mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
gongweibao
544 lines
21 KiB
Plaintext
544 lines
21 KiB
Plaintext
/*
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* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <cuda_bf16.h>
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#ifndef _WIN32
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wstrict-aliasing"
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#endif // #ifndef _WIN32
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/numeric_types.h"
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#ifndef _WIN32
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#pragma GCC diagnostic pop
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#endif // #ifndef _WIN32
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#include <cuda_runtime_api.h>
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#include <set>
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#include <vector>
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#include "cutlass_kernels/cutlass_heuristic.h"
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using namespace cutlass_extensions;
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namespace kernels {
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namespace cutlass_kernels {
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struct TileShape {
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int m;
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int n;
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};
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TileShape get_cta_shape_for_config(CutlassTileConfig tile_config) {
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switch (tile_config) {
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case CutlassTileConfig::CtaShape16x128x64_WarpShape16x32x64:
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return TileShape{16, 128};
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case CutlassTileConfig::CtaShape16x256x64_WarpShape16x64x64:
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return TileShape{16, 256};
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case CutlassTileConfig::CtaShape32x128x64_WarpShape32x32x64:
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return TileShape{32, 128};
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case CutlassTileConfig::CtaShape64x64x128_WarpShape32x64x64:
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return TileShape{64, 64};
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case CutlassTileConfig::CtaShape64x128x64_WarpShape32x64x64:
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case CutlassTileConfig::CtaShape64x128x64_WarpShape64x32x64:
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return TileShape{64, 128};
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case CutlassTileConfig::CtaShape128x64x64_WarpShape64x32x64:
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return TileShape{128, 64};
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case CutlassTileConfig::CtaShape128x128x8_WarpShape64x64x8:
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case CutlassTileConfig::CtaShape128x128x64_WarpShape64x32x64:
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case CutlassTileConfig::CtaShape128x128x64_WarpShape64x64x64:
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case CutlassTileConfig::CtaShape128x128x64_WarpShape128x32x64:
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return TileShape{128, 128};
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case CutlassTileConfig::CtaShape128x256x64_WarpShape64x64x64:
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return TileShape{128, 256};
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case CutlassTileConfig::CtaShape256x128x64_WarpShape64x64x64:
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return TileShape{256, 128};
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case CutlassTileConfig::CtaShape16x256x128_WarpShape16x64x128:
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return TileShape{16, 256};
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default:
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throw("[get_grid_shape_for_config] Invalid config");
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}
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}
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bool is_valid_split_k_factor(int64_t const m,
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int64_t const n,
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int64_t const k,
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TileShape const tile_shape,
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int const split_k_factor,
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size_t const workspace_bytes,
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bool const is_weight_only) {
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// All tile sizes have a k_tile of 64.
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static constexpr int k_tile = 128;
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// For weight-only quant, we need k and k_elements_per_split to be a multiple
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// of cta_k
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if (is_weight_only) {
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if ((k % k_tile) != 0) {
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return false;
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}
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if ((k % split_k_factor) != 0) {
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return false;
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}
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int const k_elements_per_split = k / split_k_factor;
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if ((k_elements_per_split % k_tile) != 0) {
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return false;
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}
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}
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// Check that the workspace has sufficient space for this split-k factor
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int const ctas_in_m_dim = (m + tile_shape.m - 1) / tile_shape.m;
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int const ctas_in_n_dim = (n + tile_shape.n - 1) / tile_shape.n;
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int const required_ws_bytes =
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split_k_factor == 1 ? 0 : sizeof(int) * ctas_in_m_dim * ctas_in_n_dim;
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if (required_ws_bytes > workspace_bytes) {
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return false;
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}
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return true;
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}
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std::vector<CutlassTileConfig> get_candidate_tiles(
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int const sm,
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CutlassGemmConfig::CandidateConfigTypeParam const config_type_param) {
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enum class CutlassGemmType : char { Default, WeightOnly, Simt, Int8, Fp8 };
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CutlassGemmType gemm_type = CutlassGemmType::Default;
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if (config_type_param & CutlassGemmConfig::SIMT_ONLY) {
