diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md new file mode 100644 index 0000000000000..0852fded5867e --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -0,0 +1,5 @@ +- Self Reported Review Complexity: + - [ ] Review Complexity : Low + - [ ] Review Complexity : Medium + - [ ] Review Complexity : High +- [ ] I have read the [contributing guidelines](CONTRIBUTING.md) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 0789efd18a1ab..0d16ef5f110f9 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -16,11 +16,9 @@ on: branches: - master paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] - pull_request_target: + pull_request: types: [opened, synchronize, reopened] paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] - schedule: - - cron: '2 4 * * *' concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }} @@ -115,7 +113,7 @@ jobs: server-windows: - runs-on: windows-latest + runs-on: windows-2019 steps: - name: Clone diff --git a/CMakeLists.txt b/CMakeLists.txt index b1d6afbbcfa8d..8e280f87d9dd1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -402,12 +402,26 @@ if (LLAMA_CUBLAS) endif() if (LLAMA_CUDA) - cmake_minimum_required(VERSION 3.17) + cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES find_package(CUDAToolkit) if (CUDAToolkit_FOUND) message(STATUS "CUDA found") + if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + # 52 == lowest CUDA 12 standard + # 60 == f16 CUDA intrinsics + # 61 == integer CUDA intrinsics + # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster + if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16) + set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics + else() + set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics + #set(CMAKE_CUDA_ARCHITECTURES "OFF") # use this to compile much faster, but only F16 models work + endif() + endif() + message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") + enable_language(CUDA) set(GGML_HEADERS_CUDA ggml-cuda.h) @@ -472,21 +486,6 @@ if (LLAMA_CUDA) else() set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ... endif() - - if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) - # 52 == lowest CUDA 12 standard - # 60 == f16 CUDA intrinsics - # 61 == integer CUDA intrinsics - # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster - if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16) - set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics - else() - set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics - #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work - endif() - endif() - message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") - else() message(WARNING "CUDA not found") endif() diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000..991d85e493be4 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,14 @@ +# Contributing Guidelines + +## Checklist + +* Make sure your PR follows the [coding guidelines](https://github.com/ggerganov/llama.cpp/blob/master/README.md#coding-guidelines) +* Test your changes using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library +* Execute [the full CI locally on your machine](ci/README.md) before publishing + +## PR formatting + +* Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs. + - The PR template has a series of review complexity checkboxes `[ ]` that you can mark as `[X]` for your conveience. Refer to [About task lists](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) for more information. +* If the pull request only contains documentation changes (e.g., updating READMEs, adding new wiki pages), please add `[no ci]` to the commit title. This will skip unnecessary CI checks and help reduce build times. +* When squashing multiple commits on merge, use the following format for your commit title: ` : (#)`. For example: `utils : Fix typo in utils.py (#1234)` diff --git a/README.md b/README.md index 09e8cad31bf62..ecb9d00db7957 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,6 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
  • Quantization
  • Interactive mode
  • Constrained output with grammars
  • -
  • Instruct mode
  • Obtaining and using the Facebook LLaMA 2 model
  • Seminal papers and background on the models
  • Perplexity (measuring model quality)
  • @@ -769,34 +768,6 @@ The `grammars/` folder contains a handful of sample grammars. To write your own, For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. -### Instruct mode - -1. First, download and place the `ggml` model into the `./models` folder -2. Run the `main` tool like this: - -``` -./examples/alpaca.sh -``` - -Sample run: - -``` -== Running in interactive mode. == - - Press Ctrl+C to interject at any time. - - Press Return to return control to LLaMA. - - If you want to submit another line, end your input in '\'. - - Below is an instruction that describes a task. Write a response that appropriately completes the request. - -> How many letters are there in the English alphabet? -There 26 letters in the English Alphabet -> What is the most common way of transportation in Amsterdam? -The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis -> List 5 words that start with "ca". -cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach. -> -``` - ### Obtaining and using the Facebook LLaMA 2 model - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 737bae27c7206..10b9b3d1d4d41 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -40,7 +40,7 @@ static std::string build_repetition(const std::string & item_rule, int min_items return result; } -const std::string SPACE_RULE = "\" \"?"; +const std::string SPACE_RULE = "| \" \" | \"\\n\" [ \\t]{0,20}"; struct BuiltinRule { std::string content; @@ -57,7 +57,7 @@ std::unordered_map PRIMITIVE_RULES = { {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}}, {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}}, {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}}, - {"char", {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}}, + {"char", {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}}, {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}}, {"null", {"\"null\" space", {}}}, }; diff --git a/examples/alpaca.sh b/examples/alpaca.sh deleted file mode 100755 index 8d2bae6918b62..0000000000000 --- a/examples/alpaca.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \ - --color \ - -f ./prompts/alpaca.txt \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 7 diff --git a/examples/gpt4all.sh b/examples/gpt4all.sh deleted file mode 100755 index 5fd739e55c554..0000000000000 --- a/examples/gpt4all.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main --color --instruct --threads 4 \ - --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \ - --file ./prompts/alpaca.txt \ - --batch_size 8 --ctx_size 2048 -n -1 \ - --repeat_last_n 64 --repeat_penalty 1.3 \ - --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95 diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index e18f495630616..574f5ed9c2e65 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -218,20 +218,64 @@ void IMatrixCollector::save_imatrix(int ncall) const { fname += std::to_string(ncall); } + // avoid writing imatrix entries that do not have full data + // this can happen with MoE models where some of the experts end up not being exercised by the provided training data + + int n_entries = 0; + std::vector to_store; + + bool is_first = true; // for printing + for (const auto & kv : m_stats) { + const int n_all = kv.second.counts.size(); + + if (n_all == 0) { + continue; + } + + int n_zeros = 0; + for (const int c : kv.second.counts) { + if (c == 0) { + n_zeros++; + } + } + + if (n_zeros != 0 && is_first) { + fprintf(stderr, "\n"); + is_first = false; + } + + if (n_zeros == n_all) { + fprintf(stderr, "%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str()); + continue; + } + + if (n_zeros > 0) { + fprintf(stderr, "%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all); + continue; + } + + n_entries++; + to_store.push_back(kv.first); + } + + if (to_store.size() < m_stats.size()) { + fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size()); + } + std::ofstream out(fname, std::ios::binary); - int n_entries = m_stats.size(); out.write((const char *) &n_entries, sizeof(n_entries)); - for (const auto & p : m_stats) { - int len = p.first.size(); + for (const auto & name : to_store) { + const auto & stat = m_stats.at(name); + int len = name.size(); out.write((const char *) &len, sizeof(len)); - out.write(p.first.c_str(), len); - out.write((const char *) &p.second.ncall, sizeof(p.second.ncall)); - int nval = p.second.values.size(); + out.write(name.c_str(), len); + out.write((const char *) &stat.ncall, sizeof(stat.ncall)); + int nval = stat.values.size(); out.write((const char *) &nval, sizeof(nval)); if (nval > 0) { std::vector tmp(nval); for (int i = 0; i < nval; i++) { - tmp[i] = (p.second.values[i] / static_cast(p.second.counts[i])) * static_cast(p.second.ncall); + tmp[i] = (stat.values[i] / static_cast(stat.counts[i])) * static_cast(stat.ncall); } out.write((const char*)tmp.data(), nval*sizeof(float)); } diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py index 7d889c3fe1287..ab19e20df658e 100755 --- a/examples/json_schema_to_grammar.py +++ b/examples/json_schema_to_grammar.py @@ -29,9 +29,8 @@ def __init__(self, content: str, deps: list = None): self.content = content self.deps = deps or [] -# whitespace is constrained to a single space char to prevent model "running away" in -# whitespace. Also maybe improves generation quality? -SPACE_RULE = '" "?' +# Constraining spaces to prevent model "running away". +SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}' PRIMITIVE_RULES = { 'boolean' : BuiltinRule('("true" | "false") space', []), @@ -43,7 +42,7 @@ def __init__(self, content: str, deps: list = None): 'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), 'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), 'uuid' : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []), - 'char' : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})', []), + 'char' : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []), 'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']), 'null' : BuiltinRule('"null" space', []), } diff --git a/examples/llama2-13b.sh b/examples/llama2-13b.sh deleted file mode 100755 index 92b3f6dd8b0ec..0000000000000 --- a/examples/llama2-13b.