mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-21 21:39:15 +00:00
Reorganize project folders (#6)
This commit is contained in:
Joseph Macaranas
8
client_example/18_groupnorm/CMakeLists.txt
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8
client_example/18_groupnorm/CMakeLists.txt
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@@ -0,0 +1,8 @@
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add_executable(client_groupnorm_bwd_data groupnorm_bwd_data.cpp)
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target_link_libraries(client_groupnorm_bwd_data PRIVATE composable_kernel::device_other_operations)
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add_executable(client_groupnorm_bwd_gamma_beta groupnorm_bwd_gamma_beta.cpp)
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target_link_libraries(client_groupnorm_bwd_gamma_beta PRIVATE composable_kernel::device_other_operations)
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add_executable(client_groupnorm_swish_fwd groupnorm_swish_fwd.cpp)
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target_link_libraries(client_groupnorm_swish_fwd PRIVATE composable_kernel::device_other_operations)
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182
client_example/18_groupnorm/groupnorm_bwd_data.cpp
Normal file
182
client_example/18_groupnorm/groupnorm_bwd_data.cpp
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@@ -0,0 +1,182 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <iomanip>
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#include <vector>
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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/tensor_operation/gpu/device/device_normalization_bwd_data.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/groupnorm_bwd_data.hpp"
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using DYDataType = float;
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using XDataType = float;
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using GammaDataType = float;
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using MeanInvStdDataType = float;
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using DXDataType = float;
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constexpr int Rank = 5;
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constexpr int NumReduceDim = 3;
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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(int argc, char* argv[])
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{
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ck::index_t N = 32;
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ck::index_t H = 16;
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ck::index_t W = 16;
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ck::index_t G = 64;
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ck::index_t C = 128;
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std::size_t length = N * H * W * G * C;
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std::vector<ck::index_t> strideDy = {H * W * G * C, W * G * C, G * C, C, 1};
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std::vector<ck::index_t> strideX = strideDy;
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std::vector<ck::index_t> strideDx = strideDy;
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std::vector<ck::index_t> strideGamma = {0, 0, 0, C, 1};
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std::vector<ck::index_t> strideMeanInvStd = {G, 0, 0, 1, 0};
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SimpleDeviceMem dy_dev(sizeof(DYDataType) * length);
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SimpleDeviceMem x_dev(sizeof(XDataType) * length);
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SimpleDeviceMem gamma_dev(sizeof(GammaDataType) * G * C);
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SimpleDeviceMem mean_dev(sizeof(MeanInvStdDataType) * N * G);
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SimpleDeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * N * G);
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SimpleDeviceMem dx_dev(sizeof(DXDataType) * length);
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using DeviceOp = ck::tensor_operation::device::DeviceNormalizationBwdData<DYDataType,
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XDataType,
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GammaDataType,
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MeanInvStdDataType,
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DXDataType,
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Rank,
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NumReduceDim>;
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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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bool found = false;
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int best_op_id = -1;
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float best_ave_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 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({N, H, W, G, C},
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strideDy,
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strideX,
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strideGamma,
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strideMeanInvStd,
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strideMeanInvStd,
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strideDx,
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{1, 2, 4}, // reduceDims
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dy_dev.GetDeviceBuffer(),
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x_dev.GetDeviceBuffer(),
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gamma_dev.GetDeviceBuffer(),
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mean_dev.GetDeviceBuffer(),
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inv_std_dev.GetDeviceBuffer(),
