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Commit 644df335 authored by rocking's avatar rocking
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Merge branch 'develop' into gemm_layernorm_instance

parents d99640ab 7494c1c6
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client_example/03_gemm_layernorm/gemm_add_add_layernorm_naive.cpp
View file @ 644df335
......@@ -8,7 +8,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_reduce.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/device_elementwise_instance.hpp"
......
client_example/04_contraction/CMakeLists.txt
View file @ 644df335
......@@ -4,3 +4,6 @@ target_link_libraries(client_contraction_scale PRIVATE composable_kernel::device
add_executable(client_contraction_bilinear contraction_bilinear.cpp)
target_link_libraries(client_contraction_bilinear PRIVATE composable_kernel::device_operations)
add_executable(contraction_g1m2n3k1_add_xdl_fp16 contraction_g1m2n3k1_add_xdl_fp16.cpp)
target_link_libraries(contraction_g1m2n3k1_add_xdl_fp16 PRIVATE composable_kernel::device_operations)
client_example/04_contraction/contraction_g1m2n3k1_add_xdl_fp16.cpp 0 → 100644
View file @ 644df335
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <numeric>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_contraction_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm_bias_permute.hpp"
#include "ck/library/utility/numeric.hpp"
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = Add;
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F16;
using DDataType = F16;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F16;
static constexpr ck::index_t NumDimG = 1;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 3;
static constexpr ck::index_t NumDimK = 1;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
ck::index_t G0 = 1;
ck::index_t M0 = 64;
ck::index_t M1 = 256;
ck::index_t N0 = 3;
ck::index_t N1 = 12;
ck::index_t N2 = 64;
ck::index_t K0 = 768;
// A[M0, M1, M2, K0]
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, M0, M1, K0};
std::vector<ck::index_t> a_gs_ms_ks_strides{M0 * M1 * K0, M1 * K0, K0, 1};
// B[N0, N1, N2, K0]
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, N0, N1, N2, K0};
std::vector<ck::index_t> b_gs_ns_ks_strides{N0 * N1 * N2 * K0, N1 * N2 * K0, N2 * K0, K0, 1};
// D[N0, M0, N1, M1, N2]
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, M0, M1, N0, N1, N2};
std::vector<ck::index_t> d_gs_ms_ns_strides{N0 * N1 * N2, 0, 0, N1 * N2, N2, 1};
// E[N0 M0 N1 N2 M1]
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, M0, M1, N0, N1, N2};
std::vector<ck::index_t> e_gs_ms_ns_strides{
M0 * M1 * N0 * N1 * N2, N1 * N2 * M1, 1, M0 * N1 * N2 * M1, M1 * N2, M1};
auto f_tensor_space_size = [](auto lengths, auto strides) {
std::size_t space_size = 1;
for(std::size_t i = 0; i < lengths.size(); ++i)
{
space_size += (lengths[i] - 1) * strides[i];
}
return space_size;
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) *
f_tensor_space_size(a_gs_ms_ks_lengths, a_gs_ms_ks_strides));
SimpleDeviceMem b_device_buf(sizeof(BDataType) *
f_tensor_space_size(b_gs_ns_ks_lengths, b_gs_ns_ks_strides));
SimpleDeviceMem d_device_buf(sizeof(DDataType) *
f_tensor_space_size(d_gs_ms_ns_lengths, d_gs_ms_ns_strides));
SimpleDeviceMem e_device_buf(sizeof(EDataType) *
f_tensor_space_size(e_gs_ms_ns_lengths, e_gs_ms_ns_strides));
using DeviceOp = ck::tensor_operation::device::DeviceBatchedContractionMultipleD<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
DsDataType,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Add>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto cde_element_op = CDEElementOp{};
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr =
op_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
b_gs_ns_ks_lengths,
b_gs_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
e_gs_ms_ns_lengths,
e_gs_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
ck::index_t M = ck::accumulate_n<ck::index_t>(
e_gs_ms_ns_lengths.begin() + NumDimG, NumDimM, 1, std::multiplies<>{});
