mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
db376dd8a4
* enable gfx940
* switch between intrinsic mfma routines on mi100/200 and mi300
* fix mfma_int8 on MI300
* disable 2 int8 examples on MI300
* Update cmake-ck-dev.sh
* restore gitignore file
* modify Jenkinsfile to the internal repo
* Bump rocm-docs-core from 0.24.0 to 0.29.0 in /docs/sphinx
Bumps [rocm-docs-core](https://github.com/RadeonOpenCompute/rocm-docs-core) from 0.24.0 to 0.29.0.
- [Release notes](https://github.com/RadeonOpenCompute/rocm-docs-core/releases)
- [Changelog](https://github.com/RadeonOpenCompute/rocm-docs-core/blob/develop/CHANGELOG.md)
- [Commits](https://github.com/RadeonOpenCompute/rocm-docs-core/compare/v0.24.0...v0.29.0)
---
updated-dependencies:
- dependency-name: rocm-docs-core
dependency-type: direct:production
update-type: version-update:semver-minor
...
Signed-off-by: dependabot[bot] <support@github.com>
* initial enablement of gfx950
* fix clang format
* disable examples 31 and 41 int8 on gfx950
* add code
* fix build wip
* fix xx
* now can build
* naming
* minor fix
* wip fix
* fix macro for exp2; fix warpgemm a/b in transposedC
* unify as tuple_array
* Update the required Python version to 3.9
* Update executable name in test scripts
* re-structure tuple/array to avoid spill
* Merge function templates
* Fix format
* Add constraint to array<> ctor
* Re-use function
* Some minor changes
* remove wrong code in store_raw()
* fix compile issue in transpose
* Rename enum
Rename 'cood_transform_enum' to 'coord_transform_enum'
* let more integral_constant->constant, and formating
* make sure thread_buffer can be tuple/array
* temp fix buffer_store spill
* not using custom data type by default, now we can have ISA-level same code as opt_padding
* fix compile error, fp8 not ready now
* fix fp8 duplicated move/shift/and/or problem
* Default use CK_TILE_FLOAT_TO_FP8_STOCHASTIC rounding mode
* fix scratch in fp8 kernel
* update some readme
* fix merge from upstream
* sync with upstream
* sync upstream again
* sync 22
* remove unused
* fix clang-format
* update README of ck_tile example
* fix several issue
* let python version to be 3.8 as minimal
* remove ck_tile example from default cmake target like all/install/check
* remove mistake
* 1).support receipe in generate.py 2).use simplified mask type 3).change left/right to pass into karg
* fix some bug in group-mode masking and codegen. update README
* F8 quantization for FMHA forward (#1224)
* Add SAccElementFunction, PComputeElementFunction, OAccElementFunction in pipeline
* Add element function to fmha api
* Adjust P elementwise function
* Fix bug of elementwise op, our elementwise op is not inout
* Add some elementwise op, prepare to quantization
* Let generate.py can generate different elementwise function
* To prevent compiler issue, remove the elementwise function we have not used.
* Remove f8 pipeline, we should share the same pipeline even in f8
* Remove remove_cvref_t
* Avoid warning
* Fix wrong fp8 QK/KV block gemm setting
* Check fp8 rounding error in check_err()
* Set fp8 rounding error for check_err()
* Use CK_TILE_FLOAT_TO_FP8_STANDARD as default fp8 rounding mode
* 1. codgen the f8 api and kernel
2. f8 host code
* prevent warning in filter mode
* Remove not-in-use elementwise function kargs
* Remove more not-in-use elementwise function kargs
* Small refinements in C++ source files
* Use conditional_t<> to simplify code
* Support heterogeneous argument for binary function types
* Re-use already-existing scales<> functor template
* Fix wrong value produced by saturating
* Generalize the composes<> template
* Unify saturates<> implementation
* Fix type errors in composes<>
* Extend less_equal<>
* Reuse the existing template less_equal<> in check_err()
* Add equal<float> & equal<double>
* Rename check_err() parameter
* Rename check_err() parameter
* Add FIXME comment for adding new macro in future
* Remove unnecessary cast to void
* Eliminate duplicated code
* Avoid dividing api pool into more than 2 groups
* Use more clear variable names
* Use affirmative condition in if stmt
* Remove blank lines
* Donot perfect forwarding in composes<>
* To fix compile error, revert generate.py back to 4439cc107d
* Fix bug of p element function
* Add compute element op to host softmax
* Remove element function in api interface
* Extract user parameter
* Rename pscale and oscale variable
* rename f8 to fp8
* rename more f8 to fp8
* Add pipeline::operator() without element_functor
* 1. Remove deprecated pipeline enum
2. Refine host code parameter
* Use quantization range as input
* 1. Rename max_dtype to dtype_max.
2. Rename scale to scale_s
3.Add init description
* Refine description
* prevent early return
* unify _squant kernel name in cpp, update README
* Adjust the default range.