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dx_dev.GetDeviceBuffer());
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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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size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
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SimpleDeviceMem workspace(workspace_sz);
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op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t num_byte = sizeof(DYDataType) * length + sizeof(XDataType) * length +
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sizeof(GammaDataType) * G * C +
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sizeof(MeanInvStdDataType) * N * G * 2 +
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sizeof(DXDataType) * length;
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float gb_per_sec = num_byte / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
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<< op_name << std::endl;
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if(ave_time < best_ave_time)
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{
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found = true;
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best_op_id = i;
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best_op_name = op_name;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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||||
}
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}
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||||
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// run the best intance
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||||
if(found)
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{
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
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<< best_op_name << std::endl;
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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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||||
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||||
auto argument_ptr = op_ptr->MakeArgumentPointer({N, H, W, G, C},
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strideDy,
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strideX,
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strideGamma,
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strideMeanInvStd,
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||||
strideMeanInvStd,
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strideDx,
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{1, 2, 4}, // reduceDims
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dy_dev.GetDeviceBuffer(),
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x_dev.GetDeviceBuffer(),
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gamma_dev.GetDeviceBuffer(),
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mean_dev.GetDeviceBuffer(),
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inv_std_dev.GetDeviceBuffer(),
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dx_dev.GetDeviceBuffer());
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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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size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
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SimpleDeviceMem workspace(workspace_sz);
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op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
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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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return 0;
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}
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180
client_example/18_groupnorm/groupnorm_bwd_gamma_beta.cpp
Normal file
180
client_example/18_groupnorm/groupnorm_bwd_gamma_beta.cpp
Normal file
@@ -0,0 +1,180 @@
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||||
// SPDX-License-Identifier: MIT
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||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
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#include <iomanip>
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#include <vector>
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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/device_normalization_bwd_gamma_beta.hpp"
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#include "ck/library/tensor_operation_instance/gpu/groupnorm_bwd_gamma_beta.hpp"
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using DYDataType = float;
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using XDataType = float;
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using GammaDataType = float;
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using MeanInvStdDataType = float;
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using DGammaDataType = float;
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using DBetaDataType = float;
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constexpr int Rank = 5;
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constexpr int NumReduceDim = 3;
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|
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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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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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||||
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void* GetDeviceBuffer() { return p_mem_; }
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||||
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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(int argc, char* argv[])
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{