ck::index_t N = ck::accumulate_n<ck::index_t>(
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM, NumDimN, 1, std::multiplies<>{});
ck::index_t K = ck::accumulate_n<ck::index_t>(
a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM, NumDimK, 1, std::multiplies<>{});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(DDataType) * M * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return 0;
}
client_example/06_softmax/softmax4d.cpp
View file @ 644df335
......@@ -47,8 +47,8 @@ int main(int argc, char* argv[])
ck::index_t num_elements =
std::accumulate(in_lengths.begin(), in_lengths.end(), 1, std::multiplies<ck::index_t>());
AccDataType alpha{2.0f};
AccDataType beta{2.0f};
double alpha{2.0};
double beta{2.0};
SimpleDeviceMem in(sizeof(InDataType) * num_elements);
SimpleDeviceMem out(sizeof(OutDataType) * num_elements);
......@@ -82,8 +82,8 @@ int main(int argc, char* argv[])
auto argument_ptr = op_ptr->MakeArgumentPointer(in_lengths,
in_strides,
reduce_dims,
&alpha,
&beta,
alpha,
beta,
in.GetDeviceBuffer(),
out.GetDeviceBuffer(),
PassThrough{},
......@@ -129,8 +129,8 @@ int main(int argc, char* argv[])
auto argument_ptr = op_ptr->MakeArgumentPointer(in_lengths,
in_strides,
reduce_dims,
&alpha,
&beta,
alpha,
beta,
in.GetDeviceBuffer(),
out.GetDeviceBuffer(),
PassThrough{},
......
client_example/13_batchnorm/CMakeLists.txt
View file @ 644df335
add_executable(client_batchnorm_fwd_nhwc batchnorm_fwd_nhwc.cpp)
add_executable(client_batchnorm_bwd_nhwc batchnorm_bwd_nhwc.cpp)
add_executable(client_batchnorm_infer_nhwc batchnorm_infer_nhwc.cpp)
target_link_libraries(client_batchnorm_fwd_nhwc PRIVATE composable_kernel::device_operations)
target_link_libraries(client_batchnorm_bwd_nhwc PRIVATE composable_kernel::device_operations)
target_link_libraries(client_batchnorm_infer_nhwc PRIVATE composable_kernel::device_operations)
client_example/13_batchnorm/batchnorm_infer_nhwc.cpp 0 → 100644
View file @ 644df335
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <functional>
#include <numeric>
#include <iomanip>
#include <iostream>
#include <vector>
#include "ck/ck.hpp"
#include "ck/utility/tuple.hpp"
#include "ck/library/tensor_operation_instance/gpu/batchnorm_infer.hpp"
using XDataType = float;
using YDataType = float;
using ScaleDataType = float;
using BiasDataType = float;
using MeanVarDataType = float;
constexpr int Rank = 4;
constexpr int NumBatchNormReduceDim = 3;
using Normalize = ck::tensor_operation::element_wise::NormalizeInInfer;
const double epsilon = std::numeric_limits<float>::epsilon();
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
std::array<ck::index_t, Rank> xyLengths{16, 8, 128, 256};
std::array<ck::index_t, Rank> xyStrides{8 * 128 * 256, 128 * 256, 256, 1};
std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarLengths{256};
std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarStrides{1};
std::array<int, NumBatchNormReduceDim> reduceDims{0, 1, 2};
std::array<int, Rank - NumBatchNormReduceDim> invariantDims{3};
ck::index_t numXYElement =
std::accumulate(xyLengths.begin(), xyLengths.end(), 1, std::multiplies<ck::index_t>());
ck::index_t numScaleBiasMeanVarElement = std::accumulate(scaleBiasMeanVarLengths.begin(),
scaleBiasMeanVarLengths.end(),
1,
std::multiplies<ck::index_t>());
SimpleDeviceMem x(sizeof(XDataType) * numXYElement);
SimpleDeviceMem y(sizeof(YDataType) * numXYElement);
SimpleDeviceMem scale(sizeof(ScaleDataType) * numScaleBiasMeanVarElement);
SimpleDeviceMem bias(sizeof(BiasDataType) * numScaleBiasMeanVarElement);
SimpleDeviceMem mean(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
SimpleDeviceMem variance(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
// values in variance need be non-negative
(void)hipMemset(
variance.GetDeviceBuffer(), 0, sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
std::array<ck::index_t, Rank> aligned_scaleBiasMeanVarStrides{0};
int i = 0;
for(auto dim : invariantDims)
{
assert(xyLengths[dim] == scaleBiasMeanVarLengths[i]);
