mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-04 13:41:24 +00:00
868e5c555b
* modify comment * trim unnecessary check * add gemm spec in kernel name * add TNTT gemm_gemm + atten kernel instances * refactor attention padding to better fit in unit tests This streamlines usage where "ResetNaNToMinusInf" is now hidden from user facing device op. Also added compile-time conditionals that load OOB value as NaN only after padding is enabled * add adhoc padding test for atten * shrink input value range for attention kernel validation to avoid occasional error by 1e-3 Still unsure whether this kind of deterministic floating point accurary issue is expected or not. May want to try exact same approach as the GPU kernel in the host reference GEMM+Softmax+GEMM function to see if the accuracy discrepancy goes away. Until then, shrink the input value range as it is less likely to produce errors of around ~1e-3. * attention kernel proper granular padding for all 4 dims * IsSupportedArgument checks * test more padded cases * block PadK specialization in attention kernels * workaround clang crash for gfx908 (gfx908 only) workaround for compiler crash in fused kernels on mainline #9110; #10738 seems ok error message was "fatal error: error in backend: Error while trying to spill VGPR0 from class VGPR_32: Cannot scavenge register without an emergency spill slot!" this fall back to less ideal way of handle NPadding in fused attention kernel * comment out kernels giving wrong results on MI100; MI200 doesn't seem affected
334 lines
15 KiB
C++
334 lines
15 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, 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_softmax_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_softmax_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/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_softmax.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_softmax_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 Acc0ElementOp = PassThrough;
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using B1ElementOp = PassThrough;
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using CElementOp = PassThrough;
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using AccDataType = float;
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// Ref Gemm0: various type in, fp32 out
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using ReferenceGemm0Instance = tensor_operation::host::ReferenceBatchedGemm<ADataType,
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B0DataType,
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AccDataType,
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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 Softmax: fp32 in, various type out
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using ReferenceSoftmaxInstance =
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tensor_operation::host::ReferenceSoftmax<AccDataType, ADataType, AccDataType>;
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// Ref Gemm1: various type in, various type out
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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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if(std::is_same<decltype(layout), Row>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
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std::vector<std::size_t>({batch_stride, stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
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std::vector<std::size_t>({batch_stride, 1, stride}));
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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<AccDataType> acc0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
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Tensor<ADataType> a1_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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std::srand(1); // work around test flakiness
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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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// Still unsure whether this kind of deterministic floating point accurary issue is expected
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// or not. May want to try exact same approach as the GPU kernel in the host reference
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// GEMM+Softmax+GEMM function to see if the accuracy discrepancy goes away. Until then,
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// shrink the input value range as it is less likely to produce errors of around ~1e-3.
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// a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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// b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
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// b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-5, 5});
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a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
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b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
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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<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::DeviceBatchedGemmSoftmaxGemm<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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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_softmax = ReferenceSoftmaxInstance{};
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auto ref_softmax_invoker = ref_softmax.MakeInvoker();
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auto ref_softmax_argument = ref_softmax.MakeArgument(acc0_g_m_n, a1_g_m_n, 1, 0, {2});
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ref_softmax_invoker.Run(ref_softmax_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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a1_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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// 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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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 &
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ck::utils::check_err(c_g_m_o_device_result.mData, c_g_m_o_host_result.mData);
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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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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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