mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-04 13:41:24 +00:00
db2524be2d
* add proper GEMM layout verification * Handle "auto" strides. CalculateStrides only called when tensor's strides are empty or all of them are <=0 (auto strides). CalculateStrides now supports GEMM::ColumnsMajor order. The assumption is still that it applies only to the inner two dims. ValidateStrides throws if any of the tensor's strides is <=0. profile_gemm_multiply_add updated to support "auto" strides for tensors. Manual tests for profile_gemm_multiply_add (matrix B in Row and Col modes) auto-strides bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0 bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0 bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 -1 -1 -1 -1 -1 Note, -1 should be deprecated (use 0 instead) explicit strides (same as auto) bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 128 bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 128 128 128 128 128 explicit strides (not the same as auto) bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138 bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138 mix of explicit and auto strides bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 0 invalid stride bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 64 terminate called after throwing an instance of 'std::runtime_error' what(): Invalid strides for RowMajor: mLens: 128 128 , mStrides: 64 1 Aborted (core dumped) * - add more names to ck::tensor_layout for easier namespace hierarchy checking - updated convolutional layouts to use explicit ones or BaseConvolutionalLayout where it is not clear which layout to use (TBD) - see include/ck/library/utility/convolution_host_tensor_descriptor_helper.hpp * added handling of partially initialized strides for GEMM. fixed more tests. * clang-format and more fixes * replace long dash by a simple hyphen - causes build failure in CK codegen. * increase sizeof input, otherwise output size becomes zero or negative with large filter size * select stride based on layout * specify layout explicitly to avoid errors in HostTensorDescriptor creation * add validation for higher GEMM tensor dimensions.; Add docstring to `HostTensorDescriptor` * Not clear why permute test in test/permute_scale/test_permute_scale.cpp uses a lot of invalid strides. Setting layout to BypassLayoutVerification to avoid a lot of errors * fix test (incl removing invalid config) * fix moe examples: - (in .cpp) add layout argument to non-2D tensors - (in .hpp) fix asserts/failures that show up in Debug mode, specifically addressing 2D tensor by a single index (and 3D tensor by 2d index) * fix moe_gemm2 example. * fix profile and wmma examples * clean-up early mods for ckprofile. verified with: ``` ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0 ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0 ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138 ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138 # ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 1 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 2 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 3 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 128 128 128 # ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 0 0 0 0 # ckProfiler gemm_add_relu 0 1 1 1 0 1 128 128 128 0 0 0 0 # not implemented # ckProfiler gemm_add_relu 0 2 1 1 0 1 128 128 128 0 0 0 0 # not implemented # ckProfiler gemm_add_relu 0 3 1 1 0 1 128 128 128 0 0 0 0 # not implemented ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 128 128 128 128 # ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 1 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 2 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 3 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 130 132 134 136 138 # example_gemm_add_multiply_dl_fp16 example_gemm_add_multiply_xdl_fp16 # ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 0 0 0 ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 128 128 128 ``` * temporary skip first 8 test configs - they throw error * temporary skip first 8 test configs in wmma too - they throw error --------- Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
326 lines
14 KiB
C++
326 lines
14 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <memory>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_batched_gemm_gemm.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/batched_gemm_gemm.hpp"
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#include "ck/library/utility/check_err.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_batched_gemm.hpp"
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namespace ck {
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namespace profiler {
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template <typename ADataType,
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typename B0DataType,
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typename B1DataType,
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typename CDataType,
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typename ALayout,
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typename B0Layout,
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typename B1Layout,
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typename CLayout>
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bool profile_batched_gemm_gemm_impl(bool do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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int M,
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int N,
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int K,
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int O,
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int BatchCount = 1,
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int StrideA = -1,
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int StrideB0 = -1,
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int StrideB1 = -1,
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int StrideC = -1,
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int BatchStrideA = -1,
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int BatchStrideB0 = -1,
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int BatchStrideB1 = -1,
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int BatchStrideC = -1)
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{
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using Row = tensor_layout::gemm::RowMajor;
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using Col = tensor_layout::gemm::ColumnMajor;
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using PassThrough = tensor_operation::element_wise::PassThrough;
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using AElementOp = PassThrough;
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using B0ElementOp = PassThrough;
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using B1ElementOp = PassThrough;
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using Acc0ElementOp = PassThrough;
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using CElementOp = PassThrough;
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using AccDataType = float;
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// Ref Gemm0
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using ReferenceGemm0Instance = tensor_operation::host::ReferenceBatchedGemm<ADataType,
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B0DataType,
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ADataType,
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AccDataType,
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AElementOp,
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B0ElementOp,
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CElementOp>;
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// Ref Gemm