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \ - --color \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 8 diff --git a/examples/llama2.sh b/examples/llama2.sh deleted file mode 100755 index 221b37553cfe7..0000000000000 --- a/examples/llama2.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \ - --color \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 8 diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs index cef11eab83a46..faed6a32cfc4c 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public/json-schema-to-grammar.mjs @@ -1,5 +1,5 @@ // WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first. -const SPACE_RULE = '" "?'; +const SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}'; function _buildRepetition(itemRule, minItems, maxItems, opts={}) { if (minItems === 0 && maxItems === 1) { @@ -41,7 +41,7 @@ const PRIMITIVE_RULES = { object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), uuid : new BuiltinRule('"\\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\\"" space', []), - char : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F]{4})`, []), + char : new BuiltinRule(`[^"\\\\\\x7F\\x00-\\x1F] | [\\\\] (["\\\\bfnrt] | "u" [0-9a-fA-F]{4})`, []), string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']), null : new BuiltinRule('"null" space', []), }; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6ffaa8d9fe637..80714fa58360b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -147,7 +147,7 @@ struct server_slot { int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_processed = 0; - json prompt; + std::string prompt; // when a task is submitted, we first tokenize the prompt and store it here std::vector prompt_tokens; @@ -822,13 +822,8 @@ struct server_context { continue; } - // skip the slot if it does not contains prompt - if (!slot.prompt.is_string()) { - continue; - } - // current slot's prompt - std::string slot_prompt = slot.prompt.get(); + std::string slot_prompt = slot.prompt; // length of the current slot's prompt int slot_prompt_len = slot_prompt.size(); @@ -958,13 +953,16 @@ struct server_context { if (!task.infill) { const auto & prompt = data.find("prompt"); if (prompt == data.end()) { - send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST); + send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST); return false; - } else { - slot.prompt = *prompt; } - if (slot.prompt.is_array() && slot.prompt.size() == 0) { - send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST); + + if (prompt->is_string()) { + slot.prompt = prompt->get(); + } else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) { + slot.prompt = prompt->at(0).get(); + } else { + send_error(task, "\"prompt\" must be a string or an array of strings", ERROR_TYPE_INVALID_REQUEST); return false; } } @@ -1582,14 +1580,18 @@ struct server_context { switch (task.type) { case SERVER_TASK_TYPE_COMPLETION: { - int id_slot = json_value(task.data, "id_slot", -1); - std::string prompt = json_value(task.data, "prompt", std::string()); + const int id_slot = json_value(task.data, "id_slot", -1); server_slot * slot; if (id_slot != -1) { slot = get_slot_by_id(id_slot); } else { + std::string prompt; + if (task.data.contains("prompt") && task.data.at("prompt").is_string()) { + json_value(task.data, "prompt", std::string()); + } + slot = get_available_slot(prompt); } diff --git a/flake.lock b/flake.lock index 09047ab10ad1d..7272e65fa8fa5 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1716948383, - "narHash": "sha256-SzDKxseEcHR5KzPXLwsemyTR/kaM9whxeiJohbL04rs=", + "lastModified": 1717786204, + "narHash": "sha256-4q0s6m0GUcN7q+Y2DqD27iLvbcd1G50T2lv08kKxkSI=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "ad57eef4ef0659193044870c731987a6df5cf56b", + "rev": "051f920625ab5aabe37c920346e3e69d7d34400e", "type": "github" }, "original": { diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 90a0a81ead789..7f4764d60e854 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -139,6 +139,7 @@ #define CC_PASCAL 600 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 +#define CC_TURING 750 #define CC_AMPERE 800 #define CC_OFFSET_AMD 1000000 #define CC_RDNA1 (CC_OFFSET_AMD + 1010) @@ -326,9 +327,17 @@ static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int #endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 #endif // defined(GGML_USE_HIPBLAS) -#define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#define FP16_AVAILABLE +#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL -#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#define FP16_MMA_AVAILABLE +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA + +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING +#define INT8_MMA_AVAILABLE +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING static bool fast_fp16_available(const int cc) { return cc >= CC_PASCAL && cc != 610; @@ -338,6 +347,10 @@ static bool fp16_mma_available(const int cc) { return cc < CC_OFFSET_AMD && cc >= CC_VOLTA; } +static bool int8_mma_available(const int cc) { + return cc < CC_OFFSET_AMD && cc >= CC_TURING; +} + [[noreturn]] static __device__ void