * ncols1; // Index of the first Q column for this CUDA block to work on. + + const int sequence = blockIdx.z / (ne02/ncols2); + const int head0 = blockIdx.z*ncols2 - sequence*ne02; // == blockIdx.z % (ne02/ncols2) + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float * Q_f = (const float *) (Q + nb03*sequence + nb02* head0 + nb01*col_Q_0); + const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*(head0 / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); // K and V have same shape + + const half * maskh = mask ? (const half *) (mask + nb33*(sequence % ne33) + nb31*col_Q_0) : nullptr; + + const int stride_K2 = nb11 / sizeof(half2); + const int stride_V2 = nb21 / sizeof(half2); + const int stride_mask = nb31 / sizeof(half); + + const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f; + + constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp. + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // Number of parallel warps per Q column. + static_assert(cpw == 1 || np == 1, "bad cpw / np"); + static_assert(nbatch_fa % (np*warp_size) == 0, "nbatch_fa % (np*warp_size) != 0"); + + constexpr int DKQp = (DKQ + 2*warp_size - 1) & ~(2*warp_size - 1); // DKQ padded to multiple of 2*warp_size. + constexpr int DVp = (DV + 2*warp_size - 1) & ~(2*warp_size - 1); // DV padded to multiple of 2*warp_size. + + // Q_tmp == SRAM buffer to hold Q data for the entire lifetime of the kernel. + // KV_tmp == SRAM buffer to hold fragments of K/V data while iterating over ne11. + // KV_tmp is padded to avoid memory conflicts for K (cpy_ne) and OOB accesses for V (DVp-DV). + // KQ == SRAM buffer to hold KQ fragments between KQ and VKQ matrix multiplications. + // VKQ == Accumulators in registers for the final VKQ result. +#ifdef FAST_FP16_AVAILABLE + __shared__ half2 Q_tmp[ncols * DKQ/2]; + __shared__ half2 KV_tmp[nbatch_fa * (nbatch_K/2 + cpy_ne) + DVp-DV]; + __shared__ half KQ[ncols * nbatch_fa]; + half2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}}; +#else + __shared__ float Q_tmp[ncols * DKQ]; + __shared__ float KV_tmp[nbatch_fa * (nbatch_K + cpy_ne) + DVp-DV]; + __shared__ float KQ[ncols * nbatch_fa]; + float2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}}; +#endif // FAST_FP16_AVAILABLE + + float KQ_max[cpw]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + KQ_max[j0/nwarps] = -FLT_MAX/2.0f; + } + float KQ_sum[cpw] = {0.0f}; + + // Load Q data, convert to FP16 if fast: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y / np)*cpw; + + const int j = jc / ncols2; + const int c = jc % ncols2; + + constexpr int cpy_ne_D = cpy_ne < DKQp/warp_size ? cpy_ne : DKQp/warp_size; + +#pragma unroll + for (int i0 = 0; i0 < DKQp; i0 += np*warp_size*cpy_ne_D) { + if (i0 + np*warp_size*cpy_ne_D <= DKQ || i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x*cpy_ne_D < DKQ) { + float tmp_f[cpy_ne_D] = {0.0f}; + if (ncols1 == 1 || col_Q_0 + j < ne01) { + ggml_cuda_memcpy_1 + (tmp_f, &Q_f[c*(nb02/sizeof(float)) + j*(nb01/sizeof(float)) + + i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x*cpy_ne_D]); + } + +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + tmp_f[i1] *= scale; + } + +#ifdef FAST_FP16_AVAILABLE + half2 tmp_h2[cpy_ne_D/2]; +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) { + tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]); + } + ggml_cuda_memcpy_1( + &Q_tmp[jc*(DKQ/2) + i0/2 + (threadIdx.y % np)*(warp_size*cpy_ne_D/2) + threadIdx.x*(cpy_ne_D/2)], + tmp_h2); +#else + ggml_cuda_memcpy_1( + &Q_tmp[jc* DKQ + i0 + (threadIdx.y % np)*(warp_size*cpy_ne_D) + threadIdx.x* cpy_ne_D], + tmp_f); +#endif // FAST_FP16_AVAILABLE + } + } + } + + __syncthreads(); + + // Main loop over KV cache: + const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11; + if (ncols2 == 1) { + // Branch with out-of-bounds checks. + int k_VKQ_0 = blockIdx.y*nbatch_fa; + while (k_VKQ_0 < k_VKQ_max - nbatch_fa) { + constexpr bool oob_check = false; + flash_attn_tile_iter + (Q_tmp, K_h2, V_h2, maskh, logit_softcap, slope, KQ, KV_tmp, + stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max); + k_VKQ_0 += gridDim.y*nbatch_fa; + } + if (k_VKQ_0 < k_VKQ_max) { + constexpr bool oob_check = true; + flash_attn_tile_iter + (Q_tmp, K_h2, V_h2, maskh, logit_softcap, slope, KQ, KV_tmp, + stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max); + } + } else { + // Branch without out-of-bounds checks. + for (int k_VKQ_0 = blockIdx.y*nbatch_fa; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*nbatch_fa) { + constexpr bool oob_check = false; + flash_attn_tile_iter + (Q_tmp, K_h2, V_h2, maskh, logit_softcap, slope, KQ, KV_tmp, + stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max); + } + } + +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + KQ_sum[jc0] = warp_reduce_sum(KQ_sum[jc0]); + } + + if constexpr (np > 1) { + static_assert(cpw == 1, "bad cpw"); + static_assert(nbatch_fa*nbatch_K >= nwarps*DVp, "KV_tmp too small"); + +#ifdef FAST_FP16_AVAILABLE + half2 * VKQ_combine = (half2 *) KV_tmp; +#else + float * VKQ_combine = (float *) KV_tmp; +#endif // FAST_FP16_AVAILABLE + float * KQ_sum_combine = (float *) Q_tmp; + + if (threadIdx.y % np != 0) { +#ifdef FAST_FP16_AVAILABLE + constexpr int cpy_ne_D = cpy_ne < (DVp/2)/warp_size ? cpy_ne : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1(&VKQ_combine[threadIdx.y*(DVp/2) + i0 + threadIdx.x*cpy_ne_D], &VKQ[i0/warp_size]); + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + ggml_cuda_memcpy_1( + &VKQ_combine[threadIdx.y*DVp + i0 + threadIdx.x*cpy_ne_D], ((const float *) VKQ) + i0/warp_size); + } +#endif // FAST_FP16_AVAILABLE + + if (threadIdx.x == 0) { + KQ_sum_combine[threadIdx.y] = KQ_sum[0]; + } + + return; + } + + __syncthreads(); + +#pragma unroll + for (int ip = 1; ip < np; ++ip) { +#ifdef FAST_FP16_AVAILABLE + constexpr int cpy_ne_D = cpy_ne < (DVp/2)/warp_size ? cpy_ne : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + half2 tmp[cpy_ne_D]; + ggml_cuda_memcpy_1(tmp, &VKQ_combine[(threadIdx.y + ip)*(DVp/2) + i0 + threadIdx.x*cpy_ne_D]); +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + VKQ[i0/warp_size + i1] += tmp[i1]; + } + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + float tmp[cpy_ne_D]; + ggml_cuda_memcpy_1(tmp, &VKQ_combine[(threadIdx.y + ip)*DVp + i0 + threadIdx.x*cpy_ne_D]); +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + ((float *)VKQ)[i0/warp_size + i1] += tmp[i1]; + } + } +#endif // FAST_FP16_AVAILABLE + + KQ_sum[0] += KQ_sum_combine[threadIdx.y + ip]; + } + } + + // Attention sink: adjust KQ max and sum only for the first of all parallel blocks: + if (sinks && blockIdx.y == 0) { +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y/np)*cpw; + const float sink = ((const float *) sinks)[head0 + jc % ncols2]; + + float KQ_max_new_j = fmaxf(KQ_max[jc0], sink); + const float KQ_max_scale = expf(KQ_max[jc0] - KQ_max_new_j); + KQ_max[jc0] = KQ_max_new_j; + + const float val = expf(sink - KQ_max[jc0]); + KQ_sum[jc0] = KQ_sum[jc0]*KQ_max_scale + val; + +#ifdef FAST_FP16_AVAILABLE + const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size] *= KQ_max_scale_h2; + } +#else +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size].x *= KQ_max_scale; + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size].y *= KQ_max_scale; + } +#endif // FAST_FP16_AVAILABLE + } + } + + // Write back results: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (threadIdx.y/np)*cpw; + + const int j = jc / ncols2; + const int c = jc % ncols2; + + if (ncols1 > 1 && col_Q_0 + j >= ne01) { + return; + } + + const float scale = gridDim.y == 1 ? 1.0f/KQ_sum[jc0] : 1.0f; + + const int j_dst_unrolled = ((sequence*ne01 + col_Q_0 + j)*ne02 + head0 + c)*gridDim.y + blockIdx.y; + +#ifdef FAST_FP16_AVAILABLE + constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + float2 tmp[cpy_ne_D]; +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + tmp[i1] = __half22float2(VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size + i1]); + tmp[i1].x *= scale; + tmp[i1].y *= scale; + } + if (i0 + warp_size*cpy_ne_D <= DV/2 || i0 + threadIdx.x*cpy_ne_D < DV/2) { + ggml_cuda_memcpy_1(&dst[j_dst_unrolled*DV + 2*i0 + threadIdx.x*(2*cpy_ne_D)], tmp); + } + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + if (i0 + warp_size*cpy_ne_D <= DV || i0 + threadIdx.x*cpy_ne_D < DV) { +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D/2; ++i1) { + VKQ[jc0*((DVp/2)/warp_size) + i0/(2*warp_size) + i1].x *= scale; + VKQ[jc0*((DVp/2)/warp_size) + i0/(2*warp_size) + i1].y *= scale; + } + ggml_cuda_memcpy_1( + &dst[j_dst_unrolled*DV + i0 + threadIdx.x*cpy_ne_D], + &VKQ[jc0*((DVp/2)/warp_size) + i0/(2*warp_size)]); + } + } +#endif // FAST_FP16_AVAILABLE + + if (gridDim.y != 1 && threadIdx.x == 0) { + dst_meta[j_dst_unrolled] = make_float2(KQ_max[jc0], KQ_sum[jc0]); + } + } +#else + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); + NO_DEVICE_CODE; +#endif // FLASH_ATTN_AVAILABLE +} + +template +static void launch_fattn_tile_switch_ncols1(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + const int id = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[id].cc; + const int warp_size = 32; + + constexpr size_t nbytes_shared = 0; + +#ifdef GGML_USE_HIP + if constexpr (DV <= 128) { + if (Q->ne[1] > 32/ncols2) { + constexpr int cols_per_block = 64; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } +#endif // GGML_USE_HIP + +#ifndef GGML_USE_HIP + if constexpr (DV <= 256) +#endif // GGML_USE_HIP + { + if (Q->ne[1] > 16/ncols2) { + constexpr int cols_per_block = 32; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } + + if (Q->ne[1] > 8/ncols2) { + constexpr int cols_per_block = 16; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + + if constexpr (ncols2 <= 8) { + if (Q->ne[1] > 4/ncols2) { + constexpr int cols_per_block = 8; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } + + if constexpr (ncols2 <= 4) { + if (Q->ne[1] > 2/ncols2) { + constexpr int cols_per_block = 4; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + } + + if constexpr (ncols2 <= 2) { + constexpr int cols_per_block = 2; + const int nwarps = ggml_cuda_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_cuda_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + fattn_kernel_t fattn_kernel = flash_attn_tile; + launch_fattn + (ctx, dst, fattn_kernel, nwarps, nbytes_shared, nbatch_fa, true, true, false, warp_size); + return; + } + + GGML_ABORT("fatal error"); +} + +template +static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * mask = dst->src[3]; + + float max_bias = 0.0f; + memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + + GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); + const int gqa_ratio = Q->ne[2] / K->ne[2]; + + const bool nvidia = GGML_CUDA_CC_IS_NVIDIA(ggml_cuda_info().devices[ggml_cuda_get_device()].cc); + const int gqa_limit = nvidia && gqa_ratio <= 4 ? 16 : INT_MAX; + const bool use_gqa_opt = mask && max_bias == 0.0f && Q->ne[1] <= gqa_limit && K->ne[1] % FATTN_KQ_STRIDE == 0; + + if constexpr (DV == 512) { + if (use_gqa_opt && gqa_ratio % 16 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + } + + if constexpr (DV <= 256) { + if (use_gqa_opt && gqa_ratio % 8 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 4 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 2 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + GGML_ABORT("fatal error"); +} + +template +void ggml_cuda_flash_attn_ext_tile_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + + float logit_softcap; + memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); + + if (logit_softcap == 0.0f) { + constexpr bool use_logit_softcap = false; + launch_fattn_tile_switch_ncols2(ctx, dst); + } else { + constexpr bool use_logit_softcap = true; + launch_fattn_tile_switch_ncols2(ctx, dst); + } +} void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +#define DECL_FATTN_TILE_CASE(DKQ, DV) \ + template void ggml_cuda_flash_attn_ext_tile_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_TILE_CASE( 40, 40); +extern DECL_FATTN_TILE_CASE( 64, 64); +extern DECL_FATTN_TILE_CASE( 80, 80); +extern DECL_FATTN_TILE_CASE( 96, 96); +extern DECL_FATTN_TILE_CASE(112, 112); +extern DECL_FATTN_TILE_CASE(128, 128); +extern DECL_FATTN_TILE_CASE(256, 256); +extern DECL_FATTN_TILE_CASE(576, 512); diff --git a/ggml/src/ggml-cuda/fattn-vec.cuh b/ggml/src/ggml-cuda/fattn-vec.cuh index 89ab0f1638..e1838fdded 100644 --- a/ggml/src/ggml-cuda/fattn-vec.cuh +++ b/ggml/src/ggml-cuda/fattn-vec.cuh @@ -516,8 +516,8 @@ void ggml_cuda_flash_attn_ext_vec_case_impl(ggml_backend_cuda_context & ctx, ggm const int nthreads = ggml_cuda_fattn_vec_get_nthreads_host(cc); const int nwarps = nthreads / WARP_SIZE; fattn_kernel_t fattn_kernel = flash_attn_ext_vec; - constexpr bool need_f16_K = false; - constexpr bool need_f16_V = false; + const bool need_f16_K = type_K == GGML_TYPE_F16; + const bool need_f16_V = type_V == GGML_TYPE_F16; constexpr size_t nbytes_shared = 0; launch_fattn(ctx, dst, fattn_kernel, nwarps, nbytes_shared, D, need_f16_K, need_f16_V, false); } @@ -526,11 +526,6 @@ template void ggml_cuda_flash_attn_ext_vec_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - GGML_ASSERT(K->type == type_K); - GGML_ASSERT(V->type == type_V); float logit_softcap; memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh index 1848d08836..7235f1b77a 100644 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh @@ -1,3 +1,5 @@ +#pragma once + #include "common.cuh" #if (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA) diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 0c8e7b3e41..7dee032c29 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -116,11 +116,15 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg } } -#define FATTN_VEC_CASE(D, type_K, type_V) \ - if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \ - ggml_cuda_flash_attn_ext_vec_case(ctx, dst); \ - return; \ - } \ +#define FATTN_VEC_CASE(D, type_K, type_V) \ + { \ + const bool type_K_okay = K->type == (type_K) || (K->type == GGML_TYPE_F32 && (type_K) == GGML_TYPE_F16); \ + const bool type_V_okay = V->type == (type_V) || (V->type == GGML_TYPE_F32 && (type_V) == GGML_TYPE_F16); \ + if (Q->ne[0] == (D) && type_K_okay && type_V_okay) { \ + ggml_cuda_flash_attn_ext_vec_case(ctx, dst); \ + return; \ + } \ + } \ #define FATTN_VEC_CASES_ALL_D(type_K, type_V) \ FATTN_VEC_CASE( 64, type_K, type_V) \ @@ -198,6 +202,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_NONE; #endif// FLASH_ATTN_AVAILABLE + const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; @@ -206,37 +211,32 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const const int gqa_ratio = Q->ne[2] / K->ne[2]; GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); + float max_bias = 0.0f; + memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + + // The effective batch size for the kernel can be increased by gqa_ratio. + // The kernel versions without this optimization are also used for ALiBi, if there is no mask, or if the KV cache is not padded, + const bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; + const int cc = ggml_cuda_info().devices[device].cc; - // TODO: temporary until support is extended - // https://github.com/ggml-org/llama.cpp/pull/16148#issuecomment-3343525206 - if (K->ne[1] % FATTN_KQ_STRIDE != 0) { - return BEST_FATTN_KERNEL_NONE; - } - switch (K->ne[0]) { + case 40: case 64: - case 128: - case 256: - if (V->ne[0] != K->ne[0]) { - return BEST_FATTN_KERNEL_NONE; - } - break; case 80: case 96: + case 128: case 112: + case 256: if (V->ne[0] != K->ne[0]) { return BEST_FATTN_KERNEL_NONE; } - if (!ggml_cuda_should_use_wmma_fattn(cc) && !turing_mma_available(cc)) { - return BEST_FATTN_KERNEL_NONE; - } break; case 576: if (V->ne[0] != 512) { return BEST_FATTN_KERNEL_NONE; } - if (!turing_mma_available(cc) || gqa_ratio % 16 != 0) { + if (!gqa_opt_applies || gqa_ratio % 16 != 0) { return BEST_FATTN_KERNEL_NONE; } break; @@ -251,6 +251,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const #endif // GGML_CUDA_FA_ALL_QUANTS switch (K->type) { + case GGML_TYPE_F32: case GGML_TYPE_F16: break; case GGML_TYPE_Q4_1: @@ -270,47 +271,57 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_NONE; } - const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0; - - // If Turing tensor cores available, use them except for some cases with batch size 1: - if (turing_mma_available(cc)) { - best_fattn_kernel best = BEST_FATTN_KERNEL_MMA_F16; + // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes: + const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0; + // If Turing tensor cores available, use them: + if (turing_mma_available(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40) { if (can_use_vector_kernel) { - if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) { + if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) { if (cc >= GGML_CUDA_CC_ADA_LOVELACE && Q->ne[1] == 1 && Q->ne[3] == 1 && !(gqa_ratio > 4 && K->ne[1] >= 8192)) { - best = BEST_FATTN_KERNEL_VEC; + return BEST_FATTN_KERNEL_VEC; } } else { if (cc >= GGML_CUDA_CC_ADA_LOVELACE) { if (Q->ne[1] <= 2) { - best = BEST_FATTN_KERNEL_VEC; + return BEST_FATTN_KERNEL_VEC; } } else { if (Q->ne[1] == 1) { - best = BEST_FATTN_KERNEL_VEC; + return BEST_FATTN_KERNEL_VEC; } } } - if ((gqa_ratio % 2 != 0 || !mask) && Q->ne[1] == 1) { - best = BEST_FATTN_KERNEL_VEC; // GQA-specific optimizations in the mma kernel do not apply. + if (!gqa_opt_applies && Q->ne[1] == 1) { + return BEST_FATTN_KERNEL_VEC; } } - return best; + return BEST_FATTN_KERNEL_MMA_F16; } - // Use kernels specialized for small batch sizes if possible: - if (Q->ne[1] <= 8 && can_use_vector_kernel) { - return BEST_FATTN_KERNEL_VEC; - } - - // For large batch sizes, use the WMMA kernel if possible: - if (ggml_cuda_should_use_wmma_fattn(cc)) { + // Use the WMMA kernel if possible: + if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 576) { + if (can_use_vector_kernel && Q->ne[1] <= 2) { + return BEST_FATTN_KERNEL_VEC; + } return BEST_FATTN_KERNEL_WMMA_F16; } - // If there is no suitable kernel for tensor cores or small batch sizes, use the generic kernel for large batch sizes: + // If there are no tensor cores available, use the generic tile kernel: + if (can_use_vector_kernel) { + if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) { + if (Q->ne[1] == 1) { + if (!gqa_opt_applies) { + return BEST_FATTN_KERNEL_VEC; + } + } + } else { + if (Q->ne[1] <= 2) { + return BEST_FATTN_KERNEL_VEC; + } + } + } return BEST_FATTN_KERNEL_TILE; } diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index fb691528b7..75fd6db14c 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -273,6 +273,15 @@ static ggml_cuda_device_info ggml_cuda_init() { } else if (device_name.substr(0, 21) == "NVIDIA GeForce GTX 16") { turing_devices_without_mma.push_back({ id, device_name }); } + + // Temporary performance fix: + // Setting device scheduling strategy for iGPUs with cc121 to "spinning" to avoid delays in cuda synchronize calls. + // TODO: Check for future drivers the default scheduling strategy and + // remove this call again when cudaDeviceScheduleSpin is default. + if (prop.major == 12 && prop.minor == 1) { + CUDA_CHECK(cudaSetDeviceFlags(cudaDeviceScheduleSpin)); + } + #endif // defined(GGML_USE_HIP) } @@ -2633,11 +2642,10 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { } #ifdef USE_CUDA_GRAPH -static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, +static bool check_node_graph_compatibility(ggml_cgraph * cgraph, bool use_cuda_graph) { // Loop over nodes in GGML graph to obtain info needed for CUDA graph - cuda_ctx->cuda_graph->cpy_dest_ptrs.clear(); const std::string gemma3n_per_layer_proj_src0_name = "inp_per_layer_selected"; const std::string gemma3n_per_layer_proj_src1_name = "per_layer_proj"; @@ -2688,33 +2696,11 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud #endif } - if (node->op == GGML_OP_CPY) { - - // Store the pointers which are updated for each token, such that these can be sent - // to the device and accessed using indirection from CUDA graph - cuda_ctx->cuda_graph->cpy_dest_ptrs.push_back((char *) node->src[1]->data); - - // store a pointer to each copy op CUDA kernel to identify it later - void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); - if (!ptr) { - use_cuda_graph = false; -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported copy op\n", __func__); -#endif - } - } - if (!use_cuda_graph) { break; } } - if (use_cuda_graph) { - cuda_ctx->cuda_graph->use_cpy_indirection = true; - // copy pointers to GPU so they can be accessed via indirection within CUDA graph - ggml_cuda_cpy_dest_ptrs_copy(cuda_ctx->cuda_graph.get(), cuda_ctx->cuda_graph->cpy_dest_ptrs.data(), cuda_ctx->cuda_graph->cpy_dest_ptrs.size(), cuda_ctx->stream()); - } - return use_cuda_graph; } @@ -2733,7 +2719,6 @@ static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_p static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { if (node->data != graph_node_properties->node_address && - node->op != GGML_OP_CPY && node->op != GGML_OP_VIEW) { return false; } @@ -2754,7 +2739,6 @@ static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_gra for (int i = 0; i < GGML_MAX_SRC; i++) { if (node->src[i] && node->src[i]->data != graph_node_properties->src_address[i] && - node->op != GGML_OP_CPY && node->op != GGML_OP_VIEW ) { return false; @@ -2901,7 +2885,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, } //if rms norm is the B operand, then we don't handle broadcast - if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm->src[1])) { + if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm)) { return false; } @@ -3120,7 +3104,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, if (use_cuda_graph) { cuda_graph_update_required = is_cuda_graph_update_required(cuda_ctx, cgraph); - use_cuda_graph = check_node_graph_compatibility_and_refresh_copy_ops(cuda_ctx, cgraph, use_cuda_graph); + use_cuda_graph = check_node_graph_compatibility(cgraph, use_cuda_graph); // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates. if (use_cuda_graph && cuda_graph_update_required) { @@ -3147,10 +3131,6 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed)); } - if (!use_cuda_graph) { - cuda_ctx->cuda_graph->use_cpy_indirection = false; - } - #else bool use_cuda_graph = false; bool cuda_graph_update_required = false; @@ -3645,9 +3625,10 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_CONV_2D_DW: case GGML_OP_CONV_TRANSPOSE_2D: case GGML_OP_POOL_2D: - case GGML_OP_SUM: case GGML_OP_ACC: return true; + case GGML_OP_SUM: + return ggml_is_contiguous_rows(op->src[0]); case GGML_OP_ARGSORT: // TODO: Support arbitrary column width return op->src[0]->ne[0] <= 1024; @@ -3867,7 +3848,6 @@ ggml_backend_reg_t ggml_backend_cuda_reg() { dev_ctx->device = i; dev_ctx->name = GGML_CUDA_NAME + std::to_string(i); - ggml_cuda_set_device(i); cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, i)); dev_ctx->description = prop.name; diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu index 599e085ee9..9e2aaf52d6 100644 --- a/ggml/src/ggml-cuda/mmf.cu +++ b/ggml/src/ggml-cuda/mmf.cu @@ -1,5 +1,7 @@ #include "ggml.h" #include "mmf.cuh" +#include "mmid.cuh" + void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { GGML_ASSERT( src1->type == GGML_TYPE_F32); @@ -37,6 +39,12 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr const int64_t ids_s0 = ids ? ids->nb[0] / ggml_type_size(ids->type) : 0; const int64_t ids_s1 = ids ? ids->nb[1] / ggml_type_size(ids->type) : 0; + mmf_ids_data ids_info{}; + mmf_ids_data * ids_info_ptr = nullptr; + ggml_cuda_pool_alloc ids_src_compact_dev; + ggml_cuda_pool_alloc ids_dst_compact_dev; + ggml_cuda_pool_alloc expert_bounds_dev; + // For MUL_MAT_ID the memory layout is different than for MUL_MAT: const int64_t ncols_dst = ids ? ne2 : ne1; const int64_t nchannels_dst = ids ? ne1 : ne2; @@ -54,6 +62,33 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr nchannels_y = ids->ne[0]; } + if (ids && ncols_dst > 16) { + const int64_t n_expert_used = ids->ne[0]; + const int64_t n_experts = ne02; + const int64_t n_tokens = ne12; + const int64_t ne_get_rows = n_tokens * n_expert_used; + + ids_src_compact_dev.alloc(ctx.pool(), ne_get_rows); + ids_dst_compact_dev.alloc(ctx.pool(), ne_get_rows); + expert_bounds_dev.alloc(ctx.pool(), n_experts + 1); + + const int si1 = static_cast(ids_s1); + const int sis1 = static_cast(src1->nb[2] / src1->nb[1]); + + GGML_ASSERT(sis1 > 0); + + ggml_cuda_launch_mm_ids_helper(ids_d, ids_src_compact_dev.get(), ids_dst_compact_dev.get(), expert_bounds_dev.get(), + static_cast(n_experts), static_cast(n_tokens), static_cast(n_expert_used), static_cast(ne11), si1, sis1, ctx.stream()); + CUDA_CHECK(cudaGetLastError()); + + ids_info.ids_src_compact = ids_src_compact_dev.get(); + ids_info.ids_dst_compact = ids_dst_compact_dev.get(); + ids_info.expert_bounds_dev = expert_bounds_dev.get(); + ids_info.n_experts = static_cast(n_experts); + ids_info.sis1 = sis1; + ids_info_ptr = &ids_info; + } + switch (src0->type) { case GGML_TYPE_F32: { const float * src0_d = (const float *) src0->data; @@ -61,7 +96,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr mul_mat_f_switch_cols_per_block( src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, stride_col_y/vals_per_T, stride_col_dst, ids_s0, ids_s1, ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst, - ne03, ne3, s03/vals_per_T, s13, s3, ctx.stream()); + ne03, ne3, s03/vals_per_T, s13, s3, ctx.stream(), ids_info_ptr); } break; case GGML_TYPE_F16: { const half2 * src0_d = (const half2 *) src0->data; @@ -69,7 +104,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr mul_mat_f_switch_cols_per_block( src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, stride_col_y/vals_per_T, stride_col_dst, ids_s0, ids_s1, ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst, - ne03, ne3, s03/vals_per_T, s13, s3, ctx.stream()); + ne03, ne3, s03/vals_per_T, s13, s3, ctx.stream(), ids_info_ptr); } break; case GGML_TYPE_BF16: { const nv_bfloat162 * src0_d = (const nv_bfloat162 *) src0->data; @@ -77,7 +112,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr mul_mat_f_switch_cols_per_block( src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, stride_col_y/vals_per_T, stride_col_dst, ids_s0, ids_s1, ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst, - ne03, ne3, s03/vals_per_T, s13, s3, ctx.stream()); + ne03, ne3, s03/vals_per_T, s13, s3, ctx.stream(), ids_info_ptr); } break; default: GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type)); @@ -98,10 +133,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const } if (mul_mat_id) { - if (type == GGML_TYPE_F32 && src1_ncols > 32) { + if (src0_ne[1] <= 1024 && src1_ncols > 512) { return false; - } - if ((type == GGML_TYPE_F16 || type == GGML_TYPE_BF16) && src1_ncols > 64) { + } else if(src0_ne[1] > 1024 && src1_ncols > 128) { return false; } } else { diff --git a/ggml/src/ggml-cuda/mmf.cuh b/ggml/src/ggml-cuda/mmf.cuh index a6c3adfcf1..49d5295be0 100644 --- a/ggml/src/ggml-cuda/mmf.cuh +++ b/ggml/src/ggml-cuda/mmf.cuh @@ -7,6 +7,14 @@ using namespace ggml_cuda_mma; #define MMF_ROWS_PER_BLOCK 32 +struct mmf_ids_data { + const int32_t * ids_src_compact = nullptr; + const int32_t * ids_dst_compact = nullptr; + const int32_t * expert_bounds_dev = nullptr; + int n_experts = 0; + int sis1 = 0; +}; + void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst); bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, const int src1_ncols, bool mul_mat_id); @@ -224,6 +232,250 @@ static __global__ void mul_mat_f( #endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) } + +//This kernel is for larger batch sizes of mul_mat_id +template +__launch_bounds__(ggml_cuda_get_physical_warp_size()*nwarps, 1) +static __global__ void mul_mat_f_ids( + const T * __restrict__ x, const float * __restrict__ y, + const int32_t * __restrict__ ids_src_compact, const int32_t * __restrict__ ids_dst_compact, + const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, + const int ncols, const int ncols_dst_total, const int nchannels_dst, const int stride_row, const int stride_col_y, 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 uint3 sis1_fd, const uint3 nch_fd) { +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + typedef tile<16, 8, T> tile_A; + typedef tile< 8, 8, T> tile_B; + typedef tile<16, 8, float> tile_C; + + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); + constexpr int tile_k_padded = warp_size + 4; + constexpr int ntA = rows_per_block / tile_A::I; + constexpr int ntB = (cols_per_block + tile_B::I - 1) / tile_B::I; + + const int row0 = blockIdx.x * rows_per_block; + + const int expert_idx = blockIdx.y; + const int expert_start = expert_bounds[expert_idx]; + const int expert_end = expert_bounds[expert_idx + 1]; + const int ncols_expert = expert_end - expert_start; + + const int tiles_for_expert = (ncols_expert + cols_per_block - 1) / cols_per_block; + const int tile_idx = blockIdx.z; + if (tile_idx >= tiles_for_expert) { + return; + } + + const int col_base = tile_idx * cols_per_block; + + GGML_UNUSED(channel_ratio); + + const int channel_x = expert_idx; + const int sample_dst = 0; + const int sample_x = sample_dst / sample_ratio; + const int sample_y = sample_dst; + + x += int64_t(sample_x) *stride_sample_x + channel_x *stride_channel_x + row0*stride_row; + y += int64_t(sample_y) *stride_sample_y; + dst += int64_t(sample_dst)*stride_sample_dst; + + const int32_t * ids_src_expert = ids_src_compact + expert_start; + const int32_t * ids_dst_expert = ids_dst_compact + expert_start; + + extern __shared__ char data_mmv[]; + char * compute_base = data_mmv; + + //const float2 * y2 = (const float2 *) y; + + tile_C C[ntA][ntB]; + + T * tile_xy = (T *) compute_base + threadIdx.y*(tile_A::I * tile_k_padded); + + for (int col = threadIdx.y*warp_size + threadIdx.x; col < ncols; col += nwarps*warp_size) { + tile_A A[ntA][warp_size / tile_A::J]; +#pragma unroll + for (int itA = 0; itA < ntA; ++itA) { +#pragma unroll + for (int i = 0; i < tile_A::I; ++i) { + tile_xy[i*tile_k_padded + threadIdx.x] = x[(itA*tile_A::I + i)*stride_row + col]; + } +#pragma unroll + for (int k0 = 0; k0 < warp_size; k0 += tile_A::J) { + load_ldmatrix(A[itA][k0/tile_A::J], tile_xy + k0, tile_k_padded); + } + } + + if constexpr (std::is_same_v) { + float vals_buf[2][tile_B::I]; + auto gather_tile = [&](int tile_idx_local, float *vals) { +#pragma unroll + for (int j0 = 0; j0 < tile_B::I; ++j0) { + const int j = j0 + tile_idx_local*tile_B::I; + const int global_j = col_base + j; + float val = 0.0f; + if (j < cols_per_block && global_j < ncols_expert) { + const int src_entry = ids_src_expert[global_j]; + const uint2 qrm = fast_div_modulo((uint32_t) src_entry, sis1_fd); + const int token = (int) qrm.x; + const int channel = (int) qrm.y; + if (token < ncols_dst_total) { + val = y[channel*stride_channel_y + token*stride_col_y + col]; + } + } + vals[j0] = val; + } + }; + + gather_tile(0, vals_buf[0]); + + int curr_buf = 0; + int next_buf = 1; +#pragma unroll + for (int itB = 0; itB < ntB; ++itB) { +#pragma unroll + for (int j0 = 0; j0 < tile_B::I; ++j0) { + tile_xy[j0*tile_k_padded + threadIdx.x] = vals_buf[curr_buf][j0]; + } + + if (itB + 1 < ntB) { + gather_tile(itB + 1, vals_buf[next_buf]); + } + +#pragma unroll + for (int k0 = 0; k0 < warp_size; k0 += tile_B::J) { + tile_B B; + load_ldmatrix(B, tile_xy + k0, tile_k_padded); +#pragma unroll + for (int itA = 0; itA < ntA; ++itA) { + mma(C[itA][itB], A[itA][k0/tile_B::J], B); + } + } + + if (itB + 1 < ntB) { + curr_buf ^= 1; + next_buf ^= 1; + } + } + } else if constexpr (std::is_same_v || std::is_same_v) { + float2 vals_buf[2][tile_B::I]; + auto gather_tile = [&](int tile_idx_local, float2 *vals) { +#pragma unroll + for (int j0 = 0; j0 < tile_B::I; ++j0) { + const int j = j0 + tile_idx_local*tile_B::I; + const int global_j = col_base + j; + float2 tmp = make_float2(0.0f, 0.0f); + if (j < cols_per_block && global_j < ncols_expert) { + const int src_entry = ids_src_expert[global_j]; + const uint2 qrm = fast_div_modulo((uint32_t) src_entry, sis1_fd); + const int token = (int) qrm.x; + const int channel = (int) qrm.y; + if (token < ncols_dst_total) { + tmp = *(const float2*) &y[channel*stride_channel_y + 2*(token*stride_col_y + col)]; + } + } + vals[j0] = tmp; + } + }; + + if (ntB > 0) { + gather_tile(0, vals_buf[0]); + } + + int curr_buf = 0; + int next_buf = 1; +#pragma unroll + for (int itB = 0; itB < ntB; ++itB) { +#pragma unroll + for (int j0 = 0; j0 < tile_B::I; ++j0) { + const float2 tmp = vals_buf[curr_buf][j0]; + tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y}; + } + + if (itB + 1 < ntB) { + gather_tile(itB + 1, vals_buf[next_buf]); + } + +#pragma unroll + for (int k0 = 0; k0 < warp_size; k0 += tile_B::J) { + tile_B B; + load_ldmatrix(B, tile_xy + k0, tile_k_padded); +#pragma unroll + for (int itA = 0; itA < ntA; ++itA) { + mma(C[itA][itB], A[itA][k0/tile_B::J], B); + } + } + + if (itB + 1 < ntB) { + curr_buf ^= 1; + next_buf ^= 1; + } + } + } else { + static_assert(std::is_same_v, "unsupported type"); + } + } + + float * buf_iw = (float *) compute_base; + constexpr int kiw = nwarps*rows_per_block + 4; + + if (nwarps > 1) { + __syncthreads(); + } +#pragma unroll + for (int itB = 0; itB < ntB; ++itB) { +#pragma unroll + for (int itA = 0; itA < ntA; ++itA) { +#pragma unroll + for (int l = 0; l < tile_C::ne; ++l) { + const int i = threadIdx.y*rows_per_block + itA*tile_C::I + tile_C::get_i(l); + const int j = itB*tile_C::J + tile_C::get_j(l); + buf_iw[j*kiw + i] = C[itA][itB].x[l]; + } + } + } + + if (nwarps > 1) { + __syncthreads(); + } + +#pragma unroll + for (int j0 = 0; j0 < cols_per_block; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j0 + nwarps > cols_per_block && j >= cols_per_block) { + return; + } + + float sum = 0.0f; + static_assert(rows_per_block == warp_size, "need loop/check"); +#pragma unroll + for (int i0 = 0; i0 < nwarps*rows_per_block; i0 += rows_per_block) { + const int i = i0 + threadIdx.x; + + sum += buf_iw[j*kiw + i]; + } + + const int global_j = col_base + j; + if (j < cols_per_block && global_j < ncols_expert && nchannels_dst > 0) { + const int dst_entry = ids_dst_expert[global_j]; + const uint2 qrm = fast_div_modulo((uint32_t) dst_entry, nch_fd); + const int token = (int) qrm.x; + if (token < ncols_dst_total) { + const int slot = (int) qrm.y; + dst[slot*stride_channel_dst + token*stride_col_dst + row0 + threadIdx.x] = sum; + } + } + } +#else + GGML_UNUSED_VARS(x, y, ids_src_compact, ids_dst_compact, expert_bounds, dst, + ncols, ncols_dst_total, nchannels_dst, stride_row, stride_col_y, 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, sis1_fd, nch_fd); + NO_DEVICE_CODE; +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +} + template static inline void mul_mat_f_switch_ids( const T * x, const float * y, const int32_t * ids, float * dst, @@ -232,13 +484,35 @@ static inline void mul_mat_f_switch_ids( const int64_t stride_col_id, const int64_t stride_row_id, 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 dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared_total, cudaStream_t stream) { - if (ids) { + const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared_total, cudaStream_t stream, + const mmf_ids_data * ids_data) { + const bool has_ids_data = ids_data && ids_data->ids_src_compact; + + // Use the compact-ids kernel only for larger tiles; for small ncols_dst (< 16) + // we prefer the normal mul_mat_f path with has_ids=true. + if (has_ids_data && ncols_dst > 16) { + const int max_tiles = (int) ((ncols_dst + cols_per_block - 1) / cols_per_block); + if (max_tiles == 0) { + return; + } + dim3 block_nums_ids(block_nums.x, ids_data->n_experts, max_tiles); + + const uint3 sis1_fd = ids_data->sis1 > 0 ? init_fastdiv_values((uint32_t) ids_data->sis1) : make_uint3(0, 0, 1); + const uint3 nch_fd = init_fastdiv_values((uint32_t) nchannels_dst); + + mul_mat_f_ids<<>> + (x, y, ids_data->ids_src_compact, ids_data->ids_dst_compact, ids_data->expert_bounds_dev, dst, + ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, 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, + sis1_fd, nch_fd); + } else if (ids) { const int64_t col_tiles = (ncols_dst + cols_per_block - 1) / cols_per_block; dim3 block_nums_ids = block_nums; block_nums_ids.y *= col_tiles; + mul_mat_f<<>> - (x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, + (x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); } else { @@ -258,7 +532,7 @@ void mul_mat_f_cuda( 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) { + cudaStream_t stream, const mmf_ids_data * ids_data) { typedef tile<16, 8, T> tile_A; typedef tile< 8, 8, T> tile_B; @@ -290,7 +564,7 @@ void mul_mat_f_cuda( const int nbytes_shared = std::max(nbytes_shared_iter, nbytes_shared_combine); const int nbytes_slotmap = ids ? GGML_PAD(cols_per_block, 16) * sizeof(int) : 0; const int nbytes_shared_total = nbytes_shared + nbytes_slotmap; - const int64_t grid_y = ids ? nchannels_x : nchannels_dst; // per expert when ids present + const int64_t grid_y = ids ? nchannels_x : nchannels_dst; const dim3 block_nums(nrows_x/rows_per_block, grid_y, nsamples_dst); const dim3 block_dims(warp_size, nwarps_best, 1); @@ -300,49 +574,57 @@ void mul_mat_f_cuda( mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; case 2: { mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; case 3: { mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; case 4: { mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; case 5: { mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; case 6: { mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; case 7: { mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; case 8: { mul_mat_f_switch_ids( x, y, ids, dst, ncols_x, ncols_dst, nchannels_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, - sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream); + sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, block_nums, block_dims, nbytes_shared_total, stream, + ids_data); } break; default: { GGML_ABORT("fatal error"); @@ -361,7 +643,7 @@ static void mul_mat_f_switch_cols_per_block( 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) { + cudaStream_t stream, const mmf_ids_data * ids_data) { const int ncols_case = (ids && ncols_dst > 16) ? 16 : ncols_dst; @@ -371,82 +653,82 @@ static void mul_mat_f_switch_cols_per_block( case 1: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 2: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 3: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 4: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 5: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 6: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 7: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 8: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 9: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 10: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 11: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 12: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 13: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 14: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 15: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; case 16: { mul_mat_f_cuda(x, y, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row, stride_col_y, stride_col_dst, stride_col_id, stride_row_id, 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); + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream, ids_data); } break; default: { GGML_ABORT("fatal error"); @@ -462,7 +744,7 @@ static void mul_mat_f_switch_cols_per_block( 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); + cudaStream_t stream, const mmf_ids_data * ids_data); #if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) #define DECL_MMF_CASE_EXTERN(ncols_dst) \ diff --git a/ggml/src/ggml-cuda/mmid.cu b/ggml/src/ggml-cuda/mmid.cu new file mode 100644 index 0000000000..3c61e4595a --- /dev/null +++ b/ggml/src/ggml-cuda/mmid.cu @@ -0,0 +1,164 @@ +#include "common.cuh" +#include "mmid.cuh" + +// To reduce shared memory use, store "it" and "iex_used" with 22/10 bits each. +struct mm_ids_helper_store { + uint32_t data; + + __device__ mm_ids_helper_store(const uint32_t it, const uint32_t iex_used) { + data = (it & 0x003FFFFF) | (iex_used << 22); + } + + __device__ uint32_t it() const { + return data & 0x003FFFFF; + } + + __device__ uint32_t iex_used() const { + return data >> 22; + } +}; +static_assert(sizeof(mm_ids_helper_store) == 4, "unexpected size for mm_ids_helper_store"); + +// Helper function for mul_mat_id, converts ids to a more convenient format. +// ids_src1 describes how to permute the flattened column indices of src1 in order to get a compact src1 tensor sorted by expert. +// ids_dst describes the same mapping but for the dst tensor. +// The upper and lower bounds for the ith expert in the compact src1 tensor are stored in expert_bounds[i:i+1]. +template +__launch_bounds__(ggml_cuda_get_physical_warp_size(), 1) +static __global__ void mm_ids_helper( + const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, + const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1) { + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); + const int n_expert_used = n_expert_used_template == 0 ? n_expert_used_var : n_expert_used_template; + const int expert = blockIdx.x; + + extern __shared__ char data_mm_ids_helper[]; + mm_ids_helper_store * store = (mm_ids_helper_store *) data_mm_ids_helper; + + int nex_prev = 0; // Number of columns for experts with a lower index. + int it_compact = 0; // Running index for the compact slice of this expert. + + if constexpr (n_expert_used_template == 0) { + // Generic implementation: + for (int it = 0; it < n_tokens; ++it) { + int iex_used = -1; // The index at which the expert is used, if any. + for (int iex = threadIdx.x; iex < n_expert_used; iex += warp_size) { + const int expert_used = ids[it*si1 + iex]; + nex_prev += expert_used < expert; + if (expert_used == expert) { + iex_used = iex; + } + } + + if (iex_used != -1) { + store[it_compact] = mm_ids_helper_store(it, iex_used); + } + + if (warp_reduce_any(iex_used != -1)) { + it_compact++; + } + } + } else { + // Implementation optimized for specific numbers of experts used: + static_assert(n_expert_used == 6 || warp_size % n_expert_used == 0, "bad n_expert_used"); + const int neu_padded = n_expert_used == 6 ? 