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https://github.com/ROCm/composable_kernel.git
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Merge commit 'd5ae81b2922773f7cdf4a02a2e1fd57d0e4df851' into develop
This commit is contained in:
assistant-librarian[bot]
@@ -0,0 +1,387 @@
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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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/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/batched_gemm_add_relu_gemm_add.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 A0Layout,
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typename B0Layout,
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typename D0sLayout,
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typename B1Layout,
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typename D1sLayout,
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typename E1Layout,
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typename A0DataType,
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typename B0DataType,
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typename D0sDataType,
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typename B1DataType,
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typename D1sDataType,
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typename E1DataType,
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typename A0ElementOp,
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typename B0ElementOp,
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typename CDE0ElementOp,
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typename B1ElementOp,
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typename CDE1ElementOp>
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bool profile_batched_gemm_multiple_d_gemm_multiple_d_impl(
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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 StrideA0 = -1,
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int StrideB0 = -1,
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int StrideD0 = -1,
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int StrideB1 = -1,
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int StrideD1 = -1,
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int StrideE1 = -1,
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int BatchStrideA0 = -1,
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int BatchStrideB0 = -1,
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int BatchStrideD0 = -1,
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int BatchStrideB1 = -1,
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int BatchStrideD1 = -1,
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int BatchStrideE1 = -1,
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bool fail_if_no_supported_instance = false)
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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 D0DataType = remove_cvref_t<tuple_element_t<0, D0sDataType>>;
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using D0Layout = remove_cvref_t<tuple_element_t<0, D0sLayout>>;
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using D1DataType = remove_cvref_t<tuple_element_t<0, D1sDataType>>;
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using D1Layout = remove_cvref_t<tuple_element_t<0, D1sLayout>>;
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// for reference
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using RefAcc0DataType = float;
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using RefAcc1DataType = float;
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bool pass = true;
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const int DefaultStrideA0 = ck::is_same_v<A0Layout, Row> ? K : M;
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const int DefaultStrideB0 = ck::is_same_v<B0Layout, Row> ? N : K;
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const int DefaultStrideD0 = ck::is_same_v<D0Layout, Row> ? N : M;
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const int DefaultStrideB1 = ck::is_same_v<B1Layout, Row> ? O : N;
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const int DefaultStrideD1 = ck::is_same_v<D1Layout, Row> ? O : M;
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const int DefaultStrideE1 = ck::is_same_v<E1Layout, Row> ? O : M;
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StrideA0 = (StrideA0 < 0) ? DefaultStrideA0 : StrideA0;
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StrideB0 = (StrideB0 < 0) ? DefaultStrideB0 : StrideB0;
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StrideD0 = (StrideD0 < 0) ? DefaultStrideD0 : StrideD0;
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StrideB1 = (StrideB1 < 0) ? DefaultStrideB1 : StrideB1;
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StrideD1 = (StrideD1 < 0) ? DefaultStrideD1 : StrideD1;
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StrideE1 = (StrideE1 < 0) ? DefaultStrideE1 : StrideE1;
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const int DefaultBatchStrideA0 = (ck::is_same_v<A0Layout, Col> ? K : M) * StrideA0;
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const int DefaultBatchStrideB0 = (ck::is_same_v<B0Layout, Col> ? N : K) * StrideB0;
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const int DefaultBatchStrideD0 = (ck::is_same_v<D0Layout, Col> ? N : M) * StrideD0;
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const int DefaultBatchStrideB1 = (ck::is_same_v<B1Layout, Col> ? O : N) * StrideB1;
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const int DefaultBatchStrideD1 = (ck::is_same_v<D1Layout, Col> ? O : M) * StrideD1;
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const int DefaultBatchStrideE1 = (ck::is_same_v<E1Layout, Col> ? O : M) * StrideE1;
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BatchStrideA0 = BatchStrideA0 < 0 ? DefaultBatchStrideA0 : BatchStrideA0;
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BatchStrideB0 = BatchStrideB0 < 0 ? DefaultBatchStrideB0 : BatchStrideB0;
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BatchStrideD0 = BatchStrideD0 < 0 ? DefaultBatchStrideD0 : BatchStrideD0;
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BatchStrideB1 = BatchStrideB1 < 0 ? DefaultBatchStrideB1 : BatchStrideB1;
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BatchStrideD1 = BatchStrideD1 < 0 ? DefaultBatchStrideD1 : BatchStrideD1;
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BatchStrideE1 = BatchStrideE1 < 0 ? DefaultBatchStrideE1 : BatchStrideE1;
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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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// E_m_o = A_m_k * B0_k_n * B1_n_o
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Tensor<A0DataType> a0_g_m_k(