* Refine error message and bias range
* Add fp8 benchmark and smoke test
* fix fp8 swizzle_factor=4 case
---------
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
Co-authored-by: carlushuang <carlus.huang@amd.com>
---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: illsilin <Illia.Silin@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: Jing Zhang <jizha@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Po-Yen, Chen <PoYen.Chen@amd.com>
Co-authored-by: rocking <ChunYu.Lai@amd.com>
270 lines
11 KiB
C++
270 lines
11 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/fmha.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "mask.hpp"
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#include <type_traits>
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template <typename DataType>
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struct FmhaFwdTypeConfig;
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template <>
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struct FmhaFwdTypeConfig<ck_tile::half_t>
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{
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using QDataType = ck_tile::half_t;
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using KDataType = ck_tile::half_t;
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using VDataType = ck_tile::half_t;
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using BiasDataType = ck_tile::half_t;
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using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
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using SaccDataType = float; // data type for first gemm accumulation
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using SMPLComputeDataType = float; // data type for reduction, softmax
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using PDataType = ck_tile::half_t; // data type for A matrix of second gemm
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using OaccDataType = float; // data type for second gemm accumulation
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using ODataType = ck_tile::half_t;
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};
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template <>
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struct FmhaFwdTypeConfig<ck_tile::bf16_t>
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{
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using QDataType = ck_tile::bf16_t;
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using KDataType = ck_tile::bf16_t;
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using VDataType = ck_tile::bf16_t;
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using BiasDataType = ck_tile::bf16_t;
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using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
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using SaccDataType = float; // data type for first gemm accumulation
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using SMPLComputeDataType = float; // data type for reduction, softmax
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using PDataType = ck_tile::bf16_t; // data type for A matrix of second gemm
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using OaccDataType = float; // data type for second gemm accumulation
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using ODataType = ck_tile::bf16_t;
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};
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template <>
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struct FmhaFwdTypeConfig<ck_tile::fp8_t>
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{
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using QDataType = ck_tile::fp8_t;
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using KDataType = ck_tile::fp8_t;
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using VDataType = ck_tile::fp8_t;
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using BiasDataType = float;
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using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
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using SaccDataType = float; // data type for first gemm accumulation
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using SMPLComputeDataType = float; // data type for reduction, softmax
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using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm
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using OaccDataType = float; // data type for second gemm accumulation
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using ODataType = ck_tile::fp8_t;
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};
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template <>
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struct FmhaFwdTypeConfig<ck_tile::bf8_t>
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{
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using QDataType = ck_tile::bf8_t;
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using KDataType = ck_tile::bf8_t;
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using VDataType = ck_tile::bf8_t;
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using BiasDataType = ck_tile::bf8_t;
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using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
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using SaccDataType = float; // data type for first gemm accumulation
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using SMPLComputeDataType = float; // data type for reduction, softmax
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using PDataType = ck_tile::bf8_t; // data type for A matrix of second gemm
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using OaccDataType = float; // data type for second gemm accumulation
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using ODataType = ck_tile::bf8_t;
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};
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struct FmhaMasks
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{
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using NoMask = ck_tile::GenericAttentionMask<false>;
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using GenericMask = ck_tile::GenericAttentionMask<true, true>;
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using CausalMask = ck_tile::GenericAttentionMask<true, false>;
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};
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// runtime args, some will passed to karg, some will used to compute grids/blocks
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struct fmha_fwd_args
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{
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const void* q_ptr;
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const void* k_ptr;
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const void* v_ptr;
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const void* bias_ptr;
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void* lse_ptr;
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void* o_ptr;
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const void* seqstart_q_ptr;
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const void* seqstart_k_ptr;
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const void* seqlen_k_ptr;
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ck_tile::index_t seqlen_q;
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ck_tile::index_t seqlen_k;
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ck_tile::index_t batch;
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ck_tile::index_t max_seqlen_q;
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ck_tile::index_t hdim_q;
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ck_tile::index_t hdim_v;
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ck_tile::index_t nhead_q;
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ck_tile::index_t nhead_k;
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float scale_s;
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float scale_p;
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float scale_o;
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ck_tile::index_t stride_q;
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ck_tile::index_t stride_k;
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ck_tile::index_t stride_v;
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ck_tile::index_t stride_bias;
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ck_tile::index_t stride_o;
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ck_tile::index_t nhead_stride_q;
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ck_tile::index_t nhead_stride_k;
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ck_tile::index_t nhead_stride_v;
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ck_tile::index_t nhead_stride_bias;
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ck_tile::index_t nhead_stride_lse;
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ck_tile::index_t nhead_stride_o;
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ck_tile::index_t batch_stride_q;
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ck_tile::index_t batch_stride_k;
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ck_tile::index_t batch_stride_v;
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ck_tile::index_t batch_stride_bias;
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ck_tile::index_t batch_stride_lse;
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ck_tile::index_t batch_stride_o;
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ck_tile::index_t window_size_left;
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ck_tile::index_t window_size_right;
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ck_tile::index_t mask_type;
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};
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template <typename FmhaKernel>
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auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
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{
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assert(args.nhead_q % args.nhead_k == 0);