aligned_scaleBiasMeanVarStrides[dim] = scaleBiasMeanVarStrides[i];
i++;
};
using DeviceOp = ck::tensor_operation::device::DeviceElementwise<
ck::Tuple<XDataType, MeanVarDataType, MeanVarDataType, ScaleDataType, BiasDataType>,
ck::Tuple<YDataType>,
Normalize,
Rank>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(xyLengths,
{xyStrides,
aligned_scaleBiasMeanVarStrides,
aligned_scaleBiasMeanVarStrides,
aligned_scaleBiasMeanVarStrides,
aligned_scaleBiasMeanVarStrides},
{xyStrides},
{x.GetDeviceBuffer(),
mean.GetDeviceBuffer(),
variance.GetDeviceBuffer(),
scale.GetDeviceBuffer(),
bias.GetDeviceBuffer()},
{y.GetDeviceBuffer()},
Normalize{epsilon});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t num_bytes =
numXYElement * (sizeof(XDataType) + sizeof(YDataType)) +
numScaleBiasMeanVarElement * (sizeof(ScaleDataType) + sizeof(BiasDataType) +
sizeof(MeanVarDataType) + sizeof(MeanVarDataType));
float gb_per_sec = num_bytes / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
<< op_name << std::endl;
if(ave_time < best_ave_time)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
if(found)
{
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
// run the best intance
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(xyLengths,
{xyStrides,
aligned_scaleBiasMeanVarStrides,
aligned_scaleBiasMeanVarStrides,
aligned_scaleBiasMeanVarStrides,
aligned_scaleBiasMeanVarStrides},
{xyStrides},
{x.GetDeviceBuffer(),
mean.GetDeviceBuffer(),
variance.GetDeviceBuffer(),
scale.GetDeviceBuffer(),
bias.GetDeviceBuffer()},
{y.GetDeviceBuffer()},
Normalize{epsilon});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
client_example/15_gemm_add_multiply/CMakeLists.txt 0 → 100644
View file @ 644df335
add_executable(client_gemm_add_multiply gemm_add_multiply.cpp)
target_link_libraries(client_gemm_add_multiply PRIVATE composable_kernel::device_operations)
\ No newline at end of file
client_example/15_gemm_add_multiply/gemm_add_multiply.cpp 0 → 100644
View file @ 644df335
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <vector>
#include <iostream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_add_multiply.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddMultiply = ck::tensor_operation::element_wise::AddMultiply;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AddMultiply;
using ADataType = F16;
using BDataType = F16;
using D0DataType = F16;
using D1DataType = F16;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using D0Layout = Row;
using D1Layout = Row;
using ELayout = Row;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideD0 = 0;
ck::index_t StrideD1 = 4096;
ck::index_t StrideE = 4096;
if(argc == 1)
{
// use default case
}
else if(argc == 9)
{
M = std::stoi(argv[1]);
N = std::stoi(argv[2]);
K = std::stoi(argv[3]);
StrideA = std::stoi(argv[4]);
StrideB = std::stoi(argv[5]);
StrideD0 = std::stoi(argv[6]);
StrideD1 = std::stoi(argv[7]);
StrideE = std::stoi(argv[8]);
}
else
{
printf("arg1 to 8: M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideE\n");
exit(0);
}
auto f_matrix_space_size =
[](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
using Layout = decltype(layout);
if(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
{
return (nRow - 1) * stride + nCol;
}
else
{
return (nCol - 1) * stride + nRow;
}
};
SimpleDeviceMem a_device_buf(sizeof(ADataType) * f_matrix_space_size(M, K, StrideA, ALayout{}));
SimpleDeviceMem b_device_buf(sizeof(BDataType) * f_matrix_space_size(K, N, StrideB, BLayout{}));
SimpleDeviceMem d0_m_n_device_buf(sizeof(D0DataType) *
f_matrix_space_size(M, N, StrideD0, D0Layout{}));
SimpleDeviceMem d1_m_n_device_buf(sizeof(D1DataType) *
f_matrix_space_size(M, N, StrideD1, D1Layout{}));
SimpleDeviceMem e_device_buf(sizeof(EDataType) * f_matrix_space_size(M, N, StrideE, ELayout{}));
using DeviceOp =
ck::tensor_operation::device::DeviceGemmMultipleD<ALayout,
BLayout,
ck::Tuple<D0Layout, D1Layout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<D0DataType, D1DataType>,
EDataType,
AElementOp,
