mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-07-18 01:28:27 +00:00
revert exp changes.
This commit is contained in:
Haocong WANG
@@ -25,17 +25,13 @@ add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_v2)
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add_example_executable(example_gemm_xdl_fp16_streamk_v3 gemm_xdl_fp16_streamk_v3.cpp)
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add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_streamk_v3)
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add_example_executable(example_gemm_xdl_fp16_v3 gemm_xdl_fp16_v3.cpp)
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target_compile_options(example_gemm_xdl_fp16_v3 PRIVATE -mllvm -greedy-reverse-local-assignment=1 -save-temps=$PWD -Wno-gnu-line-marker)
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add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_v3)
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add_example_executable(example_gemm_xdl_fp8_v3 gemm_xdl_fp8_v3.cpp)
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add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_v3)
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target_compile_options(example_gemm_xdl_fp8_v3 PRIVATE -mllvm -greedy-reverse-local-assignment=1 -save-temps=$PWD -Wno-gnu-line-marker)
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add_example_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp)
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add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8_v3)
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add_example_executable(example_gemm_xdl_bf16_v3 gemm_xdl_bf16_v3.cpp)
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target_compile_options(example_gemm_xdl_bf16_v3 PRIVATE -mllvm -greedy-reverse-local-assignment=1 -save-temps=$PWD -Wno-gnu-line-marker)
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add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3)
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target_compile_options(example_gemm_xdl_bf16_v3 PRIVATE -mllvm -greedy-reverse-local-assignment=1 -save-temps=$PWD -Wno-gnu-line-marker)
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add_example_executable(example_gemm_xdl_wavelet_fp16 gemm_xdl_wavelet_fp16.cpp)
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add_example_dependencies(example_gemm_xdl example_gemm_xdl_wavelet_fp16)
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@@ -86,4 +82,4 @@ add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8)
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add_custom_target(example_gemm_wmma)
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add_example_executable(example_gemm_wmma_fp16 gemm_wmma_fp16.cpp)
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add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
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add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
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@@ -12,14 +12,14 @@ using CShuffleDataType = ck::bhalf_t;
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using CDataType = ck::bhalf_t;
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using ALayout = Row;
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using BLayout = Row;
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using BLayout = Col;
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using CLayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CElementOp = PassThrough;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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// clang-format off
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using DeviceGemmV2Instance =
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@@ -28,14 +28,14 @@ using DeviceGemmV2Instance =
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ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
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PassThrough, PassThrough, PassThrough, GemmDefault,
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256,
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224, 256,
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64, 8, 1,
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128, 128,
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64, 8, 8,
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16, 16,
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7, 8,
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4, 4,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
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2, 8, 8, 0,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
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2, 8, 8, 0,
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S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>,
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1, 8, 8, 1,
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1, 2, S<1, 32, 1, 8>, 8,
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ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3>;
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// clang-format on
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@@ -19,7 +19,7 @@ using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CElementOp = PassThrough;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
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// clang-format off
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using DeviceGemmV2Instance =
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@@ -35,7 +35,7 @@ using DeviceGemmV2Instance =
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
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2, 8, 8, 0,
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S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>,
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1, 8, 8, 0,
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1, 8, 2, 0,
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1, 2, S<1, 32, 1, 8>, 8,
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ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3>;
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// clang-format on
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@@ -8,8 +8,8 @@
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using ADataType = ck::f8_t;
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using BDataType = ck::f8_t;
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using AccDataType = float;
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using CShuffleDataType = ck::bhalf_t;
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using CDataType = ck::bhalf_t;
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using CShuffleDataType = ck::half_t;
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using CDataType = ck::half_t;
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using ALayout = Row;
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using BLayout = Col;
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@@ -28,10 +28,10 @@ using DeviceGemmV2Instance =
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ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
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PassThrough, PassThrough, PassThrough, GemmDefault,
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256,
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256, 256,
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224, 256,