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f_host_tensor_descriptor(BatchCount, M, K, StrideA0, BatchStrideA0, A0Layout{}));
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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<D0DataType> d0_g_m_n(
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f_host_tensor_descriptor(BatchCount, M, N, StrideD0, BatchStrideD0, D0Layout{}));
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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<D1DataType> d1_g_m_o(
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f_host_tensor_descriptor(BatchCount, M, O, StrideD1, BatchStrideD1, D1Layout{}));
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Tensor<E1DataType> e1_g_m_o_host_result(
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f_host_tensor_descriptor(BatchCount, M, O, StrideE1, BatchStrideE1, E1Layout{}));
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Tensor<E1DataType> e1_g_m_o_device_result(
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f_host_tensor_descriptor(BatchCount, M, O, StrideE1, BatchStrideE1, E1Layout{}));
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// Host verification: Output of Gemm0 is input A of Gemm1
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Tensor<RefAcc0DataType> c0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
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Tensor<A0DataType> e0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
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Tensor<RefAcc1DataType> c1_g_m_o(f_host_tensor_descriptor(BatchCount, M, O, O, M * O, Row{}));
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std::cout << "a0_g_m_k: " << a0_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 << "d0_g_m_n: " << d0_g_m_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 << "d1_g_m_o: " << d1_g_m_o.mDesc << std::endl;
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std::cout << "e1_g_m_o: " << e1_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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a0_g_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 3});
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b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 3});
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d0_g_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 3});
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b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 3});
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d1_g_m_o.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 3});
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break;
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default:
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a0_g_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
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b0_g_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
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d0_g_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
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b1_g_n_o.GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
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d1_g_m_o.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
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}
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DeviceMem a0_g_m_k_device_buf(sizeof(A0DataType) * a0_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 d0_g_m_n_device_buf(sizeof(D0DataType) * d0_g_m_n.mDesc.GetElementSpaceSize());
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DeviceMem b1_g_n_o_device_buf(sizeof(B1DataType) * b1_g_n_o.mDesc.GetElementSize());
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DeviceMem d1_g_m_o_device_buf(sizeof(D1DataType) * d1_g_m_o.mDesc.GetElementSpaceSize());
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DeviceMem e1_g_m_o_device_buf(sizeof(E1DataType) *
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e1_g_m_o_device_result.mDesc.GetElementSize());
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a0_g_m_k_device_buf.ToDevice(a0_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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d0_g_m_n_device_buf.ToDevice(d0_g_m_n.mData.data());
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b1_g_n_o_device_buf.ToDevice(b1_g_n_o.mData.data());
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d1_g_m_o_device_buf.ToDevice(d1_g_m_o.mData.data());
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auto a0_element_op = A0ElementOp{};
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auto b0_element_op = B0ElementOp{};
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auto cde0_element_op = CDE0ElementOp{};
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auto b1_element_op = B1ElementOp{};
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auto cde1_element_op = CDE1ElementOp{};
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using DeviceOp =
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tensor_operation::device::DeviceBatchedGemmMultipleDGemmMultipleD<A0Layout,
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B0Layout,
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D0sLayout,
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B1Layout,
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D1sLayout,
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E1Layout,
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A0DataType,
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B0DataType,
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D0sDataType,
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B1DataType,
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D1sDataType,
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E1DataType,
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A0ElementOp,
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B0ElementOp,
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CDE0ElementOp,
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B1ElementOp,
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CDE1ElementOp>;
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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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if(do_verification)
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{
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// Ref Gemm0
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using ReferenceGemm0Instance = tensor_operation::host::ReferenceBatchedGemm<A0DataType,
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B0DataType,
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RefAcc0DataType,
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RefAcc0DataType,
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A0ElementOp,
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B0ElementOp,
