mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 13:11:25 +00:00
dda3a0a10b
* Simplify the macros for declaring and defining the add_device_reduce_instance_xxxx() instances * Change the types of lengths and strides from std::vector to std::array for the reduction device interfaces * Remove DeviceSoftmaxImpl's depending on DeviceReduceMultiblock * Split the cpp and hpp files for reduction instances to enable more parallel compiling * Remove the using of macros for declaring reduction instances and instance references * Update to add_device_reduce_instance_xxxx templated functions * Use ReduceOperation+InElementwiseOp+AccElementwiseOp to repace the ReduceOpId in defining add_reduce_instance_xxxx() templates * Change return format
233 lines
9.1 KiB
C++
233 lines
9.1 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#pragma once
|
|
|
|
#include <iostream>
|
|
|
|
#include "ck/ck.hpp"
|
|
#include "ck/utility/reduction_enums.hpp"
|
|
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
|
|
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.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/host_common_util.hpp"
|
|
#include "ck/library/utility/host_reduction.hpp"
|
|
|
|
#include "reduce_example_common.hpp"
|
|
|
|
template <typename InOutDataType,
|
|
typename AccDataType,
|
|
ck::ReduceTensorOp ReduceOpId,
|
|
ck::index_t Rank,
|
|
ck::index_t NumReduceDim,
|
|
bool PropagateNan>
|
|
int reduce_multiblock_atomic_add_impl(bool do_verification,
|
|
int init_method,
|
|
bool time_kernel,
|
|
const std::vector<size_t>& inLengths,
|
|
const std::array<int, NumReduceDim>& reduceDims,
|
|
float alpha,
|
|
float beta)
|
|
|
|
{
|
|
using namespace ck;
|
|
using namespace ck::tensor_operation::device;
|
|
|
|
constexpr index_t NumOutDim = (Rank - NumReduceDim == 0) ? 1 : Rank - NumReduceDim;
|
|
|
|
constexpr bool op_support_atomic_add =
|
|
(ReduceOpId == ReduceTensorOp::ADD || ReduceOpId == ReduceTensorOp::AVG);
|
|
|
|
constexpr bool invalid_reduce_1 = !op_support_atomic_add;
|
|
constexpr bool invalid_reduce_2 =
|
|
!(std::is_same<InOutDataType, float>::value || std::is_same<InOutDataType, double>::value);
|
|
|
|
constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2);
|
|
|
|
if(invalid_reduce)
|
|
{
|
|
std::cerr << "The reduction setting is invalid, exiting!" << std::endl;
|
|
return (-1);
|
|
};
|
|
|
|
using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
|
|
using InElementwiseOperation =
|
|
typename reduce_unary_operator<ReduceOpId, true, true>::InElementwiseOperation;
|
|
using AccElementwiseOperation =
|
|
typename reduce_unary_operator<ReduceOpId, true, true>::AccElementwiseOperation;
|
|
|
|
using DeviceReduceInstance =
|
|
ck::tensor_operation::device::DeviceReduceMultiBlock<InOutDataType,
|
|
AccDataType,
|
|
InOutDataType,
|
|
Rank,
|
|
NumReduceDim,
|
|
ReduceOperation,
|
|
InElementwiseOperation,
|
|
AccElementwiseOperation,
|
|
InMemoryDataOperationEnum::AtomicAdd,
|
|
PropagateNan,
|
|
false,
|
|
false, // HaveIndexInputIfOutputIndex
|
|
256,
|
|
4,
|
|
64,
|
|
1,
|
|
1,
|
|
0,
|
|
1,
|
|
1>;
|
|
|
|
Tensor<InOutDataType> in(inLengths);
|
|
|
|
std::vector<size_t> outLengths;
|
|
|
|
auto invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
|
|
|
|
if(invariantDims.empty())
|
|
outLengths.push_back(1);
|
|
else
|
|
for(auto dim : invariantDims)
|
|
outLengths.push_back(inLengths[dim]);
|
|
|
|
Tensor<InOutDataType> out_ref(outLengths);
|
|
Tensor<InOutDataType> out(outLengths);
|
|
|
|
auto inStrides = in.mDesc.GetStrides();
|
|
auto outStrides = out.mDesc.GetStrides();
|
|
|
|
size_t invariant_total_length = out.mDesc.GetElementSize();