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ck::index_t N = 32;
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ck::index_t H = 16;
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ck::index_t W = 16;
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ck::index_t G = 64;
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||||
ck::index_t C = 128;
|
||||
|
||||
std::size_t length = N * H * W * G * C;
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std::vector<ck::index_t> strideDy = {H * W * G * C, W * G * C, G * C, C, 1};
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std::vector<ck::index_t> strideX = strideDy;
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std::vector<ck::index_t> strideMeanInvStd = {G, 0, 0, 1, 0};
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std::vector<ck::index_t> strideDGammaBeta = {C, 1};
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SimpleDeviceMem dy_dev(sizeof(DYDataType) * length);
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SimpleDeviceMem x_dev(sizeof(XDataType) * length);
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SimpleDeviceMem mean_dev(sizeof(MeanInvStdDataType) * N * G);
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SimpleDeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * N * G);
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SimpleDeviceMem dgamma_dev(sizeof(DGammaDataType) * G * C);
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SimpleDeviceMem dbeta_dev(sizeof(DBetaDataType) * G * C);
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||||
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using DeviceOp =
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ck::tensor_operation::device::DeviceNormalizationBwdGammaBeta<DYDataType,
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XDataType,
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MeanInvStdDataType,
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DGammaDataType,
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DBetaDataType,
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Rank,
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NumReduceDim>;
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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;
|
||||
|
||||
std::string best_op_name;
|
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bool found = false;
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int best_op_id = -1;
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float best_ave_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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|
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
|
||||
|
||||
std::size_t num_bytes = sizeof(DYDataType) * length + sizeof(XDataType) * length +
|
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sizeof(GammaDataType) * G * C + sizeof(MeanInvStdDataType) * N * G * 2 +
|
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sizeof(DGammaDataType) * G * C + sizeof(DBetaDataType) * G * C;
|
||||
|
||||
for(int i = 0; i < op_ptrs.size(); ++i)
|
||||
{
|
||||
auto& op_ptr = op_ptrs[i];
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer({N, H, W, G, C},
|
||||
strideDy,
|
||||
strideX,
|
||||
strideMeanInvStd,
|
||||
strideMeanInvStd,
|
||||
{G, C},
|
||||
strideDGammaBeta,
|
||||
strideDGammaBeta,
|
||||
{0, 1, 2}, // reduceDims
|
||||
dy_dev.GetDeviceBuffer(),
|
||||
x_dev.GetDeviceBuffer(),
|
||||
mean_dev.GetDeviceBuffer(),
|
||||
inv_std_dev.GetDeviceBuffer(),
|
||||
dgamma_dev.GetDeviceBuffer(),
|
||||
dbeta_dev.GetDeviceBuffer());
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
|
||||
SimpleDeviceMem workspace(workspace_sz);
|
||||
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
|
||||
|
||||
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
|
||||
float gb_per_sec = num_bytes / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
|
||||
<< op_name << std::endl;
|
||||
|
||||
if(ave_time < best_ave_time)
|
||||
{
|
||||
found = true;
|
||||
best_op_id = i;
|
||||
best_op_name = op_name;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
// run the best intance
|
||||
if(found)
|
||||
{
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_op_name << std::endl;
|
||||
|
||||
auto& op_ptr = op_ptrs[best_op_id];
|
||||
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
|
||||
<< std::endl;
|
||||
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer({N, H, W, G, C},
|
||||
strideDy,
|
||||
strideX,
|
||||
strideMeanInvStd,
|
||||
strideMeanInvStd,
|
||||
{G, C},
|
||||
strideDGammaBeta,
|
||||
strideDGammaBeta,
|
||||
{0, 1, 2}, // reduceDims
|
||||
dy_dev.GetDeviceBuffer(),
|
||||
x_dev.GetDeviceBuffer(),
|
||||
mean_dev.GetDeviceBuffer(),
|
||||
inv_std_dev.GetDeviceBuffer(),
|
||||
dgamma_dev.GetDeviceBuffer(),
|
||||
dbeta_dev.GetDeviceBuffer());
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
|
||||
SimpleDeviceMem workspace(workspace_sz);
|
||||
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
|
||||
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
}
|
||||
|
||||
std::cout << "Done" << std::endl;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
236
client_example/18_groupnorm/groupnorm_swish_fwd.cpp
Normal file
236
client_example/18_groupnorm/groupnorm_swish_fwd.cpp
Normal file
@@ -0,0 +1,236 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iomanip>
|
||||
#include <vector>
|
||||
#include <iostream>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_normalization_fwd.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/gpu/normalization_fwd_swish.hpp"
|
||||
|
||||
using XDataType = ck::half_t;
|
||||
using GammaDataType = float;
|
||||
using BetaDataType = float;
|
||||
using YDataType = ck::half_t;
|
||||
using SaveMeanInvStdDataType = float;
|
||||
using Swish = ck::tensor_operation::element_wise::Swish;
|
||||
|
||||
#define SAVE_MEAN_INV_STD
|
||||
|
||||
constexpr int Rank = 5;
|
||||
constexpr int NumReduceDim = 3;
|
||||
|
||||
struct SimpleDeviceMem
|
||||
{
|
||||
SimpleDeviceMem() = delete;