8 : n_expert_used; // Padded to next higher power of 2. + for (int it0 = 0; it0 < n_tokens; it0 += warp_size/neu_padded) { + const int it = it0 + threadIdx.x / neu_padded; + + const int iex = threadIdx.x % neu_padded; // The index at which the expert is used, if any. + const int expert_used = (neu_padded == n_expert_used || iex < n_expert_used) && it < n_tokens ? + ids[it*si1 + iex] : INT_MAX; + const int iex_used = expert_used == expert ? iex : -1; + nex_prev += expert_used < expert; + + // Whether the threads at this token position have used the expert: + const int it_compact_add_self = warp_reduce_any(iex_used != -1); + + // Do a scan over threads at lower token positions in warp to get the correct index for writing data: + int it_compact_add_lower = 0; +#pragma unroll + for (int offset = neu_padded; offset < warp_size; offset += neu_padded) { + const int tmp = __shfl_up_sync(0xFFFFFFFF, it_compact_add_self, offset, warp_size); + if (threadIdx.x >= static_cast(offset)) { + it_compact_add_lower += tmp; + } + } + + if (iex_used != -1) { + store[it_compact + it_compact_add_lower] = mm_ids_helper_store(it, iex_used); + } + + // The thread with the highest index in the warp always has the sum over the whole warp, use it to increment all threads: + it_compact += __shfl_sync(0xFFFFFFFF, it_compact_add_lower + it_compact_add_self, warp_size - 1, warp_size); + } + } + nex_prev = warp_reduce_sum(nex_prev); + + for (int itc = threadIdx.x; itc < it_compact; itc += warp_size) { + const mm_ids_helper_store store_it = store[itc]; + const int it = store_it.it(); + const int iex_used = store_it.iex_used(); + ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y; + ids_dst [nex_prev + itc] = it*n_expert_used + iex_used; + } + + if (threadIdx.x != 0) { + return; + } + + expert_bounds[expert] = nex_prev; + + if (expert < static_cast(gridDim.x) - 1) { + return; + } + + expert_bounds[gridDim.x] = nex_prev + it_compact; +} + +template +static void launch_mm_ids_helper( + const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, + const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) { + GGML_ASSERT(n_tokens < (1 << 22) && "too few bits in mm_ids_helper_store"); + GGML_ASSERT(n_expert_used_var < (1 << 10) && "too few bits in mm_ids_helper_store"); + + const int id = ggml_cuda_get_device(); + const int warp_size = ggml_cuda_info().devices[id].warp_size; + const size_t smpbo = ggml_cuda_info().devices[id].smpbo; + CUDA_SET_SHARED_MEMORY_LIMIT(mm_ids_helper, smpbo); + + const dim3 num_blocks(n_experts, 1, 1); + const dim3 block_size(warp_size, 1, 1); + const size_t nbytes_shared = n_tokens*sizeof(mm_ids_helper_store); + GGML_ASSERT(nbytes_shared <= smpbo); + mm_ids_helper<<>> + (ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1); +} + +void ggml_cuda_launch_mm_ids_helper( + const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, + const int n_experts, const int n_tokens, const int n_expert_used, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) { + switch (n_expert_used) { + case 2: + launch_mm_ids_helper< 2>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + break; + case 4: + launch_mm_ids_helper< 4>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + break; + case 6: + launch_mm_ids_helper< 6>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + break; + case 8: + launch_mm_ids_helper< 8>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + break; + case 16: + launch_mm_ids_helper<16>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + break; + case 32: + launch_mm_ids_helper<32>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + break; + default: + launch_mm_ids_helper< 0>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + break; + } +} diff --git a/ggml/src/ggml-cuda/mmid.cuh b/ggml/src/ggml-cuda/mmid.cuh new file mode 100644 index 0000000000..ac090aea9e --- /dev/null +++ b/ggml/src/ggml-cuda/mmid.cuh @@ -0,0 +1,5 @@ +#pragma once + +void ggml_cuda_launch_mm_ids_helper( + const int32_t * ids, int32_t * ids_src1, int32_t * ids_dst, int32_t * expert_bounds, + int n_experts, int n_tokens, int n_expert_used, int nchannels_y, int si1, int sis1, cudaStream_t stream); diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index 12bdc629bd..a2c8760abe 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -1,141 +1,6 @@ #include "mmq.cuh" #include "quantize.cuh" - -#include - -// To reduce shared memory use, store "it" and "iex_used" with 22/10 bits each. -struct mmq_ids_helper_store { - uint32_t data; - - __device__ mmq_ids_helper_store(const uint32_t it, const uint32_t iex_used) { - data = (it & 0x003FFFFF) | (iex_used << 22); - } - - __device__ uint32_t it() const { - return data & 0x003FFFFF; - } - - __device__ uint32_t iex_used() const { - return data >> 22; - } -}; -static_assert(sizeof(mmq_ids_helper_store) == 4, "unexpected size for mmq_ids_helper_store"); - -// Helper function for mul_mat_id, converts ids to a more convenient format. -// ids_src1 describes how to permute the flattened column indices of src1 in order to get a compact src1 tensor sorted by expert. -// ids_dst describes the same mapping but for the dst tensor. -// The upper and lower bounds for the ith expert in the compact src1 tensor are stored in expert_bounds[i:i+1]. -template -__launch_bounds__(ggml_cuda_get_physical_warp_size(), 1) -static __global__ void mmq_ids_helper( - const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1) { - constexpr int warp_size = ggml_cuda_get_physical_warp_size(); - const int n_expert_used = n_expert_used_template == 0 ? n_expert_used_var : n_expert_used_template; - const int expert = blockIdx.x; - - extern __shared__ char data_mmq_ids_helper[]; - mmq_ids_helper_store * store = (mmq_ids_helper_store *) data_mmq_ids_helper; - - int nex_prev = 0; // Number of columns for experts with a lower index. - int it_compact = 0; // Running index for the compact slice of this expert. - - if constexpr (n_expert_used_template == 0) { - // Generic implementation: - for (int it = 0; it < n_tokens; ++it) { - int iex_used = -1; // The index at which the expert is used, if any. - for (int iex = threadIdx.x; iex < n_expert_used; iex += warp_size) { - const int expert_used = ids[it*si1 + iex]; - nex_prev += expert_used < expert; - if (expert_used == expert) { - iex_used = iex; - } - } - - if (iex_used != -1) { - store[it_compact] = mmq_ids_helper_store(it, iex_used); - } - - if (warp_reduce_any(iex_used != -1)) { - it_compact++; - } - } - } else { - // Implementation optimized for specific numbers of experts used: - static_assert(n_expert_used == 6 || warp_size % n_expert_used == 0, "bad n_expert_used"); - const int neu_padded = n_expert_used == 6 ? 8 : n_expert_used; // Padded to next higher power of 2. - for (int it0 = 0; it0 < n_tokens; it0 += warp_size/neu_padded) { - const int it = it0 + threadIdx.x / neu_padded; - - const int iex = threadIdx.x % neu_padded; // The index at which the expert is used, if any. - const int expert_used = (neu_padded == n_expert_used || iex < n_expert_used) && it < n_tokens ? - ids[it*si1 + iex] : INT_MAX; - const int iex_used = expert_used == expert ? iex : -1; - nex_prev += expert_used < expert; - - // Whether the threads at this token position have used the expert: - const int it_compact_add_self = warp_reduce_any(iex_used != -1); - - // Do a scan over threads at lower token positions in warp to get the correct index for writing data: - int it_compact_add_lower = 0; -#pragma unroll - for (int offset = neu_padded; offset < warp_size; offset += neu_padded) { - const int tmp = __shfl_up_sync(0xFFFFFFFF, it_compact_add_self, offset, warp_size); - if (threadIdx.x >= static_cast(offset)) { - it_compact_add_lower += tmp; - } - } - - if (iex_used != -1) { - store[it_compact + it_compact_add_lower] = mmq_ids_helper_store(it, iex_used); - } - - // The thread with the highest index in the warp always has the sum over the whole warp, use it to increment all threads: - it_compact += __shfl_sync(0xFFFFFFFF, it_compact_add_lower + it_compact_add_self, warp_size - 1, warp_size); - } - } - nex_prev = warp_reduce_sum(nex_prev); - - for (int itc = threadIdx.x; itc < it_compact; itc += warp_size) { - const mmq_ids_helper_store store_it = store[itc]; - const int it = store_it.it(); - const int iex_used = store_it.iex_used(); - ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y; - ids_dst [nex_prev + itc] = it*n_expert_used + iex_used; - } - - if (threadIdx.x != 0) { - return; - } - - expert_bounds[expert] = nex_prev; - - if (expert < static_cast(gridDim.x) - 1) { - return; - } - - expert_bounds[gridDim.x] = nex_prev + it_compact; -} - -template -static void launch_mmq_ids_helper( - const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) { - GGML_ASSERT(n_tokens < (1 << 22) && "too few bits in mmq_ids_helper_store"); - GGML_ASSERT(n_expert_used_var < (1 << 10) && "too few bits in mmq_ids_helper_store"); - - const int id = ggml_cuda_get_device(); - const int warp_size = ggml_cuda_info().devices[id].warp_size; - const size_t smpbo = ggml_cuda_info().devices[id].smpbo; - CUDA_SET_SHARED_MEMORY_LIMIT(mmq_ids_helper, smpbo); - - const dim3 num_blocks(n_experts, 1, 1); - const dim3 block_size(warp_size, 1, 1); - const size_t nbytes_shared = n_tokens*sizeof(mmq_ids_helper_store); - GGML_ASSERT(nbytes_shared <= smpbo); - mmq_ids_helper<<>> - (ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1); -} +#include "mmid.cuh" static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) { switch (args.type_x) { @@ -293,36 +158,8 @@ void ggml_cuda_mul_mat_q( const int si1 = ids->nb[1] / ggml_element_size(ids); const int sis1 = nb12 / nb11; - switch (n_expert_used) { - case 2: - launch_mmq_ids_helper< 2> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); - break; - case 4: - launch_mmq_ids_helper< 4> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); - break; - case 6: - launch_mmq_ids_helper< 6> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); - break; - case 8: - launch_mmq_ids_helper< 8> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); - break; - case 16: - launch_mmq_ids_helper<16> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); - break; - case 32: - launch_mmq_ids_helper<32> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); - break; - default: - launch_mmq_ids_helper< 0> ((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); - break; - } + ggml_cuda_launch_mm_ids_helper((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), + ne02, ne12, n_expert_used, ne11, si1, sis1, stream); CUDA_CHECK(cudaGetLastError()); } diff --git a/ggml/src/ggml-cuda/mmvf.cu b/ggml/src/ggml-cuda/mmvf.cu index 5b21ef05b3..57ab839393 100644 --- a/ggml/src/ggml-cuda/mmvf.cu +++ b/ggml/src/ggml-cuda/mmvf.cu @@ -7,14 +7,14 @@ template static __global__ void mul_mat_vec_f( const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst, 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 uint3 channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, + const uint3 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_x = ids ? ids[channel_dst] : fastdiv((uint32_t) 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_x = fastdiv((uint32_t) sample_dst, sample_ratio); const int sample_y = sample_dst; const int tid = threadIdx.x; @@ -47,8 +47,8 @@ static __global__ void mul_mat_vec_f( #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; + ggml_cuda_mad(sumf[j], tmpx.x, tmpy.x); + ggml_cuda_mad(sumf[j], tmpx.y, tmpy.y); } } } else if constexpr (std::is_same_v) { @@ -61,8 +61,8 @@ static __global__ void mul_mat_vec_f( #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; + ggml_cuda_mad(sumf[j], tmpx.x, tmpy.x); + ggml_cuda_mad(sumf[j], tmpx.y, tmpy.y); } } } else { @@ -88,16 +88,32 @@ static __global__ void mul_mat_vec_f( #endif // FP16_AVAILABLE } } else if constexpr (std::is_same_v) { +//TODO: add support for ggml_cuda_mad for hip_bfloat162 +#if defined(GGML_USE_HIP) const int * x2 = (const int *) x; 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] += ggml_cuda_cast(reinterpret_cast(&tmpx)[0]) * tmpy.x; - sumf[j] += ggml_cuda_cast(reinterpret_cast(&tmpx)[1]) * tmpy.y; + const float tmpx0 = ggml_cuda_cast(reinterpret_cast(&tmpx)[0]); + const float tmpx1 = ggml_cuda_cast(reinterpret_cast(&tmpx)[1]); + ggml_cuda_mad(sumf[j], tmpx0, tmpy.x); + ggml_cuda_mad(sumf[j], tmpx1, tmpy.y); } } +#else + const nv_bfloat162 * x2 = (const nv_bfloat162 *) x; + for (int col2 = tid; col2 < ncols2; col2 += block_size) { + const nv_bfloat162 tmpx = x2[col2]; +#pragma unroll + for (int j = 0; j < ncols_dst; ++j) { + const float2 tmpy = y2[j*stride_col_y2 + col2]; + ggml_cuda_mad(sumf[j], tmpx.x, tmpy.x); + ggml_cuda_mad(sumf[j], tmpx.y, tmpy.y); + } + } +#endif } else { static_assert(std::is_same_v, "unsupported type"); } @@ -140,8 +156,8 @@ static void launch_mul_mat_vec_f_cuda( 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; - const int64_t sample_ratio = nsamples_dst / nsamples_x; + const uint3 channel_ratio_fd = ids ? make_uint3(0, 0, 0) : init_fastdiv_values(nchannels_dst / nchannels_x); + const uint3 sample_ratio_fd = init_fastdiv_values(nsamples_dst / nsamples_x); const int device = ggml_cuda_get_device(); const int warp_size = ggml_cuda_info().devices[device].warp_size; @@ -167,50 +183,50 @@ static void launch_mul_mat_vec_f_cuda( case 32: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 64: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 96: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 128: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 160: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 192: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 224: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; case 256: { mul_mat_vec_f<<>> (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); + channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, + sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst); } break; default: { GGML_ABORT("fatal error"); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu new file mode 100644 index 0000000000..a8b15ad72a --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(112, 112); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu new file mode 100644 index 0000000000..1da1810550 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(128, 128); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu new file mode 100644 index 0000000000..bc65c723ec --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(256, 256); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu new file mode 100644 index 0000000000..10b330fa6c --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(40, 40); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu new file mode 100644 index 0000000000..254b7d2e1d --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(576, 512); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu new file mode 100644 index 0000000000..5caffac046 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(64, 64); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu new file mode 100644 index 0000000000..90abb3b186 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(80, 80); diff --git a/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu new file mode 100644 index 0000000000..7292c0aab8 --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE(96, 96); diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py index d410080fab..81a986f38c 100755 --- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py +++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py @@ -3,8 +3,17 @@ from glob import glob import os +HEAD_SIZES_KQ = [40, 64, 80, 96, 112, 128, 256, 576] + TYPES_KV = ["GGML_TYPE_F16", "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0"] +SOURCE_FATTN_TILE = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.cuh" + +DECL_FATTN_TILE_CASE({head_size_kq}, {head_size_v}); +""" + SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. #include "../fattn-vec.cuh" @@ -51,6 +60,11 @@ def get_short_name(long_quant_name): for filename in glob("*.cu"): os.remove(filename) +for head_size_kq in HEAD_SIZES_KQ: + head_size_v = head_size_kq if head_size_kq != 576 else 512 + with open(f"fattn-tile-instance-dkq{head_size_kq}-dv{head_size_v}.cu", "w") as f: + f.write(SOURCE_FATTN_TILE.format(head_size_kq=head_size_kq, head_size_v=head_size_v)) + for type_k in TYPES_KV: for type_v in TYPES_KV: with open(f"fattn-vec-instance-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f: @@ -64,7 +78,9 @@ for ncols in [8, 16, 32, 64]: with open(f"fattn-mma-f16-instance-ncols1_{ncols1}-ncols2_{ncols2}.cu", "w") as f: f.write(SOURCE_FATTN_MMA_START) - for head_size_kq in [64, 80, 96, 112, 128, 256, 576]: + for head_size_kq in HEAD_SIZES_KQ: + if head_size_kq == 40: + continue if head_size_kq != 576 and ncols2 == 16: continue if head_size_kq == 576 and ncols2 != 16: diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt index 0e2b1847e0..934aefdcb4 100644 --- a/ggml/src/ggml-hip/CMakeLists.txt +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -53,6 +53,8 @@ file(GLOB GGML_HEADERS_ROCM "../ggml-cuda/*.cuh") list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h") file(GLOB GGML_SOURCES_ROCM "../ggml-cuda/*.cu") +file(GLOB SRCS "../ggml-cuda/template-instances/fattn-tile*.cu") +list(APPEND GGML_SOURCES_ROCM ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu") list(APPEND GGML_SOURCES_ROCM ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index e23abdda97..866cd2da58 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -268,6 +268,25 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_glu(ggml_metal_library_t l return res; } +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum(ggml_metal_library_t lib, const ggml_tensor * op) { + assert(op->op == GGML_OP_SUM); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_op_sum_%s", ggml_type_name(op->src[0]->type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name); + if (res) { + return res; + } + + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + + return res; +} + ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum_rows(ggml_metal_library_t lib, const ggml_tensor * op) { GGML_ASSERT(op->src[0]->nb[0] == ggml_type_size(op->src[0]->type)); @@ -1482,3 +1501,40 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_timestep_embedding(ggml_me return res; } +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_adamw(ggml_metal_library_t lib, const ggml_tensor * op) { + assert(op->op == GGML_OP_OPT_STEP_ADAMW); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_opt_step_adamw_%s", ggml_type_name(op->src[0]->type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name); + if (res) { + return res; + } + + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + + return res; +} + +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_sgd(ggml_metal_library_t lib, const ggml_tensor * op) { + assert(op->op == GGML_OP_OPT_STEP_SGD); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_opt_step_sgd_%s", ggml_type_name(op->src[0]->type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name); + if (res) { + return res; + } + + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + + return res; +} diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 1034e4bbf6..28ae2e1765 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -109,6 +109,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_set_rows (ggml_me ggml_metal_pipeline_t ggml_metal_library_get_pipeline_repeat (ggml_metal_library_t lib, enum ggml_type tsrc); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_unary (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_glu (ggml_metal_library_t lib, const struct ggml_tensor * op); +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum_rows (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_soft_max (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_conv (ggml_metal_library_t lib, const struct ggml_tensor * op); @@ -134,6 +135,8 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad (ggml_me ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad_reflect_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_arange (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_timestep_embedding(ggml_metal_library_t lib, const struct ggml_tensor * op); +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_adamw (ggml_metal_library_t lib, const struct ggml_tensor * op); +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_sgd (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_pad( ggml_metal_library_t lib, diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 9527973015..c3c83abe4e 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -7,6 +7,8 @@ #include +#include + #ifndef TARGET_OS_VISION #define TARGET_OS_VISION 0 #endif @@ -22,6 +24,9 @@ // overload of MTLGPUFamilyMetal3 (not available in some environments) static const NSInteger MTLGPUFamilyMetal3_GGML = 5001; +// virtual address for GPU memory allocations +static atomic_uintptr_t g_addr_device = 0x000000400ULL; + #if !GGML_METAL_EMBED_LIBRARY // Here to assist with NSBundle Path Hack @interface GGMLMetalClass : NSObject @@ -656,6 +661,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te case GGML_OP_COS: case GGML_OP_LOG: return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_SUM: + return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]); case GGML_OP_SUM_ROWS: case GGML_OP_MEAN: case GGML_OP_SOFT_MAX: @@ -692,7 +699,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te return true; case GGML_OP_FLASH_ATTN_EXT: // for new head sizes, add checks here - if (op->src[0]->ne[0] != 40 && + if (op->src[0]->ne[0] != 32 && + op->src[0]->ne[0] != 40 && op->src[0]->ne[0] != 64 && op->src[0]->ne[0] != 80 && op->src[0]->ne[0] != 96 && @@ -798,6 +806,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te return false; }; } + case GGML_OP_OPT_STEP_ADAMW: + case GGML_OP_OPT_STEP_SGD: + return has_simdgroup_reduction; default: return false; } @@ -822,7 +833,7 @@ struct ggml_metal_buffer_wrapper { }; struct ggml_metal_buffer { - void * all_data; // TODO: https://github.com/ggml-org/llama.cpp/pull/15985 + void * all_data; size_t all_size; // if false, the Metal buffer data is allocated in private GPU memory and is not shared with the host @@ -960,14 +971,15 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, if (shared) { res->all_data = ggml_metal_host_malloc(size_aligned); res->is_shared = true; - res->owned = true; } else { - // dummy, non-NULL value - we'll populate this after creating the Metal buffer below - res->all_data = (void *) 0x000000400ULL; + // use virtual address from g_addr_device counter + res->all_data = (void *) atomic_fetch_add_explicit(&g_addr_device, size_aligned, memory_order_relaxed); res->is_shared = false; } res->all_size = size_aligned; + res->owned = true; + res->device = ggml_metal_device_get_obj(dev); res->queue = ggml_metal_device_get_queue(dev); @@ -978,15 +990,13 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, res->buffers[0].metal = nil; if (size_aligned > 0) { - if (props_dev->use_shared_buffers &&shared) { + if (props_dev->use_shared_buffers && shared) { res->buffers[0].metal = [res->device newBufferWithBytesNoCopy:res->all_data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; } else { res->buffers[0].metal = [res->device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate]; - - res->all_data = (void *) (res->buffers[0].metal.gpuAddress); } } @@ -1134,7 +1144,7 @@ bool ggml_metal_buffer_is_shared(ggml_metal_buffer_t buf) { void ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { if (buf->is_shared) { - memset((char *)tensor->data + offset, value, size); + memset((char *) tensor->data + offset, value, size); return; } @@ -1163,7 +1173,7 @@ void ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { if (buf->is_shared) { - memcpy((char *)tensor->data + offset, data, size); + memcpy((char *) tensor->data + offset, data, size); return; } @@ -1218,7 +1228,7 @@ void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * void ggml_metal_buffer_get_tensor(ggml_metal_buffer_t buf, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { if (buf->is_shared) { - memcpy(data, (const char *)tensor->data + offset, size); + memcpy(data, (const char *) tensor->data + offset, size); return; } diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index c9dff87305..fa2d82cefb 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -251,6 +251,7 @@ typedef struct { int32_t sect_1; int32_t sect_2; int32_t sect_3; + bool src2; } ggml_metal_kargs_rope; typedef struct { @@ -544,6 +545,10 @@ typedef struct{ float limit; } ggml_metal_kargs_glu; +typedef struct { + uint64_t np; +} ggml_metal_kargs_sum; + typedef struct { int64_t ne00; int64_t ne01; @@ -773,4 +778,12 @@ typedef struct { uint64_t nb01; } ggml_metal_kargs_argmax; +typedef struct { + int64_t np; +} ggml_metal_kargs_opt_step_adamw; + +typedef struct { + int64_t np; +} ggml_metal_kargs_opt_step_sgd; + #endif // GGML_METAL_IMPL diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 1137e21077..4f9f6bda00 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -301,6 +301,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { { n_fuse = ggml_metal_op_glu(ctx, idx); } break; + case GGML_OP_SUM: + { + n_fuse = ggml_metal_op_sum(ctx, idx); + } break; case GGML_OP_SUM_ROWS: case GGML_OP_MEAN: { @@ -410,6 +414,14 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { { n_fuse = ggml_metal_op_argmax(ctx, idx); } break; + case GGML_OP_OPT_STEP_ADAMW: + { + n_fuse = ggml_metal_op_opt_step_adamw(ctx, idx); + } break; + case GGML_OP_OPT_STEP_SGD: + { + n_fuse = ggml_metal_op_opt_step_sgd(ctx, idx); + } break; default: { GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(node->op)); @@ -840,6 +852,43 @@ int ggml_metal_op_glu(ggml_metal_op_t ctx, int idx) { return 1; } +int ggml_metal_op_sum(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + const uint64_t n = (uint64_t) ggml_nelements(op->src[0]); + + ggml_metal_kargs_sum args = { + /*.np =*/ n, + }; + + ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_sum(lib, op); + + int nth = 32; // SIMD width + + while (nth < (int) n && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) { + nth *= 2; + } + + nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)); + nth = std::min(nth, (int) n); + + const int nsg = (nth + 31) / 32; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2); + + ggml_metal_encoder_set_threadgroup_memory_size(enc, nsg * sizeof(float), 0); + + ggml_metal_encoder_dispatch_threadgroups(enc, 1, 1, 1, nth, 1, 1); + + return 1; +} + int ggml_metal_op_sum_rows(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); @@ -1546,9 +1595,8 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { !ggml_is_transposed(op->src[1]) && // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - props_dev->has_simdgroup_mm && ne00 >= 64 && - (ne11 > ne11_mm_min || (ggml_is_quantized(op->src[0]->type) && ne12 > 1))) { - //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + props_dev->has_simdgroup_mm && ne00 >= 64 && ne11 > ne11_mm_min) { + //GGML_LOG_INFO("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); // some Metal matrix data types require aligned pointers // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) @@ -2934,6 +2982,7 @@ int ggml_metal_op_rope(ggml_metal_op_t ctx, int idx) { /* sect_1 =*/ sect_1, /* sect_2 =*/ sect_2, /* sect_3 =*/ sect_3, + /* src2 =*/ op->src[2] != nullptr, }; ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_rope(lib, op); @@ -3402,3 +3451,73 @@ int ggml_metal_op_leaky_relu(ggml_metal_op_t ctx, int idx) { return 1; } + +int ggml_metal_op_opt_step_adamw(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); + GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb); + GGML_TENSOR_LOCALS( int32_t, ne, op, ne); + GGML_TENSOR_LOCALS(uint32_t, nb, op, nb); + + ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_opt_step_adamw(lib, op); + + const int64_t np = ggml_nelements(op->src[0]); + ggml_metal_kargs_opt_step_adamw args = { + /*.np =*/ np, + }; + + int ida = 0; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[2]), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[3]), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[4]), ida++); + + const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne0); + const int64_t n = (np + nth - 1) / nth; + + ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, nth, 1, 1); + + return 1; +} + +int ggml_metal_op_opt_step_sgd(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); + GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb); + GGML_TENSOR_LOCALS( int32_t, ne, op, ne); + GGML_TENSOR_LOCALS(uint32_t, nb, op, nb); + + ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_opt_step_sgd(lib, op); + + const int64_t np = ggml_nelements(op->src[0]); + ggml_metal_kargs_opt_step_sgd args = { + /*.np =*/ np, + }; + + int ida = 0; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[2]), ida++); + + const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne0); + const int64_t n = (np + nth - 1) / nth; + + ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, nth, 1, 1); + + return 1; +} diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index d4cb944621..f352738698 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -50,6 +50,7 @@ int ggml_metal_op_scale (ggml_metal_op_t ctx, int idx); int ggml_metal_op_clamp (ggml_metal_op_t ctx, int idx); int ggml_metal_op_unary (ggml_metal_op_t ctx, int idx); int ggml_metal_op_glu (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_sum (ggml_metal_op_t ctx, int idx); int ggml_metal_op_sum_rows (ggml_metal_op_t ctx, int idx); int ggml_metal_op_get_rows (ggml_metal_op_t ctx, int idx); int ggml_metal_op_set_rows (ggml_metal_op_t ctx, int idx); @@ -78,6 +79,8 @@ int ggml_metal_op_timestep_embedding(ggml_metal_op_t ctx, int idx); int ggml_metal_op_argmax (ggml_metal_op_t ctx, int idx); int ggml_metal_op_argsort (ggml_metal_op_t ctx, int idx); int ggml_metal_op_leaky_relu (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_opt_step_adamw (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_opt_step_sgd (ggml_metal_op_t ctx, int idx); #ifdef __cplusplus } diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 45d91def88..496610b154 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -1723,6 +1723,54 @@ kernel void kernel_geglu_quick_f32( } } +kernel void kernel_op_sum_f32( + constant ggml_metal_kargs_sum & args, + device const float * src0, + device float * dst, + threadgroup float * shmem_f32 [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + + if (args.np == 0) { + return; + } + + const uint nsg = (ntg.x + 31) / 32; + + float sumf = 0; + + for (int64_t i0 = tpitg.x; i0 < args.np; i0 += ntg.x) { + sumf += src0[i0]; + } + + sumf = simd_sum(sumf); + + if (tiisg == 0) { + shmem_f32[sgitg] = sumf; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + float total = 0; + + if (sgitg == 0) { + float v = 0; + + if (tpitg.x < nsg) { + v = shmem_f32[tpitg.x]; + } + + total = simd_sum(v); + + if (tpitg.x == 0) { + dst[0] = total; + } + } +} + template kernel void kernel_sum_rows( constant ggml_metal_kargs_sum_rows & args, @@ -3730,7 +3778,7 @@ kernel void kernel_rope_norm( const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); - const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; + const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f; rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); @@ -3783,7 +3831,7 @@ kernel void kernel_rope_neox( const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); - const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; + const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f; rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); @@ -3854,7 +3902,7 @@ kernel void kernel_rope_multi( const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); - const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; + const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f; rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); @@ -3921,7 +3969,7 @@ kernel void kernel_rope_vision( const float theta = theta_base * pow(args.freq_base, 2.0f * inv_ndims * p); // end of mrope - const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; + const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f; rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); @@ -5195,8 +5243,30 @@ kernel void kernel_flash_attn_ext( half, half4, simdgroup_half8x8 //float, float4, simdgroup_float8x8 +#define FA_TYPES_F32 \ + half, half4, simdgroup_half8x8, \ + float, float4x4, simdgroup_float8x8, \ + float, float4x4, simdgroup_float8x8, \ + float, simdgroup_float8x8, \ + float, float2, simdgroup_float8x8, \ + float, float4, simdgroup_float8x8 + //half, half4, simdgroup_half8x8 + typedef decltype(kernel_flash_attn_ext) flash_attn_ext_t; +template [[host_name("kernel_flash_attn_ext_f32_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk96_dv96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk112_dv112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk128_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_f16_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f16_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f16_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f16_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5209,6 +5279,7 @@ template [[host_name("kernel_flash_attn_ext_f16_dk256_dv256")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_f16_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; #if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_flash_attn_ext_bf16_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_bf16_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_bf16_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_bf16_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5221,6 +5292,7 @@ template [[host_name("kernel_flash_attn_ext_bf16_dk256_dv256")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_bf16_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; #endif +template [[host_name("kernel_flash_attn_ext_q4_0_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_0_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_0_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_0_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5232,6 +5304,7 @@ template [[host_name("kernel_flash_attn_ext_q4_0_dk192_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q4_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5243,6 +5316,7 @@ template [[host_name("kernel_flash_attn_ext_q4_1_dk192_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q4_1_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5254,6 +5328,7 @@ template [[host_name("kernel_flash_attn_ext_q5_0_dk192_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q5_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5265,6 +5340,7 @@ template [[host_name("kernel_flash_attn_ext_q5_1_dk192_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q5_1_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q8_0_dk40_dv40" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q8_0_dk64_dv64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q8_0_dk80_dv80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5800,77 +5876,103 @@ kernel void kernel_flash_attn_ext_vec( float, float4, \ float4 +#define FA_TYPES_F32 \ + half4, \ + float4, \ + float4, \ + float, \ + float, float4, \ + float4 + typedef decltype(kernel_flash_attn_ext_vec) flash_attn_ext_vec_t; -template [[host_name("kernel_flash_attn_ext_vec_f16_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk32_dv32")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_f16_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk64_dv64")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_f16_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_f16_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk128_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_f16_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk192_dv192")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_f16_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk192_dv128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_f16_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; + +template [[host_name("kernel_flash_attn_ext_vec_f32_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#endif +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #undef FA_TYPES @@ -7487,7 +7589,7 @@ kernel void kernel_mul_mv_iq1_m_f32( kernel_mul_mv_iq1_m_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } -template +template void kernel_mul_mv_iq4_nl_f32_impl( args_t args, device const char * src0, @@ -7500,13 +7602,12 @@ void kernel_mul_mv_iq4_nl_f32_impl( const short NSG = FC_mul_mv_nsg; threadgroup float * shmem_f32 = (threadgroup float *) shmem; - const int nb = args.ne00/QK4_NL; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * NSG + sgitg) * nr0; + const int first_row = (r0 * NSG + sgitg) * NR0; const uint i12 = im%args.ne12; const uint i13 = im/args.ne12; @@ -7517,6 +7618,9 @@ void kernel_mul_mv_iq4_nl_f32_impl( device const block_iq4_nl * x = (device const block_iq4_nl *) (src0 + offset0); device const float * y = (device const float *) (src1 + offset1); + const int nb = args.ne00/QK4_NL; + const int ns01 = args.nb01/args.nb00; + const short ix = tiisg/2; // 0...15 const short it = tiisg%2; // 0 or 1 @@ -7524,24 +7628,25 @@ void kernel_mul_mv_iq4_nl_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; - float sumf[nr0]={0.f}; + float sumf[NR0]={0.f}; - device const float * yb = y + ix * QK4_NL + it * 8; + device const float * yb = y + ix*QK4_NL + it*8; uint32_t aux32[2]; thread const uint8_t * q8 = (thread const uint8_t *)aux32; float4 qf1, qf2; - for (int ib = ix; ib < nb; ib += 16) { + // [TAG_MUL_MV_WEIRD] + for (int ib = ix; ib < nb && ib < ns01; ib += 16) { device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - for (short row = 0; row < nr0; row++) { - device const block_iq4_nl & xb = x[row*nb + ib]; + for (short row = 0; row < NR0; row++) { + device const block_iq4_nl & xb = x[row*ns01 + ib]; device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it); float4 acc1 = {0.f}, acc2 = {0.f}; @@ -7572,7 +7677,7 @@ void kernel_mul_mv_iq4_nl_f32_impl( device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; - for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) { + for (int row = 0; row < NR0 && first_row + row < args.ne0; ++row) { float sum_all = simd_sum(sumf[row]); if (tiisg == 0) { dst_f32[first_row + row] = sum_all; @@ -7594,7 +7699,7 @@ kernel void kernel_mul_mv_iq4_nl_f32( kernel_mul_mv_iq4_nl_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } -template +template void kernel_mul_mv_iq4_xs_f32_impl( args_t args, device const char * src0, @@ -7607,12 +7712,11 @@ void kernel_mul_mv_iq4_xs_f32_impl( const short NSG = FC_mul_mv_nsg; threadgroup float * shmem_f32 = (threadgroup float *) shmem; - const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * NSG + sgitg) * nr0; + const int first_row = (r0 * NSG + sgitg) * NR0; const uint i12 = im%args.ne12; const uint i13 = im/args.ne12; @@ -7623,6 +7727,9 @@ void kernel_mul_mv_iq4_xs_f32_impl( device const block_iq4_xs * x = (device const block_iq4_xs *) (src0 + offset0); device const float * y = (device const float *) (src1 + offset1); + const int nb = args.ne00/QK_K; + const int ns01 = args.nb01/args.nb00; + const short ix = tiisg/16; // 0 or 1 const short it = tiisg%16; // 0...15 const short ib = it/2; @@ -7632,7 +7739,7 @@ void kernel_mul_mv_iq4_xs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; - float sumf[nr0]={0.f}; + float sumf[NR0]={0.f}; device const float * yb = y + ix * QK_K + ib * 32 + il * 8; @@ -7641,15 +7748,16 @@ void kernel_mul_mv_iq4_xs_f32_impl( float4 qf1, qf2; - for (int ibl = ix; ibl < nb; ibl += 2) { + // [TAG_MUL_MV_WEIRD] + for (int ibl = ix; ibl < nb && ibl < ns01; ibl += 2) { device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - for (short row = 0; row < nr0; ++row) { - device const block_iq4_xs & xb = x[row*nb + ibl]; + for (short row = 0; row < NR0; ++row) { + device const block_iq4_xs & xb = x[row*ns01 + ibl]; device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il); float4 acc1 = {0.f}, acc2 = {0.f}; @@ -7679,7 +7787,7 @@ void kernel_mul_mv_iq4_xs_f32_impl( device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; - for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) { + for (int row = 0; row < NR0 && first_row + row < args.ne0; ++row) { float sum_all = simd_sum(sumf[row]); if (tiisg == 0) { dst_f32[first_row + row] = sum_all; @@ -7701,7 +7809,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( kernel_mul_mv_iq4_xs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } -template +template void kernel_mul_mv_mxfp4_f32_impl( args_t args, device const char * src0, @@ -7714,13 +7822,12 @@ void kernel_mul_mv_mxfp4_f32_impl( const short NSG = FC_mul_mv_nsg; threadgroup float * shmem_f32 = (threadgroup float *) shmem; - const int nb = args.ne00/QK_MXFP4; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * NSG + sgitg) * nr0; + const int first_row = (r0 * NSG + sgitg) * NR0; const uint i12 = im%args.ne12; const uint i13 = im/args.ne12; @@ -7731,6 +7838,9 @@ void kernel_mul_mv_mxfp4_f32_impl( device const block_mxfp4 * x = (device const block_mxfp4 *) (src0 + offset0); device const float * y = (device const float *) (src1 + offset1); + const int nb = args.ne00/QK_MXFP4; + const int ns01 = args.nb01/args.nb00; // this can be larger than nb for permuted src0 tensors + const short ix = tiisg/2; // 0...15 const short it = tiisg%2; // 0 or 1 @@ -7738,20 +7848,22 @@ void kernel_mul_mv_mxfp4_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; - float sumf[nr0]={0.f}; + float sumf[NR0]={0.f}; - device const float * yb = y + ix * QK_MXFP4 + it * 8; + device const float * yb = y + ix*QK_MXFP4 + it*8; + + // note: just the check `ib < nb` is enough, but adding the redundant `&& ib < ns01` check makes the kernel a bit faster + // no idea why that is - needs some deeper investigation [TAG_MUL_MV_WEIRD] + for (int ib = ix; ib < nb && ib < ns01; ib += 16) { + device const float4 * y4 = (device const float4 *) yb; - for (int ib = ix; ib < nb; ib += 16) { - device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; -#pragma unroll(nr0) - for (short row = 0; row < nr0; row++) { - device const block_mxfp4 & xb = x[row*nb + ib]; + FOR_UNROLL (short row = 0; row < NR0; row++) { + device const block_mxfp4 & xb = x[row*ns01 + ib]; device const uint8_t * q2 = (device const uint8_t *)(xb.qs + 8*it); float4 acc1 = yl[0]*float4(shmem_f32[q2[0] & 0x0F], shmem_f32[q2[1] & 0x0F], shmem_f32[q2[2] & 0x0F], shmem_f32[q2[3] & 0x0F]); @@ -7769,7 +7881,7 @@ void kernel_mul_mv_mxfp4_f32_impl( device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; - for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) { + for (int row = 0; row < NR0 && first_row + row < args.ne0; ++row) { float sum_all = simd_sum(sumf[row]); if (tiisg == 0) { dst_f32[first_row + row] = sum_all; @@ -8744,3 +8856,51 @@ kernel void kernel_pool_2d_avg_f32( o_ptr[cur_oh * args.OW + cur_ow] = res; } + +kernel void kernel_opt_step_adamw_f32( + constant ggml_metal_kargs_opt_step_adamw & args, + device float * x, + device const float * g, + device float * g_m, + device float * g_v, + device const float * pars, + uint gid[[thread_position_in_grid]]) { + + if (gid >= args.np) { + return; + } + + const float alpha = pars[0]; + const float beta1 = pars[1]; + const float beta2 = pars[2]; + const float eps = pars[3]; + const float wd = pars[4]; + const float beta1h = pars[5]; + const float beta2h = pars[6]; + + const float gi = g[gid]; + const float gmi = g_m[gid] * beta1 + gi * (1.0f - beta1); + const float gvi = g_v[gid] * beta2 + gi * gi * (1.0f - beta2); + + g_m[gid] = gmi; + g_v[gid] = gvi; + + const float mh = gmi * beta1h; + const float vh = sqrt(gvi * beta2h) + eps; + + x[gid] = x[gid] * (1.0f - alpha * wd) - alpha * mh / vh; +} + +kernel void kernel_opt_step_sgd_f32( + constant ggml_metal_kargs_opt_step_sgd & args, + device float * x, + device const float * g, + device const float * pars, + uint gid[[thread_position_in_grid]]) { + + if (gid >= args.np) { + return; + } + + x[gid] = x[gid] * (1.0f - pars[0] * pars[1]) - pars[0] * g[gid]; +} diff --git a/ggml/src/ggml-musa/CMakeLists.txt b/ggml/src/ggml-musa/CMakeLists.txt index f8477a2ef3..d76cb51977 100644 --- a/ggml/src/ggml-musa/CMakeLists.txt +++ b/ggml/src/ggml-musa/CMakeLists.txt @@ -30,6 +30,8 @@ if (MUSAToolkit_FOUND) list(APPEND GGML_HEADERS_MUSA "../ggml-musa/mudnn.cuh") file(GLOB GGML_SOURCES_MUSA "../ggml-cuda/*.cu") + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-tile*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu") list(APPEND GGML_SOURCES_MUSA ${SRCS}) file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index 7e6c843846..6f6bba55e2 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -93,6 +93,7 @@ set(GGML_OPENCL_KERNELS mul_mv_id_mxfp4_f32_flat mul_mm_f32_f32_l4_lm mul_mm_f16_f32_l4_lm + mul_mm_q8_0_f32_l4_lm mul norm relu diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 79d2148744..2ec896fd0e 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -408,6 +408,7 @@ struct ggml_backend_opencl_context { cl_program program_mul_mv_id_mxfp4_f32_flat; cl_program program_mul_mm_f32_f32_l4_lm; cl_program program_mul_mm_f16_f32_l4_lm; + cl_program program_mul_mm_q8_0_f32_l4_lm; cl_kernel kernel_add, kernel_add_row, kernel_add_f16, kernel_add_row_f16; cl_kernel kernel_mul, kernel_mul_row, kernel_mul_f16, kernel_mul_row_f16; @@ -480,6 +481,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_mul_mv_id_mxfp4_f32_flat; cl_kernel kernel_mul_mm_f32_f32_l4_lm; cl_kernel kernel_mul_mm_f16_f32_l4_lm; + cl_kernel kernel_mul_mm_q8_0_f32_l4_lm; std::vector profiling_info; @@ -1191,6 +1193,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // mul_mm_q8_0_f32_l4_lm + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "mul_mm_q8_0_f32_l4_lm.cl.h" + }; +#else + const std::string kernel_src = read_file("mul_mm_q8_0_f32_l4_lm.cl"); +#endif + backend_ctx->program_mul_mm_q8_0_f32_l4_lm = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + + CL_CHECK((backend_ctx->kernel_mul_mm_q8_0_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_q8_0_f32_l4_lm, "kernel_mul_mm_q8_0_f32_l4_lm", &err), err)); + GGML_LOG_CONT("."); + } + // mul { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -2348,8 +2366,13 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) { svm_caps & CL_DEVICE_SVM_ATOMICS ? "true" : "false"); if (opencl_c_version.major >= 3) { + // Assume it is not available for 3.0, since it is optional in 3.0. + // If compiling against 3.0, then we can query. + backend_ctx->non_uniform_workgroups = false; +#if CL_TARGET_OPENCL_VERSION >= 300 CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_NON_UNIFORM_WORK_GROUP_SUPPORT, sizeof(cl_bool), &backend_ctx->non_uniform_workgroups, 0)); +#endif } else { GGML_ASSERT(opencl_c_version.major == 2); // Non-uniform workgroup sizes is mandatory feature in v2.x. @@ -2681,7 +2704,7 @@ static bool ggml_opencl_can_fuse(const struct ggml_cgraph * cgraph, int node_idx // if rms_norm is the B operand, then we don't handle broadcast if (rms_norm == mul->src[1] && - !ggml_are_same_shape(mul->src[0], rms_norm->src[1])) { + !ggml_are_same_shape(mul->src[0], rms_norm)) { return false; } @@ -6956,6 +6979,44 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); return; } + case GGML_TYPE_Q8_0: { + if (ne11 < 32) { + break; + } + kernel = backend_ctx->kernel_mul_mm_q8_0_f32_l4_lm; + nth0 = 128; // calculated as (BM*BN)/(TM*TN) + + int batch_stride_a = ne00*ne01; + int batch_stride_b = ne10*ne11; + int batch_stride_d = ne0*ne1; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q8_0->q)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q8_0->d)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne11)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne10)); // stride_a + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10)); // stride_b + CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne01)); // stride_d + CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &batch_stride_a)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &batch_stride_b)); + CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &batch_stride_d)); + CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2)); + CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3)); + + // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed. + size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13}; + size_t local_work_size[] = {(size_t)nth0, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + return; + } default: break; } diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl index 9c0bab135a..a6d7479037 100644 --- a/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl +++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl @@ -4,6 +4,7 @@ #define ACC_TYPE4 float4 #define DATA_TYPE float #define DATA_TYPE4 float4 +#define MASK_DATA_TYPE half #define CONVERT_ACC4(x) (x) #define CONVERT_DATA4(x) (x) @@ -148,7 +149,7 @@ __kernel void flash_attn_f32( if (k_row1 >= n_kv) score1 = -INFINITY; if (mask_base != NULL) { - const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base + my_query_row * mask_nb1); + const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1); if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0]; if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1]; } @@ -281,7 +282,7 @@ __kernel void flash_attn_f32_q1( } ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale; if (mask_base != NULL) { - const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base); + const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base); score += slope * (ACC_TYPE)mask_ptr[k_idx]; } if (logit_softcap > 0.0f) { @@ -317,7 +318,7 @@ __kernel void flash_attn_f32_q1( } ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale; if (mask_base != NULL) { - const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base); + const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base); score += slope * (ACC_TYPE)mask_ptr[k_idx]; } if (logit_softcap > 0.0f) { diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl index 9599a0e157..1a1bfe144f 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl @@ -79,19 +79,33 @@ kernel void kernel_mul_mm_f16_f32_l4_lm( for (int block = 0; block < ne00; block += BK) { for (int l = 0; l < BM; l += loadstride_a) { + if (loadc_a + l < ne01) { const int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a; - buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = src0[idx].s0; - buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = src0[idx].s1; - buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = src0[idx].s2; - buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = src0[idx].s3; + buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = src0[idx].s0; + buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = src0[idx].s1; + buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = src0[idx].s2; + buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = src0[idx].s3; + } else { + buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = 0.0h; + buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = 0.0h; + buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = 0.0h; + buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = 0.0h; + } } for (int l = 0; l < BN; l += loadstride_b) { - const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b; - buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0; - buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1; - buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2; - buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3; + if (loadc_b + l < ne11) { + const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b; + buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0; + buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1; + buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2; + buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3; + } else { + buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0h; + buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0h; + buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0h; + buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0h; + } } barrier(CLK_LOCAL_MEM_FENCE); diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl index 58c5178e39..39a5d4868f 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl @@ -79,19 +79,33 @@ kernel void kernel_mul_mm_f32_f32_l4_lm( for (int block = 0; block < ne00; block += BK) { for (int l = 0; l < BM; l += loadstride_a) { - const int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a; - buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = src0[idx].s0; - buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = src0[idx].s1; - buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = src0[idx].s2; - buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = src0[idx].s3; + if (loadc_a + l < ne01) { + const int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a; + buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = src0[idx].s0; + buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = src0[idx].s1; + buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = src0[idx].s2; + buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = src0[idx].s3; + } else { + buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = 0.0f; + buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = 0.0f; + buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = 0.0f; + buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = 0.0f; + } } for (int l = 0; l < BN; l += loadstride_b) { - const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b; - buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0; - buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1; - buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2; - buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3; + if (loadc_b + l < ne11) { + const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b; + buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0; + buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1; + buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2; + buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3; + } else { + buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f; + buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f; + buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f; + buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f; + } } barrier(CLK_LOCAL_MEM_FENCE); diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl new file mode 100644 index 0000000000..fd47e8a89d --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl @@ -0,0 +1,154 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +#define LOAD_VEC_A 4 +#define LOAD_VEC_B 4 + +#define BM 64 +#define BN 64 +#define BK 32 +#define TM 4 +#define TN 8 + +kernel void kernel_mul_mm_q8_0_f32_l4_lm( + global char4 * src0_q, + global half * src0_d, + global float4 * src1, + ulong offset1, + global float * dst, + ulong offsetd, + + int ne00, + int ne01, + int ne02, + int ne11, + int ne12, + + int stride_a, + int stride_b, + int stride_d, + + int batch_stride_a, + int batch_stride_b, + int batch_stride_d, + + int r2, + int r3 +) { + src1 = (global float4*)((global char*)src1 + offset1); + dst = (global float *)((global char*)dst + offsetd); + + local float buf_a[BM * BK]; + local float buf_b[BN * BK]; + + const int batch_idx = get_global_id(2); + + const int i13 = batch_idx / ne12; + const int i12 = batch_idx % ne12; + + const int i03 = i13 / r3; + const int i02 = i12 / r2; + + const int batch_idx_a = i03 * ne02 + i02; + + const int ir = get_group_id(0); + const int ic = get_group_id(1); + + const int tid = get_local_id(0); + const int th_r = tid % (BM / TM); + const int th_c = tid / (BM / TM); + + const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A); + const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A); + const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B); + const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B); + + const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK; + const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK; + + int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A; + int pos_b = (batch_idx * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B; + + float sums[TM * TN]; + float cache_a[TM]; + float cache_b[TN]; + + for (int i = 0; i < TM * TN; i++) { + sums[i] = 0.0f; + } + + for (int block = 0; block < ne00; block += BK) { + for (int l = 0; l < BM; l += loadstride_a) { + if (loadc_a + l < ne01) { + int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a; + int ib = idx / 8; + int iqs = idx % 8; + + float d = (float)src0_d[ib]; + global char4 * qs = src0_q + ib*8 + iqs; + char4 q = *qs; + float4 v = convert_float4(q)*d; + + buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = v.s0; + buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = v.s1; + buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = v.s2; + buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = v.s3; + } else { + buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = 0.0f; + buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = 0.0f; + buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = 0.0f; + buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = 0.0f; + } + } + + for (int l = 0; l < BN; l += loadstride_b) { + if (loadc_b + l < ne11) { + int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b; + buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0; + buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1; + buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2; + buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3; + } else { + buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f; + buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f; + buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f; + buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f; + } + } + + barrier(CLK_LOCAL_MEM_FENCE); + + pos_a += BK / LOAD_VEC_A; + pos_b += BK / LOAD_VEC_B; + + for (int i = 0; i < BK; i++) { + for (int j = 0; j < TM; j++) { + cache_a[j] = buf_a[(i) * BM + th_r * TM + j]; + } + + for (int j = 0; j < TN; j++) { + cache_b[j] = buf_b[(i) * BN + th_c * TN + j]; + } + + for (int cc = 0; cc < TN; cc++) { + for (int cr = 0; cr < TM; cr++) { + const int sums_idx = cc*TM + cr; + sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]); + } + } + } + barrier(CLK_LOCAL_MEM_FENCE); + } + + const int dr = ir * BM + th_r * TM; + const int dc = ic * BN + th_c * TN; + + const int offsets = batch_idx * batch_stride_d; + + for (int cc = 0; cc < TN; cc++) { + for (int cr = 0; cr < TM; cr++) { + if (dr + cr < ne01 && dc + cc < ne11) { + dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr]; + } + } + } +} diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp index 410a67b019..6ff3215d5a 100644 --- a/ggml/src/ggml-sycl/backend.hpp +++ b/ggml/src/ggml-sycl/backend.hpp @@ -18,6 +18,7 @@ #include "concat.hpp" #include "conv.hpp" #include "convert.hpp" +#include "count-equal.hpp" #include "cpy.hpp" #include "dequantize.hpp" #include "dmmv.hpp" @@ -28,6 +29,7 @@ #include "mmvq.hpp" #include "norm.hpp" #include "outprod.hpp" +#include "pad.hpp" #include "quantize.hpp" #include "quants.hpp" #include "rope.hpp" diff --git a/ggml/src/ggml-sycl/binbcast.cpp b/ggml/src/ggml-sycl/binbcast.cpp index e0a1de0f32..0a3883ae1e 100644 --- a/ggml/src/ggml-sycl/binbcast.cpp +++ b/ggml/src/ggml-sycl/binbcast.cpp @@ -303,10 +303,6 @@ inline void ggml_sycl_op_sub(ggml_backend_sycl_context & ctx, ggml_tensor *dst) ggml_sycl_op_bin_bcast>(ctx, dst->src[0], dst->src[1], dst); } -inline void ggml_sycl_op_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - ggml_sycl_op_bin_bcast>(ctx, dst->src[0], dst->src[1], dst); -} - inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { ggml_sycl_op_bin_bcast>(ctx, dst->src[0], dst->src[1], dst); @@ -332,11 +328,6 @@ void ggml_sycl_sub(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_op_sub(ctx, dst); } -void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); - ggml_sycl_op_count_equal(ctx, dst); -} - void ggml_sycl_mul(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); ggml_sycl_op_mul(ctx, dst); diff --git a/ggml/src/ggml-sycl/binbcast.hpp b/ggml/src/ggml-sycl/binbcast.hpp index 34c4064f52..9cce0f053a 100644 --- a/ggml/src/ggml-sycl/binbcast.hpp +++ b/ggml/src/ggml-sycl/binbcast.hpp @@ -16,12 +16,6 @@ static __dpct_inline__ float op_sub(const float a, const float b) { return a - b; } -static __dpct_inline__ float op_count_equal(const float a, const float b) { - return (a == b) ? 