mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 10:09:41 +00:00
Maxpool bwd (#750)
* Add maxpool f32 kernel and example
* Revise copyright
* Add device pool bwd device op
* Support f16 and bf16
* Add compute datatype for reference code.
Prevent error in bf16
* Fix type error
* Remove layout
* Fix bf16 error
* Add f16 and bf16 example
* Add more operations
* Implement IsSupportedArgument
* Add changelog
* Add comment
* Add comment
* Remove useless header
* Move initialize of workspace to the run
* Move set din zero to the device operator
* Save din_length_raw
* Remove useless header
* Calculate gridsize according to the number of CU
* Calculate gridSize according to the number of CU.
Remove useless header
* Add put example
* Remove useless header
* Fix CI fail
[ROCm/composable_kernel commit: 341ad95665]
This commit is contained in:
rocking
3
example/49_maxpool2d_bwd/CMakeLists.txt
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3
example/49_maxpool2d_bwd/CMakeLists.txt
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add_example_executable(example_maxpool2d_bwd_bf16 maxpool2d_bwd_bf16.cpp)
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add_example_executable(example_maxpool2d_bwd_fp16 maxpool2d_bwd_fp16.cpp)
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add_example_executable(example_maxpool2d_bwd_fp32 maxpool2d_bwd_fp32.cpp)
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62
example/49_maxpool2d_bwd/maxpool2d_bwd_bf16.cpp
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62
example/49_maxpool2d_bwd/maxpool2d_bwd_bf16.cpp
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// 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/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/utility/reduction_enums.hpp"
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#include "maxpool2d_bwd_common.hpp"
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using InDataType = ck::bhalf_t;
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using OutDataType = ck::bhalf_t;
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using IndexDataType = int32_t;
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using ComputeDataType = float;
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using DInDataType = ck::bhalf_t;
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using DOutDataType = ck::bhalf_t;
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static constexpr bool PropagateNan = false;
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int main()
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{
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bool do_verification = true;
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bool time_kernel = false;
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// Pool shape
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ck::index_t N = 1;
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ck::index_t C = 1;
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ck::index_t Y = 3;
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ck::index_t X = 3;
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ck::index_t Hi = 32;
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ck::index_t Wi = 32;
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ck::index_t window_stride_h = 1;
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ck::index_t window_stride_w = 1;
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ck::index_t in_left_pad_h = 0;
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ck::index_t in_left_pad_w = 0;
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ck::index_t in_right_pad_h = 0;
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ck::index_t in_right_pad_w = 0;
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bool pass = maxpool_bwd_test<InDataType,
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OutDataType,
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IndexDataType,
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ComputeDataType,
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DInDataType,
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DOutDataType,
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PropagateNan>(do_verification,
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time_kernel,
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N,
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C,
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Y,
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X,
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Hi,
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Wi,
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window_stride_h,
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window_stride_w,
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in_left_pad_h,
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in_left_pad_w,
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in_right_pad_h,
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in_right_pad_w);
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return (pass ? 0 : 1);
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}
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222
example/49_maxpool2d_bwd/maxpool2d_bwd_common.hpp
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222
example/49_maxpool2d_bwd/maxpool2d_bwd_common.hpp
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@@ -0,0 +1,222 @@
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// 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_index_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 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_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C<
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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, // OutputIndex
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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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DeviceIndexPoolBwdImpl<DOutDataType, IndexDataType, DInDataType, 4>;
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const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1;
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const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / 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> 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}, {C_ * H * W, 1_uz, W * C_, C_});
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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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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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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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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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62
example/49_maxpool2d_bwd/maxpool2d_bwd_fp16.cpp
Normal file
62
example/49_maxpool2d_bwd/maxpool2d_bwd_fp16.cpp
Normal file
@@ -0,0 +1,62 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
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|
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#include <iostream>
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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/utility/reduction_enums.hpp"
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#include "maxpool2d_bwd_common.hpp"
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using InDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using IndexDataType = int32_t;
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using ComputeDataType = float;
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using DInDataType = ck::half_t;
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using DOutDataType = ck::half_t;