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128, 16, 16,
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16, 16,
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8, 8,
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7, 8,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
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2, 16, 16, 0,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
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@@ -40,14 +40,8 @@ using DeviceGemmV2Instance =
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ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3, ck::f8_t>;
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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CDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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CElementOp,
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ck::f8_t>;
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using ReferenceGemmInstance = ck::tensor_operation::host::
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ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
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#include "run_gemm_example_v2.inc"
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@@ -1,5 +1,3 @@
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add_example_executable(example_gemm_bilinear_wmma_fp16 gemm_bilinear_wmma_fp16.cpp)
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add_example_executable(example_gemm_bilinear_wmma_int8 gemm_bilinear_wmma_int8.cpp)
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add_example_executable(example_gemm_bilinear_xdl_fp16 gemm_bilinear_xdl_fp16.cpp)
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add_example_executable(example_gemm_bilinear_xdl_fp16_v3 gemm_bilinear_xdl_fp16_v3.cpp)
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target_compile_options(example_gemm_bilinear_xdl_fp16_v3 PRIVATE -mllvm -greedy-reverse-local-assignment=1 -save-temps=$PWD -Wno-gnu-line-marker)
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@@ -1,308 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
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#include "ck/library/utility/check_err.hpp"
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struct AlphaBetaAdd
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{
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AlphaBetaAdd(float alpha, float beta) : alpha_(alpha), beta_(beta){};
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template <typename E, typename C, typename D>
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__host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
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template <>
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__host__ __device__ constexpr void operator()<ck::half_t, float, ck::half_t>(
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ck::half_t& e, const float& c, const ck::half_t& d) const
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{
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e = ck::type_convert<ck::half_t>(alpha_ * c + beta_ * ck::type_convert<float>(d));
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};
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float alpha_;
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float beta_;
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};
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F16 = ck::half_t;
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using F32 = float;
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F32;
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using DDataType = F16;
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using EDataType = F16;
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using ALayout = Row;
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using BLayout = Col;
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using DLayout = Row;
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using ELayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = AlphaBetaAdd;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
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using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3<
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ALayout,
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BLayout,
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DsLayout,
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ELayout,
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ADataType,
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BDataType,
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DsDataType,
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EDataType,
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AccDataType,
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CShuffleDataType,
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AElementOp,
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BElementOp,
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CDEElementOp,
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GemmDefault,
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256,
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128,
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128,
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64,
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8,
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8,
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16,
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16,
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4,
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4,
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S<8, 32, 1>,
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S<1, 0, 2>,
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S<1, 0, 2>,
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2,
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8,
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8,
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0,
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S<8, 32, 1>,
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S<1, 0, 2>,
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S<1, 0, 2>,
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2,
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8,
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8,
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0,
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1,
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2,
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S<1, 32, 1, 8>,
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S<8, 8, 1>,
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ck::BlockGemmPipelineScheduler::Intrawave,
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ck::BlockGemmPipelineVersion::v3>;
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int main(int argc, char* argv[])
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = false;
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// GEMM shape
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ck::index_t M = 3840;
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ck::index_t N = 4096;
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ck::index_t K = 4096;
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ck::index_t StrideA = 4096;
