mirror of
https://github.com/ROCm/composable_kernel.git
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a3d9a2cd42
* Rename folder * Add layernorm 4d fwd example * Rename original layernorm example * Add layernorm 4d f16 test * Add layernorm4d_fwd client example * Support layernorm4D in ckProfiler * Rename groupnorm to groupnorm fwd in example * Rename layernorm and group fwd in test * Rename normalization to normalization_fwd (instances) * Add fwd to DeviceNormalization * Rename external api header * Rename folder, because we can also add bwd in this folder * Add fwd in layernorm and groupnorm (profiler * Fix compile error --------- Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
237 lines
10 KiB
C++
237 lines
10 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 <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_fwd.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/normalization_fwd_swish.hpp"
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using XDataType = ck::half_t;
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using GammaDataType = float;
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using BetaDataType = float;
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using YDataType = ck::half_t;
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using SaveMeanInvStdDataType = float;
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using Swish = ck::tensor_operation::element_wise::Swish;
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#define SAVE_MEAN_INV_STD
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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 xy_size = N * H * W * G * C;
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std::size_t gamma_beta_size = G * C;
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std::vector<ck::index_t> xy_strides = {H * W * G * C, W * G * C, G * C, C, 1};
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std::vector<ck::index_t> gamma_beta_strides = {0, 0, 0, C, 1};
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std::vector<ck::index_t> save_mean_inv_std_strides = {G, 1};
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SimpleDeviceMem x_device_buf(sizeof(XDataType) * xy_size);
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SimpleDeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_beta_size);
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SimpleDeviceMem beta_device_buf(sizeof(BetaDataType) * gamma_beta_size);
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SimpleDeviceMem y_device_buf(sizeof(YDataType) * xy_size);
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#ifdef SAVE_MEAN_INV_STD
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SimpleDeviceMem save_mean_device_buf(sizeof(SaveMeanInvStdDataType) * N * G);
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SimpleDeviceMem save_inv_std_device_buf(sizeof(SaveMeanInvStdDataType) * N * G);
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#endif
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using DeviceOp = ck::tensor_operation::device::DeviceNormalizationFwd<XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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SaveMeanInvStdDataType,
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Swish,
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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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const auto& generic_op_ptr = op_ptrs[0];
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auto generic_argument_ptr =
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generic_op_ptr->MakeArgumentPointer({N, H, W, G, C}, // lengths
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xy_strides, // xStrides
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gamma_beta_strides, // gammaStrides
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gamma_beta_strides, // betaStrides
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xy_strides, // yStrides
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save_mean_inv_std_strides, // save_mean Strides
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save_mean_inv_std_strides, // save_inv_std Strides
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{1, 2, 4}, // reduceDims
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1e-6,
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x_device_buf.GetDeviceBuffer(),
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gamma_device_buf.GetDeviceBuffer(),
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beta_device_buf.GetDeviceBuffer(),
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y_device_buf.GetDeviceBuffer(),
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#ifdef SAVE_MEAN_INV_STD
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save_mean_device_buf.GetDeviceBuffer(),
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save_inv_std_device_buf.GetDeviceBuffer(),
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#else
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nullptr,
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nullptr,
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#endif
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Swish{});
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if(!generic_op_ptr->IsSupportedArgument(generic_argument_ptr.get()))
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{
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throw std::runtime_error(
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"The generic kernel instance should be able to support any input shapes");
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};
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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 =
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op_ptr->MakeArgumentPointer({N, H, W, G, C}, // lengths
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xy_strides, // xStrides
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gamma_beta_strides, // gammaStrides
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gamma_beta_strides, // betaStrides
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xy_strides, // yStrides
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save_mean_inv_std_strides, // save_mean Strides
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save_mean_inv_std_strides, // save_inv_std Strides
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{1, 2, 4}, // reduceDims
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1e-6,
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x_device_buf.GetDeviceBuffer(),
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gamma_device_buf.GetDeviceBuffer(),
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beta_device_buf.GetDeviceBuffer(),
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y_device_buf.GetDeviceBuffer(),
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#ifdef SAVE_MEAN_INV_STD
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save_mean_device_buf.GetDeviceBuffer(),
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save_inv_std_device_buf.GetDeviceBuffer(),
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#else
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nullptr,
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nullptr,
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#endif
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Swish{});
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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 =
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sizeof(XDataType) * xy_size + sizeof(GammaDataType) * gamma_beta_size +
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sizeof(BetaDataType) * gamma_beta_size + sizeof(YDataType) * xy_size;
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#ifdef SAVE_MEAN_INV_STD
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num_byte += sizeof(SaveMeanInvStdDataType) * N * G * 2;
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#endif
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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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// 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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auto argument_ptr =
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op_ptr->MakeArgumentPointer({N, H, W, G, C}, // lengths
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xy_strides, // xStrides
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gamma_beta_strides, // gammaStrides
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gamma_beta_strides, // betaStrides
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xy_strides, // yStrides
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save_mean_inv_std_strides, // save_mean Strides
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save_mean_inv_std_strides, // save_inv_std Strides
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{1, 2, 4}, // reduceDims
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1e-6,
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x_device_buf.GetDeviceBuffer(),
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gamma_device_buf.GetDeviceBuffer(),
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beta_device_buf.GetDeviceBuffer(),
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y_device_buf.GetDeviceBuffer(),
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#ifdef SAVE_MEAN_INV_STD
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save_mean_device_buf.GetDeviceBuffer(),
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save_inv_std_device_buf.GetDeviceBuffer(),
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#else
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nullptr,
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nullptr,
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#endif
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Swish{});
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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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