mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 02:02:46 +00:00
Grouped conv1d client example (#589)
* add conv1d fwd client example
* change 07_grouped_conv2d_fwd to 07_grouped_convnd_fwd
* add conv1d bwd weight
---------
Co-authored-by: zjing14 <zhangjing14@gmail.com>
[ROCm/composable_kernel commit: 830d37a7d5]
This commit is contained in:
ltqin
@@ -1,2 +0,0 @@
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add_executable(client_grouped_conv2d_fwd grouped_conv2d_fwd.cpp)
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target_link_libraries(client_grouped_conv2d_fwd PRIVATE composable_kernel::device_operations)
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5
client_example/07_grouped_convnd_fwd/CMakeLists.txt
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5
client_example/07_grouped_convnd_fwd/CMakeLists.txt
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@@ -0,0 +1,5 @@
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add_executable(client_grouped_conv2d_fwd grouped_conv2d_fwd.cpp)
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target_link_libraries(client_grouped_conv2d_fwd PRIVATE composable_kernel::device_operations)
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add_executable(client_grouped_conv1d_fwd grouped_conv1d_fwd.cpp)
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target_link_libraries(client_grouped_conv1d_fwd PRIVATE composable_kernel::device_operations)
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229
client_example/07_grouped_convnd_fwd/grouped_conv1d_fwd.cpp
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229
client_example/07_grouped_convnd_fwd/grouped_conv1d_fwd.cpp
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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_forward.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.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::GNWC;
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using WeiLayout = ck::tensor_layout::convolution::GKXC;
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using OutLayout = ck::tensor_layout::convolution::GNWK;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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static constexpr ck::index_t NumDimSpatial = 1;
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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 X = 3;
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static constexpr ck::index_t Wi = 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, Wi, C};
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std::array<ck::index_t, NumDimSpatial + 3> in_strides{0, 0, 0, 1};
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std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, X, C};
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std::array<ck::index_t, NumDimSpatial + 3> wei_strides{0, 0, 0, 1};
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std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, Wo, K};
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std::array<ck::index_t, NumDimSpatial + 3> out_strides{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 GNWC/GKXC/GNWK to GNCW/GKCX/GNCW
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std::rotate(rbegin(in_lengths),
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std::next(rbegin(in_lengths)),
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std::next(rbegin(in_lengths), NumDimSpatial + 1));
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std::rotate(rbegin(in_strides),
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std::next(rbegin(in_strides)),
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std::next(rbegin(in_strides), NumDimSpatial + 1));
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std::rotate(rbegin(wei_lengths),
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std::next(rbegin(wei_lengths)),
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std::next(rbegin(wei_lengths), NumDimSpatial + 1));
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std::rotate(rbegin(wei_strides),
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std::next(rbegin(wei_strides)),
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std::next(rbegin(wei_strides), NumDimSpatial + 1));
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std::rotate(rbegin(out_lengths),
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std::next(rbegin(out_lengths)),
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std::next(rbegin(out_lengths), NumDimSpatial + 1));
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std::rotate(rbegin(out_strides),
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std::next(rbegin(out_strides)),
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std::next(rbegin(out_strides), NumDimSpatial + 1));
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std::array<ck::index_t, NumDimSpatial> filter_strides{1};
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std::array<ck::index_t, NumDimSpatial> filter_dilations{1};
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std::array<ck::index_t, NumDimSpatial> input_left_pads{1};
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std::array<ck::index_t, NumDimSpatial> input_right_pads{1};
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SimpleDeviceMem in(sizeof(InDataType) * G * N * Wi * C);
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SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * G * N * Wo * K);
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<>,
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OutLayout,
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InDataType,
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WeiDataType,
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ck::Tuple<>,
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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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{},
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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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{},
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{},
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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 * Wo * X;
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std::size_t num_bytes = sizeof(InDataType) * G * N * Wi * C +
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sizeof(WeiDataType) * G * K * X * C +
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sizeof(OutDataType) * G * N * 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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{},
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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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{},
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{},
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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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@@ -1,7 +1,9 @@
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add_executable(client_grouped_conv1d_bwd_weight_fp16 grouped_conv1d_bwd_weight_fp16.cpp)
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add_executable(client_grouped_conv2d_bwd_weight_fp16 grouped_conv2d_bwd_weight_fp16.cpp)
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add_executable(client_grouped_conv3d_bwd_weight_fp16 grouped_conv3d_bwd_weight_fp16.cpp)
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add_executable(client_grouped_conv3d_bwd_weight_fp32 grouped_conv3d_bwd_weight_fp32.cpp)
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target_link_libraries(client_grouped_conv1d_bwd_weight_fp16 PRIVATE composable_kernel::device_operations)
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target_link_libraries(client_grouped_conv2d_bwd_weight_fp16 PRIVATE composable_kernel::device_operations)
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target_link_libraries(client_grouped_conv3d_bwd_weight_fp16 PRIVATE composable_kernel::device_operations)
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target_link_libraries(client_grouped_conv3d_bwd_weight_fp32 PRIVATE composable_kernel::device_operations)
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@@ -0,0 +1,37 @@
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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 "common.hpp"
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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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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::GNWC;
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using WeiLayout = ck::tensor_layout::convolution::GKXC;
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using OutLayout = ck::tensor_layout::convolution::GNWK;
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static constexpr ck::index_t NumDimSpatial = 1;
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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 X = 3;
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static constexpr ck::index_t Wi = 28;
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static constexpr ck::index_t Wo = 28;
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int main()
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{
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return run_grouped_conv_bwd_weight<NumDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InLayout,
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WeiLayout,
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OutLayout>(G, N, K, C, {Wi}, {X}, {Wo}, {1}, {1}, {1}, {1})
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? EXIT_SUCCESS
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: EXIT_FAILURE;
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}
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