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https://github.com/ROCm/composable_kernel.git
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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>
232 lines
11 KiB
C++
232 lines
11 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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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/utility/reduction_enums.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_pool2d_fwd_nhwc_nhwc.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_max_pool_bwd_impl.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.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_pool_fwd.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_maxpool_bwd.hpp"
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template <typename InDataType,
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typename OutDataType,
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typename IndexDataType,
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typename ComputeDataType,
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typename DInDataType,
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typename DOutDataType,
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bool PropagateNan>
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bool maxpool_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 Y,
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ck::index_t X,
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ck::index_t Hi,
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ck::index_t Wi,
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ck::index_t window_stride_h,
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ck::index_t window_stride_w,
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ck::index_t window_dilation_h,
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ck::index_t window_dilation_w,
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ck::index_t in_left_pad_h,
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ck::index_t in_left_pad_w,
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ck::index_t in_right_pad_h,
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ck::index_t in_right_pad_w)
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{
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using DevicePoolFwdInstance =
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ck::tensor_operation::device::DevicePool2dFwd_NHWC_NHWC<InDataType, // InDataType
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OutDataType, // OutDataType
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IndexDataType, // IndexDataType
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ComputeDataType, // ComputeDataType
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ck::ReduceTensorOp::MAX,
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true,
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64, // BlockSize
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64, // ReduceMThreadClusterSize
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1, // ReduceKThreadClusterSize
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4, // ReduceMThreadSliceSize
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1, // ReduceKThreadSliceSize
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1>; // InSrcOutDstVectorSize
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using DeviceMaxPoolBwdInstance = ck::tensor_operation::device::
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DeviceMaxPoolBwdImpl<DOutDataType, IndexDataType, DInDataType, 4>;
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const ck::index_t Ys = (Y - 1) * window_dilation_h + 1;
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const ck::index_t Xs = (X - 1) * window_dilation_w + 1;
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const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
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const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
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const std::vector<ck::index_t> window_spatial_lengths{Y, X};
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const std::vector<ck::index_t> window_strides{window_stride_h, window_stride_w};
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const std::vector<ck::index_t> window_dilations{window_dilation_h, window_dilation_w};
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const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w};
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const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w};
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auto f_host_tensor_descriptor =
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[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W) {
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using namespace ck::literals;
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// reference need Tensor with NCHW order
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return HostTensorDescriptor({N_, C_, H, W},
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{C_ * H * W, 1_uz, W * C_, C_},
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ck::tensor_layout::convolution::NCHW{});
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};
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// in
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Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi));
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// out
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Tensor<OutDataType> out_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
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Tensor<OutDataType> out_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
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// indices
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Tensor<IndexDataType> indices_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
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Tensor<IndexDataType> indices_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
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// dout
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Tensor<DOutDataType> dout_n_c_ho_wo(f_host_tensor_descriptor(N, C, Ho, Wo));
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// din
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Tensor<DInDataType> din_n_c_hi_wi_host(f_host_tensor_descriptor(N, C, Hi, Wi));
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Tensor<DInDataType> din_n_c_hi_wi_device(f_host_tensor_descriptor(N, C, Hi, Wi));
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std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
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std::cout << "out_n_c_ho_wo: " << out_n_c_ho_wo_host.mDesc << std::endl;
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std::cout << "indices_n_c_ho_wo: " << indices_n_c_ho_wo_host.mDesc << std::endl;
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std::cout << "dout_n_c_ho_wo: " << dout_n_c_ho_wo.mDesc << std::endl;
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std::cout << "din_n_c_hi_wi: " << din_n_c_hi_wi_host.mDesc << std::endl;
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in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{-1.0, 1.0});
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dout_n_c_ho_wo.GenerateTensorValue(GeneratorTensor_3<DOutDataType>{-1.0, 1.0});
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DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpaceSize());
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DeviceMem out_device_buf(sizeof(OutDataType) *
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out_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
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DeviceMem indices_device_buf(sizeof(IndexDataType) *
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indices_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
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DeviceMem dout_device_buf(sizeof(DOutDataType) * dout_n_c_ho_wo.mDesc.GetElementSpaceSize());
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DeviceMem din_device_buf(sizeof(DInDataType) *