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gemm_type = CutlassGemmType::Simt;
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} else if (config_type_param & CutlassGemmConfig::WEIGHT_ONLY) {
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gemm_type = CutlassGemmType::WeightOnly;
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} else if (config_type_param & CutlassGemmConfig::INT8_ONLY) {
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gemm_type = CutlassGemmType::Int8;
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} else if (config_type_param & CutlassGemmConfig::FP8_ONLY) {
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gemm_type = CutlassGemmType::Fp8;
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}
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std::vector<CutlassTileConfig> base_configs{
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CutlassTileConfig::CtaShape32x128x64_WarpShape32x32x64,
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CutlassTileConfig::CtaShape64x128x64_WarpShape32x64x64};
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if (sm >= 75) {
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base_configs.push_back(
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CutlassTileConfig::CtaShape128x128x64_WarpShape64x32x64);
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}
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switch (gemm_type) {
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case CutlassGemmType::Simt:
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return {CutlassTileConfig::CtaShape128x128x8_WarpShape64x64x8};
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case CutlassGemmType::WeightOnly:
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if (sm >= 75) {
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return {CutlassTileConfig::CtaShape16x128x64_WarpShape16x32x64,
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CutlassTileConfig::CtaShape16x256x64_WarpShape16x64x64,
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CutlassTileConfig::CtaShape32x128x64_WarpShape32x32x64,
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CutlassTileConfig::CtaShape64x128x64_WarpShape64x32x64,
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CutlassTileConfig::CtaShape128x128x64_WarpShape128x32x64};
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} else {
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return {CutlassTileConfig::CtaShape32x128x64_WarpShape32x32x64,
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CutlassTileConfig::CtaShape64x128x64_WarpShape64x32x64};
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}
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case CutlassGemmType::Int8:
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return {CutlassTileConfig::CtaShape32x128x64_WarpShape32x32x64,
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CutlassTileConfig::CtaShape64x128x64_WarpShape64x32x64,
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CutlassTileConfig::CtaShape128x64x64_WarpShape64x32x64,
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CutlassTileConfig::CtaShape64x64x128_WarpShape32x64x64,
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CutlassTileConfig::CtaShape128x256x64_WarpShape64x64x64,
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CutlassTileConfig::CtaShape256x128x64_WarpShape64x64x64};
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case CutlassGemmType::Fp8:
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if (config_type_param & CutlassGemmConfig::GROUPED_GEMM) {
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if (sm == 89) {
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return {CutlassTileConfig::CtaShape16x256x128_WarpShape16x64x128,
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CutlassTileConfig::CtaShape32x128x64_WarpShape32x32x64,
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CutlassTileConfig::CtaShape64x128x64_WarpShape64x32x64,
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CutlassTileConfig::CtaShape64x64x128_WarpShape32x64x64,
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CutlassTileConfig::CtaShape128x64x64_WarpShape64x32x64,
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CutlassTileConfig::CtaShape128x256x64_WarpShape64x64x64,
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CutlassTileConfig::CtaShape256x128x64_WarpShape64x64x64};
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} else {
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// no valid ampere style fp8 configs for sm90
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return {};
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}
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}
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default:
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return base_configs;
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}
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}
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std::vector<CutlassTileConfigSM90> get_candidate_tiles_sm90(
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int const sm, CutlassGemmConfig::CandidateConfigTypeParam const config) {
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#ifdef FAST_BUILD
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// Fast build disables all configs except this one for SM90
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return {CutlassTileConfigSM90::CtaShape128x128x128B};
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#else
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if (config & CutlassGemmConfig::GROUPED_GEMM) {
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return {CutlassTileConfigSM90::CtaShape128x16x128B,
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CutlassTileConfigSM90::CtaShape128x32x128B,
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CutlassTileConfigSM90::CtaShape128x64x128B,
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CutlassTileConfigSM90::CtaShape128x128x128B,
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CutlassTileConfigSM90::CtaShape128x256x128B,
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CutlassTileConfigSM90::CtaShape256x128x128B};
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} else {
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return {CutlassTileConfigSM90::CtaShape64x16x128B,
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CutlassTileConfigSM90::CtaShape64x32x128B,
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CutlassTileConfigSM90::CtaShape64x64x128B,
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CutlassTileConfigSM90::CtaShape64x128x128B,
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CutlassTileConfigSM90::CtaShape64x256x128B,
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CutlassTileConfigSM90::CtaShape128x16x128B,
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CutlassTileConfigSM90::CtaShape128x32x128B,
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CutlassTileConfigSM90::CtaShape128x64x128B,
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CutlassTileConfigSM90::CtaShape128x128x128B,
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CutlassTileConfigSM90::CtaShape128x256x128B};
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}
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#endif
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}
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// We only compile CUTLASS kernels with multi-cast along M if the M tile is >=
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// 128. This is purely to improve compilation speed.
