Skip to content

GitLab

"...resnet50_tensorflow.git" did not exist on "b8ceb49c1578d33b0ff02cc7c4e8c2f40f0083ff"
Unverified Commit 14932e8d authored by Qianfeng's avatar Qianfeng Committed by GitHub
Browse files

Add examples for reduction fp16/fp32/bp16/int8/fp64 for 3d/4d/5d (#342) 

* Update the reduce_blockwise example to support user specified data type and input+reducing dimensions

* Add examples for using reduce_multiblock_atomic_add

* Add more running examples to the default command-line

* Remove un-necessary header including

* Update to the example README.md
parent 6c3c06bf
Hide whitespace changes
Inline Side-by-side
Showing with 946 additions and 237 deletions
example/12_reduce/CMakeLists.txt
View file @ 14932e8d
add_example_executable(example_reduce_blockwise reduce_blockwise.cpp)
add_example_executable(example_reduce_multiblock_atomic_add reduce_multiblock_atomic_add.cpp)
add_example_executable(example_reduce_blockwise_two_call reduce_blockwise_two_call.cpp)
example/12_reduce/README.md
View file @ 14932e8d
......@@ -2,20 +2,41 @@
## Run ```example_reduce_blockwise```
```bash
# -D <xxx> : input 4-d tensor lengths
# -D <xxx> : input 3d/4d/5d tensor lengths
# -R <xxx> : reduce dimension ids
# -v <x> : verification (0=no, 1=yes)
#arg1: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg2: time kernel (0=no, 1=yes)
./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 1 1
#arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64, 7: int4)
#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg3: time kernel (0=no, 1=yes)
./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 0 2 1
```
Result
```
./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 1 1
launch_and_time_kernel: grid_dim {240, 1, 1}, block_dim {256, 1, 1}
./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 0 2 1
launch_and_time_kernel: grid_dim {240, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 0.282592 ms, 222.641 GB/s, DeviceReduceBlockWise<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
Perf: 0.238063 ms, 264.285 GB/s, DeviceReduceBlockWise<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
```
## Run ```example_reduce_multiblock_atomic_add```
```bash
# -D <xxx> : input 3d/4d/5d tensor lengths
# -R <xxx> : reduce dimension ids
# -v <x> : verification (0=no, 1=yes)
#arg1: data type (0: fp32, 1: fp64)
#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg3: time kernel (0=no, 1=yes)
./bin/example_reduce_multiblock_atomic_add -D 16,64,32,960 -v 1 0 2 0
```
Result
```
./bin/example_reduce_multiblock_atomic_add -D 16,64,32,960 -v 1 0 2 0
Perf: 0 ms, inf GB/s, DeviceReduceMultiBlock<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
echo $?
0
```
# Instructions for ```example_reduce_blockwise_two_call```
......
