mirror of
https://github.com/ROCm/composable_kernel.git
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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
This commit is contained in:
Qianfeng
@@ -1,2 +1,3 @@
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add_example_executable(example_reduce_blockwise reduce_blockwise.cpp)
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add_example_executable(example_reduce_multiblock_atomic_add reduce_multiblock_atomic_add.cpp)
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add_example_executable(example_reduce_blockwise_two_call reduce_blockwise_two_call.cpp)
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@@ -2,20 +2,41 @@
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## Run ```example_reduce_blockwise```
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```bash
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# -D <xxx> : input 4-d tensor lengths
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# -D <xxx> : input 3d/4d/5d tensor lengths
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# -R <xxx> : reduce dimension ids
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# -v <x> : verification (0=no, 1=yes)
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#arg1: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
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#arg2: time kernel (0=no, 1=yes)
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./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 1 1
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#arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64, 7: int4)
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#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
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#arg3: time kernel (0=no, 1=yes)
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./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 0 2 1
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```
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Result
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```
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./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 1 1
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launch_and_time_kernel: grid_dim {240, 1, 1}, block_dim {256, 1, 1}
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./bin/example_reduce_blockwise -D 16,64,32,960 -v 1 0 2 1
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launch_and_time_kernel: grid_dim {240, 1, 1}, block_dim {256, 1, 1}
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Warm up 1 time
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Start running 10 times...
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Perf: 0.282592 ms, 222.641 GB/s, DeviceReduceBlockWise<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
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Perf: 0.238063 ms, 264.285 GB/s, DeviceReduceBlockWise<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
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```
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## Run ```example_reduce_multiblock_atomic_add```
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```bash
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# -D <xxx> : input 3d/4d/5d tensor lengths
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# -R <xxx> : reduce dimension ids
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# -v <x> : verification (0=no, 1=yes)
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#arg1: data type (0: fp32, 1: fp64)
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#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
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#arg3: time kernel (0=no, 1=yes)
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./bin/example_reduce_multiblock_atomic_add -D 16,64,32,960 -v 1 0 2 0
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```
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Result
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```
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./bin/example_reduce_multiblock_atomic_add -D 16,64,32,960 -v 1 0 2 0
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Perf: 0 ms, inf GB/s, DeviceReduceMultiBlock<256,M_C4_S1,K_C64_S1,InSrcVectorDim_0_InSrcVectorSize_1_OutDstVectorSize_1>
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echo $?
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0
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```
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# Instructions for ```example_reduce_blockwise_two_call```
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@@ -2,64 +2,17 @@
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <getopt.h>
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#include "ck/ck.hpp"
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#include "ck/utility/reduction_enums.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
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#include "ck/tensor_operation/gpu/device/device_reduce_multiblock.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/host_common_util.hpp"
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#include "ck/library/utility/host_reduction.hpp"
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#include "reduce_blockwise_impl.hpp"
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#include "reduce_example_common.hpp"
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using namespace ck;
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using namespace ck::tensor_operation::device;
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using InDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using AccDataType = float;
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constexpr int Rank = 4;
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constexpr int NumReduceDim = 3;
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constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::NORM2;
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constexpr bool PropagateNan = true;
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constexpr bool OutputIndex = false;
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using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
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using InElementwiseOperation =
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typename reduce_unary_operator<ReduceOpId, true, true>::InElementwiseOperation;
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using AccElementwiseOperation =
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typename reduce_unary_operator<ReduceOpId, true, true>::AccElementwiseOperation;
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using DeviceReduceInstance = DeviceReduceMultiBlock<InDataType,
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AccDataType,
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OutDataType,
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Rank,
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NumReduceDim,
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ReduceOperation,
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InElementwiseOperation,
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AccElementwiseOperation,
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InMemoryDataOperationEnum::Set,
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PropagateNan,
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OutputIndex,
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false, // HaveIndexInputIfOutputIndex
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256,
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4,
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64,
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1,
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1,
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0,
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1,
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1>;
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static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
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{"verify", required_argument, nullptr, 'v'},
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{"help", no_argument, nullptr, '?'},
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@@ -72,10 +25,12 @@ class SimpleAppArgs
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public:
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std::vector<size_t> inLengths = {16, 64, 32, 960};
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std::vector<int> reduceDims = {0, 1, 2};
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std::vector<float> scales = {1.0f, 0.0f};
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bool do_verification = true;
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int init_method = 1;
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int data_type = 1;
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int init_method = 2;
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bool time_kernel = true;
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public:
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@@ -84,13 +39,17 @@ class SimpleAppArgs
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std::cout << "Usage of " << cmd << std::endl;
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std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths"
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<< std::endl;
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std::cout << "--reduceDims or -R, comma separated list of to-reduce dimensions"
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<< std::endl;
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std::cout << "--verify or -v, 1/0 to indicate whether to verify the reduction result by "
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"comparing with the host-based reduction"
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<< std::endl;