1.0f : 0.0f; -} - -void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst); - static __dpct_inline__ float op_mul(const float a, const float b) { return a * b; } diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index d66d7ade90..338fa08cda 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -195,7 +195,8 @@ struct optimize_feature { struct sycl_device_info { int cc; // compute capability - // int nsm; // number of streaming multiprocessors + int nsm; // number of streaming multiprocessors (CUDA) maps to the maximum + // number of compute units on a SYCL device. // size_t smpb; // max. shared memory per block size_t smpbo; // max. shared memory per block (with opt-in) bool vmm; // virtual memory support diff --git a/ggml/src/ggml-sycl/count-equal.cpp b/ggml/src/ggml-sycl/count-equal.cpp new file mode 100644 index 0000000000..b0a8b4820d --- /dev/null +++ b/ggml/src/ggml-sycl/count-equal.cpp @@ -0,0 +1,79 @@ +#include "count-equal.hpp" + +#include + +template +static void count_equal(const T *__restrict__ x, const T *__restrict__ y, + int64_t *__restrict__ dst, const int64_t dk, + const int64_t k) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int64_t i0 = (int64_t)item_ct1.get_group(2) * dk; + const int64_t i1 = sycl::min(i0 + dk, k); + + int nequal = 0; + + for (int64_t i = i0 + item_ct1.get_local_id(2); i < i1; i += WARP_SIZE) { + const T xi = x[i]; + const T yi = y[i]; + nequal += xi == yi; + } + + nequal = warp_reduce_sum(nequal); + + if (item_ct1.get_local_id(2) != 0) { + return; + } + + dpct::atomic_fetch_add( + (int *)dst, nequal); +} + +void ggml_sycl_count_equal(ggml_backend_sycl_context &ctx, ggml_tensor *dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == src1->type); + GGML_ASSERT( dst->type == GGML_TYPE_I64); + + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + int64_t * dst_d = (int64_t *) dst->data; + + dpct::queue_ptr stream = ctx.stream(); + const int id = get_current_device_id(); + const int nsm = ggml_sycl_info().devices[id].nsm; + + const int64_t ne = ggml_nelements(src0); + GGML_ASSERT(ne < (1 << 30) && "atomicAdd implementation only supports int"); + const int64_t dne = + GGML_PAD((ne + 4 * nsm - 1) / (4 * nsm), SYCL_COUNT_EQUAL_CHUNK_SIZE); + + SYCL_CHECK(CHECK_TRY_ERROR(stream->memset(dst_d, 0, ggml_nbytes(dst)))); + + const dpct::dim3 block_dims(WARP_SIZE, 1, 1); + const dpct::dim3 block_nums( + std::min((int64_t)4 * nsm, (ne + SYCL_COUNT_EQUAL_CHUNK_SIZE - 1) / + SYCL_COUNT_EQUAL_CHUNK_SIZE), + 1, 1); + + switch (src0->type) { + case GGML_TYPE_I32: { + const int *src0_d = (const int *)src0->data; + const int *src1_d = (const int *)src1->data; + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + count_equal(src0_d, src1_d, dst_d, dne, ne); + GGML_UNUSED(item_ct1); + }); + + } break; + default: + GGML_ASSERT(false); + break; + } +} diff --git a/ggml/src/ggml-sycl/count-equal.hpp b/ggml/src/ggml-sycl/count-equal.hpp new file mode 100644 index 0000000000..f7f4fcbd0b --- /dev/null +++ b/ggml/src/ggml-sycl/count-equal.hpp @@ -0,0 +1,9 @@ +#ifndef GGML_SYCL_COUNT_EQUAL_HPP +#define GGML_SYCL_COUNT_EQUAL_HPP +#include "common.hpp" + +#define SYCL_COUNT_EQUAL_CHUNK_SIZE 128 + +void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +#endif //GGML_SYCL_COUNT_EQUAL_HPP diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index c2da2fb48a..aeeb387595 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -328,26 +328,6 @@ static void upscale(const T *x, T *dst, const int nb00, const int nb01, dst[index] = *(const T *)((const char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); } -template -static void pad(const T *x, T *dst, const int ne0, const int ne00, const int ne01, const int ne02, - const sycl::nd_item<3> &item_ct1) { - int nidx = SYCL_LOCAL_ID_CALC(item_ct1, 2); - if (nidx >= ne0) { - return; - } - - // operation - int offset_dst = nidx + item_ct1.get_group(1) * ne0 + - item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); - if (nidx < ne00 && item_ct1.get_group(1) < (size_t) ne01 && item_ct1.get_group(0) < (size_t) ne02) { - int offset_src = nidx + item_ct1.get_group(1) * ne00 + - item_ct1.get_group(0) * ne00 * ne01; - dst[offset_dst] = x[offset_src]; - } else { - dst[offset_dst] = static_cast(0.0f); - } -} - template static void clamp(const T * x, T * dst, const float min, const float max, const int k, const sycl::nd_item<1> &item_ct1) { @@ -431,18 +411,6 @@ static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01, }); } -template -static void pad_sycl(const T *x, T *dst, const int ne00, - const int ne01, const int ne02, const int ne0, - const int ne1, const int ne2, queue_ptr stream) { - int num_blocks = ceil_div(ne0, 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); }); -} - template static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) { #if defined (GGML_SYCL_F16) @@ -596,40 +564,6 @@ static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx } } -template -static inline void dispatch_ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) { -#if defined (GGML_SYCL_F16) - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); -#else - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); -#endif - GGML_ASSERT(dst->src[0]->type == dst->type); - GGML_ASSERT(dst->src[0]->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors - dpct::queue_ptr main_stream = ctx.stream(); - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); - switch (dst->type) { -#if defined (GGML_SYCL_F16) - case GGML_TYPE_F16: - { - auto data_pts = cast_data(dst); - kernel_invoker(data_pts.src, data_pts.dst, (int)dst->src[0]->ne[0], (int)dst->src[0]->ne[1], (int)dst->src[0]->ne[2], (int)dst->ne[0], - (int)dst->ne[1], (int)dst->ne[2], main_stream, std::forward(args)...); - break; - } -#endif - case GGML_TYPE_F32: - { - auto data_pts = cast_data(dst); - kernel_invoker(data_pts.src, data_pts.dst, (int)dst->src[0]->ne[0], (int)dst->src[0]->ne[1], (int)dst->src[0]->ne[2], (int)dst->ne[0], - (int)dst->ne[1], (int)dst->ne[2], main_stream, std::forward(args)...); - break; - } - default: - GGML_ABORT("GGML tensor type not supported!\n"); - } -} } // namespace ggml_sycl_detail @@ -919,14 +853,6 @@ static inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, ggml_te }); } -static inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - ggml_sycl_detail::dispatch_ggml_sycl_op_pad(ctx, dst, - [](const auto* src, auto* dst_ptr, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, - queue_ptr stream) { - ggml_sycl_detail::pad_sycl(src, dst_ptr, ne00, ne01, ne02, ne0, ne1, ne2, stream); - }); -} - static inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { float min_val; float max_val; @@ -1119,10 +1045,6 @@ void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_op_upscale(ctx, dst); } -void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { - scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); - ggml_sycl_op_pad(ctx, dst); -} void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index 50749e87d7..4347431728 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -67,8 +67,6 @@ void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst); -void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst); - void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_sgn(ggml_backend_sycl_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index e4cc3c8ed8..f3407a813d 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -85,9 +85,11 @@ static ggml_sycl_device_info ggml_sycl_init() { info.devices[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + info.devices[i].nsm = prop.get_max_compute_units(); info.devices[i].opt_feature.reorder = device.ext_oneapi_architecture_is(syclex::arch_category::intel_gpu); - info.max_work_group_sizes[i] = prop.get_max_work_group_size(); info.devices[i].smpbo = prop.get_local_mem_size(); + + info.max_work_group_sizes[i] = prop.get_max_work_group_size(); } for (int id = 0; id < info.device_count; ++id) { @@ -1512,60 +1514,70 @@ static inline void ggml_sycl_swap(T & a, T & b) { template __dpct_inline__ static void k_argsort_f32_i32(const float *x, int *dst, const int ncols, int ncols_pad, - const sycl::nd_item<3> &item_ct1, uint8_t *dpct_local) { + const int tasks_per_thread, const sycl::nd_item<3> &item_ct1, + uint8_t *dpct_local) { // bitonic sort - int col = item_ct1.get_local_id(2); + int col_index = item_ct1.get_local_id(2); int row = item_ct1.get_group(1); - if (col >= ncols_pad) { - return; + for (int i = 0; i < tasks_per_thread; i++) { + int col = col_index * tasks_per_thread + i; + if (col >= ncols_pad) { + return; + } } const float * x_row = x + row * ncols; auto dst_row = (int *)dpct_local; // initialize indices - dst_row[col] = col; + for (int i=0;i 0; j /= 2) { - int ixj = col ^ j; - if (ixj > col) { - if ((col & k) == 0) { - if (dst_row[col] >= ncols || - (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] > x_row[dst_row[ixj]] : - x_row[dst_row[col]] < x_row[dst_row[ixj]])) - ) { - ggml_sycl_swap(dst_row[col], dst_row[ixj]); - } - } else { - if (dst_row[ixj] >= ncols || - (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] < x_row[dst_row[ixj]] : - x_row[dst_row[col]] > x_row[dst_row[ixj]])) - ) { - ggml_sycl_swap(dst_row[col], dst_row[ixj]); + for (int i = 0; i < tasks_per_thread; i++) { + int col = col_index * tasks_per_thread + i; + int ixj = col ^ j; + if (ixj > col) { + if ((col & k) == 0) { + if (dst_row[col] >= ncols || + (dst_row[ixj] < ncols && + (order == GGML_SORT_ORDER_ASC + ? x_row[dst_row[col]] > x_row[dst_row[ixj]] + : x_row[dst_row[col]] < + x_row[dst_row[ixj]]))) { + ggml_sycl_swap(dst_row[col], dst_row[ixj]); + } + } else { + if (dst_row[ixj] >= ncols || + (dst_row[col] < ncols && + (order == GGML_SORT_ORDER_ASC + ? x_row[dst_row[col]] < x_row[dst_row[ixj]] + : x_row[dst_row[col]] > + x_row[dst_row[ixj]]))) { + ggml_sycl_swap(dst_row[col], dst_row[ixj]); + } } } + item_ct1.barrier(sycl::access::fence_space::local_space); } - /* - DPCT1118:1: SYCL group functions and algorithms must be encountered - in converged control flow. You may need to adjust the code. - */ - item_ct1.barrier(sycl::access::fence_space::local_space); } } // copy the result to dst without the padding - if (col < ncols) { - dst[row * ncols + col] = dst_row[col]; + for (int i = 0; i < tasks_per_thread; i++) { + int col = col_index * tasks_per_thread + i; + if (col < ncols) { + dst[row * ncols + col] = dst_row[col]; + } } } - static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past, const sycl::nd_item<3> &item_ct1) { const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) + @@ -1738,11 +1750,20 @@ static int next_power_of_2(int x) { static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, const int nrows, ggml_sort_order order, - queue_ptr stream) { + queue_ptr stream, int device) { // bitonic sort requires ncols to be power of 2 const int ncols_pad = next_power_of_2(ncols); - const sycl::range<3> block_dims(1, 1, ncols_pad); + int nth = 1; + int max_block_size = ggml_sycl_info().max_work_group_sizes[device]; + while (nth < ncols_pad && nth < max_block_size) + nth *= 2; + if (nth > max_block_size) + nth = max_block_size; + + const int tasks_per_thread = ncols_pad / nth; + + const sycl::range<3> block_dims(1, 1, nth); const sycl::range<3> block_nums(1, nrows, 1); const size_t shared_mem = ncols_pad * sizeof(int); @@ -1755,8 +1776,9 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, 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() + x, dst, ncols, ncols_pad, tasks_per_thread, item_ct1, + dpct_local_acc_ct1 + .get_multi_ptr() .get()); }); }); @@ -1769,8 +1791,9 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, 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() + x, dst, ncols, ncols_pad, tasks_per_thread, item_ct1, + dpct_local_acc_ct1 + .get_multi_ptr() .get()); }); }); @@ -2128,6 +2151,30 @@ inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor * sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream); } +inline void ggml_sycl_op_mean(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + + const float * src0_dd = static_cast(dst->src[0]->data); + float * dst_dd = static_cast(dst->data); + + const int64_t ncols = dst->src[0]->ne[0]; + const int64_t nrows = ggml_nrows(dst->src[0]); + + sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream); + + main_stream->parallel_for( + sycl::range<1>(nrows), + [=](sycl::id<1> row) { + dst_dd[row] /= ncols; + } + ); +} + + inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_I32); @@ -2142,7 +2189,8 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - argsort_f32_i32_sycl(src0_dd, (int *) dst_dd, ncols, nrows, order, main_stream); + argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, + main_stream, ctx.device); } inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { @@ -3511,6 +3559,12 @@ static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor * ds ggml_sycl_op_sum_rows(ctx, dst); } +static void ggml_sycl_mean(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + GGML_ASSERT(ggml_is_contiguous(dst->src[0])); + ggml_sycl_op_mean(ctx, dst); +} + static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); GGML_ASSERT(ggml_is_contiguous(dst->src[0])); @@ -3760,6 +3814,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_SUM_ROWS: ggml_sycl_sum_rows(ctx, dst); break; + case GGML_OP_MEAN: + ggml_sycl_mean(ctx, dst); + break; case GGML_OP_ARGSORT: ggml_sycl_argsort(ctx, dst); break; @@ -4407,14 +4464,14 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST; case GGML_OP_SUM: case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: case GGML_OP_ARGSORT: return ggml_is_contiguous(op->src[0]); case GGML_OP_POOL_2D: case GGML_OP_ACC: return true; case GGML_OP_PAD: - return (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) && - (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0); + return ggml_is_contiguous(op->src[0]); case GGML_OP_LEAKY_RELU: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_RWKV_WKV6: diff --git a/ggml/src/ggml-sycl/pad.cpp b/ggml/src/ggml-sycl/pad.cpp new file mode 100644 index 0000000000..413712c584 --- /dev/null +++ b/ggml/src/ggml-sycl/pad.cpp @@ -0,0 +1,97 @@ +// +// MIT license +// Copyright (C) 2025 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +//#include "common.hpp" +#include "pad.hpp" + +static void pad_f32(const float * src, float * dst, + const int lp0, const int rp0, const int lp1, const int rp1, + const int lp2, const int rp2, const int lp3, const int rp3, + const int ne0, const int ne1, const int ne2, const int ne3) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + int i0 = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + int i1 = item_ct1.get_group(1); + int i2 = item_ct1.get_group(0) % ne2; + int i3 = item_ct1.get_group(0) / ne2; + if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + // operation + const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; + if ((i0 >= lp0 && i0 < ne0 - rp0) && + (i1 >= lp1 && i1 < ne1 - rp1) && + (i2 >= lp2 && i2 < ne2 - rp2) && + (i3 >= lp3 && i3 < ne3 - rp3)) { + const int64_t i00 = i0 - lp0; + const int64_t i01 = i1 - lp1; + const int64_t i02 = i2 - lp2; + const int64_t i03 = i3 - lp3; + const int64_t ne02 = ne2 - lp2 - rp2; + const int64_t ne01 = ne1 - lp1 - rp1; + const int64_t ne00 = ne0 - lp0 - rp0; + + const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + + i02 * (ne00 * ne01) + i01 * ne00 + i00; + + dst[dst_idx] = src[src_idx]; + } else { + dst[dst_idx] = 0.0f; + } +} + +static void pad_f32_sycl(const float *src, float *dst, const int lp0, + const int rp0, const int lp1, const int rp1, + const int lp2, const int rp2, const int lp3, + const int rp3, const int ne0, const int ne1, + const int ne2, const int ne3, + dpct::queue_ptr stream) { + int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; + dpct::dim3 gridDim(num_blocks, ne1, ne2 * ne3); + 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_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1, + ne2, ne3); + }); +} + +void ggml_sycl_op_pad(ggml_backend_sycl_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; + dpct::queue_ptr stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(src0)); + + const int32_t lp0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t rp0 = ((const int32_t*)(dst->op_params))[1]; + const int32_t lp1 = ((const int32_t*)(dst->op_params))[2]; + const int32_t rp1 = ((const int32_t*)(dst->op_params))[3]; + const int32_t lp2 = ((const int32_t*)(dst->op_params))[4]; + const int32_t rp2 = ((const int32_t*)(dst->op_params))[5]; + const int32_t lp3 = ((const int32_t*)(dst->op_params))[6]; + const int32_t rp3 = ((const int32_t*)(dst->op_params))[7]; + + pad_f32_sycl(src0_d, dst_d, + lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream); +} + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_pad(ctx, dst); +} diff --git a/ggml/src/ggml-sycl/pad.hpp b/ggml/src/ggml-sycl/pad.hpp new file mode 100644 index 0000000000..b099e9b73a --- /dev/null +++ b/ggml/src/ggml-sycl/pad.hpp @@ -0,0 +1,24 @@ +// +// MIT license +// Copyright (C) 2025 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_PAD_HPP +#define GGML_SYCL_PAD_HPP + +#include "common.hpp" + +#define SYCL_PAD_BLOCK_SIZE 256 + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +#endif // GGML_SYCL_PAD_HPP diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt index 83a83887b5..de01336cd3 100644 --- a/ggml/src/ggml-vulkan/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/CMakeLists.txt @@ -1,9 +1,18 @@ cmake_minimum_required(VERSION 3.19) cmake_policy(SET CMP0114 NEW) cmake_policy(SET CMP0116 NEW) +if (POLICY CMP0147) + # Parallel build custom build steps + cmake_policy(SET CMP0147 NEW) +endif() find_package(Vulkan COMPONENTS glslc REQUIRED) +if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") + # Parallel build object files + add_definitions(/MP) +endif() + function(detect_host_compiler) if (CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows") find_program(HOST_C_COMPILER NAMES cl gcc clang NO_CMAKE_FIND_ROOT_PATH) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 3cd89c7116..1674dc66ab 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -2649,11 +2649,13 @@ static void ggml_vk_load_shaders(vk_device& device) { } \ } + CREATE_FA(GGML_TYPE_F32, f32, FA_SCALAR, ) CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, ) CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_SCALAR, ) CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_SCALAR, ) #if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) if (device->coopmat1_fa_support) { + CREATE_FA(GGML_TYPE_F32, f32, FA_COOPMAT1, _cm1) CREATE_FA(GGML_TYPE_F16, f16, FA_COOPMAT1, _cm1) CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_COOPMAT1, _cm1) CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_COOPMAT1, _cm1) @@ -2661,6 +2663,7 @@ static void ggml_vk_load_shaders(vk_device& device) { #endif #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) if (device->coopmat2) { + CREATE_FA(GGML_TYPE_F32, f32, FA_COOPMAT2, _cm2) CREATE_FA(GGML_TYPE_F16, f16, FA_COOPMAT2, _cm2) CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_COOPMAT2, _cm2) CREATE_FA(GGML_TYPE_Q4_1, q4_1, FA_COOPMAT2, _cm2) @@ -7457,8 +7460,16 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx } const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type)); - const uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type)); - const uint32_t v_stride = (uint32_t)(nbv1 / ggml_type_size(v->type)); + uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type)); + uint32_t v_stride = (uint32_t)(nbv1 / ggml_type_size(v->type)); + + // For F32, the shader treats it as a block of size 4 (for vec4 loads) + if (k->type == GGML_TYPE_F32) { + k_stride /= 4; + } + if (v->type == GGML_TYPE_F32) { + v_stride /= 4; + } uint32_t alignment = fa_align(path, HSK, HSV, k->type, small_rows); bool aligned = (KV % alignment) == 0 && @@ -12660,6 +12671,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm } switch (op->src[1]->type) { case GGML_TYPE_F16: + case GGML_TYPE_F32: case GGML_TYPE_Q4_0: case GGML_TYPE_Q8_0: // supported in scalar and coopmat2 paths diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl index 6a5bb4574d..67baedf7c6 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl @@ -1,6 +1,18 @@ #include "types.glsl" +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufF32 { + vec4 block; +}; + +float16_t dequantFuncF32(const in decodeBufF32 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const vec4 v = bl.block; + const uint idx = coordInBlock[1]; + const f16vec4 vf16 = f16vec4(v); + return vf16[idx]; +} + layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ4_0 { block_q4_0_packed16 block; }; @@ -717,4 +729,6 @@ float16_t dequantFuncMXFP4(const in decodeBufMXFP4 bl, const in uint blockCoords #define dequantFuncA dequantFuncIQ4_NL #elif defined(DATA_A_MXFP4) #define dequantFuncA dequantFuncMXFP4 +#elif defined(DATA_A_F32) +#define dequantFuncA dequantFuncF32 #endif diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl index 9b1f153bf7..eb93903c46 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl @@ -64,13 +64,31 @@ layout (binding = 4) readonly buffer S {float data_s[];}; layout (binding = 5) writeonly buffer O {D_TYPE data_o[];}; -#if defined(A_TYPE_PACKED16) #define BINDING_IDX_K 0 #define BINDING_IDX_V 1 +#if defined(DATA_A_F32) +layout (binding = 1) readonly buffer K_PACKED {vec4 k_data_packed[];} k_packed; +layout (binding = 2) readonly buffer V_PACKED {vec4 v_data_packed[];} v_packed; +#elif defined(A_TYPE_PACKED16) layout (binding = 1) readonly buffer K_PACKED16 {A_TYPE_PACKED16 k_data_packed16[];} k_packed; layout (binding = 2) readonly buffer V_PACKED16 {A_TYPE_PACKED16 v_data_packed16[];} v_packed; #endif +#if defined(DATA_A_F32) +#undef BLOCK_SIZE +#define BLOCK_SIZE 4 +#define BLOCK_BYTE_SIZE 16 + +vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { + // iqs is currently always zero in the flash attention shaders + if (binding_idx == BINDING_IDX_K) { + return k_packed.k_data_packed[a_offset + ib]; + } else { + return v_packed.v_data_packed[a_offset + ib]; + } +} +#endif + #if defined(DATA_A_Q4_0) #define BLOCK_BYTE_SIZE 18 diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp index 85400ac5fc..a20788c4b5 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp @@ -313,12 +313,12 @@ void main() { sums[i] = coopmat(0.0f); } #else - ACC_TYPE sums[WMITER * TM * WNITER * TN]; + ACC_TYPE_VEC2 sums[WMITER * TM * WNITER * TN/2]; FLOAT_TYPE_VEC2 cache_a[WMITER * TM]; - FLOAT_TYPE_VEC2 cache_b[TN]; + FLOAT_TYPE_VEC2 cache_b; - [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) { - sums[i] = ACC_TYPE(0.0f); + [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN/2; i++) { + sums[i] = ACC_TYPE_VEC2(0.0f, 0.0f); } #endif @@ -360,20 +360,22 @@ void main() { cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + i]; } } - [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { - [[unroll]] for (uint j = 0; j < TN; j++) { - cache_b[j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * SHMEM_STRIDE + i]; - } - [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { - [[unroll]] for (uint cc = 0; cc < TN; cc++) { - [[unroll]] for (uint cr = 0; cr < TM; cr++) { - const uint sums_idx = (wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr; - sums[sums_idx] = fma(ACC_TYPE(cache_a[wsir * TM + cr].x), ACC_TYPE(cache_b[cc].x), fma(ACC_TYPE(cache_a[wsir * TM + cr].y), ACC_TYPE(cache_b[cc].y), sums[sums_idx])); + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint cc = 0; cc < TN; cc++) { + cache_b = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + i]; + + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + [[unroll]] for (uint cr = 0; cr < TM / 2; cr++) { + // [WNITER][TN][WMITER][TM / 2] -> [wsic][cc][wsir][cr] + const uint sums_idx = (wsic * TN + cc) * WMITER * (TM / 2) + wsir * (TM / 2) + cr; + sums[sums_idx].x = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].y), ACC_TYPE(cache_b.y), sums[sums_idx].x)); + sums[sums_idx].y = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].y), ACC_TYPE(cache_b.y), sums[sums_idx].y)); } } } } + } #endif @@ -388,8 +390,9 @@ void main() { } } #else - [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) { - sums[i] = clamp(sums[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); + [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN/2; i++) { + sums[i].x = clamp(sums[i].x, -ACC_TYPE_MAX, ACC_TYPE_MAX); + sums[i].y = clamp(sums[i].y, -ACC_TYPE_MAX, ACC_TYPE_MAX); } #endif #endif @@ -463,14 +466,21 @@ void main() { const u16vec2 row_idx = row_ids[row_i - ic * BN]; #endif // MUL_MAT_ID - [[unroll]] for (uint cr = 0; cr < TM; cr++) { + [[unroll]] for (uint cr = 0; cr < TM / 2; cr++) { + const uint sums_idx = (wsic * TN + cc) * WMITER * (TM / 2) + wsir * (TM / 2) + cr; #ifdef MUL_MAT_ID - if (dr_warp + cr < p.M) { - data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); + if (dr_warp + 2 * cr < p.M) { + data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + 2 * cr] = D_TYPE(sums[sums_idx].x); + } + if (dr_warp + 2 * cr + 1 < p.M) { + data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + 2 * cr + 1] = D_TYPE(sums[sums_idx].y); } #else - if (dr_warp + cr < p.M && dc_warp + cc < p.N) { - data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); + if (dr_warp + 2 * cr < p.M && dc_warp + cc < p.N) { + data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + 2 * cr] = D_TYPE(sums[sums_idx].x); + } + if (dr_warp + 2 * cr + 1 < p.M && dc_warp + cc < p.N) { + data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + 2 * cr + 1] = D_TYPE(sums[sums_idx].y); } #endif // MUL_MAT_ID } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index f0cc24ff31..184f3f3a7d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -611,9 +611,6 @@ void process_shaders() { } for (const auto& tname : type_names) { - if (tname == "f32") { - continue; - } if (tname == "bf16") continue; #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) @@ -630,7 +627,7 @@ void process_shaders() { if (tname == "f16") { string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp", merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"COOPMAT", "1"}}), true, true, false, f16acc); - } else if (tname == "q4_0" || tname == "q8_0") { + } else if (tname == "q4_0" || tname == "q8_0" || tname == "f32") { std::string data_a_key = "DATA_A_" + to_uppercase(tname); string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp", merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname)}, {"COOPMAT", "1"}}), true, true, false, f16acc); @@ -639,7 +636,7 @@ void process_shaders() { if (tname == "f16") { string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}}), true, false, false, f16acc); - } else if (tname == "q4_0" || tname == "q8_0") { + } else if (tname == "q4_0" || tname == "q8_0" || tname == "f32") { std::string data_a_key = "DATA_A_" + to_uppercase(tname); string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, false, f16acc); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 2bce1375ba..86f1c31afd 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1144,9 +1144,13 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = { "EXP", "GELU_ERF", "XIELU", + "FLOOR", + "CEIL", + "ROUND", + "TRUNC", }; -static_assert(GGML_UNARY_OP_COUNT == 16, "GGML_UNARY_OP_COUNT != 16"); +static_assert(GGML_UNARY_OP_COUNT == 20, "GGML_UNARY_OP_COUNT != 20"); static const char * GGML_GLU_OP_NAME[GGML_GLU_OP_COUNT] = { "REGLU", @@ -2749,6 +2753,62 @@ static struct ggml_tensor * ggml_glu_impl( return result; } +// ggml_floor + +struct ggml_tensor * ggml_floor( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_FLOOR); +} + +struct ggml_tensor * ggml_floor_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_FLOOR); +} + +// ggml_ceil + +struct ggml_tensor * ggml_ceil( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_CEIL); +} + +struct ggml_tensor * ggml_ceil_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_CEIL); +} + +//ggml_round + +struct ggml_tensor * ggml_round( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_ROUND); +} + +struct ggml_tensor * ggml_round_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ROUND); +} + +//ggml_trunc + +struct ggml_tensor * ggml_trunc( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_TRUNC); +} + +struct ggml_tensor * ggml_trunc_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TRUNC); +} + struct ggml_tensor * ggml_glu( struct ggml_context * ctx, struct ggml_tensor * a, diff --git a/gguf-py/gguf/scripts/gguf_convert_endian.py b/gguf-py/gguf/scripts/gguf_convert_endian.py index 211a3f536a..0bda490a20 100755 --- a/gguf-py/gguf/scripts/gguf_convert_endian.py +++ b/gguf-py/gguf/scripts/gguf_convert_endian.py @@ -91,6 +91,7 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None tensor.tensor_type not in ( gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16, + gguf.GGMLQuantizationType.BF16, ): raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}") logger.info(f"* Preparing to convert from {file_endian} to {order}") @@ -148,6 +149,11 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None # restore old shape in case it's ever used tensor.data.resize(oldshape) + elif tensor.tensor_type == gguf.GGMLQuantizationType.BF16: + # Special case for BF16 + # It is 2-bytes data, but by default view loads it as 1-byte data. + # Change to correct view before byteswapping. + tensor.data.view(dtype=np.uint16).byteswap(inplace=True) else: # Handle other tensor types tensor.data.byteswap(inplace=True) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 869e4dccf0..b7e00b275b 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -5,6 +5,7 @@ #include static const std::map LLM_ARCH_NAMES = { + { LLM_ARCH_CLIP, "clip" }, // dummy, only used by llama-quantize { LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_LLAMA4, "llama4" }, { LLM_ARCH_DECI, "deci" }, @@ -275,6 +276,10 @@ static const std::map LLM_KV_NAMES = { }; static const std::map> LLM_TENSOR_NAMES = { + { + LLM_ARCH_CLIP, + {}, + }, { LLM_ARCH_LLAMA, { diff --git a/src/llama-arch.h b/src/llama-arch.h index c3ae71655b..c41de89859 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -9,6 +9,7 @@ // enum llm_arch { + LLM_ARCH_CLIP, LLM_ARCH_LLAMA, LLM_ARCH_LLAMA4, LLM_ARCH_DECI, diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index a24853c63a..f29a1e98c9 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -261,12 +261,17 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) { } } -static void print_mask(float * data, int64_t n_tokens, int64_t n_kv, int64_t n_swa, llama_swa_type swa_type) { +static void print_mask(const float * data, int64_t n_tokens, int64_t n_kv, int64_t n_swa, llama_swa_type swa_type) { LLAMA_LOG_DEBUG("%s: === Attention mask ===\n", __func__); - const char * swa_type_str = (swa_type == LLAMA_SWA_TYPE_NONE) ? "LLAMA_SWA_TYPE_NONE" : - (swa_type == LLAMA_SWA_TYPE_STANDARD) ? "LLAMA_SWA_TYPE_STANDARD" : - (swa_type == LLAMA_SWA_TYPE_CHUNKED) ? "LLAMA_SWA_TYPE_CHUNKED" : - (swa_type == LLAMA_SWA_TYPE_SYMMETRIC) ? "LLAMA_SWA_TYPE_SYMMETRIC" : "unknown"; + const char * swa_type_str = "unknown"; + + switch (swa_type) { + case LLAMA_SWA_TYPE_NONE: swa_type_str = "LLAMA_SWA_TYPE_NONE"; break; + case LLAMA_SWA_TYPE_STANDARD: swa_type_str = "LLAMA_SWA_TYPE_STANDARD"; break; + case LLAMA_SWA_TYPE_CHUNKED: swa_type_str = "LLAMA_SWA_TYPE_CHUNKED"; break; + case LLAMA_SWA_TYPE_SYMMETRIC: swa_type_str = "LLAMA_SWA_TYPE_SYMMETRIC"; break; + }; + LLAMA_LOG_DEBUG("%s: n_swa : %d, n_kv: %d, swq_type: %s\n", __func__, (int)n_swa, (int)n_kv, swa_type_str); LLAMA_LOG_DEBUG("%s: '0' = can attend, '∞' = masked\n", __func__); LLAMA_LOG_DEBUG("%s: Rows = query tokens, Columns = key/value tokens\n\n", __func__); @@ -295,50 +300,67 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) { const int64_t n_kv = ubatch->n_tokens; const int64_t n_tokens = ubatch->n_tokens; - GGML_ASSERT(kq_mask); - GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer)); + const auto fill_mask = [&](float * data, int n_swa, llama_swa_type swa_type) { + 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]; + const llama_pos p1 = ubatch->pos[i1]; - float * data = (float *) kq_mask->data; + const uint64_t idst = h*(n_kv*n_tokens) + i1*n_kv; - // [TAG_NO_CACHE_ISWA] - GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "TODO: implement"); - - 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 i0 = 0; i0 < n_tokens; ++i0) { - float f = -INFINITY; - - for (int s = 0; s < ubatch->n_seq_id[i0]; ++s) { + for (int i0 = 0; i0 < n_tokens; ++i0) { const llama_seq_id s0 = ubatch->seq_id[i0][0]; + const llama_pos p0 = ubatch->pos[i0]; + // mask different sequences if (s0 != s1) { - continue; // skip different sequences + continue; } - if (cparams.causal_attn && ubatch->pos[i0] > ubatch->pos[i1]) { - continue; // skip future tokens for causal attention + // mask future tokens + if (cparams.causal_attn && p0 > p1) { + continue; } - // TODO: this does not take into account that some layers are SWA and others are note (i.e. iSWA) [TAG_NO_CACHE_ISWA] - //if (hparams.is_masked_swa(ubatch->pos[i0], ubatch->pos[i1])) { - // continue; // skip masked tokens for SWA - //} - - // TODO: reimplement this like in llama_kv_cache_unified - if (hparams.use_alibi) { - f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]); - } else { - f = 0.0f; + // apply SWA if any + if (llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) { + continue; } + + data[idst + i0] = hparams.use_alibi ? -std::abs(p0 - p1) : 0.0f; } - data[h*(n_kv*n_tokens) + i1*n_kv + i0] = f; } } + }; + + { + GGML_ASSERT(self_kq_mask); + GGML_ASSERT(ggml_backend_buffer_is_host(self_kq_mask->buffer)); + + float * data = (float *) self_kq_mask->data; + + std::fill(data, data + ggml_nelements(self_kq_mask), -INFINITY); + + fill_mask(data, 0, LLAMA_SWA_TYPE_NONE); + + if (debug) { + print_mask(data, n_tokens, n_kv, 0, LLAMA_SWA_TYPE_NONE); + } } - if (debug) { - print_mask(data, n_tokens, n_kv, hparams.n_swa, hparams.swa_type); + + if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { + GGML_ASSERT(self_kq_mask_swa); + GGML_ASSERT(ggml_backend_buffer_is_host(self_kq_mask_swa->buffer)); + + float * data = (float *) self_kq_mask_swa->data; + + std::fill(data, data + ggml_nelements(self_kq_mask_swa), -INFINITY); + + fill_mask(data, hparams.n_swa, hparams.swa_type); + + if (debug) { + print_mask(data, n_tokens, n_kv, hparams.n_swa, hparams.swa_type); + } } } @@ -1299,12 +1321,9 @@ ggml_tensor * llm_graph_context::build_attn_mha( k = ggml_permute(ctx0, k, 0, 2, 1, 3); v = ggml_permute(ctx0, v, 0, 2, 1, 3); - const auto n_kv = k->ne[1]; - ggml_tensor * cur; - // TODO: replace hardcoded padding with ggml-provided padding - if (cparams.flash_attn && (n_kv % 256 == 0) && kq_b == nullptr) { + if (cparams.flash_attn && kq_b == nullptr) { GGML_ASSERT(kq_b == nullptr && "Flash attention does not support KQ bias yet"); if (v_trans) { @@ -1419,10 +1438,20 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con auto inp = std::make_unique(hparams, cparams); // note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch - inp->kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1); - ggml_set_input(inp->kq_mask); + inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1); + ggml_set_input(inp->self_kq_mask); - inp->kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->kq_mask, GGML_TYPE_F16) : inp->kq_mask; + inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask; + + if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { + inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1); + ggml_set_input(inp->self_kq_mask_swa); + + inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa; + } else { + inp->self_kq_mask_swa = nullptr; + inp->self_kq_mask_swa_cnv = nullptr; + } return (llm_graph_input_attn_no_cache *) res->add_input(std::move(inp)); } @@ -1447,7 +1476,9 @@ ggml_tensor * llm_graph_context::build_attn( ggml_build_forward_expand(gf, k_cur); ggml_build_forward_expand(gf, v_cur); - const auto & kq_mask = inp->get_kq_mask(); + const bool is_swa = hparams.is_swa(il); + + const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask(); // [TAG_NO_CACHE_PAD] // TODO: if ubatch.equal_seqs() == true, we can split the three tensors below into ubatch.n_seqs_unq streams diff --git a/src/llama-graph.h b/src/llama-graph.h index dc84b79428..d0c3934f67 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -257,10 +257,14 @@ public: void set_input(const llama_ubatch * ubatch) override; - ggml_tensor * get_kq_mask() const { return kq_mask_cnv; } + ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; } + ggml_tensor * get_kq_mask_swa() const { return self_kq_mask_swa_cnv; } - ggml_tensor * kq_mask = nullptr; // F32 [n_tokens, n_batch, 1, 1] - ggml_tensor * kq_mask_cnv = nullptr; // [n_tokens, n_batch, 1, 1] + // n_tokens == n_batch + ggml_tensor * self_kq_mask = nullptr; // F32 [n_tokens, n_batch/n_stream, 1, n_stream] + ggml_tensor * self_kq_mask_cnv = nullptr; // [n_tokens, n_batch/n_stream, 1, n_stream] + ggml_tensor * self_kq_mask_swa = nullptr; // F32 [n_tokens, n_batch/n_stream, 1, n_stream] + ggml_tensor * self_kq_mask_swa_cnv = nullptr; // [n_tokens, n_batch/n_stream, 1, n_stream] const llama_hparams hparams; const llama_cparams cparams; diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index c04ac58f1a..db65d69eab 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -140,7 +140,11 @@ uint32_t llama_hparams::n_embd_s() const { } bool llama_hparams::is_recurrent(uint32_t il) const { - return recurrent_layer_arr[il]; + if (il < n_layer) { + return recurrent_layer_arr[il]; + } + + GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer); } uint32_t llama_hparams::n_pos_per_embd() const { diff --git a/src/llama-model.cpp b/src/llama-model.cpp index a5fe5b749c..5002bd42ff 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -478,7 +478,8 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_GENERAL_NAME, name, false); // everything past this point is not vocab-related - if (hparams.vocab_only) { + // for CLIP models, we only need to load tensors, no hparams + if (hparams.vocab_only || ml.get_arch() == LLM_ARCH_CLIP) { return; } @@ -11358,8 +11359,8 @@ struct llm_build_gemma3n_iswa : public llm_graph_context { } }; -struct llm_build_gemma_embedding_iswa : public llm_graph_context { - llm_build_gemma_embedding_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { +struct llm_build_gemma_embedding : public llm_graph_context { + llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_k; ggml_tensor * cur; @@ -11376,8 +11377,7 @@ struct llm_build_gemma_embedding_iswa : public llm_graph_context { // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); - // TODO: support cacheless iSWA embeddings [TAG_NO_CACHE_ISWA] - auto * inp_attn = build_attn_inp_kv_iswa(); + auto * inp_attn = build_attn_inp_no_cache(); ggml_tensor * inp_out_ids = build_inp_out_ids(); @@ -16313,10 +16313,10 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba { } ggml_tensor * build_layer_ffn( - ggml_tensor * cur, - ggml_tensor * inpSA, - const llama_model & model, - const int il) { + ggml_tensor * cur, + ggml_tensor * inpSA, + const llama_model & model, + const int il) { // For Granite architectures - scale residual if (hparams.f_residual_scale) { @@ -19378,7 +19378,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, case LLM_ARCH_NOMIC_BERT_MOE: case LLM_ARCH_NEO_BERT: case LLM_ARCH_WAVTOKENIZER_DEC: - //case LLM_ARCH_GEMMA_EMBEDDING: // TODO: disabled until the cacheless SWA logic is fixed [TAG_NO_CACHE_ISWA] + case LLM_ARCH_GEMMA_EMBEDDING: case LLM_ARCH_DREAM: case LLM_ARCH_LLADA: case LLM_ARCH_LLADA_MOE: @@ -19671,7 +19671,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { } break; case LLM_ARCH_GEMMA_EMBEDDING: { - llm = std::make_unique(*this, params); + llm = std::make_unique(*this, params); } break; case LLM_ARCH_STARCODER2: { @@ -20014,6 +20014,7 @@ int32_t llama_n_head(const llama_model * model) { llama_rope_type llama_model_rope_type(const llama_model * model) { switch (model->arch) { // these models do not use RoPE + case LLM_ARCH_CLIP: case LLM_ARCH_GPT2: case LLM_ARCH_GPTJ: case LLM_ARCH_MPT: diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index b8391a4f2c..91b127789c 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -1995,6 +1995,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: }); } + bool is_clip_model = false; for (const auto * it : tensors) { const struct ggml_tensor * tensor = it->tensor; @@ -2008,12 +2009,14 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) { qs.has_output = true; } + + is_clip_model |= name.rfind("mm.", 0) == 0; // check the "mm." prefix } qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer; // sanity checks for models that have attention layers - if (qs.n_attention_wv != 0) + if (qs.n_attention_wv != 0 && !is_clip_model) { const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin(); // attention layers have a non-zero number of kv heads @@ -2139,6 +2142,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: bool quantize = ggml_n_dims(tensor) >= 2 && is_quantizable(name, model.arch, params); quantize &= params->quantize_output_tensor || name != "output.weight"; + // do not quantize specific multimodal tensors + quantize &= name.find(".position_embd.") == std::string::npos; + ggml_type new_type; void * new_data; size_t new_size; diff --git a/src/llama.cpp b/src/llama.cpp index fe5a7a8354..ab2e9868af 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -124,6 +124,9 @@ static int llama_model_load(const std::string & fname, std::vector } catch(const std::exception & e) { throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what())); } + if (model.arch == LLM_ARCH_CLIP) { + throw std::runtime_error("CLIP cannot be used as main model, use it with --mmproj instead"); + } try { model.load_vocab(ml); } catch(const std::exception & e) { @@ -312,6 +315,7 @@ struct llama_model * llama_model_load_from_splits( LLAMA_LOG_ERROR("%s: list of splits is empty\n", __func__); return nullptr; } + splits.reserve(n_paths); for (size_t i = 0; i < n_paths; ++i) { splits.push_back(paths[i]); } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 2fa16b497a..d5c5a2a665 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -4588,20 +4588,31 @@ struct test_topk_moe: public test_case { struct test_sum : public test_case { const ggml_type type; const std::array ne; + const std::array permute; + bool _use_permute; std::string vars() override { - return VARS_TO_STR2(type, ne); + std::string v = VARS_TO_STR2(type, ne); + if (_use_permute) v += "," + VAR_TO_STR(permute); + return v; } test_sum(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 5, 4, 3}) - : type(type), ne(ne) {} + std::array ne = {10, 5, 4, 3}, + std::array permute = {0, 0, 0, 0}) + : type(type), ne(ne), permute(permute), + _use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_set_param(a); ggml_set_name(a, "a"); + if (_use_permute) { + a = ggml_permute(ctx, a, permute[0], permute[1], permute[2], permute[3]); + ggml_set_name(a, "a_permuted"); + } + ggml_tensor * out = ggml_sum(ctx, a); ggml_set_name(out, "out"); @@ -6354,6 +6365,19 @@ static std::vector> make_test_cases_eval() { } } +#if 0 + { + // Test paths in OpenCL + std::vector ns = {32, 64, 128, 256, 512, 1024, 4096}; + std::vector ks = {896, 1536, 4096}; + for (auto n : ns) { + for (auto k : ks) { + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 1024, n, k, {1, 1}, {1, 1})); + } + } + } +#endif + #if 1 for (ggml_type type_a : base_types) { for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) { @@ -6724,6 +6748,9 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_sum()); test_cases.emplace_back(new test_sum_rows()); + test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 2, 1, 3})); // row-contiguous but non-contiguous + test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 3, 2, 1})); + test_cases.emplace_back(new test_sum(GGML_TYPE_F32, {11, 5, 6, 3}, {0, 1, 3, 2})); test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, true, false)); test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, false, true)); test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 11, 5, 6, 3 }, true, true)); @@ -6734,6 +6761,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 1024, 1, 1 })); test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 1024, 1, 1 })); test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 })); + test_cases.emplace_back(new test_sum(GGML_TYPE_F32, { 33, 256, 1, 1 }, { 1, 0, 2, 3 })); // sum dst not-contiguous test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, { 33, 256, 1, 1 })); test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 33, 256, 1, 1 })); test_cases.emplace_back(new test_mean(GGML_TYPE_F32, { 32769, 1, 1, 1 })); @@ -6779,7 +6807,7 @@ static std::vector> make_test_cases_eval() { for (int nb : { 1, 3, 32, 35, }) { for (ggml_prec prec : {GGML_PREC_F32, GGML_PREC_DEFAULT}) { if (hsk != 128 && prec == GGML_PREC_DEFAULT) continue; - for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { + for (ggml_type type_KV : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { test_cases.emplace_back(new test_flash_attn_ext( hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV)); // run fewer test cases permuted @@ -6911,7 +6939,7 @@ static std::vector> make_test_cases_perf() { } // qwen3-30b-a3b - for (int bs : {1, 4, 8, 32, 64, 128, 512}) { + for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) { for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) { for (ggml_type type_b : {GGML_TYPE_F32}) { test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 128, 8, false, 768, bs, 2048, 1)); @@ -6919,6 +6947,15 @@ static std::vector> make_test_cases_perf() { } } + for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) { + for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) { + for (ggml_type type_b : {GGML_TYPE_F32}) { + test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 1792, bs, 2048, 1)); + } + } + } + + // gpt-oss-20b for (int bs : {1, 4, 8, 512}) { for (ggml_type type_a : {GGML_TYPE_MXFP4}) { diff --git a/tests/test-chat-parser.cpp b/tests/test-chat-parser.cpp index 0b275befb8..4766518fe6 100644 --- a/tests/test-chat-parser.cpp +++ b/tests/test-chat-parser.cpp @@ -524,6 +524,64 @@ static void test_json_with_dumped_args() { R"({"foo": "bar", "args": {"arg1": [)", R"({"foo":"bar","args":"{\"arg1\":["})" ); + + // Unicode tests + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u0)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u0"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u00)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u00"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u000)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u000"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\u0000)", + R"({"foo":"bar","args":"{\"arg1\":\"\\u0000"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud8)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud8"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud80)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud80"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\u)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\u"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\ud)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\ud"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\udc)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\udc0)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc0"})" + ); + test_with_args( + R"({"foo": "bar", "args": {"arg1": "\ud800\udc00)", + R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc00"})" + ); } static void test_positions() { diff --git a/tests/test-json-partial.cpp b/tests/test-json-partial.cpp index bc136beceb..39da9276ef 100644 --- a/tests/test-json-partial.cpp +++ b/tests/test-json-partial.cpp @@ -58,7 +58,7 @@ static void test_json_healing() { for (const auto & input : inputs) { common_json out; assert_equals(true, common_json_parse(input, "$foo", out)); - assert_equals(expected, out.json.dump()); + assert_equals(expected, out.json.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true)); assert_equals(expected_marker, out.healing_marker.json_dump_marker); } }; @@ -228,6 +228,56 @@ static void test_json_healing() { R"({"key":"$foo"})", R"(:"$foo)" ); + // Test unicode escape sequences + test( + { + R"({"a":"\u)", + }, + R"({"a":"\u0000$foo"})", + R"(0000$foo)" + ); + test( + { + R"({"a":"\u00)", + }, + R"({"a":"\u0000$foo"})", + R"(00$foo)" + ); + test( + { + R"({"a":"\ud300)", + }, + R"({"a":"\ud300$foo"})", + R"($foo)" + ); + test( + { + R"({"a":"\ud800)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"(\udc00$foo)" + ); + test( + { + R"({"a":"\ud800\)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"(udc00$foo)" + ); + test( + { + R"({"a":"\ud800\u)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"(dc00$foo)" + ); + test( + { + R"({"a":"\ud800\udc00)", + }, + R"({"a":"\ud800\udc00$foo"})", + R"($foo)" + ); } int main() { diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 5026edcebe..1c62ebe968 100644 Binary files a/tools/server/public/index.html.gz and b/tools/server/public/index.html.gz differ diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 60326e8e50..8737fba124 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -1585,23 +1585,31 @@ struct server_prompt_cache { } } + // average size per token + const float size_per_token = std::max(1.0f, float(size()) / (std::max(1, n_tokens()))); + + // dynamically increase the token limit if it can fit in the memory limit + const size_t limit_tokens_cur = limit_size > 0 ? std::max(limit_tokens, limit_size/size_per_token) : limit_tokens; + if (limit_tokens > 0) { - while (states.size() > 1 && n_tokens() > limit_tokens) { + while (states.size() > 1 && n_tokens() > limit_tokens_cur) { if (states.empty()) { break; } - SRV_WRN(" - cache token limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0)); + SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n", + limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0)); states.pop_front(); } } - SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens)\n", - states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens); + SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n", + states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur); for (const auto & state : states) { - SRV_WRN(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n", (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0)); + SRV_WRN(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n", + (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0)); } } }; @@ -3804,7 +3812,7 @@ struct server_context { if (slot.n_past > 0 && slot.n_past < (int) slot.prompt.tokens.size()) { const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id); if (pos_min == -1) { - SLT_ERR(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min); + SLT_ERR(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min); GGML_ABORT("pos_min == -1, but n_past > 0 - should not happen: https://github.com/ggml-org/llama.cpp/pull/13833#discussion_r2116181237"); } @@ -3831,14 +3839,14 @@ struct server_context { { const auto token = slot.prompt.tokens[i]; - const auto piece = common_token_to_piece(ctx, token); + const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx, token) : "[mtmd]"; ss0 << piece; st0 << std::setw(8) << token; } { const auto token = slot.task->tokens[i]; - const auto piece = common_token_to_piece(ctx, token); + const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx, token) : "[mtmd]"; ss1 << piece; st1 << std::setw(8) << token; } @@ -3852,7 +3860,7 @@ struct server_context { } if (pos_min > pos_min_thold) { - SLT_WRN(slot, "n_past = %d, cache_tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min, n_swa); + SLT_WRN(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min, n_swa); // search for a context checkpoint const auto it = std::find_if( @@ -4020,7 +4028,7 @@ struct server_context { } } - // SLT_INF(slot, "new cache_tokens: %s\n", slot.cache_tokens.str().c_str()); + // SLT_INF(slot, "new slot.prompt.tokens: %s\n", slot.slot.prompt.tokens.str().c_str()); SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, batch.n_tokens, (float) slot.n_past / slot.n_prompt_tokens()); @@ -5401,15 +5409,6 @@ int main(int argc, char ** argv) { const json body = json::parse(req.body); - // TODO: implement - //int top_n = 1; - //if (body.count("top_n") != 1) { - // top_n = body.at("top_n"); - //} else { - // res_error(res, format_error_response("\"top_n\" must be provided", ERROR_TYPE_INVALID_REQUEST)); - // return; - //} - // if true, use TEI API format, otherwise use Jina API format // Jina: https://jina.ai/reranker/ // TEI: https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/rerank @@ -5434,6 +5433,8 @@ int main(int argc, char ** argv) { return; } + int top_n = json_value(body, "top_n", (int)documents.size()); + // create and queue the task json responses = json::array(); bool error = false; @@ -5474,7 +5475,8 @@ int main(int argc, char ** argv) { body, responses, is_tei_format, - documents); + documents, + top_n); res_ok(res, root); }; diff --git a/tools/server/tests/unit/test_rerank.py b/tools/server/tests/unit/test_rerank.py index 0b63c7821e..ded8267109 100644 --- a/tools/server/tests/unit/test_rerank.py +++ b/tools/server/tests/unit/test_rerank.py @@ -102,3 +102,45 @@ def test_rerank_usage(query, doc1, doc2, n_tokens): assert res.status_code == 200 assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] assert res.body['usage']['prompt_tokens'] == n_tokens + + +@pytest.mark.parametrize("top_n,expected_len", [ + (None, len(TEST_DOCUMENTS)), # no top_n parameter + (2, 2), + (4, 4), + (99, len(TEST_DOCUMENTS)), # higher than available docs +]) +def test_rerank_top_n(top_n, expected_len): + global server + server.start() + data = { + "query": "Machine learning is", + "documents": TEST_DOCUMENTS, + } + if top_n is not None: + data["top_n"] = top_n + + res = server.make_request("POST", "/rerank", data=data) + assert res.status_code == 200 + assert len(res.body["results"]) == expected_len + + +@pytest.mark.parametrize("top_n,expected_len", [ + (None, len(TEST_DOCUMENTS)), # no top_n parameter + (2, 2), + (4, 4), + (99, len(TEST_DOCUMENTS)), # higher than available docs +]) +def test_rerank_tei_top_n(top_n, expected_len): + global server + server.start() + data = { + "query": "Machine learning is", + "texts": TEST_DOCUMENTS, + } + if top_n is not None: + data["top_n"] = top_n + + res = server.make_request("POST", "/rerank", data=data) + assert res.status_code == 200 + assert len(res.body) == expected_len diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp index f175115f4f..cc48f5a9d0 100644 --- a/tools/server/utils.hpp +++ b/tools/server/utils.hpp @@ -849,47 +849,44 @@ static json format_response_rerank( const json & request, const json & ranks, bool is_tei_format, - std::vector & texts) { - json res; - if (is_tei_format) { - // TEI response format - res = json::array(); - bool return_text = json_value(request, "return_text", false); - for (const auto & rank : ranks) { - int index = json_value(rank, "index", 0); - json elem = json{ - {"index", index}, - {"score", json_value(rank, "score", 0.0)}, - }; - if (return_text) { - elem["text"] = std::move(texts[index]); - } - res.push_back(elem); - } - } else { - // Jina response format - json results = json::array(); - int32_t n_tokens = 0; - for (const auto & rank : ranks) { - results.push_back(json{ - {"index", json_value(rank, "index", 0)}, - {"relevance_score", json_value(rank, "score", 0.0)}, - }); - - n_tokens += json_value(rank, "tokens_evaluated", 0); - } - - res = json{ - {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", "list"}, - {"usage", json{ - {"prompt_tokens", n_tokens}, - {"total_tokens", n_tokens} - }}, - {"results", results} + std::vector & texts, + int top_n) { + int32_t n_tokens = 0; + bool return_text = is_tei_format && json_value(request, "return_text", false); + std::vector elements; // Temporary vector to hold unsorted elements + std::string score_label = is_tei_format ? "score" : "relevance_score"; + for (const auto & rank : ranks) { + int index = json_value(rank, "index", 0); + json elem = json{ + {"index", index}, + {score_label, json_value(rank, "score", 0.0)}, }; + n_tokens += json_value(rank, "tokens_evaluated", 0); + if (return_text) { + elem["text"] = std::move(texts[index]); + } + elements.push_back(elem); } + std::sort(elements.begin(), elements.end(), [score_label](const json& a, const json& b) { + return json_value(a, score_label, 0.0) > json_value(b, score_label, 0.0); + }); + + elements.resize(std::min(top_n, (int)elements.size())); + json results = elements; + + if (is_tei_format) return results; + + json res = json{ + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", json{ + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} + }}, + {"results", results} + }; + return res; } @@ -1240,9 +1237,10 @@ public: // allowed to resize ^ ^ // disallowed to resize ^ ^ ^ if (n > 0) { - llama_token last_token = tokens[n - 1]; // make sure we never remove tokens in the middle of an image - if (last_token == LLAMA_TOKEN_NULL) { + // note that the case where we keep a full image at the end is allowed: + // tokens[n - 1] == LLAMA_TOKEN_NULL && tokens[n] != LLAMA_TOKEN_NULL + if (tokens[n - 1] == LLAMA_TOKEN_NULL && tokens[n] == LLAMA_TOKEN_NULL) { find_chunk(n - 1); // will throw an error if the token is not begin-of-chunk } } diff --git a/tools/server/webui/package-lock.json b/tools/server/webui/package-lock.json index c300ecaa77..9cd6ef9138 100644 --- a/tools/server/webui/package-lock.json +++ b/tools/server/webui/package-lock.json @@ -50,6 +50,7 @@ "eslint-plugin-svelte": "^3.0.0", "fflate": "^0.8.2", "globals": "^16.0.0", + "mdast": "^3.0.0", "mdsvex": "^0.12.3", "playwright": "^1.53.0", "prettier": "^3.4.2", @@ -66,6 +67,7 @@ "tw-animate-css": "^1.3.5", "typescript": "^5.0.0", "typescript-eslint": "^8.20.0", + "unified": "^11.0.5", "uuid": "^13.0.0", "vite": "^7.0.4", "vite-plugin-devtools-json": "^0.2.0", @@ -2128,6 +2130,66 @@ "node": ">=14.0.0" } }, + "node_modules/@tailwindcss/oxide-wasm32-wasi/node_modules/@emnapi/core": { + "version": "1.4.3", + "dev": true, + "inBundle": true, + "license": "MIT", + "optional": true, + "dependencies": { + "@emnapi/wasi-threads": "1.0.2", + "tslib": "^2.4.0" + } + }, + "node_modules/@tailwindcss/oxide-wasm32-wasi/node_modules/@emnapi/runtime": { + "version": "1.4.3", + "dev": true, + "inBundle": true, + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, + "node_modules/@tailwindcss/oxide-wasm32-wasi/node_modules/@emnapi/wasi-threads": { + "version": "1.0.2", + "dev": true, + "inBundle": true, + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, + "node_modules/@tailwindcss/oxide-wasm32-wasi/node_modules/@napi-rs/wasm-runtime": { + "version": "0.2.11", + "dev": true, + "inBundle": true, + "license": "MIT", + "optional": true, + "dependencies": { + "@emnapi/core": "^1.4.3", + "@emnapi/runtime": "^1.4.3", + "@tybys/wasm-util": "^0.9.0" + } + }, + "node_modules/@tailwindcss/oxide-wasm32-wasi/node_modules/@tybys/wasm-util": { + "version": "0.9.0", + "dev": true, + "inBundle": true, + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, + "node_modules/@tailwindcss/oxide-wasm32-wasi/node_modules/tslib": { + "version": "2.8.0", + "dev": true, + "inBundle": true, + "license": "0BSD", + "optional": true + }, "node_modules/@tailwindcss/oxide-win32-arm64-msvc": { "version": "4.1.11", "resolved": "https://registry.npmjs.org/@tailwindcss/oxide-win32-arm64-msvc/-/oxide-win32-arm64-msvc-4.1.11.tgz", @@ -4946,6 +5008,13 @@ "url": "https://github.com/sponsors/wooorm" } }, + "node_modules/mdast": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/mdast/-/mdast-3.0.0.tgz", + "integrity": "sha512-xySmf8g4fPKMeC07jXGz971EkLbWAJ83s4US2Tj9lEdnZ142UP5grN73H1Xd3HzrdbU5o9GYYP/y8F9ZSwLE9g==", + "dev": true, + "license": "MIT" + }, "node_modules/mdast-util-find-and-replace": { "version": "3.0.2", "resolved": "https://registry.npmjs.org/mdast-util-find-and-replace/-/mdast-util-find-and-replace-3.0.2.tgz", diff --git a/tools/server/webui/package.json b/tools/server/webui/package.json index 7bf21bf57c..e073cd32f0 100644 --- a/tools/server/webui/package.json +++ b/tools/server/webui/package.json @@ -52,6 +52,7 @@ "eslint-plugin-svelte": "^3.0.0", "fflate": "^0.8.2", "globals": "^16.0.0", + "mdast": "^3.0.0", "mdsvex": "^0.12.3", "playwright": "^1.53.0", "prettier": "^3.4.2", @@ -68,6 +69,7 @@ "tw-animate-css": "^1.3.5", "typescript": "^5.0.0", "typescript-eslint": "^8.20.0", + "unified": "^11.0.5", "uuid": "^13.0.0", "vite": "^7.0.4", "vite-plugin-devtools-json": "^0.2.0", diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte index 66369b2f1c..cc2631b830 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte @@ -2,8 +2,9 @@ import { Check, X } from '@lucide/svelte'; import { Card } from '$lib/components/ui/card'; import { Button } from '$lib/components/ui/button'; - import { ChatAttachmentsList } from '$lib/components/app'; + import { ChatAttachmentsList, MarkdownContent } from '$lib/components/app'; import { INPUT_CLASSES } from '$lib/constants/input-classes'; + import { config } from '$lib/stores/settings.svelte'; import ChatMessageActions from './ChatMessageActions.svelte'; interface Props { @@ -55,6 +56,7 @@ let isMultiline = $state(false); let messageElement: HTMLElement | undefined = $state(); + const currentConfig = config(); $effect(() => { if (!messageElement || !message.content.trim()) return; @@ -123,9 +125,18 @@ class="max-w-[80%] rounded-[1.125rem] bg-primary px-3.75 py-1.5 text-primary-foreground data-[multiline]:py-2.5" data-multiline={isMultiline ? '' : undefined} > - - {message.content} - + {#if currentConfig.renderUserContentAsMarkdown} +
+ +
+ {:else} + + {message.content} + + {/if} {/if} diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte index 666febf0d2..374eb05ab0 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte @@ -7,6 +7,7 @@ ChatMessages, ChatProcessingInfo, EmptyFileAlertDialog, + ChatErrorDialog, ServerErrorSplash, ServerInfo, ServerLoadingSplash, @@ -22,10 +23,11 @@ activeMessages, activeConversation, deleteConversation, + dismissErrorDialog, + errorDialog, isLoading, sendMessage, - stopGeneration, - setMaxContextError + stopGeneration } from '$lib/stores/chat.svelte'; import { supportsVision, @@ -34,7 +36,6 @@ serverWarning, serverStore } from '$lib/stores/server.svelte'; - import { contextService } from '$lib/services'; import { parseFilesToMessageExtras } from '$lib/utils/convert-files-to-extra'; import { isFileTypeSupported } from '$lib/utils/file-type'; import { filterFilesByModalities } from '$lib/utils/modality-file-validation'; @@ -79,6 +80,7 @@ showCenteredEmpty && !activeConversation() && activeMessages().length === 0 && !isLoading() ); + let activeErrorDialog = $derived(errorDialog()); let isServerLoading = $derived(serverLoading()); async function handleDeleteConfirm() { @@ -105,6 +107,12 @@ } } + function handleErrorDialogOpenChange(open: boolean) { + if (!open) { + dismissErrorDialog(); + } + } + function handleDragOver(event: DragEvent) { event.preventDefault(); } @@ -183,21 +191,6 @@ const extras = result?.extras; - // Check context limit using real-time slots data - const contextCheck = await contextService.checkContextLimit(); - - if (contextCheck && contextCheck.wouldExceed) { - const errorMessage = contextService.getContextErrorMessage(contextCheck); - - setMaxContextError({ - message: errorMessage, - estimatedTokens: contextCheck.currentUsage, - maxContext: contextCheck.maxContext - }); - - return false; - } - // Enable autoscroll for user-initiated message sending userScrolledUp = false; autoScrollEnabled = true; @@ -461,6 +454,13 @@ }} /> + +