no_device_code( const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { @@ -379,7 +392,7 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { } static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #pragma unroll @@ -412,7 +425,7 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { } static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) { -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX return __float2half(fmaxf(__half2float(a), __half2float(b))); diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh index c00f8606a5c85..37b3b99323b20 100644 --- a/ggml-cuda/fattn-common.cuh +++ b/ggml-cuda/fattn-common.cuh @@ -74,7 +74,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( const int sumi = __dp4a(v, u, 0); -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { const half2 * Q_ds = (const half2 *) Q_ds_v; @@ -122,7 +122,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( const int sumi = __dp4a(v, u, 0); -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { const half2 * Q_ds = (const half2 *) Q_ds_v; @@ -181,7 +181,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( const int sumi = __dp4a(v, u, 0); -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { const half2 * Q_ds = (const half2 *) Q_ds_v; @@ -236,7 +236,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( const int sumi = __dp4a(v, u, 0); -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { const half2 * Q_ds = (const half2 *) Q_ds_v; @@ -314,7 +314,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16( GGML_UNUSED(Q_q8); GGML_UNUSED(Q_ds_v); -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { const half2 * Q_h2 = (const half2 *) Q_v; @@ -407,7 +407,7 @@ static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ const int q0 = x[ib].qs[iqs]; const int q = ((q0 >> (4*shift)) & 0x0F) - 8; -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { return ((half) d)*((half) q); } @@ -428,7 +428,7 @@ static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ const int q0 = x[ib].qs[iqs]; const int q = ((q0 >> (4*shift)) & 0x0F); -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { return __low2half(dm)*((half) q) + __high2half(dm); } @@ -453,7 +453,7 @@ static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ const int qh = ((qh0 >> idq) << 4) & 0x10; const int q = (ql | qh) - 16; -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { return ((half) d)*((half) q); } @@ -478,7 +478,7 @@ static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ const int qh = ((qh0 >> idq) << 4) & 0x10; const int q = (ql | qh); -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { return __low2half(dm)*((half) q) + __high2half(dm); } @@ -497,7 +497,7 @@ static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ const T d = x[ib].d; const int q = x[ib].qs[iqs]; -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE if (std::is_same::value) { return ((half) d)*((half) q); } diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index cb11d7212ca28..c6c35134d4db5 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -43,7 +43,7 @@ static __global__ void flash_attn_tile_ext_f16( const int ne1, const int ne2, const int ne3) { -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE //In this kernel Q, K, V are matrices while i, j, k are matrix indices. const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh index 9e1aa2c6b6885..02a4ad072bbd6 100644 --- a/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -40,7 +40,7 @@ static __global__ void flash_attn_vec_ext_f16( const int ne1, const int ne2, const int ne3) { -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE //In this kernel Q, K, V are matrices while i, j, k are matrix indices. constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16(type_K); diff --git a/ggml-cuda/fattn-wmma-f16.cuh b/ggml-cuda/fattn-wmma-f16.cuh index 59cd30d7837c9..ae23222426097 100644 --- a/ggml-cuda/fattn-wmma-f16.cuh +++ b/ggml-cuda/fattn-wmma-f16.cuh @@ -1,9 +1,9 @@ #include "common.cuh" #include "fattn-common.cuh" -#if FP16_MMA_AVAILABLE +#ifdef FP16_MMA_AVAILABLE #include -#endif +#endif // FP16_MMA_AVAILABLE // D == head size, VKQ_stride == num VKQ rows calculated in parallel: template @@ -45,7 +45,7 @@ static __global__ void flash_attn_ext_f16( const int ne1, const int ne2, const int ne3) { -#if FP16_MMA_AVAILABLE +#ifdef FP16_MMA_AVAILABLE //In this kernel Q, K, V are matrices while i, j, k are matrix indices. const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on. diff --git a/ggml-cuda/mma.cuh b/ggml-cuda/mma.cuh new file mode 100644 index 0000000000000..71e8e342918aa --- /dev/null +++ b/ggml-cuda/mma.cuh @@ -0,0 +1,95 @@ +#include "common.cuh" + +struct mma_int_A_I16K8 { + static constexpr int I = 16; + static constexpr int K = 8; + static constexpr int ne = 4; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_i(const int l) { + const int ret = (l%2) * (I/2) + threadIdx.x / (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < I); + return ret; + } + + static __device__ __forceinline__ int get_k(const int l) { + const int ret = (l/2) * (K/2) + threadIdx.x % (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < K); + return ret; + } +}; + +struct mma_int_B_J8K8 { + static