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std::cout << "Arg1 -- init method (0=no init, 1=single integer value, 2=scope integer "
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std::cout << "Arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64, 7: int4)"
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<< std::endl;
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std::cout << "Arg2 -- init method (0=no init, 1=single integer value, 2=scope integer "
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"value, 3=decimal value)"
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<< std::endl;
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std::cout << "Arg2 -- time kernel (0=no, 1=yes)" << std::endl;
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std::cout << "Arg3 -- time kernel (0=no, 1=yes)" << std::endl;
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};
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int processArgs(int argc, char* argv[])
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@@ -101,7 +60,7 @@ class SimpleAppArgs
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while(1)
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{
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ch = getopt_long(argc, argv, "D:v:l:", long_options, &option_index);
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ch = getopt_long(argc, argv, "D:R:v:l:", long_options, &option_index);
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if(ch == -1)
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break;
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switch(ch)
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@@ -112,6 +71,12 @@ class SimpleAppArgs
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inLengths = getTypeValuesFromString<size_t>(optarg);
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break;
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case 'R':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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reduceDims = getTypeValuesFromString<int>(optarg);
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break;
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case 'v':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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@@ -129,9 +94,12 @@ class SimpleAppArgs
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};
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};
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if(optind + 2 > argc)
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if(optind + 3 > argc)
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{
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throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
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};
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data_type = std::atoi(argv[optind++]);
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init_method = std::atoi(argv[optind++]);
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time_kernel = static_cast<bool>(std::atoi(argv[optind]));
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@@ -145,198 +113,152 @@ class SimpleAppArgs
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};
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};
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template <typename InOutDataType,
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typename AccDataType,
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ReduceTensorOp ReduceOpId,
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index_t PropagateNan,
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index_t OutputIndex>
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bool reduce_blockwise_test(bool do_verification,
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int init_method,
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bool time_kernel,
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const std::vector<size_t>& inLengths,
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const std::vector<int>& reduceDims,
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float alpha,
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float beta)
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{
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bool matched = false;
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int result = 0;
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const auto tuple_object = reduce_shape_instances{};
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static_for<0, std::tuple_size<reduce_shape_instances>::value, 1>{}([&](auto i) {
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if(matched)
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return;
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using ShapeType = remove_cvref_t<decltype(std::get<i>(tuple_object))>;
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if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
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return;
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result = reduce_blockwise_impl<InOutDataType,
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AccDataType,
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ReduceOpId,
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ShapeType::Rank_,
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ShapeType::NumReduceDim_,
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PropagateNan,
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OutputIndex>(
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do_verification, init_method, time_kernel, inLengths, reduceDims, alpha, beta);
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matched = true;
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});
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return (result == 0) ? true : false;
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};
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constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::AVG;
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constexpr bool PropagateNan = true;
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constexpr bool OutputIndex = false;
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int main(int argc, char* argv[])
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{
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const std::vector<int> reduceDims{0, 1, 2};
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const std::vector<int> invariantDims{3};
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SimpleAppArgs args;
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bool pass = true;
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if(argc > 1)
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{
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if(args.processArgs(argc, argv) < 0)
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SimpleAppArgs arg;
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if(arg.processArgs(argc, argv) < 0)
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return (-1);
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};
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constexpr bool op_support_indices =
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(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
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ReduceOpId == ReduceTensorOp::AMAX);
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// if input is half type, no reason to use float for indiced reduction operation and must use
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// float for non-indiced reduction operation for accuracy
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constexpr bool invalid_reduce_1 =
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std::is_same<InDataType, ck::half_t>::value &&
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((!op_support_indices && !std::is_same<AccDataType, float>::value) ||
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(op_support_indices && !std::is_same<AccDataType, ck::half_t>::value));
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// if input is float type, no reason to use double for indiced reduction operation
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constexpr bool invalid_reduce_2 =
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std::is_same<InDataType, float>::value &&
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(op_support_indices && !std::is_same<AccDataType, float>::value);
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// indices option can only be used when it is really needed
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constexpr bool invalid_reduce_3 = (!op_support_indices && OutputIndex);
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constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2 || invalid_reduce_3);
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if constexpr(invalid_reduce)
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std::cout << "Reduction setting is not supported, exiting!" << std::endl;
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Tensor<InDataType> in(args.inLengths);
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std::vector<size_t> outLengths;
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if(invariantDims.empty())
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outLengths.push_back(1);
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else
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for(auto dim : invariantDims)
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outLengths.push_back(args.inLengths[dim]);
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Tensor<OutDataType> out_ref(outLengths);
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Tensor<OutDataType> out(outLengths);
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Tensor<int> out_indices_ref(outLengths);
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Tensor<int> out_indices(outLengths);