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ck::index_t StrideB = 4096;
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ck::index_t StrideD = 4096;
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ck::index_t StrideE = 4096;
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float alpha = 1.0f;
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float beta = 1.0f;
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if(argc == 1)
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{
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// use default case
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}
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else if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else if(argc == 6)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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alpha = std::stof(argv[4]);
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beta = std::stof(argv[5]);
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}
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else if(argc == 13)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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M = std::stoi(argv[4]);
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N = std::stoi(argv[5]);
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K = std::stoi(argv[6]);
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StrideA = std::stoi(argv[7]);
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StrideB = std::stoi(argv[8]);
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StrideD = std::stoi(argv[9]);
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StrideE = std::stoi(argv[10]);
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alpha = std::stof(argv[11]);
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beta = std::stof(argv[12]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: time kernel (0=no, 1=yes)\n");
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printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, alpha, "
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"beta\n");
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exit(0);
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}
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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using namespace ck::literals;
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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DLayout{}));
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Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
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std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
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break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem d_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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d_device_buf.ToDevice(d_m_n.mData.data());
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e_device_buf.ToDevice(e_m_n_device_result.mData.data());
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto cde_element_op = CDEElementOp{alpha, beta};
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// do GEMM
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auto device_op = DeviceOpInstance{};
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auto invoker = device_op.MakeInvoker();
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auto argument =
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device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
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e_device_buf.GetDeviceBuffer(),
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M,
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N,
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K,
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StrideA,
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StrideB,
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std::array<ck::index_t, 1>{StrideD},
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StrideE,
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1,
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a_element_op,
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b_element_op,
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cde_element_op);
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if(!device_op.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_gemm with the specified compilation parameters does "
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"not support this GEMM problem");
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
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<< std::endl;
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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_m_n({M, N});
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
|
||||
}
|
||||
}
|
||||
|
||||
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
|
||||
|
||||
return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -1,4 +1,3 @@
|
||||
add_example_executable(example_gemm_multiply_multiply_xdl_fp8 gemm_multiply_multiply_xdl_fp8.cpp)
|
||||
target_compile_options(example_gemm_multiply_multiply_xdl_fp8 PRIVATE -mllvm -greedy-reverse-local-assignment=1 -save-temps=$PWD -Wno-gnu-line-marker)
|
||||
add_example_executable(example_gemm_multiply_multiply_xdl_fp8_ab_scale gemm_multiply_multiply_xdl_fp8_ab_scale.cpp)
|
||||
add_example_executable(example_gemm_add_add_xdl_fp16 gemm_add_add_xdl_fp16.cpp)
|
||||
|
||||
@@ -24,9 +24,9 @@
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using BF16 = ck::bhalf_t;
|
||||
using FP8 = ck::f8_t;
|
||||
using F32 = float;
|
||||
using F16 = ck::half_t;
|
||||
using FP8 = ck::f8_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
@@ -38,7 +38,7 @@ using CShuffleDataType = F32;
|
||||
using D0DataType = F32;
|
||||
using D1DataType = F32;
|
||||
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
|
||||
using EDataType = BF16;
|
||||
using EDataType = F16;
|
||||
|
||||
using A0Layout = Row;
|
||||
using B0Layout = Col;
|
||||
@@ -54,12 +54,12 @@ struct MultiplyMultiply
|
||||
operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
|
||||
|
||||
template <>
|
||||
__host__ __device__ constexpr void operator()<ck::bhalf_t, float, float, float>(
|
||||
ck::bhalf_t& e, const float& c, const float& d0, const float& d1) const
|
||||
__host__ __device__ constexpr void operator()<ck::half_t, float, float, float>(
|
||||
ck::half_t& e, const float& c, const float& d0, const float& d1) const
|
||||
{
|
||||
const float x0_f = c * d0 * d1;
|
||||
|
||||
e = ck::type_convert<ck::bhalf_t>(x0_f);
|
||||
e = ck::type_convert<ck::half_t>(x0_f);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -69,7 +69,7 @@ using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = MultiplyMultiply;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNPadding;
|
||||
|
||||
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3
|
||||
// clang-format off
|
||||
@@ -80,16 +80,7 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShu
|
||||
///###### RRR
|
||||
///< Row, Row, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 256, 128, 64, 16, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, 1, 1, S<1, 32, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, FP8>;
|
||||
///###### RCR
|
||||
< Row, Col, DsLayout, ELayout,
|
||||
A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
|