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bool supports_mcast_along_m(CutlassTileConfigSM90 const tile) {
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#ifdef FAST_BUILD
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return false;
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#else
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std::set<CutlassTileConfigSM90> valid_tiles{
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CutlassTileConfigSM90::CtaShape128x16x128B,
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CutlassTileConfigSM90::CtaShape128x32x128B,
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CutlassTileConfigSM90::CtaShape128x64x128B,
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CutlassTileConfigSM90::CtaShape128x128x128B,
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CutlassTileConfigSM90::CtaShape128x256x128B,
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CutlassTileConfigSM90::CtaShape256x128x128B};
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return valid_tiles.count(tile) == 1;
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#endif
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}
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// We only compile CUTLASS kernels with multi-cast along N if the N tile is >=
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// 128. This is purely to improve compilation speed.
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bool supports_mcast_along_n(CutlassTileConfigSM90 const tile) {
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#ifdef FAST_BUILD
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return false;
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#else
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std::set<CutlassTileConfigSM90> valid_tiles{
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CutlassTileConfigSM90::CtaShape64x128x128B,
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CutlassTileConfigSM90::CtaShape64x256x128B,
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CutlassTileConfigSM90::CtaShape128x128x128B,
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CutlassTileConfigSM90::CtaShape128x256x128B,
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CutlassTileConfigSM90::CtaShape256x128x128B};
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return valid_tiles.count(tile) == 1;
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#endif
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}
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// SM100 (Blackwell) candidate tile configurations
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std::vector<CutlassTileConfigSM100> get_candidate_tiles_sm100(
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int /*sm*/, CutlassGemmConfig::CandidateConfigTypeParam const config) {
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#ifdef FAST_BUILD
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return {CutlassTileConfigSM100::CtaShape128x128x128B};
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#else
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/* Grouped-GEMM path first (Blackwell uses 1-SM and 2-SM “cluster” kernels)
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*/
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if (config & CutlassGemmConfig::GROUPED_GEMM) {
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if (config & CutlassGemmConfig::FP4_ONLY) // nvfp4 / mx_fp4
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{
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return {/* 1 SM (M=128) */
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CutlassTileConfigSM100::CtaShape128x128x128B,
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CutlassTileConfigSM100::CtaShape128x256x128B,
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/* 2 SM (M=256) */
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CutlassTileConfigSM100::CtaShape256x128x128B,
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CutlassTileConfigSM100::CtaShape256x256x128B,
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/* slim tiles for very tall matrices */
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CutlassTileConfigSM100::CtaShape128x64x128B,
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CutlassTileConfigSM100::CtaShape256x64x128B};
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}
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/* Fp8 / Fp16 grouped-GEMM */
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return {CutlassTileConfigSM100::CtaShape128x128x128B,
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CutlassTileConfigSM100::CtaShape128x256x128B,
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CutlassTileConfigSM100::CtaShape256x128x128B,
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CutlassTileConfigSM100::CtaShape256x256x128B};
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}
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/* Non-grouped path (plain GEMM or weight-only) */
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return {/* 1 SM tiles */
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CutlassTileConfigSM100::CtaShape64x64x128B,
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CutlassTileConfigSM100::CtaShape64x128x128B,
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CutlassTileConfigSM100::CtaShape64x256x128B,
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CutlassTileConfigSM100::CtaShape128x64x128B,
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CutlassTileConfigSM100::CtaShape128x128x128B,
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CutlassTileConfigSM100::CtaShape128x256x128B,
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/* 2 SM tiles */
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CutlassTileConfigSM100::CtaShape256x64x128B,
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CutlassTileConfigSM100::CtaShape256x128x128B,
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CutlassTileConfigSM100::CtaShape256x256x128B};
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#endif
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}
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// M-multicast support for SM100.
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bool supports_mcast_along_m_sm100(CutlassTileConfigSM100 tile) {
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#ifdef FAST_BUILD
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return false;
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#else
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std::set<CutlassTileConfigSM100> m_tiles{
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CutlassTileConfigSM100::CtaShape128x64x128B,
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CutlassTileConfigSM100::CtaShape128x128x128B,
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CutlassTileConfigSM100::CtaShape128x256x128B,
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CutlassTileConfigSM100::CtaShape256x64x128B,
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CutlassTileConfigSM100::CtaShape256x128x128B,
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CutlassTileConfigSM100::CtaShape256x256x128B};
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return m_tiles.count(tile) == 1;
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#endif
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}
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// N-multicast support for SM100.
