mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-16 10:59:55 +00:00
Extend permute scale support up to 6D (#1168)
* Extend permute scale support up to 6D
* Fixes
* Fixes
* Update profiler/README.md
Co-authored-by: Lisa <lisajdelaney@gmail.com>
* Update profiler/README.md
Co-authored-by: Lisa <lisajdelaney@gmail.com>
* Update profiler/README.md
Co-authored-by: Lisa <lisajdelaney@gmail.com>
* Update profiler/README.md
Co-authored-by: Lisa <lisajdelaney@gmail.com>
* Update profiler/README.md
Co-authored-by: Lisa <lisajdelaney@gmail.com>
* Update profiler/README.md
Co-authored-by: Lisa <lisajdelaney@gmail.com>
* Update profiler/README.md
Co-authored-by: Lisa <lisajdelaney@gmail.com>
---------
Co-authored-by: Lisa <lisajdelaney@gmail.com>
[ROCm/composable_kernel commit: 66736edb95]
This commit is contained in:
Bartłomiej Kocot
188
profiler/include/profiler/profile_permute_scale_impl.hpp
Normal file
188
profiler/include/profiler/profile_permute_scale_impl.hpp
Normal file
@@ -0,0 +1,188 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iomanip>
|
||||
#include <random>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_elementwise_scale.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/gpu/permute_scale.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
#include "ck/library/utility/literals.hpp"
|
||||
|
||||
namespace ck {
|
||||
template <typename HostTensorA,
|
||||
typename HostTensorB,
|
||||
typename AElementOp,
|
||||
typename BElementOp,
|
||||
typename ScaleElementOp>
|
||||
void reference_permute_scale(HostTensorB& b_tensor,
|
||||
const HostTensorA& a_tensor,
|
||||
AElementOp a_tensor_op,
|
||||
BElementOp b_tensor_op,
|
||||
ScaleElementOp scale_op)
|
||||
{
|
||||
b_tensor.ForEach([&](auto& self, auto idx) {
|
||||
auto tmp_val = a_tensor(idx);
|
||||
b_tensor_op(tmp_val, tmp_val);
|
||||
scale_op(tmp_val, tmp_val);
|
||||
a_tensor_op(self(idx), tmp_val);
|
||||
});
|
||||
}
|
||||
|
||||
namespace profiler {
|
||||
|
||||
template <typename ADataType, typename BDataType, index_t NumDim>
|
||||
bool profile_permute_scale_impl(int do_verification,
|
||||
int init_method,
|
||||
bool do_log,
|
||||
bool time_kernel,
|
||||
std::vector<index_t> lengths_vector,
|
||||
std::vector<index_t> input_strides_vector,
|
||||
std::vector<index_t> output_strides_vector)
|
||||
{
|
||||
bool pass = true;
|
||||
bool instance_found = false;
|
||||
|
||||
using ElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using UnaryOp = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using Scale = ck::tensor_operation::element_wise::Scale;
|
||||
float scale = 2.f;
|
||||
|
||||
Tensor<ADataType> a(lengths_vector, input_strides_vector);
|
||||
Tensor<BDataType> b(lengths_vector, output_strides_vector);
|
||||
Tensor<BDataType> host_b(lengths_vector, output_strides_vector);
|
||||
|
||||
std::cout << "A: " << a.mDesc << std::endl;
|
||||
std::cout << "B: " << b.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1: a.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2}); break;
|
||||
default: a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}); break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a.mData.data());
|
||||
|
||||
std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
|
||||
std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ADataType>,
|
||||
ck::Tuple<BDataType>,
|
||||
ElementOp,
|
||||
UnaryOp,
|
||||
Scale,
|
||||
NumDim>;
|
||||
|
||||
// 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;
|
||||
|
||||
std::string best_instance_name;
|
||||
float best_ave_time = std::numeric_limits<float>::max();
|
||||
float best_gb_per_sec = 0;
|
||||
float best_tflops = 0;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
reference_permute_scale(host_b, a, ElementOp{}, UnaryOp{}, Scale{scale});
|
||||
}
|
||||
|
||||
auto copy = [](const auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
|
||||
std::array<ck::index_t, NumDim> lengths{};
|
||||
std::array<ck::index_t, NumDim> input_strides{};
|
||||
std::array<ck::index_t, NumDim> output_strides{};
|
||||
copy(lengths_vector, lengths);
|
||||
copy(input_strides_vector, input_strides);
|
||||
copy(output_strides_vector, output_strides);
|
||||
|
||||
for(auto& op_ptr : op_ptrs)
|
||||
{
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(lengths,
|
||||
{input_strides},
|
||||
{output_strides},
|
||||
input,
|
||||
output,
|
||||
ElementOp{},
|
||||
UnaryOp{},
|
||||
Scale{scale});
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
instance_found = true;
|
||||
|
||||
b_device_buf.SetZero();
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
b_device_buf.FromDevice(b.mData.data());
|
||||
|
||||
pass &= ck::utils::check_err(
|
||||
b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
|
||||
|
||||
if(do_log)
|
||||
{
|
||||
LogRangeAsType<float>(std::cout << "a : ", a.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "b: ", b.mData, ",") << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
float ave_time =
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = std::size_t(2) * a.mDesc.GetElementSpaceSize() / sizeof(ADataType);
|
||||
|
||||
std::size_t num_btype = sizeof(ADataType) * a.mDesc.GetElementSpaceSize() +
|
||||
sizeof(BDataType) * b.mDesc.GetElementSpaceSize();
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_instance_name = op_name;
|
||||
best_tflops = tflops;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
if(time_kernel)
|
||||
{
|
||||
std::cout << "Best perf = " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_instance_name << std::endl;
|
||||
}
|
||||
|
||||
return pass && instance_found;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user