example/12_reduce/reduce_blockwise.cpp
View file @ 14932e8d
......@@ -2,64 +2,17 @@
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#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/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_blockwise_impl.hpp"
#include "reduce_example_common.hpp"
using namespace ck;
using namespace ck::tensor_operation::device;
using InDataType = ck::half_t;
using OutDataType = ck::half_t;
using AccDataType = float;
constexpr int Rank = 4;
constexpr int NumReduceDim = 3;
constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::NORM2;
constexpr bool PropagateNan = true;
constexpr bool OutputIndex = false;
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 = DeviceReduceMultiBlock<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
InMemoryDataOperationEnum::Set,
PropagateNan,
OutputIndex,
false, // HaveIndexInputIfOutputIndex
256,
4,
64,
1,
1,
0,
1,
1>;
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"verify", required_argument, nullptr, 'v'},
{"help", no_argument, nullptr, '?'},
......@@ -72,10 +25,12 @@ class SimpleAppArgs
public:
std::vector<size_t> inLengths = {16, 64, 32, 960};
std::vector<int> reduceDims = {0, 1, 2};
std::vector<float> scales = {1.0f, 0.0f};
bool do_verification = true;
int init_method = 1;
int data_type = 1;
int init_method = 2;
bool time_kernel = true;
public:
......@@ -84,13 +39,17 @@ class SimpleAppArgs
std::cout << "Usage of " << cmd << std::endl;
std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths"
<< std::endl;
std::cout << "--reduceDims or -R, comma separated list of to-reduce dimensions"
<< std::endl;
std::cout << "--verify or -v, 1/0 to indicate whether to verify the reduction result by "
"comparing with the host-based reduction"
<< std::endl;
std::cout << "Arg1 -- init method (0=no init, 1=single integer value, 2=scope integer "
std::cout << "Arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64, 7: int4)"
<< std::endl;
std::cout << "Arg2 -- init method (0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
std::cout << "Arg2 -- time kernel (0=no, 1=yes)" << std::endl;
std::cout << "Arg3 -- time kernel (0=no, 1=yes)" << std::endl;
};
int processArgs(int argc, char* argv[])
......@@ -101,7 +60,7 @@ class SimpleAppArgs
while(1)
{
ch = getopt_long(argc, argv, "D:v:l:", long_options, &option_index);
ch = getopt_long(argc, argv, "D:R:v:l:", long_options, &option_index);
if(ch == -1)
break;
switch(ch)
......@@ -112,6 +71,12 @@ class SimpleAppArgs
inLengths = getTypeValuesFromString<size_t>(optarg);
break;
case 'R':
if(!optarg)
throw std::runtime_error("Invalid option format!");
reduceDims = getTypeValuesFromString<int>(optarg);
break;
case 'v':
if(!optarg)
throw std::runtime_error("Invalid option format!");
......@@ -129,9 +94,12 @@ class SimpleAppArgs
};
};
if(optind + 2 > argc)
if(optind + 3 > argc)
{
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
};
data_type = std::atoi(argv[optind++]);
init_method = std::atoi(argv[optind++]);
time_kernel = static_cast<bool>(std::atoi(argv[optind]));
......@@ -145,198 +113,152 @@ class SimpleAppArgs
};
};
int main(int argc, char* argv[])
template <typename InOutDataType,
typename AccDataType,
ReduceTensorOp ReduceOpId,
index_t PropagateNan,
index_t OutputIndex>
bool reduce_blockwise_test(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
float alpha,
float beta)
{
const std::vector<int> reduceDims{0, 1, 2};
const std::vector<int> invariantDims{3};
bool matched = false;
int result = 0;
SimpleAppArgs args;
const auto tuple_object = reduce_shape_instances{};
if(argc > 1)
{
if(args.processArgs(argc, argv) < 0)
return (-1);
};
constexpr bool op_support_indices =
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
// if input is half type, no reason to use float for indiced reduction operation and must use
// float for non-indiced reduction operation for accuracy
constexpr bool invalid_reduce_1 =
std::is_same<InDataType, ck::half_t>::value &&
((!op_support_indices && !std::is_same<AccDataType, float>::value) ||
(op_support_indices && !std::is_same<AccDataType, ck::half_t>::value));