BElementOp,
CDEElementOp>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto cde_element_op = CDEElementOp{};
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(
a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 2>{d0_m_n_device_buf.GetDeviceBuffer(),
d1_m_n_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(
a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 2>{d0_m_n_device_buf.GetDeviceBuffer(),
d1_m_n_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 2>{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
client_example/15_reduce/CMakeLists.txt 0 → 100644
View file @ 644df335
add_executable(client_reduce_nhwc_c reduce_nhwc_c.cpp)
target_link_libraries(client_reduce_nhwc_c PRIVATE composable_kernel::device_operations)
client_example/15_reduce/reduce_nhwc_c.cpp 0 → 100644
View file @ 644df335
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <functional>
#include <numeric>
#include <iomanip>
#include <iostream>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/reduce/reduce.hpp"
using InDataType = float;
using OutDataType = float;
using AccDataType = float;
using ReduceAdd = ck::reduce::Add;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using UnaryDivide = ck::tensor_operation::element_wise::UnaryDivide;
constexpr bool PropagateNan = false;
constexpr bool OutputIndex = false;
constexpr int Rank = 4;
constexpr int NumReduceDim = 3;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
std::array<ck::index_t, Rank> in_lengths{16, 8, 128, 256};
std::array<ck::index_t, Rank> in_strides{8 * 128 * 256, 128 * 256, 256, 1};
std::array<ck::index_t, Rank - NumReduceDim> out_lengths{256};
std::array<ck::index_t, Rank - NumReduceDim> out_strides{1};
std::array<int, NumReduceDim> reduce_dims{0, 1, 2};
ck::index_t num_in_elements =
std::accumulate(in_lengths.begin(), in_lengths.end(), 1, std::multiplies<ck::index_t>());
ck::index_t num_out_elements =
std::accumulate(out_lengths.begin(), out_lengths.end(), 1, std::multiplies<ck::index_t>());
ck::index_t reduce_length = 1;
for(auto dim : reduce_dims)
reduce_length *= in_lengths[dim];
double alpha{1.0};
double beta{0.0};
SimpleDeviceMem in(sizeof(InDataType) * num_in_elements);
SimpleDeviceMem out(sizeof(OutDataType) * num_out_elements);
using DeviceOp = ck::tensor_operation::device::DeviceReduce<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceAdd,
PassThrough,
UnaryDivide,
PropagateNan,
OutputIndex>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(in_lengths,
in_strides,
out_lengths,
out_strides,
reduce_dims,
alpha,
beta,
in.GetDeviceBuffer(),
nullptr,
out.GetDeviceBuffer(),
nullptr,
PassThrough{},
UnaryDivide{reduce_length});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t num_bytes = num_in_elements * sizeof(InDataType) +
(beta == 0.0f ? 1 : 2) * num_out_elements * sizeof(OutDataType);
float gb_per_sec = num_bytes / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
<< op_name << std::endl;
if(ave_time < best_ave_time)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
// run the best intance
if(found)
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(in_lengths,
in_strides,
out_lengths,
out_strides,
reduce_dims,
alpha,
beta,
in.GetDeviceBuffer(),
nullptr,
out.GetDeviceBuffer(),
nullptr,
PassThrough{},
UnaryDivide{reduce_length});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
example/01_gemm/CMakeLists.txt
View file @ 644df335
......@@ -17,12 +17,14 @@ endif(USE_BITINT_EXTENSION_INT4)
add_custom_target(example_gemm_xdl)
add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
add_example_executable(example_gemm_xdl_wavelet_fp16 gemm_xdl_wavelet_fp16.cpp)
add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
add_dependencies(example_gemm_xdl example_gemm_xdl_fp16)
add_dependencies(example_gemm_xdl example_gemm_xdl_bf16)
add_dependencies(example_gemm_xdl example_gemm_xdl_int8)
add_dependencies(example_gemm_xdl example_gemm_xdl_wavelet_fp16)
if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_gemm_xdl_int4 gemm_xdl_int4.cpp)
......
example/01_gemm/gemm_xdl_fp16.cpp
View file @ 644df335
......@@ -12,6 +12,8 @@ using AccDataType = float;
using CShuffleDataType = float;
using CDataType = ck::half_t;
using F16 = ck::half_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
......@@ -29,7 +31,7 @@ using DeviceGemmInstance0 = ck::tensor_operation::device::DeviceGemmXdl
// ######| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>;
// clang-format on
// // clang-format on
// clang-format off
using DeviceGemmInstance1 = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
......@@ -40,7 +42,7 @@ using DeviceGemmInstance1 = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffl
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
using DeviceGemmInstance = DeviceGemmInstance0;
using DeviceGemmInstance = DeviceGemmInstance1;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
......
example/01_gemm/gemm_xdl_wavelet_fp16.cpp 0 → 100644
View file @ 644df335
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_xdl_waveletmodel_cshuffle.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using AccDataType = float;
using CShuffleDataType = float;
using CDataType = ck::half_t;
using F16 = ck::half_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_WaveletModel_CShuffle
// clang-format off
// ######| ALayout| BLayout| CLayout| AData| BData| AccData| CShuffle| CData| A| B| C| GEMM| NumGemmK| ABBlockTransfer| BlockGemm| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// ######| | | | Type| Type| Type| DataType| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| ThreadGroupSize| ThreadGroupSize| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
// ######| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, F16, CDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1,8>, 8>;
// clang-format on
using DeviceGemmInstance = DeviceGemmInstance;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/12_reduce/reduce_blockwise_impl.hpp
View file @ 644df335
......@@ -9,6 +9,7 @@
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_reduce.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
......@@ -16,7 +17,6 @@
#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"
......@@ -236,38 +236,57 @@ int reduce_blockwise_impl(bool do_verification,
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
static_cast<int32_t>(reduce_total_length));
std::array<index_t, Rank> arrInLengths;
std::array<index_t, Rank> arrInStrides;
std::array<index_t, NumOutDim> arrOutLengths;
std::array<index_t, NumOutDim> arrOutStrides;
ck::ranges::copy(inLengths, arrInLengths.begin());
ck::ranges::copy(inStrides, arrInStrides.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
if(do_verification)
{
ReductionHost<InOutDataType,
using ReferenceReduceInstance =
ck::tensor_operation::host::ReferenceReduce<InOutDataType,
AccDataType,
InOutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
Rank,
NumReduceDim,
PropagateNan,
OutputIndex>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
OutputIndex>;
hostReduce.Run(alpha,
auto reduce_ref = ReferenceReduceInstance{};
auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths,
arrInStrides,
arrOutLengths,
arrOutStrides,
reduceDims,
static_cast<double>(alpha),
static_cast<double>(beta),
in.mData.data(),
beta,
nullptr,
out_ref.mData.data(),
out_indices_ref.mData.data(),
in_elementwise_op,
acc_elementwise_op);
if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
<< std::endl;
return (false);
};
std::array<index_t, Rank> arrInLengths;
std::array<index_t, Rank> arrInStrides;
std::array<index_t, NumOutDim> arrOutLengths;
std::array<index_t, NumOutDim> arrOutStrides;
auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
ck::ranges::copy(inLengths, arrInLengths.begin());
ck::ranges::copy(inStrides, arrInStrides.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
invoker_ptr_ref->Run(argument_ptr_ref.get());
};
auto reduce = DeviceReduceInstance{};
......@@ -276,8 +295,8 @@ int reduce_blockwise_impl(bool do_verification,
arrOutLengths,
arrOutStrides,
reduceDims,
alpha,
beta,
static_cast<double>(alpha),
static_cast<double>(beta),
in_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
......@@ -287,8 +306,7 @@ int reduce_blockwise_impl(bool do_verification,
if(!reduce.IsSupportedArgument(argument_ptr.get()))
{
std::cerr
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
std::cerr << "The runtime parameters not supported by the DeviceReduce instance, exiting!"