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din_n_c_hi_wi_device.mDesc.GetElementSpaceSize());
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in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
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dout_device_buf.ToDevice(dout_n_c_ho_wo.mData.data());
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auto pool_fwd = DevicePoolFwdInstance{};
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auto pool_fwd_invoker_ptr = pool_fwd.MakeInvokerPointer();
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auto pool_fwd_argument_ptr = pool_fwd.MakeArgumentPointer(
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static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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static_cast<IndexDataType*>(indices_device_buf.GetDeviceBuffer()),
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{N, C, Hi, Wi},
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window_spatial_lengths,
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{N, C, Ho, Wo},
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{C * Hi * Wi, 1, Wi * C, C},
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{C * Ho * Wo, 1, Wo * C, C},
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{C * Ho * Wo, 1, Wo * C, C},
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window_strides,
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window_dilations,
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input_left_pads,
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input_right_pads,
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{2, 3});
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if(!pool_fwd.IsSupportedArgument(pool_fwd_argument_ptr.get()))
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{
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throw std::runtime_error("wrong! pool_fwd 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_fwd =
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pool_fwd_invoker_ptr->Run(pool_fwd_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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auto pool_bwd = DeviceMaxPoolBwdInstance{};
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auto pool_bwd_invoker_ptr = pool_bwd.MakeInvokerPointer();
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auto pool_bwd_argument_ptr = pool_bwd.MakeArgumentPointer(
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static_cast<DOutDataType*>(dout_device_buf.GetDeviceBuffer()),
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static_cast<IndexDataType*>(indices_device_buf.GetDeviceBuffer()),
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static_cast<DInDataType*>(din_device_buf.GetDeviceBuffer()),
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dout_n_c_ho_wo.mDesc.GetElementSpaceSize(),
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din_n_c_hi_wi_device.mDesc.GetElementSpaceSize(),
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window_spatial_lengths,
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window_strides,
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window_dilations);
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if(!pool_bwd.IsSupportedArgument(pool_bwd_argument_ptr.get()))
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{
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throw std::runtime_error("wrong! pool_bwd with the specified compilation parameters does "
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"not support this problem");
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}
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size_t pool_bwd_workspace_sz = pool_bwd.GetWorkSpaceSize(pool_bwd_argument_ptr.get());
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DeviceMem pool_bwd_workspace_device_buf(pool_bwd_workspace_sz);
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pool_bwd.SetWorkSpacePointer(pool_bwd_argument_ptr.get(),
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pool_bwd_workspace_device_buf.GetDeviceBuffer());
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float ave_time_bwd =
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pool_bwd_invoker_ptr->Run(pool_bwd_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::cout << "Pool fwd perf: " << ave_time_fwd << " ms" << std::endl;
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std::cout << "Pool bwd perf: " << ave_time_bwd << " ms" << std::endl;
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bool pass = true;
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if(do_verification)
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{
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using ReferencePoolingFwdInstance =
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ck::tensor_operation::host::ReferencePoolingFwd<4,
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2,
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InDataType,
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OutDataType,
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ComputeDataType,
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IndexDataType,
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ck::ReduceTensorOp::MAX,
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PropagateNan,
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true>;
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auto ref_pooling_fwd = ReferencePoolingFwdInstance{};
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auto ref_pooling_fwd_invoker = ref_pooling_fwd.MakeInvoker();
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auto ref_pooling_fwd_argument = ref_pooling_fwd.MakeArgument(in_n_c_hi_wi,
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out_n_c_ho_wo_host,
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indices_n_c_ho_wo_host,
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window_spatial_lengths,
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window_strides,
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window_dilations,
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input_left_pads,
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input_right_pads);
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ref_pooling_fwd_invoker.Run(ref_pooling_fwd_argument);
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using ReferencePoolingBwdInstance =
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ck::tensor_operation::host::ReferenceMaxPoolBwd<DOutDataType,
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IndexDataType,
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ComputeDataType,
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DInDataType,
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PassThrough>;
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auto ref_pooling_bwd = ReferencePoolingBwdInstance{};
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auto ref_pooling_bwd_invoker = ref_pooling_bwd.MakeInvoker();
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auto ref_pooling_bwd_argument = ref_pooling_bwd.MakeArgument(
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dout_n_c_ho_wo, indices_n_c_ho_wo_host, din_n_c_hi_wi_host, PassThrough{});
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ref_pooling_bwd_invoker.Run(ref_pooling_bwd_argument);
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out_device_buf.FromDevice(out_n_c_ho_wo_device.mData.data());
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indices_device_buf.FromDevice(indices_n_c_ho_wo_device.mData.data());
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din_device_buf.FromDevice(din_n_c_hi_wi_device.mData.data());
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pass = pass && ck::utils::check_err(out_n_c_ho_wo_device, out_n_c_ho_wo_host);
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pass = pass && ck::utils::check_err(indices_n_c_ho_wo_device, indices_n_c_ho_wo_host);
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pass = pass && ck::utils::check_err(din_n_c_hi_wi_device, din_n_c_hi_wi_host);
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}
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return (pass);
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};
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