// if input is float type, no reason to use double for indiced reduction operation
constexpr bool invalid_reduce_2 =
std::is_same<InDataType, float>::value &&
(op_support_indices && !std::is_same<AccDataType, float>::value);
// indices option can only be used when it is really needed
constexpr bool invalid_reduce_3 = (!op_support_indices && OutputIndex);
static_for<0, std::tuple_size<reduce_shape_instances>::value, 1>{}([&](auto i) {
if(matched)
return;
constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2 || invalid_reduce_3);
using ShapeType = remove_cvref_t<decltype(std::get<i>(tuple_object))>;
if constexpr(invalid_reduce)
std::cout << "Reduction setting is not supported, exiting!" << std::endl;
if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
return;
Tensor<InDataType> in(args.inLengths);
result = reduce_blockwise_impl<InOutDataType,
AccDataType,
ReduceOpId,
ShapeType::Rank_,
ShapeType::NumReduceDim_,
PropagateNan,
OutputIndex>(
do_verification, init_method, time_kernel, inLengths, reduceDims, alpha, beta);
std::vector<size_t> outLengths;
matched = true;
});
if(invariantDims.empty())
outLengths.push_back(1);
else
for(auto dim : invariantDims)
outLengths.push_back(args.inLengths[dim]);
Tensor<OutDataType> out_ref(outLengths);
Tensor<OutDataType> out(outLengths);
Tensor<int> out_indices_ref(outLengths);
Tensor<int> out_indices(outLengths);
return (result == 0) ? true : false;
};
auto inStrides = in.mDesc.GetStrides();
auto outStrides = out.mDesc.GetStrides();
constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::AVG;
constexpr bool PropagateNan = true;
constexpr bool OutputIndex = false;
size_t invariant_total_length = out.mDesc.GetElementSize();
size_t reduce_total_length = in.mDesc.GetElementSize() / invariant_total_length;
int main(int argc, char* argv[])
{
bool pass = true;
float alpha = args.scales[0];
float beta = args.scales[1];
if(argc > 1)
{
SimpleAppArgs arg;
std::size_t num_thread = 1;
if(arg.processArgs(argc, argv) < 0)
return (-1);
if(args.do_verification)
{
switch(args.init_method)
if(arg.data_type == 0)
{
case 0: break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
break;
default:
in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0}, num_thread);
pass = reduce_blockwise_test<ck::half_t, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
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(InDataType) * in.mDesc.GetElementSpaceSize());
DeviceMem out_dev(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
in_dev.ToDevice(in.mData.data());
if(beta != 0.0f)
out_dev.ToDevice(out.mData.data());
size_t indicesSizeInBytes = OutputIndex ? out.mDesc.GetElementSize() * sizeof(int32_t) : 0;
DeviceMem out_index_dev(indicesSizeInBytes);
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(args.do_verification)
{
ReductionHost<InDataType,
AccDataType,
OutDataType,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
Rank,
NumReduceDim,
PropagateNan,
OutputIndex>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run(alpha,
in.mData.data(),
beta,
out_ref.mData.data(),
out_indices_ref.mData.data(),
in_elementwise_op,
acc_elementwise_op);
};
std::vector<ck::index_t> i_inLengths;
std::vector<ck::index_t> i_inStrides;
std::vector<ck::index_t> i_outLengths;
std::vector<ck::index_t> i_outStrides;
i_inLengths.assign(args.inLengths.begin(), args.inLengths.end());
i_inStrides.assign(inStrides.begin(), inStrides.end());
i_outLengths.assign(outLengths.begin(), outLengths.end());
i_outStrides.assign(outStrides.begin(), outStrides.end());
auto reduce = DeviceReduceInstance{};
auto argument_ptr = reduce.MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
in_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
out_index_dev.GetDeviceBuffer(),
in_elementwise_op,
acc_elementwise_op);
if(!reduce.IsSupportedArgument(argument_ptr.get()))
{
std::cout
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
<< std::endl;
};
std::string reduce_name = reduce.GetTypeString();
auto invoker_ptr = reduce.MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, args.time_kernel});