constexpr int J = 8; + static constexpr int K = 8; + static constexpr int ne = 2; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_j(const int /* l */) { + const int ret = threadIdx.x / (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < J); + return ret; + } + + static __device__ __forceinline__ int get_k(const int l) { + const int ret = l * (K/2) + threadIdx.x % (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < K); + return ret; + } +}; + +struct mma_int_C_I16J8 { + static constexpr int I = 16; + static constexpr int J = 8; + static constexpr int ne = 4; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_i(const int l) { + const int ret = (l/2) * (I/2) + threadIdx.x / (J/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < I); + return ret; + } + + static __device__ __forceinline__ int get_j(const int l) { + const int ret = 2 * (threadIdx.x % (J/2)) + l%2; + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < J); + return ret; + } + + __device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) { +#ifdef INT8_MMA_AVAILABLE +#if __CUDA_ARCH__ >= CC_AMPERE + asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};" + : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_A.x[2]), "r"(mma_A.x[3]), "r"(mma_B.x[0]), "r"(mma_B.x[1])); +#else + // On Turing m16n8k32 mma is not available, use 4x m8n8k16 mma instead: + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[0]), "+r"(x[1]) + : "r"(mma_A.x[0]), "r"(mma_B.x[0])); + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[1]), "r"(mma_B.x[0])); + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[0]), "+r"(x[1]) + : "r"(mma_A.x[2]), "r"(mma_B.x[1])); + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[3]), "r"(mma_B.x[1])); +#endif // __CUDA_ARCH__ >= CC_AMPERE +#else + GGML_UNUSED(mma_A); + GGML_UNUSED(mma_B); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE + } +}; diff --git a/ggml-cuda/mmq.cuh b/ggml-cuda/mmq.cuh index 3ccae8a0c36fa..62111f376ec81 100644 --- a/ggml-cuda/mmq.cuh +++ b/ggml-cuda/mmq.cuh @@ -2,6 +2,7 @@ #include "common.cuh" #include "vecdotq.cuh" +#include "mma.cuh" #include #include @@ -14,6 +15,7 @@ typedef void (*load_tiles_mmq_t)( typedef void (*vec_dot_mmq_t)( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y, float * __restrict__ sum, const int & k0); +typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1); struct block_q8_1_mmq { half2 ds[4]; @@ -141,15 +143,15 @@ template static __device__ __forceinlin } template -static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mul_mat( +static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y, float * __restrict__ sum, const int & k0) { GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - const float * x_dmf = (const float *) x_dm; - const int * y_qs = (const int *) y + 4; - const half2 * y_ds = (const half2 *) y; + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { @@ -170,12 +172,76 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mul_mat( } sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl - (&x_ql[i*(WARP_SIZE + 1) + k0], u, x_dmf[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0], + (&x_ql[i*(WARP_SIZE + 1) + k0], u, x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0], y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]); } } } +template +static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mma( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + mma_A A; + float dA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = k0 + mma_A::get_k(l) % QI4_0; + const int shift = 4*(mma_A::get_k(l) / QI4_0); + + A.x[l] = __vsubss4((x_ql[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F, 0x08080808); + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/B.J)*C.ne + l] += dA[l/2]*__low2float(dsB[l%2])*C.x[l]; + } + } +} + template static __device__ __forceinline__ void load_tiles_q4_1( const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { @@ -215,7 +281,7 @@ template static __device__ __forceinlin } template -static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mul_mat( +static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y, float * __restrict__ sum, const int & k0) { @@ -249,6 +315,70 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mul_mat( } } +template +static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mma( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + mma_A A; + half2 dmA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = k0 + mma_A::get_k(l) % QI4_0; + const int shift = 4*(mma_A::get_k(l) / QI4_0); + + A.x[l] = (x_ql[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dmA[l] = x_dm[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + const half2 dmA_dsB = dmA[l/2]*dsB[l%2]; + sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB); + } + } +} + template static __device__ __forceinline__ void load_tiles_q5_0( const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { @@ -308,7 +438,7 @@ template static __device__ __forceinlin } template -static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mul_mat( +static __device__ __forceinline__ void vec_dot_q5_0_q8_1_dp4a( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y, float * __restrict__ sum, const