Files
composable_kernel/client_example/26_reduce/reduce_nhwc_c.cpp
Vidyasagar Ananthan 92c67a824f [DOCS] Documentation Addition (Readme updates) (#2495)
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Update README.md to clarify CK Tile API description and remove outdated references to the Tile Engine.

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Remove references to the Tile Engine in README files across multiple examples

* GH-2368 Adding a basic glossary

GH-2368 Minor edits

GH-2368 Adding missing READMEs and standardization.

resolving readme updates

GH-2368 Minor improvements to documentation.

Improving some readmes.

Further improvement for readmes.

Cleaned up the documentation in 'client_example' (#2468)

Update for PR

Update ACRONYMS.md to remove trivial terms

Update ACRONYMS.md to provide detailed explanations for BF16 and BF8 formats

Apply suggestion from @spolifroni-amd

Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>

Apply suggestion from @spolifroni-amd

Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>

Update README.md to clarify CK Tile API description and remove outdated references to the Tile Engine.

revise 37_transpose readme

revise 36_copy readme

Remove references to the Tile Engine in README files for 19_gemm_multi_d and 35_batched_transpose, and update distribution links for clarity.

Remove references to the Tile Engine in multiple README files and update distribution links for consistency and clarity.

Remove references to the Tile Engine in README files across multiple examples

Refine README files by removing outdated references to the Tile Engine

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* Content Modification of CK Tile Example

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---------

Co-authored-by: AviralGoelAMD <aviral.goel@amd.com>
Co-authored-by: ThomasNing <thomas.ning@amd.com>
2025-10-16 03:10:57 -07:00

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6.8 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <functional>
#include <numeric>
#include <iomanip>
#include <iostream>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/reduce/reduce.hpp"
using InDataType = float;
using OutDataType = float;
using AccDataType = float;
using ReduceAdd = ck::reduce::Add;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using UnaryDivide = ck::tensor_operation::element_wise::UnaryDivide;
constexpr bool PropagateNan = false;
constexpr bool OutputIndex = false;
constexpr int Rank = 4;
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[])
{
std::array<ck::index_t, Rank> in_lengths{16, 8, 128, 256};
std::array<ck::index_t, Rank> in_strides{8 * 128 * 256, 128 * 256, 256, 1};
std::array<ck::index_t, Rank - NumReduceDim> out_lengths{256};
std::array<ck::index_t, Rank - NumReduceDim> out_strides{1};
std::array<int, NumReduceDim> reduce_dims{0, 1, 2};
ck::index_t num_in_elements =
std::accumulate(in_lengths.begin(), in_lengths.end(), 1, std::multiplies<ck::index_t>());
ck::index_t num_out_elements =
std::accumulate(out_lengths.begin(), out_lengths.end(), 1, std::multiplies<ck::index_t>());
ck::index_t reduce_length = 1;
for(auto dim : reduce_dims)
reduce_length *= in_lengths[dim];
double alpha{1.0};
double beta{0.0};
SimpleDeviceMem in(sizeof(InDataType) * num_in_elements);
SimpleDeviceMem out(sizeof(OutDataType) * num_out_elements);
using DeviceOp = ck::tensor_operation::device::DeviceReduce<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceAdd,
PassThrough,
UnaryDivide,
PropagateNan,
OutputIndex>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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(in_lengths,
in_strides,
out_lengths,
out_strides,
reduce_dims,
alpha,
beta,
in.GetDeviceBuffer(),
nullptr,
out.GetDeviceBuffer(),
nullptr,
PassThrough{},
UnaryDivide{reduce_length});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t num_bytes = num_in_elements * sizeof(InDataType) +
(beta == 0.0f ? 1 : 2) * num_out_elements * sizeof(OutDataType);
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;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
// run the best intance
if(found)
{
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(in_lengths,
in_strides,
out_lengths,
out_strides,
reduce_dims,
alpha,
beta,
in.GetDeviceBuffer(),
nullptr,
out.GetDeviceBuffer(),
nullptr,
PassThrough{},
UnaryDivide{reduce_length});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}