<< std::endl;
return (-2);
......
example/12_reduce/reduce_blockwise_two_call.cpp
View file @ 644df335
......@@ -12,13 +12,13 @@
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_reduce.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"
using namespace ck;
using namespace ck::tensor_operation::device;
......@@ -98,7 +98,7 @@ int main(int argc, char* argv[])
// used by the host reduction
const std::array<int, 2> reduceDims = {3, 4};
const std::array<int, 3> invariantDims = {0, 1, 2};
// const std::array<int, 3> invariantDims = {0, 1, 2};
const std::vector<size_t> inLengths_1 = {64, 320, 80, 4, 128};
......@@ -191,42 +191,61 @@ int main(int argc, char* argv[])
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
static_cast<int32_t>(reduce_total_length));
std::array<index_t, 5> arrInLengths_1;
std::array<index_t, 5> arrInStrides_1;
std::array<index_t, 4> arrInLengths_2;
std::array<index_t, 4> arrInStrides_2;
std::array<index_t, 3> arrOutLengths;
std::array<index_t, 3> arrOutStrides;
ck::ranges::copy(inLengths_1, arrInLengths_1.begin());
ck::ranges::copy(inStrides_1, arrInStrides_1.begin());
ck::ranges::copy(inLengths_2, arrInLengths_2.begin());
ck::ranges::copy(inStrides_2, arrInStrides_2.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
if(do_verify)
{
ReductionHost<InOutDataType,
using ReferenceReduceInstance =
ck::tensor_operation::host::ReferenceReduce<InOutDataType,
AccDataType,
InOutDataType,
5,
2,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
5, // Rank
2, // NumReduceDim
PropagateNan,
OutputIndex>
hostReduce(in_1.mDesc, out_ref.mDesc, invariantDims, reduceDims);
OutputIndex>;
auto reduce_ref = ReferenceReduceInstance{};
hostReduce.Run(alpha,
auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths_1,
arrInStrides_1,
arrOutLengths,
arrOutStrides,
reduceDims,
static_cast<double>(alpha),
static_cast<double>(beta),
in_1.mData.data(),
beta,
nullptr,
out_ref.mData.data(),
nullptr,
in_elementwise_op,
acc_elementwise_op);
if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
<< std::endl;
return (false);
};
std::array<index_t, 5> arrInLengths_1;
std::array<index_t, 5> arrInStrides_1;
std::array<index_t, 4> arrInLengths_2;
std::array<index_t, 4> arrInStrides_2;
std::array<index_t, 3> arrOutLengths;
std::array<index_t, 3> arrOutStrides;
auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
ck::ranges::copy(inLengths_1, arrInLengths_1.begin());
ck::ranges::copy(inStrides_1, arrInStrides_1.begin());
ck::ranges::copy(inLengths_2, arrInLengths_2.begin());
ck::ranges::copy(inStrides_2, arrInStrides_2.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
invoker_ptr_ref->Run(argument_ptr_ref.get());
};
auto reduce_1 = DeviceReduceInstance_1{};
......@@ -235,8 +254,8 @@ int main(int argc, char* argv[])
arrInLengths_2,
arrInStrides_2,
reduceDims_1,
1.0f,
0.0f,
1.0,
0.0,
in_1_dev.GetDeviceBuffer(),
nullptr,
in_2_dev.GetDeviceBuffer(),
......@@ -246,8 +265,7 @@ int main(int argc, char* argv[])
if(!reduce_1.IsSupportedArgument(argument_ptr_1.get()))
{
std::cout
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
std::cout << "The runtime parameters seems supported by the DeviceReduce instance, exiting!"