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PassThrough>;
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// Ref Gemm1
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using ReferenceGemm1Instance = tensor_operation::host::ReferenceBatchedGemm<A0DataType,
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B1DataType,
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RefAcc1DataType,
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RefAcc1DataType,
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PassThrough,
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B1ElementOp,
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PassThrough>;
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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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a0_g_m_k, b0_g_k_n, c0_g_m_n, a0_element_op, b0_element_op, PassThrough{});
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ref_gemm0_invoker.Run(ref_gemm0_argument);
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// cde0_elementwise
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// Note that we also convert from Acc0DataType to A0DataType to match what the device
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// operation does
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e0_g_m_n.ForEach([&](auto&, auto idx) {
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RefAcc0DataType out;
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cde0_element_op(out, c0_g_m_n(idx), d0_g_m_n(idx));
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e0_g_m_n(idx) = ck::type_convert<A0DataType>(out);
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});
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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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e0_g_m_n, b1_g_n_o, c1_g_m_o, PassThrough{}, b1_element_op, PassThrough{});
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ref_gemm1_invoker.Run(ref_gemm1_argument);
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// cde1_elementwise
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e1_g_m_o_host_result.ForEach([&](auto&, auto idx) {
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cde1_element_op(e1_g_m_o_host_result(idx), c1_g_m_o(idx), d1_g_m_o(idx));
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});
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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 instances_supported = 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<A0DataType*>(a0_g_m_k_device_buf.GetDeviceBuffer()),
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static_cast<B0DataType*>(b0_g_k_n_device_buf.GetDeviceBuffer()),
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std::array<const void*, 1>{d0_g_m_n_device_buf.GetDeviceBuffer()},
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static_cast<B1DataType*>(b1_g_n_o_device_buf.GetDeviceBuffer()),
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std::array<const void*, 1>{d1_g_m_o_device_buf.GetDeviceBuffer()},
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static_cast<E1DataType*>(e1_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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StrideA0,
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StrideB0,
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std::array<ck::index_t, 1>{StrideD0},
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StrideB1,
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std::array<ck::index_t, 1>{StrideD1},
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StrideE1,
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BatchStrideA0,
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BatchStrideB0,
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std::array<ck::index_t, 1>{BatchStrideD0},
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BatchStrideB1,
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std::array<ck::index_t, 1>{BatchStrideD1},
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BatchStrideE1,
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a0_element_op,
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b0_element_op,
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cde0_element_op,
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b1_element_op,
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cde1_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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++instances_supported;
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std::string op_name = op_ptr->GetTypeString();
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||||
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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 =
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(sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(D0DataType) * N +
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sizeof(B1DataType) * N * O + sizeof(E1DataType) * M * O + sizeof(D1DataType) * 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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||||
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||||
if(do_verification)
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||||
{
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||||
e1_g_m_o_device_buf.FromDevice(e1_g_m_o_device_result.mData.data());
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||||
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||||
pass = pass & ck::utils::check_err(e1_g_m_o_device_result, e1_g_m_o_host_result);
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||||
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||||
if(do_log)
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||||
{
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||||
LogRangeAsType<float>(
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std::cout << "e1_g_m_o_host_result : ", e1_g_m_o_host_result.mData, ",")
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||||
<< std::endl;
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||||
LogRangeAsType<float>(
|
||||
std::cout << "e1_g_m_o_device_result : ", e1_g_m_o_device_result.mData, ",")
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||||
<< std::endl;
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||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
if(instances_supported == 0)
|
||||
{
|
||||
if(do_log)
|
||||
{
|
||||
std::cout << "Warning! No supported instances found." << std::endl;
|
||||
}
|
||||
|
||||
if(fail_if_no_supported_instance)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
};
|
||||
|
||||
printf("\033[36mFound %d supported instances\033[0m\n", instances_supported);
|
||||
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
||||
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user