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using ReferenceGemm1Instance = tensor_operation::host::ReferenceBatchedGemm<ADataType,
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B1DataType,
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CDataType,
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AccDataType,
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AElementOp,
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B1ElementOp,
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CElementOp>;
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bool pass = true;
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const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
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const int DefaultStrideB0 = ck::is_same_v<B0Layout, Row> ? N : K;
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const int DefaultStrideB1 = ck::is_same_v<B1Layout, Row> ? O : N;
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const int DefaultStrideC = ck::is_same_v<CLayout, Row> ? O : M;
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StrideA = (StrideA < 0) ? DefaultStrideA : StrideA;
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StrideB0 = (StrideB0 < 0) ? DefaultStrideB0 : StrideB0;
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StrideB1 = (StrideB1 < 0) ? DefaultStrideB1 : StrideB1;
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StrideC = (StrideC < 0) ? DefaultStrideC : StrideC;
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const int DefaultBatchStrideA = (ck::is_same_v<ALayout, Col> ? K : M) * StrideA;
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const int DefaultBatchStrideB0 = (ck::is_same_v<B0Layout, Col> ? N : K) * StrideB0;
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const int DefaultBatchStrideB1 = (ck::is_same_v<B1Layout, Col> ? O : N) * StrideB1;
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const int DefaultBatchStrideC = (ck::is_same_v<CLayout, Col> ? O : M) * StrideC;
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BatchStrideA = BatchStrideA < 0 ? DefaultBatchStrideA : BatchStrideA;
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BatchStrideB0 = BatchStrideB0 < 0 ? DefaultBatchStrideB0 : BatchStrideB0;
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BatchStrideB1 = BatchStrideB1 < 0 ? DefaultBatchStrideB1 : BatchStrideB1;
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BatchStrideC = BatchStrideC < 0 ? DefaultBatchStrideC : BatchStrideC;
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auto f_host_tensor_descriptor = [](std::size_t batch_count,
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std::size_t row,
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std::size_t col,
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std::size_t stride,
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std::size_t batch_stride,
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auto layout) {
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using namespace ck::literals;
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if(std::is_same<decltype(layout), Row>::value)
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{
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return HostTensorDescriptor(
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{batch_count, row, col}, {batch_stride, stride, 1_uz}, layout);
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}
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else
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{
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return HostTensorDescriptor(
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{batch_count, row, col}, {batch_stride, 1_uz, stride}, layout);
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}
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};
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// C_m_o = A_m_k * B0_k_n * B1_n_o
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Tensor<ADataType> a_g_m_k(
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f_host_tensor_descriptor(BatchCount, M, K, StrideA, BatchStrideA, ALayout{}));
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Tensor<B0DataType> b0_g_k_n(
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f_host_tensor_descriptor(BatchCount, K, N, StrideB0, BatchStrideB0, B0Layout{}));
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Tensor<B1DataType> b1_g_n_o(
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f_host_tensor_descriptor(BatchCount, N, O, StrideB1, BatchStrideB1, B1Layout{}));
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Tensor<CDataType> c_g_m_o_host_result(
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f_host_tensor_descriptor(BatchCount, M, O, StrideC, BatchStrideC, CLayout{}));
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Tensor<CDataType> c_g_m_o_device_result(
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f_host_tensor_descriptor(BatchCount, M, O, StrideC, BatchStrideC, CLayout{}));
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// Host verification: Output of Gemm0 is input A of Gemm1
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Tensor<ADataType> acc0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
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std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
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std::cout << "b0_g_k_n: " << b0_g_k_n.mDesc << std::endl;
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std::cout << "b1_g_n_o: " << b1_g_n_o.mDesc << std::endl;
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std::cout << "c_g_m_o: " << c_g_m_o_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_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 3});
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b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 3});
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b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 3});
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break;
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case 2:
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a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b0_g_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{0.0, 1.0});
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b1_g_n_o.GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
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break;
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case 3:
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a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b0_g_k_n.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
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b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
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break;
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default:
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a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
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b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
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b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
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}
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DeviceMem a_g_m_k_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSize());
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DeviceMem b0_g_k_n_device_buf(sizeof(B0DataType) * b0_g_k_n.mDesc.GetElementSize());
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DeviceMem b1_g_n_o_device_buf(sizeof(B1DataType) * b1_g_n_o.mDesc.GetElementSize());
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DeviceMem c_g_m_o_device_buf(sizeof(CDataType) * c_g_m_o_device_result.mDesc.GetElementSize());
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a_g_m_k_device_buf.ToDevice(a_g_m_k.mData.data());