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auto kargs = [&] {
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// create group mode kernel arguments
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if constexpr(FmhaKernel::kIsGroupMode)
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{
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return FmhaKernel::MakeKargs(args.q_ptr,
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args.k_ptr,
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args.v_ptr,
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args.bias_ptr,
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args.lse_ptr,
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args.o_ptr,
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args.seqstart_q_ptr,
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args.seqstart_k_ptr,
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args.seqlen_k_ptr,
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args.hdim_q,
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args.hdim_v,
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args.nhead_q / args.nhead_k,
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args.scale_s,
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args.scale_p,
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args.scale_o,
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args.stride_q,
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args.stride_k,
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args.stride_v,
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args.stride_bias,
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args.stride_o,
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args.nhead_stride_q,
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args.nhead_stride_k,
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args.nhead_stride_v,
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args.nhead_stride_bias,
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args.nhead_stride_lse,
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args.nhead_stride_o,
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args.window_size_left,
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args.window_size_right,
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args.mask_type);
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}
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else
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{ // create batch mode kernel arguments
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return FmhaKernel::MakeKargs(args.q_ptr,
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args.k_ptr,
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args.v_ptr,
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args.bias_ptr,
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args.lse_ptr,
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args.o_ptr,
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args.seqlen_q,
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args.seqlen_k,
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args.hdim_q,
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args.hdim_v,
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args.nhead_q / args.nhead_k,
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args.scale_s,
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args.scale_p,
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args.scale_o,
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args.stride_q,
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args.stride_k,
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args.stride_v,
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args.stride_bias,
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args.stride_o,
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args.nhead_stride_q,
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args.nhead_stride_k,
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args.nhead_stride_v,
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args.nhead_stride_bias,
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args.nhead_stride_lse,
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args.nhead_stride_o,
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args.batch_stride_q,
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args.batch_stride_k,
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args.batch_stride_v,
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args.batch_stride_bias,
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args.batch_stride_lse,
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args.batch_stride_o,
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args.window_size_left,
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args.window_size_right,
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args.mask_type);
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}
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}();
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dim3 grids = FmhaKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v);
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return ck_tile::make_tuple(kargs, grids);
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}
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// this is used to pattern-match internl kernel implementation, not to instantiate kernel
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template <ck_tile::index_t HDim_,
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typename DataType_,
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bool kIsGroupMode_,
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ck_tile::index_t kM0_,
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ck_tile::index_t kN0_,
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ck_tile::index_t kK0_,
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ck_tile::index_t kN1_,
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ck_tile::index_t kK1_,
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ck_tile::index_t kK0BlockLength_,
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bool kIsVLayoutRowMajor_,
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ck_tile::BlockFmhaPipelineEnum FmhaPipelineEnum_,
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typename FmhaMask_,
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bool kHasBias_,
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bool kStoreLse_,
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bool kDoFp8StaticQuant_,
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bool kPadS_,
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bool kPadSK_,
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bool kPadD_,
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bool kPadDv_>
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struct fmha_fwd_traits_
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{
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static constexpr ck_tile::index_t HDim = HDim_;
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using DataType = ck_tile::remove_cvref_t<DataType_>;
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static constexpr bool kIsGroupMode = kIsGroupMode_;
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static constexpr ck_tile::index_t kM0 = kM0_;
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static constexpr ck_tile::index_t kN0 = kN0_;
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static constexpr ck_tile::index_t kK0 = kK0_;
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static constexpr ck_tile::index_t kN1 = kN1_;
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static constexpr ck_tile::index_t kK1 = kK1_;
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static constexpr ck_tile::index_t kK0BlockLength = kK0BlockLength_;
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static constexpr bool kIsVLayoutRowMajor = kIsVLayoutRowMajor_;
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static constexpr auto FmhaPipelineEnum = FmhaPipelineEnum_;
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using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
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static constexpr bool kHasBias = kHasBias_;
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static constexpr bool kStoreLse = kStoreLse_;
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static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
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static constexpr bool kPadS = kPadS_;
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static constexpr bool kPadSK = kPadSK_;
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static constexpr bool kPadD = kPadD_;
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static constexpr bool kPadDv = kPadDv_;
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};
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template <typename Traits_>
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float fmha_fwd_(const ck_tile::stream_config&, fmha_fwd_args);
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// This is the public API, will be generated by script
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struct fmha_fwd_traits
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{
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int hdim_q;
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int hdim_v;
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std::string data_type;
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bool is_group_mode;
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bool is_v_rowmajor;
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mask_enum mask_type;
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bool has_bias;
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bool has_lse;
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bool do_fp8_static_quant;
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// TODO: padding check is inside this api
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};
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float fmha_fwd(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
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