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Add client example of grouped conv2d backward data (data type: fp16) (#481)
* Improve example reusability
* Remove no-longer used file
* Rename folder of grouped_conv_bwd_data example
* Add normal grouped conv bwd example
* Add interface 'DeviceGroupedConvBwdData'
* Prettify comment of device op type arguments
* Add grouped conv2d/conv3d backward data fp16 instances
* Fix wrong template argument
* Add grouped_conv2d_bwd_data client example
* Use simpler expression to calculate memory size
* Fix formating
* Remove grouped_conv3d_bw_data instances
Underlying device operator is not ready to handle 3D input
* Remove no-longer necessary include directive
* Add missing include directive
* Use more realistic conv param in example
[ROCm/composable_kernel commit: 9e57a290af]
This commit is contained in:
Po Yen Chen
2
client_example/09_grouped_conv2d_bwd_data/CMakeLists.txt
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2
client_example/09_grouped_conv2d_bwd_data/CMakeLists.txt
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add_executable(client_grouped_conv2d_bwd_data grouped_conv2d_bwd_data.cpp)
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target_link_libraries(client_grouped_conv2d_bwd_data PRIVATE composable_kernel::device_operations)
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <iterator>
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#include <numeric>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_data.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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using InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using InLayout = ck::tensor_layout::convolution::GNHWC;
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using WeiLayout = ck::tensor_layout::convolution::GKYXC;
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using OutLayout = ck::tensor_layout::convolution::GNHWK;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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static constexpr ck::index_t NumDimSpatial = 2;
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static constexpr ck::index_t G = 32;
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static constexpr ck::index_t N = 256;
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static constexpr ck::index_t K = 192;
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static constexpr ck::index_t C = 192;
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static constexpr ck::index_t Y = 3;
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static constexpr ck::index_t X = 3;
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static constexpr ck::index_t Hi = 28;
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static constexpr ck::index_t Wi = 28;
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static constexpr ck::index_t Ho = 28;
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static constexpr ck::index_t Wo = 28;
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int main()
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{
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std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, Hi, Wi, C};
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std::array<ck::index_t, NumDimSpatial + 3> in_strides{0, 0, 0, 0, 1};
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std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, Y, X, C};
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std::array<ck::index_t, NumDimSpatial + 3> wei_strides{0, 0, 0, 0, 1};
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std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, Ho, Wo, K};
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std::array<ck::index_t, NumDimSpatial + 3> out_strides{0, 0, 0, 0, 1};
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std::partial_sum(rbegin(in_lengths),
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std::prev(rend(in_lengths)),
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std::next(rbegin(in_strides)),
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std::multiplies<>{});
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std::partial_sum(rbegin(wei_lengths),
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std::prev(rend(wei_lengths)),
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std::next(rbegin(wei_strides)),
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std::multiplies<>{});
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std::partial_sum(rbegin(out_lengths),
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std::prev(rend(out_lengths)),
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std::next(rbegin(out_strides)),
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std::multiplies<>{});
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// transpose GNHWC/GKYXC/GNHWK to GNCHW/GKCYX/GNCHW
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std::rotate(
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rbegin(in_lengths), std::next(rbegin(in_lengths)), std::next(rbegin(in_lengths), 3));
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std::rotate(
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rbegin(in_strides), std::next(rbegin(in_strides)), std::next(rbegin(in_strides), 3));
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std::rotate(
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rbegin(wei_lengths), std::next(rbegin(wei_lengths)), std::next(rbegin(wei_lengths), 3));
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std::rotate(
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rbegin(wei_strides), std::next(rbegin(wei_strides)), std::next(rbegin(wei_strides), 3));
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std::rotate(
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rbegin(out_lengths), std::next(rbegin(out_lengths)), std::next(rbegin(out_lengths), 3));
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std::rotate(
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rbegin(out_strides), std::next(rbegin(out_strides)), std::next(rbegin(out_strides), 3));
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std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
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std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
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std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1};
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std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
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SimpleDeviceMem in(sizeof(InDataType) * G * N * Hi * Wi * C);
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SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * G * N * Ho * Wo * K);
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvBwdData<NumDimSpatial,
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InLayout,
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WeiLayout,
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OutLayout,
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InDataType,
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WeiDataType,
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OutDataType,
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PassThrough,
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PassThrough,
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PassThrough>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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int best_op_id = -1;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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float best_tflops = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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wei_lengths,
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wei_strides,
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out_lengths,
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out_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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input_right_pads,
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PassThrough{},
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PassThrough{},
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PassThrough{});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t flop = std::size_t(2) * G * N * K * C * Ho * Wo * Y * X;
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std::size_t num_bytes = sizeof(InDataType) * G * N * Hi * Wi * C +
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sizeof(WeiDataType) * G * K * Y * X * C +
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sizeof(OutDataType) * G * N * Ho * Wo * K;
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_op_id = i;
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best_op_name = op_name;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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best_tflops = tflops;
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}
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}
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else
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{
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std::cerr << op_name << " does not support this problem" << std::endl;
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}
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}
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if(best_op_id < 0)
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{
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std::cerr << "no suitable instance" << std::endl;
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return EXIT_FAILURE;
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}
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std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
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<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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// run the best intance
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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wei_lengths,
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wei_strides,
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out_lengths,
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out_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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input_right_pads,
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PassThrough{},
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PassThrough{},
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PassThrough{});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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}
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