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b0_g_k_n_device_buf.ToDevice(b0_g_k_n.mData.data());
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b1_g_n_o_device_buf.ToDevice(b1_g_n_o.mData.data());
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auto a_element_op = AElementOp{};
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auto b0_element_op = B0ElementOp{};
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auto acc0_element_op = Acc0ElementOp{};
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auto b1_element_op = B1ElementOp{};
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auto c_element_op = CElementOp{};
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using DeviceOp = tensor_operation::device::DeviceBatchedGemmGemm<ALayout,
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B0Layout,
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B1Layout,
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CLayout,
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ADataType,
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B0DataType,
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B1DataType,
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CDataType,
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AElementOp,
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B0ElementOp,
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Acc0ElementOp,
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B1ElementOp,
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CElementOp>;
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// get device op instances
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const auto op_ptrs = tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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// early fail when no instances are found
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if(op_ptrs.size() == 0)
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{
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return false;
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}
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if(do_verification)
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{
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auto ref_gemm0 = ReferenceGemm0Instance{};
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auto ref_gemm0_invoker = ref_gemm0.MakeInvoker();
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auto ref_gemm0_argument = ref_gemm0.MakeArgument(
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a_g_m_k, b0_g_k_n, acc0_g_m_n, a_element_op, b0_element_op, PassThrough{});
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ref_gemm0_invoker.Run(ref_gemm0_argument);
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auto ref_gemm1 = ReferenceGemm1Instance{};
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auto ref_gemm1_invoker = ref_gemm1.MakeInvoker();
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auto ref_gemm1_argument = ref_gemm1.MakeArgument(
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acc0_g_m_n, b1_g_n_o, c_g_m_o_host_result, PassThrough{}, b1_element_op, c_element_op);
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ref_gemm1_invoker.Run(ref_gemm1_argument);
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}
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std::string best_op_name;
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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int num_supported_instances = 0;
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// profile device op instances
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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static_cast<ADataType*>(a_g_m_k_device_buf.GetDeviceBuffer()),
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static_cast<B0DataType*>(b0_g_k_n_device_buf.GetDeviceBuffer()),
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static_cast<B1DataType*>(b1_g_n_o_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_g_m_o_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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O,
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BatchCount,
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StrideA,
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StrideB0,
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StrideB1,
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StrideC,
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BatchStrideA,
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BatchStrideB0,
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BatchStrideB1,
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BatchStrideC,
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a_element_op,
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b0_element_op,
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acc0_element_op,
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b1_element_op,
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c_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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num_supported_instances++;
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std::string op_name = op_ptr->GetTypeString();
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float ave_time =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
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std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N +
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sizeof(B1DataType) * N * O + sizeof(CDataType) * M * O) *
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BatchCount;
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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
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<< " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_op_name = op_name;
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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if(do_verification)
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{
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c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data());
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pass = pass & ck::utils::check_err(c_g_m_o_device_result, c_g_m_o_host_result);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a_g_m_k: ", a_g_m_k.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "b0_g_k_n : ", b0_g_k_n.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "b1_g_n_o : ", b1_g_n_o.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_g_m_o_host_result : ", c_g_m_o_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_g_m_o_device_result : ", c_g_m_o_device_result.mData, ",")
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<< std::endl;
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}
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}
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}
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else
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{
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std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
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}
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}
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printf("\033[36mFound %d supported instances\033[0m\n", num_supported_instances);
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
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<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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return pass;
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}
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} // namespace profiler
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} // namespace ck
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