mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-17 11:30:02 +00:00
ae36ead7f5
* Do not hardcode stride
* devicePool2DFwd Inherit devicePool3DFwd
* Move instance declaration out of common
* Add dilation
* use the pool3d rank, because pool2d inherit pooo3d
* calculate Do Ho Wo for the dilation
* Fix header name
* Modify ckProfiler
* Remove pool2d instance
* Remove pool2d in profiler
* Remove pool2d and add dilation
* In to client example, this commit revise following:
1. Add dilation.
2. Use pool3d to implement pool2d
* Refine naming and IsSupportedArgument()
* Add dilation to maxpool bwd example
* clang format
* 1. Remove useless header
2. Fix copyright
3. Refine naming
* Add layout parameter to pool fwd
* clang format
* Fix merge error
* Fix compile error
* Remove layout parameter in derived class
* Refine changlog
* Fix compile error
* Fix compiler error
* Add layout to external api and profiler
[ROCm/composable_kernel commit: f60f0a5e03]
87 lines
4.4 KiB
C++
87 lines
4.4 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 "gtest/gtest.h"
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#include "profiler/profile_pool3d_fwd_impl.hpp"
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#include "test_pool_fwd_common.hpp"
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template <typename Tuple>
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class TestMaxPool3dFwd : public ::testing::Test
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{
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protected:
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using InDataType = std::tuple_element_t<0, Tuple>;
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using OutDataType = std::tuple_element_t<1, Tuple>;
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using ComputeDataType = std::tuple_element_t<2, Tuple>;
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using IndexDataType = std::tuple_element_t<3, Tuple>;
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std::vector<PoolingParam> params;
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void Run()
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{
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for(auto param : params)
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{
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// max pool
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bool success =
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ck::profiler::profile_pool3d_fwd_impl<InDataType,
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OutDataType,
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ComputeDataType,
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IndexDataType,
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ck::tensor_layout::convolution::NDHWC,
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ck::tensor_layout::convolution::NDHWC,
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ck::ReduceTensorOp::MAX,
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false,
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false>(true,
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2,
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false,
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false,
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param.length_,
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param.window_spatial_lengths_,
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param.window_strides_,
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param.window_dilations_,
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param.input_left_pads_,
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param.input_right_pads_);
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EXPECT_TRUE(success);
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// max pool + index
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success = ck::profiler::profile_pool3d_fwd_impl<InDataType,
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OutDataType,
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ComputeDataType,
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IndexDataType,
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ck::tensor_layout::convolution::NDHWC,
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ck::tensor_layout::convolution::NDHWC,
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ck::ReduceTensorOp::MAX,
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false,
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true>(true,
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2,
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false,
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false,
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param.length_,
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param.window_spatial_lengths_,
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param.window_strides_,
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param.window_dilations_,
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param.input_left_pads_,
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param.input_right_pads_);
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EXPECT_TRUE(success);
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}
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}
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};
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#ifdef __fp16__
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using KernelTypes =
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::testing::Types<std::tuple<F16, F16, F32, I32>, std::tuple<F32, F32, F32, I32>>;
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#else
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using KernelTypes = ::testing::Types<std::tuple<F32, F32, F32, I32>>;
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#endif
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TYPED_TEST_SUITE(TestMaxPool3dFwd, KernelTypes);
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TYPED_TEST(TestMaxPool3dFwd, Test_Pool)
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{
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// length, window_length, window_stride, window_dilation, left_pad, right_pad
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this->params = {{{1, 1, 1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}},
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{{2, 16, 64, 64, 64}, {64, 64, 64}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}},
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{{2, 16, 64, 64, 64}, {4, 4, 4}, {4, 4, 4}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0}},
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{{2, 32, 30, 30, 30}, {2, 2, 2}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}}};
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this->Run();
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}
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