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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>
153 lines
6.5 KiB
C++
153 lines
6.5 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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#include <iostream>
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#include "ck/ck.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_avgpool_bwd.hpp"
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template <typename TensorLayout>
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std::vector<ck::index_t> f_tensor_strides_ncdhw(ck::index_t N_,
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ck::index_t C_,
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ck::index_t D,
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ck::index_t H,
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ck::index_t W,
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TensorLayout layout)
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{
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using namespace ck::literals;
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(void)N_;
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
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return {C_ * D * H * W, D * H * W, H * W, W, 1_uz};
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else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
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return {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_};
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throw std::runtime_error("Avgpool3d_bwd: problem with layout. ");
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return {0, 0, 0, 0, 0};
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};
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template <typename TensorLayout>
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HostTensorDescriptor f_host_tensor_descriptor(std::size_t N_,
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std::size_t C_,
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std::size_t D,
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std::size_t H,
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std::size_t W,
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TensorLayout layout)
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{
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using namespace ck::literals;
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
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{
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return HostTensorDescriptor(
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{N_, C_, D, H, W}, {C_ * D * H * W, D * H * W, H * W, W, 1_uz}, layout);
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}
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else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
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{
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return HostTensorDescriptor(
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{N_, C_, D, H, W}, {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_}, layout);
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}
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throw std::runtime_error("Avgpool3d_bwd: problem with layout. ");
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return HostTensorDescriptor({0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, layout);
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};
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template <typename DevicePoolBwdInstance,
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typename DOutDataType,
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typename DInDataType,
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typename DOutLayout,
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typename DInLayout>
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bool pool3d_bwd_test(bool do_verification,
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bool time_kernel,
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ck::index_t N,
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ck::index_t C,
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ck::index_t Di,
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ck::index_t Hi,
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ck::index_t Wi,
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std::vector<ck::index_t> window_lengths,
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std::vector<ck::index_t> window_strides,
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std::vector<ck::index_t> window_dilations,
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std::vector<ck::index_t> dinput_left_pads,
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std::vector<ck::index_t> dinput_right_pads)
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{
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auto OutSpatialLength = [&](auto InSpatialLength, int index) {
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ck::index_t left_pad = dinput_left_pads[index];
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ck::index_t right_pad = dinput_right_pads[index];
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ck::index_t window_len = window_lengths[index];
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ck::index_t stride = window_strides[index];
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ck::index_t dilation = window_dilations[index];
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ck::index_t eff = (window_len - 1) * dilation + 1;
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return (InSpatialLength + left_pad + right_pad - eff) / stride + 1;
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};
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ck::index_t Do = OutSpatialLength(Di, 0);
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ck::index_t Ho = OutSpatialLength(Hi, 1);
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ck::index_t Wo = OutSpatialLength(Wi, 2);
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Tensor<DOutDataType> dout(f_host_tensor_descriptor(N, C, Do, Ho, Wo, DOutLayout{}));
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Tensor<DInDataType> din_dev(f_host_tensor_descriptor(N, C, Di, Hi, Wi, DInLayout{}));
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Tensor<DInDataType> din_host(f_host_tensor_descriptor(N, C, Di, Hi, Wi, DInLayout{}));
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std::cout << "dout: " << dout.mDesc << std::endl;
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std::cout << "din_host: " << din_host.mDesc << std::endl;
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dout.GenerateTensorValue(GeneratorTensor_3<DOutDataType>{0.0, 1.0});
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DeviceMem dout_device_buf(sizeof(DOutDataType) * dout.mDesc.GetElementSpaceSize());
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DeviceMem din_device_buf(sizeof(DInDataType) * din_dev.mDesc.GetElementSpaceSize());
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dout_device_buf.ToDevice(dout.mData.data());
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din_device_buf.SetZero();
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auto pool = DevicePoolBwdInstance{};
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auto invoker_ptr = pool.MakeInvokerPointer();
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auto argument_ptr =
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pool.MakeArgumentPointer(static_cast<DOutDataType*>(dout_device_buf.GetDeviceBuffer()),
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static_cast<DInDataType*>(din_device_buf.GetDeviceBuffer()),
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{N, C, Do, Ho, Wo},
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{N, C, Di, Hi, Wi},
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f_tensor_strides_ncdhw(N, C, Do, Ho, Wo, DOutLayout{}),
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f_tensor_strides_ncdhw(N, C, Di, Hi, Wi, DInLayout{}),
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window_lengths,
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window_strides,
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window_dilations,
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dinput_left_pads,
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dinput_right_pads);
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if(!pool.IsSupportedArgument(argument_ptr.get()))
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{
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throw std::runtime_error("wrong! device_op with the specified compilation parameters does "
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"not support this problem");
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}
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::cout << "Perf: " << ave_time << std::endl;
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bool pass = true;
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if(do_verification)
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{
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auto ref_pool =
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ck::tensor_operation::host::ReferenceAvgPoolBwd<3, DInDataType, DOutDataType>();
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auto ref_invoker = ref_pool.MakeInvoker();
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auto ref_argument = ref_pool.MakeArgument(din_host,
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dout,
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window_lengths,
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window_strides,
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window_dilations,
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dinput_left_pads,
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dinput_right_pads);
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ref_invoker.Run(ref_argument);
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din_device_buf.FromDevice(din_dev.mData.data());
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pass = ck::utils::check_err(din_dev, din_host);
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}
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return pass;
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}
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