||||
AElementOp, BElementOp, CDEElementOp, GemmSpec, 256,
|
||||
256, 256, 128,
|
||||
16, 16,
|
||||
16, 16,
|
||||
8, 8,
|
||||
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
|
||||
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
|
||||
1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, FP8>;
|
||||
< Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 256, 128, 64, 16, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, FP8>;
|
||||
// clang-format on
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
@@ -265,8 +256,7 @@ int main(int argc, char* argv[])
|
||||
AccDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
FP8>;
|
||||
PassThrough>;
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
|
||||
@@ -1,84 +1,84 @@
|
||||
# ckProfiler
|
||||
set(PROFILER_SOURCES
|
||||
profiler.cpp
|
||||
# profile_gemm.cpp
|
||||
# profile_reduce.cpp
|
||||
# profile_groupnorm_bwd_data.cpp
|
||||
# profile_groupnorm_fwd.cpp
|
||||
# profile_layernorm_bwd_data.cpp
|
||||
# profile_layernorm_bwd_gamma_beta.cpp
|
||||
# profile_groupnorm_bwd_gamma_beta.cpp
|
||||
# profile_layernorm_fwd.cpp
|
||||
# profile_max_pool3d_fwd.cpp
|
||||
# profile_avg_pool3d_bwd.cpp
|
||||
# profile_max_pool3d_bwd.cpp
|
||||
# profile_softmax.cpp
|
||||
# profile_batchnorm_fwd.cpp
|
||||
# profile_batchnorm_bwd.cpp
|
||||
# profile_batchnorm_infer.cpp
|
||||
# profile_conv_tensor_rearrange.cpp
|
||||
# profile_transpose.cpp
|
||||
# profile_permute_scale.cpp
|
||||
profile_gemm.cpp
|
||||
profile_reduce.cpp
|
||||
profile_groupnorm_bwd_data.cpp
|
||||
profile_groupnorm_fwd.cpp
|
||||
profile_layernorm_bwd_data.cpp
|
||||
profile_layernorm_bwd_gamma_beta.cpp
|
||||
profile_groupnorm_bwd_gamma_beta.cpp
|
||||
profile_layernorm_fwd.cpp
|
||||
profile_max_pool3d_fwd.cpp
|
||||
profile_avg_pool3d_bwd.cpp
|
||||
profile_max_pool3d_bwd.cpp
|
||||
profile_softmax.cpp
|
||||
profile_batchnorm_fwd.cpp
|
||||
profile_batchnorm_bwd.cpp
|
||||
profile_batchnorm_infer.cpp
|
||||
profile_conv_tensor_rearrange.cpp
|
||||
profile_transpose.cpp
|
||||
profile_permute_scale.cpp
|
||||
)
|
||||
|
||||
if(GPU_TARGETS MATCHES "gfx9")
|
||||
# if(DTYPES MATCHES "fp32" OR DTYPES MATCHES "fp64" OR NOT DEFINED DTYPES)
|
||||
# list(APPEND PROFILER_SOURCES profile_contraction_bilinear.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_contraction_scale.cpp)
|
||||
# endif()
|
||||
# if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_reduce.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_batched_gemm_gemm.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_batched_gemm_add_relu_gemm_add.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_add.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_add_add_fastgelu.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_add_fastgelu.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_gemm.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_streamk.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_fastgelu.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_add_relu.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_add_silu.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_add_relu_add_layernorm.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_gemm_fixed_nk.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_gemm_two_stage.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_gemm_fastgelu.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_gemm_tile_loop.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_gemm_multiply_tile_loop.cpp)
|
||||
# endif()
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_multiply_add.cpp)
|
||||
# if(GPU_TARGETS MATCHES "gfx94")
|
||||
if(DTYPES MATCHES "fp32" OR DTYPES MATCHES "fp64" OR NOT DEFINED DTYPES)
|
||||
list(APPEND PROFILER_SOURCES profile_contraction_bilinear.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_contraction_scale.cpp)
|
||||
endif()
|
||||
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_reduce.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_batched_gemm_gemm.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_batched_gemm_add_relu_gemm_add.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_add.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_add_add_fastgelu.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_add_fastgelu.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_gemm.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_streamk.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_fastgelu.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_add_relu.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_add_silu.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_add_relu_add_layernorm.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_gemm_fixed_nk.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_gemm_two_stage.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_gemm_fastgelu.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_gemm_tile_loop.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_gemm_multiply_tile_loop.cpp)
|
||||
endif()
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_multiply_add.cpp)
|
||||
if(GPU_TARGETS MATCHES "gfx94")
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_multiply_multiply.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_ab_scale.cpp)
|
||||
# endif()
|
||||
# list(APPEND PROFILER_SOURCES profile_batched_gemm.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_batched_gemm_reduce.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_add_multiply.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_bias_add_reduce.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_splitk.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_ab_scale.cpp)
|
||||
endif()
|
||||
list(APPEND PROFILER_SOURCES profile_batched_gemm.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_batched_gemm_reduce.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_add_multiply.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_bias_add_reduce.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_splitk.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_universal.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_universal_reduce.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_universal_streamk.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_conv_fwd_bias_relu.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_conv_fwd_bias_relu_add.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_conv_bwd_data.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_conv_fwd.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_conv_fwd_outelementop.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_universal_reduce.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_universal_streamk.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_conv_fwd_bias_relu.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_conv_fwd_bias_relu_add.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_conv_bwd_data.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_conv_fwd.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_conv_fwd_outelementop.cpp)
|
||||
|
||||
endif()
|
||||
|
||||
# if(GPU_TARGETS MATCHES "gfx11" OR GPU_TARGETS MATCHES "gfx12" OR GPU_TARGETS MATCHES "gfx9")
|
||||
# if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_bilinear.cpp)
|
||||
# endif()
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_conv_fwd.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_conv_bwd_data.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_conv_bwd_weight.cpp)