int & k0) { @@ -343,6 +473,68 @@ static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mul_mat( } } +template +static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mma( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + mma_A A; + float dA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = 2*(k0 + mma_A::get_k(l) % QI5_0) + mma_A::get_k(l) / QI5_0; + + A.x[l] = x_ql[i*(2*WARP_SIZE + 1) + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + float dB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dB[l] = y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/B.J)*C.ne + l] += dA[l/2]*dB[l%2]*C.x[l]; + } + } +} template static __device__ __forceinline__ void load_tiles_q5_1( const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, @@ -400,7 +592,7 @@ template static __device__ __forceinlin } template -static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mul_mat( +static __device__ __forceinline__ void vec_dot_q5_1_q8_1_dp4a( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y, float * __restrict__ sum, const int & k0) { @@ -434,6 +626,69 @@ static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mul_mat( } } +template +static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mma( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + mma_A A; + half2 dmA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = 2*(k0 + mma_A::get_k(l) % QI5_1) + mma_A::get_k(l) / QI5_1; + + A.x[l] = x_ql[i*(2*WARP_SIZE + 1) + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dmA[l] = x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + const half2 dmA_dsB = dmA[l/2]*dsB[l%2]; + sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB); + } + } +} + template static __device__ __forceinline__ void load_tiles_q8_0( const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { @@ -475,7 +730,7 @@ template static __device__ __forceinlin } template -static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mul_mat( +static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y, float * __restrict__ sum, const int & k0) { @@ -500,6 +755,69 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mul_mat( } } +template +static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma( + const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + mma_A A; + float dA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = k0 + mma_A::get_k(l); + + A.x[l] = x_ql[i*(WARP_SIZE + 1) + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + float dB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = k0 + mma_B::get_k(l); + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dB[l] = y_df[j*MMQ_TILE_Y_K + k0/QI8_1]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/B.J)*C.ne + l] += C.x[l]*dA[l/2]*dB[l%2]; + } + } +} + template static __device__ __forceinline__ void load_tiles_q2_K( const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { @@ -989,6 +1307,57 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mul_mat( } } +template +static __device__ __forceinline__ void mmq_write_back_dp4a(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) { +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = blockIdx.y*mmq_x + j0 + threadIdx.y; + + if (j >= ne1) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = blockIdx.x*mmq_y + i0 + threadIdx.x; + + if (need_check && i >= ne0) { + continue; + } + + dst[j*ne0 + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; + } + } +} + +template +static __device__ __forceinline__ void mmq_write_back_mma(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) { + typedef mma_int_C_I16J8 mma_C; + + const int i0 = threadIdx.y*mma_C::I; + static_assert(nwarps*mma_C::I == mmq_y, "nwarps*mma_C::I != mmq_y"); + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += mma_C::J) { +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + const int j = blockIdx.y*mmq_x + j0 + mma_C::get_j(l); + + if (j >= ne1) { + continue; + } + + const int i = blockIdx.x*mmq_y + i0 + mma_C::get_i(l); + + if (need_check && i >= ne0) { + continue; + } + + dst[j*ne0 + i] = sum[(j0/mma_C::J)*mma_C::ne + l]; + } + } +} + // ------------------------------------------------------------------------------------------------------------------------------------- template @@ -998,35 +1367,65 @@ template struct mmq_type_traits { static constexpr int vdr = VDR_Q4_0_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_0; - static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_0_q8_1_mul_mat; +#ifdef INT8_MMA_AVAILABLE + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_0_q8_1_mma; + static constexpr mmq_write_back_t write_back = mmq_write_back_mma; +#else + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_0_q8_1_dp4a; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; +#endif // INT8_MMA_AVAILABLE }; template struct mmq_type_traits { static constexpr int vdr = VDR_Q4_1_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_1; - static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_1_q8_1_mul_mat; +#ifdef INT8_MMA_AVAILABLE + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_1_q8_1_mma; + static constexpr mmq_write_back_t write_back = mmq_write_back_mma; +#else + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_1_q8_1_dp4a; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; +#endif // INT8_MMA_AVAILABLE }; template struct mmq_type_traits { static constexpr int vdr = VDR_Q5_0_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_0; - static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_0_q8_1_mul_mat; +#ifdef INT8_MMA_AVAILABLE + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_0_q8_1_mma; + static constexpr mmq_write_back_t write_back = mmq_write_back_mma; +#else + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_0_q8_1_dp4a; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; +#endif // INT8_MMA_AVAILABLE }; template struct mmq_type_traits { static constexpr int vdr = VDR_Q5_1_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_1; - static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_1_q8_1_mul_mat; +#ifdef INT8_MMA_AVAILABLE + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_1_q8_1_mma; + static constexpr mmq_write_back_t write_back = mmq_write_back_mma; +#else + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_1_q8_1_dp4a; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; +#endif // INT8_MMA_AVAILABLE }; template struct mmq_type_traits { static constexpr int vdr = VDR_Q8_0_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q8_0; - static constexpr vec_dot_mmq_t vec_dot = vec_dot_q8_0_q8_1_mul_mat; +#ifdef INT8_MMA_AVAILABLE + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q8_0_q8_1_mma; + static constexpr mmq_write_back_t write_back = mmq_write_back_mma; +#else + static constexpr vec_dot_mmq_t vec_dot = vec_dot_q8_0_q8_1_dp4a; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; +#endif // INT8_MMA_AVAILABLE }; template @@ -1034,6 +1433,7 @@ struct mmq_type_traits { static constexpr int vdr = VDR_Q2_K_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q2_K; static constexpr vec_dot_mmq_t vec_dot = vec_dot_q2_K_q8_1_mul_mat; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; }; template @@ -1041,6 +1441,7 @@ struct mmq_type_traits { static constexpr int vdr = VDR_Q3_K_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q3_K; static constexpr vec_dot_mmq_t vec_dot = vec_dot_q3_K_q8_1_mul_mat; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; }; template @@ -1048,6 +1449,7 @@ struct mmq_type_traits { static constexpr int vdr = VDR_Q4_K_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_K; static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_K_q8_1_mul_mat; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; }; template @@ -1055,6 +1457,7 @@ struct mmq_type_traits { static constexpr int vdr = VDR_Q5_K_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_K; static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_K_q8_1_mul_mat; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; }; template @@ -1062,6 +1465,7 @@ struct mmq_type_traits { static constexpr int vdr = VDR_Q6_K_Q8_1_MMQ; static constexpr load_tiles_mmq_t load_tiles = load_tiles_q6_K; static constexpr vec_dot_mmq_t vec_dot = vec_dot_q6_K_q8_1_mul_mat; + static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; }; static int mmq_need_sum(const ggml_type type_x) { @@ -1118,6 +1522,7 @@ static __global__ void mul_mat_q( constexpr int vdr = mmq_type_traits::vdr; constexpr load_tiles_mmq_t load_tiles = mmq_type_traits::load_tiles; constexpr vec_dot_mmq_t vec_dot = mmq_type_traits::vec_dot; + constexpr mmq_write_back_t write_back = mmq_type_traits::write_back; constexpr tile_x_sizes txs = get_tile_x_sizes_device(type); @@ -1137,7 +1542,7 @@ static __global__ void mul_mat_q( const int * y = (const int *) yc + blockIdx.y*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int)); - float sum[(mmq_x/nwarps) * (mmq_y/WARP_SIZE)] = {0.0f}; + float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f}; for (int kb0 = 0; kb0 < blocks_per_row_x; kb0 += blocks_per_warp) { @@ -1164,25 +1569,7 @@ static __global__ void mul_mat_q( } } -#pragma unroll - for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { - const int j = blockIdx.y*mmq_x + j0 + threadIdx.y; - - if (j >= ne1) { - return; - } - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { - const int i = blockIdx.x*mmq_y + i0 + threadIdx.x; - - if (need_check && i >= ne0) { - continue; - } - - dst[j*ne0 + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; - } - } + write_back(sum, dst, ne0, ne1); } struct mmq_args { @@ -1256,10 +1643,10 @@ void mul_mat_q_case(const mmq_args & args, cudaStream_t stream) { launch_mul_mat_q(args, stream); break; case 16: - launch_mul_mat_q(args, stream); + launch_mul_mat_q(args, stream); break; case 24: - launch_mul_mat_q(args, stream); + launch_mul_mat_q(args, stream); break; case 32: launch_mul_mat_q(args, stream); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 0a645b2e1db80..42fc0df203537 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -13089,10 +13089,12 @@ void *ggml_sycl_host_malloc(size_t size) try { return nullptr; } + ggml_sycl_set_device(g_main_device); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + void * ptr = nullptr; - //allow to use dpct::get_in_order_queue() for host malloc dpct::err0 err = CHECK_TRY_ERROR( - ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue())); + ptr = (void *)sycl::malloc_host(size, *main_stream)); if (err != 0) { // clear the error @@ -13113,8 +13115,9 @@ catch (sycl::exception const &exc) { } void ggml_sycl_host_free(void *ptr) try { - //allow to use dpct::get_in_order_queue() for host malloc - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue()))); + ggml_sycl_set_device(g_main_device); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *main_stream))); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ diff --git a/grammars/README.md b/grammars/README.md index 3ffc7cec09257..2ec21a4c091cf 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -94,6 +94,8 @@ This guide provides a brief overview. Check out the GBNF files in this directory ./main -m --grammar-file grammars/some-grammar.gbnf -p 'Some prompt' ``` +`llama.cpp` can also convert JSON schemas to grammars either ahead of time or at each request, see below. + ## Troubleshooting Grammars currently have performance gotchas (see https://github.com/ggerganov/llama.cpp/issues/4218). @@ -103,3 +105,40 @@ Grammars currently have performance gotchas (see https://github.com/ggerganov/ll A common pattern is to allow repetitions of a pattern `x` up to N times. While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) may result in extremely slow sampling. Instead, you can write `x{0,N}` (or `(x (x (x ... (x)?...)?)?)?` w/ N-deep nesting in earlier llama.cpp versions). + +## Using GBNF grammars + +You can use GBNF grammars: + +- In the [server](../examples/server)'s completion endpoints, passed as the `grammar` body field +- In the [main](../examples/main) CLI, passed as the `--grammar` & `--grammar-file` flags +- With the [gbnf-validator](../examples/gbnf-validator) tool, to test them against strings. + +## JSON Schemas → GBNF + +`llama.cpp` supports converting a subset of https://json-schema.org/ to GBNF grammars: + +- In the [server](../examples/server): + - For any completion endpoints, passed as the `json_schema` body field + - For the `/chat/completions` endpoint, passed inside the `result_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}`) +- In the [main](../examples/main) CLI, passed as the `--json` / `-j` flag +- To convert to a grammar ahead of time: + - in CLI, with [json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) + - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI) + +Take a look at [tests](../../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggerganov/llama.cpp/pull/5978, https://github.com/ggerganov/llama.cpp/pull/6659 & https://github.com/ggerganov/llama.cpp/pull/6555). + +Here is also a non-exhaustive list of **unsupported** features: + +- `additionalProperties`: to be fixed in https://github.com/ggerganov/llama.cpp/pull/7840 +- `minimum`, `exclusiveMinimum`, `maximum`, `exclusiveMaximum` + - `integer` constraints to be implemented in https://github.com/ggerganov/llama.cpp/pull/7797 +- Remote `$ref`s in the C++ version (Python & JavaScript versions fetch https refs) +- Mixing `properties` w/ `anyOf` / `oneOf` in the same type (https://github.com/ggerganov/llama.cpp/issues/7703) +- `string` formats `uri`, `email` +- [`contains`](https://json-schema.org/draft/2020-12/json-schema-core#name-contains) / `minContains` +- `uniqueItems` +- `$anchor` (cf. [dereferencing](https://json-schema.org/draft/2020-12/json-schema-core#name-dereferencing)) +- [`not`](https://json-schema.org/draft/2020-12/json-schema-core#name-not) +- [Conditionals](https://json-schema.org/draft/2020-12/json-schema-core#name-keywords-for-applying-subsche) `if` / `then` / `else` / `dependentSchemas` +- [`patternProperties`](https://json-schema.org/draft/2020-12/json-schema-core#name-patternproperties) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index a8a80752e6562..b6448c87b72ec 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -16,10 +16,10 @@ array ::= string ::= "\"" ( [^"\\\x7F\x00-\x1F] | - "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes + "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes )* "\"" ws -number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws +number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws # Optional space: by convention, applied in this grammar after literal chars when allowed -ws ::= ([ \t\n] ws)? +ws ::= | " " | "\n" [ \t]{0,20} diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index 31a3202f863c1..b3dc6f9b16277 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -25,10 +25,10 @@ array ::= string ::= "\"" ( [^"\\\x7F\x00-\x1F] | - "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes + "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes )* "\"" ws -number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws +number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [1-9] [0-9]{0,15})? ws # Optional space: by convention, applied in this grammar after literal chars when allowed -ws ::= ([ \t\n] ws)? +ws ::= | " " | "\n" [ \t]{0,20} diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index 052c0807310ce..a33104dea3598 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -105,14 +105,14 @@ static void test_all(const std::string & lang, std::function