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* 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>
174 lines
6.7 KiB
C++
174 lines
6.7 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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bool run_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
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{
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#if defined(BUILD_INT4_EXAMPLE) && defined(CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4)
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static_assert(sizeof(ck::int4_t) == sizeof(int8_t));
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#endif
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using namespace ck::literals;
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auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
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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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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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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<QuantDataType> quant_b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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// assume scale tensor is [1, n]
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Tensor<ScaleDataType> scale_k_n(
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HostTensorDescriptor({K, N}, {0, 1_uz}, ck::tensor_layout::BypassLayoutVerification()));
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switch(config.init_method)
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{
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case 0: break;
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case 1:
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ck::utils::FillUniformDistributionIntegerValue<ADataType>{-1.f, 1.f}(a_m_k);
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ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{-1.f, 1.f}(quant_b_k_n);
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ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{-1.f, 1.f}(scale_k_n);
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break;
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case 2:
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ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
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ck::utils::FillUniformDistribution<QuantDataType>{-1.f, 1.f}(quant_b_k_n);
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ck::utils::FillUniformDistribution<ScaleDataType>{-1.f, 1.f}(scale_k_n);
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break;
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default:
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ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
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ck::utils::FillUniformDistribution<QuantDataType>{-1.f, 1.f}(quant_b_k_n);
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ck::utils::FillUniformDistribution<ScaleDataType>{-1.f, 1.f}(scale_k_n);
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}
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UnsignedWeightPreprocessor<QuantDataType> preprocessor;
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Tensor<BDataType> b_k_n = preprocessor(quant_b_k_n);
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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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 << "scale_k_n: " << scale_k_n.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
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#ifdef BUILD_INT4_EXAMPLE
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DeviceMem a_m_k_device_buf(sizeof(KernelADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_device_buf(sizeof(KernelBDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_device_buf(sizeof(KernelCDataType) *
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c_m_n_device_result.mDesc.GetElementSpaceSize());
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const Tensor<KernelADataType> a_m_k_converted(a_m_k);
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const Tensor<KernelBDataType> b_k_n_converted(b_k_n);
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a_m_k_device_buf.ToDevice(a_m_k_converted.mData.data());
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b_k_n_device_buf.ToDevice(b_k_n_converted.mData.data());
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#else
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DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem scale_k_n_device_buf(sizeof(ScaleDataType) * scale_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
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a_m_k_device_buf.ToDevice(a_m_k.mData.data());
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b_k_n_device_buf.ToDevice(b_k_n.mData.data());
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scale_k_n_device_buf.ToDevice(scale_k_n.mData.data());
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#endif
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto c_element_op = CElementOp{};
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// do GEMM
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auto gemm = DeviceGemmInstance{};
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auto invoker = gemm.MakeInvoker();
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auto argument = gemm.MakeArgument(
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#ifdef BUILD_INT4_EXAMPLE
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static_cast<KernelADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<KernelBDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<KernelCDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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#else
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static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<ScaleDataType*>(scale_k_n_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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#endif
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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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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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if(!gemm.IsSupportedArgument(argument))
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{
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std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
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return true;
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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std::size_t flop = 2_uz * 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(CDataType) * 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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<< gemm.GetTypeString() << std::endl;
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if(config.do_verification)
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{
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(a_m_k,
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quant_b_k_n,
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scale_k_n,
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c_m_n_host_result,
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a_element_op,
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b_element_op,
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c_element_op);
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ref_invoker.Run(ref_argument);
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#ifdef BUILD_INT4_EXAMPLE
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Tensor<CDataType> c_m_n_device_result_converted(c_m_n_host_result.mDesc);
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c_m_n_device_buf.FromDevice(c_m_n_device_result_converted.mData.data());
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c_m_n_device_result = c_m_n_device_result_converted.CopyAsType<CDataType>();
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return ck::utils::check_err(c_m_n_device_result_converted, c_m_n_host_result);
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#else
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c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
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return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
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#endif
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}
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return true;
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}
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bool run_gemm_example(int argc, char* argv[])
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{
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ProblemSize problem_size;
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ExecutionConfig config;
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return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
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}
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