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auto inStrides = in.mDesc.GetStrides();
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auto outStrides = out.mDesc.GetStrides();
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size_t invariant_total_length = out.mDesc.GetElementSize();
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size_t reduce_total_length = in.mDesc.GetElementSize() / invariant_total_length;
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float alpha = args.scales[0];
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float beta = args.scales[1];
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std::size_t num_thread = 1;
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if(args.do_verification)
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{
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switch(args.init_method)
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if(arg.data_type == 0)
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{
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case 0: break;
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case 1:
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in.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
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break;
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case 2:
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in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
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break;
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default:
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in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0}, num_thread);
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pass = reduce_blockwise_test<ck::half_t, float, ReduceOpId, PropagateNan, OutputIndex>(
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arg.do_verification,
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arg.init_method,
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arg.time_kernel,
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arg.inLengths,
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arg.reduceDims,
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arg.scales[0],
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arg.scales[1]);
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}
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if(beta != 0.0f)
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for(size_t i = 0; i < out_ref.mDesc.GetElementSpaceSize(); i++)
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out.mData[i] = out_ref.mData[i];
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};
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// these buffers are usually provided by the user application
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DeviceMem in_dev(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
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DeviceMem out_dev(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
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in_dev.ToDevice(in.mData.data());
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if(beta != 0.0f)
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out_dev.ToDevice(out.mData.data());
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size_t indicesSizeInBytes = OutputIndex ? out.mDesc.GetElementSize() * sizeof(int32_t) : 0;
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DeviceMem out_index_dev(indicesSizeInBytes);
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InElementwiseOperation in_elementwise_op;
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AccElementwiseOperation acc_elementwise_op;
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std::tie(in_elementwise_op, acc_elementwise_op) =
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reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
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static_cast<int32_t>(reduce_total_length));
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if(args.do_verification)
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{
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ReductionHost<InDataType,
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AccDataType,
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OutDataType,
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ReduceOperation,
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InElementwiseOperation,
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AccElementwiseOperation,
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Rank,
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NumReduceDim,
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PropagateNan,
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OutputIndex>
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hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
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hostReduce.Run(alpha,
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in.mData.data(),
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beta,
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out_ref.mData.data(),
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out_indices_ref.mData.data(),
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in_elementwise_op,
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acc_elementwise_op);
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};
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|
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std::vector<ck::index_t> i_inLengths;
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std::vector<ck::index_t> i_inStrides;
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std::vector<ck::index_t> i_outLengths;
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std::vector<ck::index_t> i_outStrides;
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i_inLengths.assign(args.inLengths.begin(), args.inLengths.end());
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i_inStrides.assign(inStrides.begin(), inStrides.end());
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i_outLengths.assign(outLengths.begin(), outLengths.end());
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i_outStrides.assign(outStrides.begin(), outStrides.end());
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auto reduce = DeviceReduceInstance{};
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auto argument_ptr = reduce.MakeArgumentPointer(i_inLengths,
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i_inStrides,
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i_outLengths,
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i_outStrides,
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reduceDims,
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alpha,
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beta,
|
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in_dev.GetDeviceBuffer(),
|
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nullptr,
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out_dev.GetDeviceBuffer(),
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out_index_dev.GetDeviceBuffer(),
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in_elementwise_op,
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acc_elementwise_op);
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if(!reduce.IsSupportedArgument(argument_ptr.get()))
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{
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std::cout
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<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
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<< std::endl;
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};
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std::string reduce_name = reduce.GetTypeString();
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auto invoker_ptr = reduce.MakeInvokerPointer();
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, args.time_kernel});
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|
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std::size_t num_bytes = invariant_total_length * reduce_total_length * sizeof(InDataType) +
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invariant_total_length * sizeof(OutDataType);
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|
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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|
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std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, " << reduce_name
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<< std::endl;
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|
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bool pass = true;
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|
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if(args.do_verification)
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{
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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);
|
||||
}
|
||||
};
|
||||
|
||||
275
example/12_reduce/reduce_blockwise_impl.hpp
Normal file
275
example/12_reduce/reduce_blockwise_impl.hpp
Normal file
@@ -0,0 +1,275 @@
|
||||
// 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);
|
||||
}
|
||||
48
example/12_reduce/reduce_example_common.hpp
Normal file
48
example/12_reduce/reduce_example_common.hpp
Normal file
@@ -0,0 +1,48 @@
|
||||
// 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>>;
|
||||
212
example/12_reduce/reduce_multiblock_atomic_add.cpp
Normal file
212
example/12_reduce/reduce_multiblock_atomic_add.cpp
Normal file
@@ -0,0 +1,212 @@
|
||||
// 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);
|
||||
};
|
||||
230
example/12_reduce/reduce_multiblock_atomic_add_impl.hpp
Normal file
230
example/12_reduce/reduce_multiblock_atomic_add_impl.hpp
Normal file
@@ -0,0 +1,230 @@
|
||||
// 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);
|
||||
}
|
||||
Reference in New Issue
Block a user