<< std::endl;
};
......@@ -260,8 +278,8 @@ int main(int argc, char* argv[])
arrOutLengths,
arrOutStrides,
reduceDims_2,
alpha,
beta,
static_cast<double>(alpha),
static_cast<double>(beta),
in_2_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
......
example/12_reduce/reduce_multiblock_atomic_add_impl.hpp
View file @ 644df335
......@@ -9,6 +9,7 @@
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_reduce.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
......@@ -16,7 +17,6 @@
#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"
......@@ -149,38 +149,57 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
static_cast<int32_t>(reduce_total_length));
std::array<index_t, Rank> arrInLengths;
std::array<index_t, Rank> arrInStrides;
std::array<index_t, NumOutDim> arrOutLengths;
std::array<index_t, NumOutDim> arrOutStrides;
ck::ranges::copy(inLengths, arrInLengths.begin());
ck::ranges::copy(inStrides, arrInStrides.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
if(do_verification)
{
ReductionHost<InOutDataType,
using ReferenceReduceInstance =
ck::tensor_operation::host::ReferenceReduce<InOutDataType,
AccDataType,
InOutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
Rank,
NumReduceDim,
PropagateNan,
false>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
false>;
hostReduce.Run(alpha,
auto reduce_ref = ReferenceReduceInstance{};
auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths,
arrInStrides,
arrOutLengths,
arrOutStrides,
reduceDims,
static_cast<double>(alpha),
static_cast<double>(beta),
in.mData.data(),
beta,
nullptr,
out_ref.mData.data(),
nullptr,
in_elementwise_op,
acc_elementwise_op);
if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
<< std::endl;
return (false);
};
std::array<index_t, Rank> arrInLengths;
std::array<index_t, Rank> arrInStrides;
std::array<index_t, NumOutDim> arrOutLengths;
std::array<index_t, NumOutDim> arrOutStrides;
auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
ck::ranges::copy(inLengths, arrInLengths.begin());
ck::ranges::copy(inStrides, arrInStrides.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
invoker_ptr_ref->Run(argument_ptr_ref.get());
};
auto reduce = DeviceReduceInstance{};
......@@ -189,8 +208,8 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
arrOutLengths,
arrOutStrides,
reduceDims,
alpha,
beta,
static_cast<double>(alpha),
static_cast<double>(beta),
in_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
......@@ -200,8 +219,7 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
if(!reduce.IsSupportedArgument(argument_ptr.get()))
{
std::cerr
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
std::cerr << "The runtime parameters not supported by the DeviceReduce instance, exiting!"
<< std::endl;
return (-2);
......
example/19_binary_elementwise/broadcast_add_2d_amn_bn.cpp
View file @ 644df335
......@@ -6,7 +6,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
......@@ -23,7 +23,7 @@ using CDataType = F16;
using Add = ck::tensor_operation::element_wise::Add;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ABDataType, ABDataType>,
ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
ck::Tuple<CDataType>,
Add,
2,
......
example/19_binary_elementwise/broadcast_add_3d_am_bmnk.cpp
View file @ 644df335
......@@ -6,7 +6,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
......@@ -23,7 +23,7 @@ using CDataType = F16;
using Add = ck::tensor_operation::element_wise::Add;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ABDataType, ABDataType>,
ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
ck::Tuple<CDataType>,
Add,
3,
......
example/19_binary_elementwise/elementwise_add_1d.cpp
View file @ 644df335
......@@ -5,7 +5,7 @@
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
......@@ -21,7 +21,7 @@ using CDataType = F16;
using Add = ck::tensor_operation::element_wise::Add;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ABDataType, ABDataType>,
ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
ck::Tuple<CDataType>,
Add,
1,
......
example/19_binary_elementwise/elementwise_add_4d.cpp
View file @ 644df335
......@@ -6,7 +6,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
......@@ -23,7 +23,7 @@ using CDataType = F16;
using Add = ck::tensor_operation::element_wise::Add;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ABDataType, ABDataType>,
ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
ck::Tuple<CDataType>,
Add,
4,
......
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