|
|
size_t reduce_total_length = in.mDesc.GetElementSize() / invariant_total_length;
|
|
|
|
std::size_t num_thread = 1;
|
|
|
|
if(do_verification)
|
|
{
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
in.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
|
|
if(beta != 0.0f)
|
|
out_ref.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
|
|
break;
|
|
case 2:
|
|
in.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
|
|
if(beta != 0.0f)
|
|
out_ref.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
|
|
break;
|
|
default:
|
|
in.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0}, num_thread);
|
|
if(beta != 0.0f)
|
|
out_ref.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0},
|
|
num_thread);
|
|
}
|
|
|
|
if(beta != 0.0f)
|
|
for(size_t i = 0; i < out_ref.mDesc.GetElementSpaceSize(); i++)
|
|
out.mData[i] = out_ref.mData[i];
|
|
};
|
|
|
|
// these buffers are usually provided by the user application
|
|
DeviceMem in_dev(sizeof(InOutDataType) * in.mDesc.GetElementSpaceSize());
|
|
DeviceMem out_dev(sizeof(InOutDataType) * out.mDesc.GetElementSpaceSize());
|
|
|
|
in_dev.ToDevice(in.mData.data());
|
|
|
|
if(beta != 0.0f)
|
|
out_dev.ToDevice(out.mData.data());
|
|
|
|
InElementwiseOperation in_elementwise_op;
|
|
AccElementwiseOperation acc_elementwise_op;
|
|
|
|
std::tie(in_elementwise_op, acc_elementwise_op) =
|
|
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
|
|
static_cast<int32_t>(reduce_total_length));
|
|
|
|
if(do_verification)
|
|
{
|
|
ReductionHost<InOutDataType,
|
|
AccDataType,
|
|
InOutDataType,
|
|
ReduceOperation,
|
|
InElementwiseOperation,
|
|
AccElementwiseOperation,
|
|
Rank,
|
|
NumReduceDim,
|
|
PropagateNan,
|
|
false>
|
|
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
|
|
|
|
hostReduce.Run(alpha,
|
|
in.mData.data(),
|
|
beta,
|
|
out_ref.mData.data(),
|
|
nullptr,
|
|
in_elementwise_op,
|
|
acc_elementwise_op);
|
|
};
|
|
|
|
std::array<index_t, Rank> arrInLengths;
|
|
std::array<index_t, Rank> arrInStrides;
|
|
std::array<index_t, NumOutDim> arrOutLengths;
|
|
std::array<index_t, NumOutDim> arrOutStrides;
|
|
|
|
std::copy(inLengths.begin(), inLengths.end(), arrInLengths.begin());
|
|
std::copy(inStrides.begin(), inStrides.end(), arrInStrides.begin());
|
|
std::copy(outLengths.begin(), outLengths.end(), arrOutLengths.begin());
|
|
std::copy(outStrides.begin(), outStrides.end(), arrOutStrides.begin());
|
|
|
|
auto reduce = DeviceReduceInstance{};
|
|
|
|
auto argument_ptr = reduce.MakeArgumentPointer(arrInLengths,
|
|
arrInStrides,
|
|
arrOutLengths,
|
|
arrOutStrides,
|
|
reduceDims,
|
|
alpha,
|
|
beta,
|
|
in_dev.GetDeviceBuffer(),
|
|
nullptr,
|
|
out_dev.GetDeviceBuffer(),
|
|
nullptr,
|
|
in_elementwise_op,
|
|
acc_elementwise_op);
|
|
|
|
if(!reduce.IsSupportedArgument(argument_ptr.get()))
|
|
{
|
|
std::cerr
|
|
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
|
|
<< std::endl;
|
|
|
|
return (-2);
|
|
};
|
|
|
|
std::string reduce_name = reduce.GetTypeString();
|
|
|
|
auto invoker_ptr = reduce.MakeInvokerPointer();
|
|
|
|
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
|
|
|
std::size_t num_bytes = invariant_total_length * reduce_total_length * sizeof(InOutDataType) +
|
|
invariant_total_length * sizeof(InOutDataType);
|
|
|
|
float gb_per_sec = num_bytes / 1.E6 / avg_time;
|
|
|
|
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, " << reduce_name
|
|
<< std::endl;
|
|
|
|
bool pass = true;
|
|
|
|
if(do_verification)
|
|
{
|
|
out_dev.FromDevice(out.mData.data());
|
|
pass = pass && ck::utils::check_err(out.mData, out_ref.mData);
|
|
};
|
|
|
|
return (pass ? 0 : 1);
|
|
}
|