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bool supports_mcast_along_n_sm100(CutlassTileConfigSM100 tile) {
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#ifdef FAST_BUILD
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return false;
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#else
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std::set<CutlassTileConfigSM100> n_tiles{
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CutlassTileConfigSM100::CtaShape64x128x128B,
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CutlassTileConfigSM100::CtaShape64x256x128B,
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CutlassTileConfigSM100::CtaShape128x128x128B,
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CutlassTileConfigSM100::CtaShape128x256x128B,
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CutlassTileConfigSM100::CtaShape256x128x128B};
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return n_tiles.count(tile) == 1;
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#endif
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}
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std::vector<CutlassGemmConfig> get_candidate_configs(
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int sm,
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int const max_split_k,
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CutlassGemmConfig::CandidateConfigTypeParam const config_type_param) {
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if (sm == 90 && (config_type_param & CutlassGemmConfig::HOPPER)) {
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std::vector<CutlassTileConfigSM90> tiles =
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get_candidate_tiles_sm90(sm, config_type_param);
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std::vector<CutlassGemmConfig> candidate_configs;
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for (auto const& tile_config : tiles) {
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CutlassGemmConfig config(tile_config,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_1x1x1);
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candidate_configs.push_back(config);
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bool const has_m_mcast = supports_mcast_along_m(tile_config);
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bool const has_n_mcast = supports_mcast_along_n(tile_config);
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if (has_m_mcast) {
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CutlassGemmConfig config(tile_config,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_2x1x1);
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candidate_configs.push_back(config);
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}
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if (has_n_mcast) {
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CutlassGemmConfig config(tile_config,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_1x2x1);
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candidate_configs.push_back(config);
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}
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if (has_m_mcast && has_n_mcast) {
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CutlassGemmConfig config(tile_config,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_2x2x1);
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candidate_configs.push_back(config);
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}
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}
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return candidate_configs;
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} else if (sm == 100 &&
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(config_type_param &
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CutlassGemmConfig::BLACKWELL)) // Assuming SM100 for Blackwell
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{
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std::vector<CutlassTileConfigSM100> tiles =
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get_candidate_tiles_sm100(sm, config_type_param);
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std::vector<CutlassGemmConfig> candidate_configs;
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for (auto const& tile_config_sm100 : tiles) {
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// SM100 uses MainloopScheduleType::AUTO, EpilogueScheduleType::AUTO
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// similar to SM90. Cluster shapes are also handled similarly.
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CutlassGemmConfig config(tile_config_sm100,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_1x1x1);
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candidate_configs.push_back(config);
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bool const has_m_mcast = supports_mcast_along_m_sm100(tile_config_sm100);
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bool const has_n_mcast = supports_mcast_along_n_sm100(tile_config_sm100);
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if (has_m_mcast) {
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CutlassGemmConfig mcast_m_config(tile_config_sm100,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_2x1x1);
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candidate_configs.push_back(mcast_m_config);
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}
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if (has_n_mcast) {
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CutlassGemmConfig mcast_n_config(tile_config_sm100,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_1x2x1);
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candidate_configs.push_back(mcast_n_config);
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}
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if (has_m_mcast && has_n_mcast) {
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CutlassGemmConfig mcast_mn_config(tile_config_sm100,
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MainloopScheduleType::AUTO,
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EpilogueScheduleType::AUTO,
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ClusterShape::ClusterShape_2x2x1);
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candidate_configs.push_back(mcast_mn_config);
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}
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}
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return candidate_configs;
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}
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// Fallback to older architecture configurations
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std::vector<CutlassTileConfig> tiles =
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get_candidate_tiles(sm, config_type_param);
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std::vector<CutlassGemmConfig>
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candidate_configs; // Already declared above for SM90 path, ensure scope
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// is correct or redeclare if necessary.
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// It's fine here as it's within an else if / else
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// block.
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bool const int8_configs_only =
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config_type_param & CutlassGemmConfig::INT8_ONLY;
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int const min_stages = int8_configs_only ? 3 : 2;
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int const max_stages = int8_configs_only ? 6 : (sm >= 80 ? 4 : 2);
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for (auto const& tile_config : tiles) {
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for (int stages = min_stages; stages <= max_stages; ++stages) {
|
|
CutlassGemmConfig config(tile_config, SplitKStyle::NO_SPLIT_K, 1, stages);
|
|
candidate_configs.push_back(config);
|
|
if (sm >= 75) {
|
|
for (int split_k_factor = 2; split_k_factor <= max_split_k;
|
|
++split_k_factor) {
|
|
auto config = CutlassGemmConfig{
|
|
tile_config, SplitKStyle::SPLIT_K_SERIAL, split_k_factor, stages};
|
|
candidate_configs.push_back(config);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return candidate_configs;
|
|
}
|
|
|
|
CutlassGemmConfig estimate_best_config_from_occupancies(
|
|
std::vector<CutlassGemmConfig> const& candidate_configs,
|
|
std::vector<int> const& occupancies,
|
|
int64_t const m,
|
|
int64_t const n,
|
|
int64_t const k,
|
|
int64_t const num_experts,
|
|
int const split_k_limit,
|
|
size_t const workspace_bytes,
|
|
int const multi_processor_count,
|
|
int const is_weight_only) {
|
|
if (occupancies.size() != candidate_configs.size()) {
|
|
throw(
|
|
"[estimate_best_config_from_occupancies] occpancies and "
|
|
"candidate configs vectors must have equal length.");
|
|
}
|
|
|
|
CutlassGemmConfig best_config;
|
|
// Score will be [0, 1]. The objective is to minimize this score.