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static constexpr bool PropagateNan = false;
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|
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int main()
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{
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bool do_verification = true;
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bool time_kernel = false;
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|
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// Pool shape
|
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ck::index_t N = 1;
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ck::index_t C = 1;
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ck::index_t Y = 3;
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ck::index_t X = 3;
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ck::index_t Hi = 32;
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ck::index_t Wi = 32;
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ck::index_t window_stride_h = 1;
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ck::index_t window_stride_w = 1;
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ck::index_t in_left_pad_h = 0;
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ck::index_t in_left_pad_w = 0;
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ck::index_t in_right_pad_h = 0;
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ck::index_t in_right_pad_w = 0;
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bool pass = maxpool_bwd_test<InDataType,
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OutDataType,
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IndexDataType,
|
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ComputeDataType,
|
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DInDataType,
|
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DOutDataType,
|
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PropagateNan>(do_verification,
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time_kernel,
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N,
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C,
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Y,
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X,
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Hi,
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Wi,
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window_stride_h,
|
||||
window_stride_w,
|
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in_left_pad_h,
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in_left_pad_w,
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||||
in_right_pad_h,
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in_right_pad_w);
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|
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return (pass ? 0 : 1);
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}
|
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62
example/49_maxpool2d_bwd/maxpool2d_bwd_fp32.cpp
Normal file
62
example/49_maxpool2d_bwd/maxpool2d_bwd_fp32.cpp
Normal file
@@ -0,0 +1,62 @@
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// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/utility/reduction_enums.hpp"
|
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|
||||
#include "maxpool2d_bwd_common.hpp"
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||||
|
||||
using InDataType = float;
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||||
using OutDataType = float;
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||||
using IndexDataType = int32_t;
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||||
using ComputeDataType = float;
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||||
using DInDataType = float;
|
||||
using DOutDataType = float;
|
||||
|
||||
static constexpr bool PropagateNan = false;
|
||||
|
||||
int main()
|
||||
{
|
||||
bool do_verification = true;
|
||||
bool time_kernel = false;
|
||||
|
||||
// Pool shape
|
||||
ck::index_t N = 1;
|
||||
ck::index_t C = 1;
|
||||
ck::index_t Y = 2;
|
||||
ck::index_t X = 2;
|
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ck::index_t Hi = 32;
|
||||
ck::index_t Wi = 32;
|
||||
ck::index_t window_stride_h = 2;
|
||||
ck::index_t window_stride_w = 2;
|
||||
ck::index_t in_left_pad_h = 0;
|
||||
ck::index_t in_left_pad_w = 0;
|
||||
ck::index_t in_right_pad_h = 0;
|
||||
ck::index_t in_right_pad_w = 0;
|
||||
|
||||
bool pass = maxpool_bwd_test<InDataType,
|
||||
OutDataType,
|
||||
IndexDataType,
|
||||
ComputeDataType,
|
||||
DInDataType,
|
||||
DOutDataType,
|
||||
PropagateNan>(do_verification,
|
||||
time_kernel,
|
||||
N,
|
||||
C,
|
||||
Y,
|
||||
X,
|
||||
Hi,
|
||||
Wi,
|
||||
window_stride_h,
|
||||
window_stride_w,
|
||||
in_left_pad_h,
|
||||
in_left_pad_w,
|
||||
in_right_pad_h,
|
||||
in_right_pad_w);
|
||||
|
||||
return (pass ? 0 : 1);
|
||||
}
|
||||
1
example/50_put_element/CMakeLists.txt
Normal file
1
example/50_put_element/CMakeLists.txt
Normal file
@@ -0,0 +1 @@
|
||||
add_example_executable(example_put_element_fp16 put_element_fp16.cpp)
|
||||
88
example/50_put_element/put_element_fp16.cpp
Normal file
88
example/50_put_element/put_element_fp16.cpp
Normal file
@@ -0,0 +1,88 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_put_element_impl.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
|
||||
using XDataType = ck::half_t;
|
||||
using YDataType = ck::half_t;
|
||||
using IndexDataType = int32_t;
|
||||
|
||||
using YElementwiseOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using DeviceInstance =
|
||||
ck::tensor_operation::device::DevicePutElementImpl<XDataType, // XDataType
|
||||
IndexDataType, // IndexDataType
|
||||
YDataType, // YDataType
|
||||
YElementwiseOp,
|
||||
ck::InMemoryDataOperationEnum::Set,
|
||||
1>;
|
||||
|
||||
int main()
|
||||
{
|
||||
bool do_verification = true;
|
||||
bool time_kernel = false;
|
||||
|
||||
int N = 1024;
|
||||
|
||||
Tensor<XDataType> x(HostTensorDescriptor{N, 1});
|
||||
Tensor<IndexDataType> indices(HostTensorDescriptor{N, 1});
|
||||
Tensor<YDataType> y(HostTensorDescriptor{N, 1});
|
||||
|
||||
x.GenerateTensorValue(GeneratorTensor_3<XDataType>{-1.0, 1.0});
|
||||
for(int i = 0; i < N; ++i)
|
||||
indices(i) = i;
|
||||
|
||||
DeviceMem x_device_buf(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
|
||||
DeviceMem y_device_buf(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
|
||||
DeviceMem indices_device_buf(sizeof(IndexDataType) * indices.mDesc.GetElementSpaceSize());
|
||||
|
||||
x_device_buf.ToDevice(x.mData.data());
|
||||
indices_device_buf.ToDevice(indices.mData.data());
|
||||
|
||||
auto put_instance = DeviceInstance{};
|
||||
auto put_invoker_ptr = put_instance.MakeInvokerPointer();
|
||||
auto put_argument_ptr = put_instance.MakeArgumentPointer(
|
||||
static_cast<XDataType*>(x_device_buf.GetDeviceBuffer()),
|
||||
static_cast<IndexDataType*>(indices_device_buf.GetDeviceBuffer()),
|
||||
static_cast<YDataType*>(y_device_buf.GetDeviceBuffer()),
|
||||
N,
|
||||
N,
|
||||
YElementwiseOp{});
|
||||
|
||||
if(!put_instance.IsSupportedArgument(put_argument_ptr.get()))
|
||||
{
|
||||
throw std::runtime_error("argument is not supported!");
|
||||
}
|
||||
|
||||
float ave_time =
|
||||
put_invoker_ptr->Run(put_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::cout << "perf: " << ave_time << " ms" << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<YDataType> y_host(HostTensorDescriptor{N, 1});
|
||||
|
||||
for(int i = 0; i < N; ++i)
|
||||
{
|
||||
IndexDataType idx = indices(i);
|
||||
y_host(idx) = x(i);
|
||||
}
|
||||
|
||||
y_device_buf.FromDevice(y.mData.data());
|
||||
pass = ck::utils::check_err(y, y_host);
|
||||
}
|
||||
|
||||
return (pass ? 0 : 1);
|
||||
}
|
||||
Reference in New Issue
Block a user