std::size_t num_bytes = invariant_total_length * reduce_total_length * sizeof(InDataType) +
invariant_total_length * sizeof(OutDataType);
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(args.do_verification)
{
out_dev.FromDevice(out.mData.data());
pass = pass && ck::utils::check_err(out.mData, out_ref.mData);
if(OutputIndex)
else if(arg.data_type == 1)
{
out_index_dev.FromDevice(out_indices.mData.data());
pass = pass && ck::utils::check_err(out_indices.mData, out_indices_ref.mData);
};
pass = reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 3)
{
pass = reduce_blockwise_test<int8_t, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 5)
{
pass = reduce_blockwise_test<ck::bhalf_t, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 6)
{
pass = reduce_blockwise_test<double, double, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
}
else
{
// for testing half_t
pass =
pass && reduce_blockwise_test<ck::half_t, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing float
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing double
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing bhalf_t
pass = pass &&
reduce_blockwise_test<ck::bhalf_t, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing int8_t
pass =
pass && reduce_blockwise_test<int8_t, int32_t, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing 3D input
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 960}, {0, 1}, 1.0f, 0.0f);
// for testing 5D input
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 2, 960}, {0, 1, 2, 3}, 1.0f, 0.0f);
};
return (pass ? 0 : 1);
}
};
example/12_reduce/reduce_blockwise_impl.hpp 0 → 100644
View file @ 14932e8d
// 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/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,
bool OutputIndex>
int reduce_blockwise_impl(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
float alpha,
float beta)
{
using namespace ck;
using namespace ck::tensor_operation::device;
constexpr bool op_support_indices =
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool invalid_reduce_1 = OutputIndex && !op_support_indices;
// 1) If InOutDataType is half_t, must use half_t as AccDataType for indexable reduction
// operations 2) If InOutDataType is half_t, must use float as AccDataType for non-indexable
// reduction operations
constexpr bool invalid_reduce_2 =
std::is_same<InOutDataType, half_t>::value &&
((!op_support_indices && !std::is_same<AccDataType, float>::value) ||
(op_support_indices && !std::is_same<AccDataType, half_t>::value));
// 1) If InOutDataType is float, must use float as AccDataType for indexable reduction
// operations
constexpr bool invalid_reduce_3 =
std::is_same<InOutDataType, float>::value &&
(op_support_indices && !std::is_same<AccDataType, float>::value);
// 1) If InOutDataType is int8_t, must use int8_t as AccDataType for indexable reduction
// operations 2) If InOutDataType is int8_t, must use int32_t as AccDataType for non-indexable
// reduction operations
constexpr bool invalid_reduce_4 =
std::is_same<InOutDataType, int8_t>::value &&
((!op_support_indices && !std::is_same<AccDataType, int32_t>::value) ||
(op_support_indices && !std::is_same<AccDataType, int8_t>::value));
// 1) If InOutDataType is int8_t, the supported operation must be either indexable operations or
// ADD/AVG
constexpr bool invalid_reduce_5 = std::is_same<InOutDataType, int8_t>::value &&
(!op_support_indices && ReduceOpId != ReduceTensorOp::ADD &&
ReduceOpId != ReduceTensorOp::AVG);
// 1) If InOutDataType is bhalf_t, must use float as AccDataType for all reduction operations
constexpr bool invalid_reduce_6 =
std::is_same<InOutDataType, bhalf_t>::value && !std::is_same<AccDataType, float>::value;
constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2 || invalid_reduce_3 ||
invalid_reduce_4 || invalid_reduce_5 || invalid_reduce_6);
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::Set,
PropagateNan,
OutputIndex,
false, // HaveIndexInputIfOutputIndex
256, // BlockSize
4, // MThreadClusterSize
64, // KThreadClusterSize