|
|
// It represents the fraction of SM resources unused in the last wave.
|
|
float config_score = 1.0f;
|
|
int config_waves = INT_MAX;
|
|
int current_m_tile = 0;
|
|
|
|
int const max_split_k = n >= multi_processor_count * 256 ? 1 : split_k_limit;
|
|
for (int ii = 0; ii < candidate_configs.size(); ++ii) {
|
|
CutlassGemmConfig candidate_config = candidate_configs[ii];
|
|
TileShape tile_shape =
|
|
get_cta_shape_for_config(candidate_config.tile_config);
|
|
int occupancy = occupancies[ii];
|
|
|
|
if (occupancy == 0) {
|
|
continue;
|
|
}
|
|
|
|
// Keep small tile sizes when possible.
|
|
if (best_config.tile_config != CutlassTileConfig::ChooseWithHeuristic &&
|
|
m < current_m_tile && current_m_tile < tile_shape.m) {
|
|
continue;
|
|
}
|
|
|
|
int const ctas_in_m_dim = (m + tile_shape.m - 1) / tile_shape.m;
|
|
int const ctas_in_n_dim = (n + tile_shape.n - 1) / tile_shape.n;
|
|
|
|
for (int split_k_factor = 1; split_k_factor <= max_split_k;
|
|
++split_k_factor) {
|
|
if (is_valid_split_k_factor(m,
|
|
n,
|
|
k,
|
|
tile_shape,
|
|
split_k_factor,
|
|
workspace_bytes,
|
|
is_weight_only)) {
|
|
int const ctas_per_wave = occupancy * multi_processor_count;
|
|
int const ctas_for_problem =
|
|
ctas_in_m_dim * ctas_in_n_dim * split_k_factor;
|
|
|
|
int const num_waves_total =
|
|
(ctas_for_problem + ctas_per_wave - 1) / ctas_per_wave;
|
|
float const num_waves_fractional =
|
|
ctas_for_problem / float(ctas_per_wave);
|
|
float const current_score =
|
|
float(num_waves_total) - num_waves_fractional;
|
|
|
|
float const score_slack = 0.1f;
|
|
if (current_score < config_score ||
|
|
((config_waves > num_waves_total) &&
|
|
(current_score < config_score + score_slack))) {
|
|
config_score = current_score;
|
|
config_waves = num_waves_total;
|
|
SplitKStyle split_style = split_k_factor > 1
|
|
? SplitKStyle::SPLIT_K_SERIAL
|
|
: SplitKStyle::NO_SPLIT_K;
|
|
best_config = CutlassGemmConfig(candidate_config.tile_config,
|
|
split_style,
|
|
split_k_factor,
|
|
candidate_config.stages);
|
|
current_m_tile = tile_shape.m;
|
|
} else if (current_score == config_score &&
|
|
(best_config.stages < candidate_config.stages ||
|
|
split_k_factor < best_config.split_k_factor ||
|
|
current_m_tile < tile_shape.m)) {
|
|
// Prefer deeper pipeline or smaller split-k
|
|
SplitKStyle split_style = split_k_factor > 1
|
|
? SplitKStyle::SPLIT_K_SERIAL
|
|
: SplitKStyle::NO_SPLIT_K;
|
|
best_config = CutlassGemmConfig(candidate_config.tile_config,
|
|
split_style,
|
|
split_k_factor,
|
|
candidate_config.stages);
|
|
current_m_tile = tile_shape.m;
|
|
config_waves = num_waves_total;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (best_config.tile_config == CutlassTileConfig::ChooseWithHeuristic) {
|
|
throw("Heurisitc failed to find a valid config.");
|
|
}
|
|
|
|
return best_config;
|
|
}
|
|
|
|
} // namespace cutlass_kernels
|
|
} // namespace kernels
|