|
||||
|
||||
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
|
||||
{
|
||||
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
|
||||
}
|
||||
|
||||
void* GetDeviceBuffer() { return p_mem_; }
|
||||
|
||||
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
|
||||
|
||||
void* p_mem_;
|
||||
};
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
ck::index_t N = 32;
|
||||
ck::index_t H = 16;
|
||||
ck::index_t W = 16;
|
||||
ck::index_t G = 64;
|
||||
ck::index_t C = 128;
|
||||
|
||||
std::size_t xy_size = N * H * W * G * C;
|
||||
std::size_t gamma_beta_size = G * C;
|
||||
|
||||
std::vector<ck::index_t> xy_strides = {H * W * G * C, W * G * C, G * C, C, 1};
|
||||
std::vector<ck::index_t> gamma_beta_strides = {0, 0, 0, C, 1};
|
||||
std::vector<ck::index_t> save_mean_inv_std_strides = {G, 1};
|
||||
|
||||
SimpleDeviceMem x_device_buf(sizeof(XDataType) * xy_size);
|
||||
SimpleDeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_beta_size);
|
||||
SimpleDeviceMem beta_device_buf(sizeof(BetaDataType) * gamma_beta_size);
|
||||
SimpleDeviceMem y_device_buf(sizeof(YDataType) * xy_size);
|
||||
#ifdef SAVE_MEAN_INV_STD
|
||||
SimpleDeviceMem save_mean_device_buf(sizeof(SaveMeanInvStdDataType) * N * G);
|
||||
SimpleDeviceMem save_inv_std_device_buf(sizeof(SaveMeanInvStdDataType) * N * G);
|
||||
#endif
|
||||
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceNormalizationFwd<XDataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
YDataType,
|
||||
SaveMeanInvStdDataType,
|
||||
Swish,
|
||||
Rank,
|
||||
NumReduceDim>;
|
||||
|
||||
// get device op instances
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
const auto& generic_op_ptr = op_ptrs[0];
|
||||
|
||||
auto generic_argument_ptr =
|
||||
generic_op_ptr->MakeArgumentPointer({N, H, W, G, C}, // lengths
|
||||
xy_strides, // xStrides
|
||||
gamma_beta_strides, // gammaStrides
|
||||
gamma_beta_strides, // betaStrides
|
||||
xy_strides, // yStrides
|
||||
save_mean_inv_std_strides, // save_mean Strides
|
||||
save_mean_inv_std_strides, // save_inv_std Strides
|
||||
{1, 2, 4}, // reduceDims
|
||||
1e-6,
|
||||
x_device_buf.GetDeviceBuffer(),
|
||||
gamma_device_buf.GetDeviceBuffer(),
|
||||
beta_device_buf.GetDeviceBuffer(),
|
||||
y_device_buf.GetDeviceBuffer(),
|
||||
#ifdef SAVE_MEAN_INV_STD
|
||||
save_mean_device_buf.GetDeviceBuffer(),
|
||||
save_inv_std_device_buf.GetDeviceBuffer(),
|
||||
#else
|
||||
nullptr,
|
||||
nullptr,
|
||||
#endif
|
||||
Swish{});
|
||||
|
||||
if(!generic_op_ptr->IsSupportedArgument(generic_argument_ptr.get()))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"The generic kernel instance should be able to support any input shapes");
|
||||
};
|
||||
|
||||
std::string best_op_name;
|
||||
bool found = false;
|
||||
int best_op_id = -1;
|
||||
float best_ave_time = std::numeric_limits<float>::max();
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
// profile device operation instances
|
||||
std::cout << "Run all instances and do timing" << std::endl;
|
||||
|
||||
for(int i = 0; i < op_ptrs.size(); ++i)
|
||||
{
|
||||
auto& op_ptr = op_ptrs[i];
|
||||
auto argument_ptr =
|
||||
op_ptr->MakeArgumentPointer({N, H, W, G, C}, // lengths
|
||||
xy_strides, // xStrides
|
||||
gamma_beta_strides, // gammaStrides
|
||||
gamma_beta_strides, // betaStrides
|
||||
xy_strides, // yStrides
|
||||
save_mean_inv_std_strides, // save_mean Strides
|
||||
save_mean_inv_std_strides, // save_inv_std Strides
|
||||
{1, 2, 4}, // reduceDims
|
||||
1e-6,
|
||||
x_device_buf.GetDeviceBuffer(),
|
||||
gamma_device_buf.GetDeviceBuffer(),
|
||||
beta_device_buf.GetDeviceBuffer(),
|
||||
y_device_buf.GetDeviceBuffer(),
|
||||
#ifdef SAVE_MEAN_INV_STD
|
||||
save_mean_device_buf.GetDeviceBuffer(),
|
||||
save_inv_std_device_buf.GetDeviceBuffer(),
|
||||
#else
|
||||
nullptr,
|
||||
nullptr,
|
||||
#endif
|
||||
Swish{});
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
|
||||
SimpleDeviceMem workspace(workspace_sz);
|
||||
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
|
||||
|
||||
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
|
||||
|
||||
std::size_t num_byte =
|
||||
sizeof(XDataType) * xy_size + sizeof(GammaDataType) * gamma_beta_size +
|
||||
sizeof(BetaDataType) * gamma_beta_size + sizeof(YDataType) * xy_size;
|
||||
|
||||
#ifdef SAVE_MEAN_INV_STD
|
||||
num_byte += sizeof(SaveMeanInvStdDataType) * N * G * 2;
|
||||
#endif
|
||||
|
||||
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
|
||||
<< op_name << std::endl;
|
||||
|
||||
if(ave_time < best_ave_time)
|
||||
{
|
||||
found = true;
|
||||
best_op_id = i;
|
||||
best_op_name = op_name;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
// run the best intance
|
||||
if(found)
|
||||
{
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_op_name << std::endl;
|
||||
|
||||
auto& op_ptr = op_ptrs[best_op_id];
|
||||
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
|
||||
<< std::endl;
|
||||
|
||||
auto argument_ptr =
|
||||
op_ptr->MakeArgumentPointer({N, H, W, G, C}, // lengths
|
||||
xy_strides, // xStrides
|
||||
gamma_beta_strides, // gammaStrides
|
||||
gamma_beta_strides, // betaStrides
|
||||
xy_strides, // yStrides
|
||||
save_mean_inv_std_strides, // save_mean Strides
|
||||
save_mean_inv_std_strides, // save_inv_std Strides
|
||||
{1, 2, 4}, // reduceDims
|
||||
1e-6,
|
||||
x_device_buf.GetDeviceBuffer(),
|
||||
gamma_device_buf.GetDeviceBuffer(),
|
||||
beta_device_buf.GetDeviceBuffer(),
|
||||
y_device_buf.GetDeviceBuffer(),
|
||||
#ifdef SAVE_MEAN_INV_STD
|
||||
save_mean_device_buf.GetDeviceBuffer(),
|
||||
save_inv_std_device_buf.GetDeviceBuffer(),
|
||||
#else
|
||||
nullptr,
|
||||
nullptr,
|
||||
#endif
|
||||
Swish{});
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
|
||||
SimpleDeviceMem workspace(workspace_sz);
|
||||
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
|
||||
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
}
|
||||
|
||||
std::cout << "Done" << std::endl;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user