1, // MThreadSliceSize
1, // KThreadSliceSize
0, // InSrcVectorDim
1, // InSrceVectorSize
1>; // OutDstVectorSize
Tensor<InOutDataType> in(inLengths);
std::vector<size_t> outLengths;
std::vector<int> 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);
Tensor<int> out_indices_ref(outLengths);
Tensor<int> out_indices(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());
size_t indicesSizeInBytes = OutputIndex ? out.mDesc.GetElementSize() * sizeof(int32_t) : 0;
DeviceMem out_index_dev(indicesSizeInBytes);
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,
OutputIndex>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run(alpha,
in.mData.data(),
beta,
out_ref.mData.data(),
out_indices_ref.mData.data(),
in_elementwise_op,
acc_elementwise_op);
};
std::vector<ck::index_t> i_inLengths;
std::vector<ck::index_t> i_inStrides;
std::vector<ck::index_t> i_outLengths;
std::vector<ck::index_t> i_outStrides;
i_inLengths.assign(inLengths.begin(), inLengths.end());
i_inStrides.assign(inStrides.begin(), inStrides.end());
i_outLengths.assign(outLengths.begin(), outLengths.end());
i_outStrides.assign(outStrides.begin(), outStrides.end());
auto reduce = DeviceReduceInstance{};
auto argument_ptr = reduce.MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
in_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
out_index_dev.GetDeviceBuffer(),
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);
if(OutputIndex)
{
out_index_dev.FromDevice(out_indices.mData.data());
pass = pass && ck::utils::check_err(out_indices.mData, out_indices_ref.mData);
};
};
return (pass ? 0 : 1);
}
example/12_reduce/reduce_example_common.hpp 0 → 100644
View file @ 14932e8d
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
template <ck::index_t Rank, ck::index_t NumReduceDim>
std::vector<int> get_invariant_dims(const std::vector<int>& reduceDims)
{
assert(NumReduceDim == reduceDims.size());
int reduceFlag = 0;
// flag the bits for the reduceDims
for(int i = 0; i < NumReduceDim; i++)
{
reduceFlag |= 1 << reduceDims[i];
};
std::vector<int> invariantDims;
// collect invariant dimensions
for(int i = 0; i < Rank; i++)
if((reduceFlag & (1 << i)) == 0)
{
invariantDims.push_back(i);
};
return invariantDims;
};
template <ck::index_t Rank, ck::index_t NumReduceDim>
struct ReduceShape
{
static constexpr ck::index_t Rank_ = Rank;
static constexpr ck::index_t NumReduceDim_ = NumReduceDim;
};
using reduce_shape_instances = std::tuple<ReduceShape<3, 1>,
ReduceShape<3, 2>,
ReduceShape<4, 1>,
ReduceShape<4, 2>,
ReduceShape<4, 3>,
ReduceShape<5, 1>,
ReduceShape<5, 2>,
ReduceShape<5, 3>,
ReduceShape<5, 4>>;
example/12_reduce/reduce_multiblock_atomic_add.cpp 0 → 100644
View file @ 14932e8d
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#include "ck/utility/reduction_enums.hpp"
#include "reduce_multiblock_atomic_add_impl.hpp"
#include "reduce_example_common.hpp"
using namespace ck;
using namespace ck::tensor_operation::device;
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"verify", required_argument, nullptr, 'v'},
{"help", no_argument, nullptr, '?'},
{nullptr, 0, nullptr, 0}};
class SimpleAppArgs
{
private:
int option_index = 0;
public:
std::vector<size_t> inLengths = {16, 64, 32, 960};
std::vector<int> reduceDims = {0, 1, 2};
std::vector<float> scales = {1.0f, 0.0f};
bool do_verification = true;
int data_type = 1;
int init_method = 2;
bool time_kernel = true;
public:
void show_usage(const char* cmd)
{
std::cout << "Usage of " << cmd << std::endl;
std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths"
<< std::endl;
std::cout << "--reduceDims or -R, comma separated list of to-reduce dimensions"
<< std::endl;
std::cout << "--verify or -v, 1/0 to indicate whether to verify the reduction result by "
"comparing with the host-based reduction"
<< std::endl;
std::cout << "Arg1: data type (0: fp32, 1: fp64)" << std::endl;
std::cout << "Arg2 -- init method (0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
std::cout << "Arg3 -- time kernel (0=no, 1=yes)" << std::endl;
};
int processArgs(int argc, char* argv[])
{