|
||||
# endif()
|
||||
if(GPU_TARGETS MATCHES "gfx11" OR GPU_TARGETS MATCHES "gfx12" OR GPU_TARGETS MATCHES "gfx9")
|
||||
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_bilinear.cpp)
|
||||
endif()
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_conv_fwd.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_conv_bwd_data.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_conv_bwd_weight.cpp)
|
||||
endif()
|
||||
|
||||
# if(DL_KERNELS)
|
||||
# list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_grouped_conv_bwd_weight.cpp)
|
||||
# endif()
|
||||
if(DL_KERNELS)
|
||||
list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_grouped_conv_bwd_weight.cpp)
|
||||
endif()
|
||||
|
||||
set(PROFILER_EXECUTABLE ckProfiler)
|
||||
|
||||
@@ -91,85 +91,85 @@ if(NOT WIN32 AND ${hip_VERSION_FLAT} GREATER 600241132)
|
||||
endif()
|
||||
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE utility getopt::getopt)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_fwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_bwd_data_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_bwd_gamma_beta_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool3d_fwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_avg_pool3d_bwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_max_pool_bwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_image_to_column_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_column_to_image_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_transpose_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_permute_scale_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_fwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_bwd_data_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_bwd_gamma_beta_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool3d_fwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_avg_pool3d_bwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_max_pool_bwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_image_to_column_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_column_to_image_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_transpose_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_permute_scale_instance)
|
||||
|
||||
if(GPU_TARGETS MATCHES "gfx9")
|
||||
# if(DTYPES MATCHES "fp32" OR DTYPES MATCHES "fp64" OR NOT DEFINED DTYPES)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_bilinear_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_instance)
|
||||
# endif()
|
||||
# if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_add_fastgelu_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_fastgelu_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_gemm_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_add_relu_gemm_add_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_streamk_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_fastgelu_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_silu_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_add_layernorm_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fixed_nk_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_tile_loop_instance)
|
||||
# endif()
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_reduce_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_multiply_add_instance)
|
||||
# if(GPU_TARGETS MATCHES "gfx94")
|
||||
if(DTYPES MATCHES "fp32" OR DTYPES MATCHES "fp64" OR NOT DEFINED DTYPES)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_bilinear_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_instance)
|
||||
endif()
|
||||
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_add_fastgelu_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_fastgelu_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_gemm_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_add_relu_gemm_add_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_streamk_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_fastgelu_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_silu_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_add_layernorm_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fixed_nk_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_tile_loop_instance)
|
||||
endif()
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_reduce_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_multiply_add_instance)
|
||||
if(GPU_TARGETS MATCHES "gfx94")
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_multiply_multiply_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_ab_scale_instance)
|
||||
# endif()
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_ab_scale_instance)
|
||||
endif()
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_reduce_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_streamk_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_multiply_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_reduce_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bias_add_reduce_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_add_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_fwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv1d_bwd_data_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv3d_bwd_data_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_bwd_data_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_bwd_weight_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_convscale_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_convinvscale_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_reduce_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_streamk_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_multiply_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_reduce_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bias_add_reduce_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_add_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_fwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv1d_bwd_data_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv3d_bwd_data_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_bwd_data_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_bwd_weight_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_convscale_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_convinvscale_instance)
|
||||
endif()
|
||||
|
||||
# if(GPU_TARGETS MATCHES "gfx9" OR GPU_TARGETS MATCHES "gfx11" OR GPU_TARGETS MATCHES "gfx12")
|
||||
# if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bilinear_instance)
|
||||
# endif()
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_data_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_fwd_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance)
|
||||
# endif()
|
||||
if(GPU_TARGETS MATCHES "gfx9" OR GPU_TARGETS MATCHES "gfx11" OR GPU_TARGETS MATCHES "gfx12")
|
||||
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bilinear_instance)
|
||||
endif()
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_data_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_fwd_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance)
|
||||
endif()
|
||||
|
||||
# if(DL_KERNELS)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_bwd_weight_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance)
|
||||
# endif()
|
||||
if(DL_KERNELS)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_bwd_weight_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance)
|
||||
endif()
|
||||
|
||||
rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)
|
||||
|
||||
Reference in New Issue
Block a user