using ck::host_common::getTypeValuesFromString;
int ch;
while(1)
{
ch = getopt_long(argc, argv, "D:R:v:l:", long_options, &option_index);
if(ch == -1)
break;
switch(ch)
{
case 'D':
if(!optarg)
throw std::runtime_error("Invalid option format!");
inLengths = getTypeValuesFromString<size_t>(optarg);
break;
case 'R':
if(!optarg)
throw std::runtime_error("Invalid option format!");
reduceDims = getTypeValuesFromString<int>(optarg);
break;
case 'v':
if(!optarg)
throw std::runtime_error("Invalid option format!");
do_verification = static_cast<bool>(std::atoi(optarg));
break;
case '?':
if(std::string(long_options[option_index].name) == "help")
{
show_usage(argv[0]);
return (-1);
};
break;
default: show_usage(argv[0]); return (-1);
};
};
if(optind + 3 > argc)
{
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
};
data_type = std::atoi(argv[optind++]);
init_method = std::atoi(argv[optind++]);
time_kernel = static_cast<bool>(std::atoi(argv[optind]));
if(scales.empty())
{
scales.push_back(1.0f);
scales.push_back(0.0f);
};
return (0);
};
};
template <typename InOutDataType,
typename AccDataType,
ReduceTensorOp ReduceOpId,
index_t PropagateNan>
bool reduce_multiblock_atomic_add_test(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
float alpha,
float beta)
{
bool matched = false;
int result = 0;
const auto tuple_object = reduce_shape_instances{};
static_for<0, std::tuple_size<reduce_shape_instances>::value, 1>{}([&](auto i) {
if(matched)
return;
using ShapeType = remove_cvref_t<decltype(std::get<i>(tuple_object))>;
if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
return;
result = reduce_multiblock_atomic_add_impl<InOutDataType,
AccDataType,
ReduceOpId,
ShapeType::Rank_,
ShapeType::NumReduceDim_,
PropagateNan>(
do_verification, init_method, time_kernel, inLengths, reduceDims, alpha, beta);
matched = true;
});
return (result == 0) ? true : false;
};
constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::AVG;
constexpr bool PropagateNan = true;
int main(int argc, char* argv[])
{
bool pass = true;
if(argc > 1)
{
SimpleAppArgs arg;
if(arg.processArgs(argc, argv) < 0)
return (-1);
if(arg.data_type == 0)
{
pass = reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 1)
{
pass = reduce_multiblock_atomic_add_test<double, double, ReduceOpId, PropagateNan>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
}
else
{
// for testing float
pass = pass && reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
true, 2, false, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing double
pass = pass && reduce_multiblock_atomic_add_test<double, double, ReduceOpId, PropagateNan>(
true, 2, false, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing 3D input
pass = pass && reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
true, 2, false, {16, 64, 960}, {0, 1}, 1.0f, 0.0f);
// for testing 5D input
pass = pass && reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
true, 2, false, {16, 64, 32, 2, 960}, {0, 1, 2, 3}, 1.0f, 0.0f);
};
return (pass ? 0 : 1);
};
example/12_reduce/reduce_multiblock_atomic_add_impl.hpp 0 → 100644
View file @ 14932e8d
// 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/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::vector<int>& reduceDims,
float alpha,
float beta)
{
using namespace ck;
using namespace ck::tensor_operation::device;
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;
std::vector<int> 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::vector<ck::index_t> i_inLengths;
std::vector<ck::index_t> i_inStrides;
std::vector<ck::index_t> i_outLengths;
std::vector<ck::index_t> i_outStrides;
i_inLengths.assign(inLengths.begin(), inLengths.end());
i_inStrides.assign(inStrides.begin(), inStrides.end());
i_outLengths.assign(outLengths.begin(), outLengths.end());
i_outStrides.assign(outStrides.begin(), outStrides.end());
auto reduce = DeviceReduceInstance{};
auto argument_ptr = reduce.MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
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);
}
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment