mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 21:21:22 +00:00
Softmax client example (#396)
* Update Softmax device operation interface. * Update ckProfiler. * Update Softmax UT. * Update example. * Client example. * Clang format Co-authored-by: Adam Osewski <aosewski@amd.com>
This commit is contained in:
Adam Osewski
@@ -3,19 +3,10 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
|
||||
#include "ck/utility/reduction_operator.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_base.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_normalization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_reduce_multiblock.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_reduce_common.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_softmax.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_set_buffer_value.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
@@ -24,227 +15,54 @@ namespace device {
|
||||
template <typename InDataType,
|
||||
typename AccDataType,
|
||||
typename OutDataType,
|
||||
index_t Rank,
|
||||
index_t NumReduceDim,
|
||||
index_t BlockSize,
|
||||
index_t MThreadClusterSize,
|
||||
index_t KThreadClusterSize,
|
||||
index_t MThreadSliceSize,
|
||||
index_t KThreadSliceSize,
|
||||
index_t InSrcVectorDim,
|
||||
index_t InSrcVectorSize,
|
||||
index_t OutDstVectorSize>
|
||||
struct DeviceSoftmax : public DeviceNormalization
|
||||
typename InElementwiseOp,
|
||||
typename AccElementwiseOp,
|
||||
index_t Rank>
|
||||
struct DeviceSoftmax : public BaseOperator
|
||||
{
|
||||
static constexpr index_t kRank = Rank;
|
||||
static constexpr index_t kNumReduceDim = NumReduceDim;
|
||||
//
|
||||
// @brief Makes a pointer to Argument class.
|
||||
//
|
||||
// @param[in] inLengths Input tensor extent(s) from high to low dimension
|
||||
// @param[in] inStrides Input tensor stride(s) from high to low dimension
|
||||
// @param[in] reduceDims The dimension(s) the normalization operation is applied
|
||||
// @param[in] alpha Typeless pointer in host memory storing the alpha scaling
|
||||
// value as type AccDataType
|
||||
// @param[in] beta Typeless pointer in host memory storing the beta scaling
|
||||
// value as type AccDataType
|
||||
// @param[in] in_dev Typeless const pointer in device memory storing the input
|
||||
// tensor
|
||||
// @param out_dev Typeless pointer in device memory storing the output tensor
|
||||
// @param[in] in_elementwise_op The input elementwise operation.
|
||||
// @param[in] acc_elementwise_op The accumulation elementwise operation.
|
||||
//
|
||||
// @return Unique pointer to the Argument class.
|
||||
//
|
||||
virtual std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const std::vector<index_t> inLengths,
|
||||
const std::vector<index_t> inStrides,
|
||||
const std::vector<int> reduceDims,
|
||||
const void* alpha,
|
||||
const void* beta,
|
||||
const void* in_dev,
|
||||
void* out_dev,
|
||||
InElementwiseOp in_elementwise_op,
|
||||
AccElementwiseOp acc_elementwise_op) = 0;
|
||||
|
||||
virtual index_t GetRank() const override { return kRank; }
|
||||
|
||||
virtual index_t GetNumReduceDim() const override { return kNumReduceDim; }
|
||||
|
||||
using PassThrough = tensor_operation::element_wise::PassThrough;
|
||||
|
||||
// Used for freeloading of some handy functions from DeviceReduceMultiBlock
|
||||
using Reduction = DeviceReduceMultiBlock<InDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
Rank,
|
||||
NumReduceDim,
|
||||
reduce::Add,
|
||||
PassThrough, // InElementwiseOperation
|
||||
PassThrough, // AccElementwiseOperation
|
||||
InMemoryDataOperationEnum::Set,
|
||||
false, // PropagateNan
|
||||
false, // OutputIndex
|
||||
false, // HaveIndexInputIfOutputIndex
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
InSrcVectorDim,
|
||||
InSrcVectorSize,
|
||||
1>; // OutDstVectorSize
|
||||
|
||||
using GridDesc_M_K = decltype(Reduction::MakeSrc2dDescriptor({1}, {1}, 1, 1));
|
||||
|
||||
using GridwiseSoftmaxGeneric = GridwiseSoftmax_mk_to_mk<InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K,
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
InSrcVectorDim,
|
||||
InSrcVectorSize,
|
||||
OutDstVectorSize,
|
||||
false>;
|
||||
|
||||
using GridwiseSoftmaxSweepOnce = GridwiseSoftmax_mk_to_mk<InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K,
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
InSrcVectorDim,
|
||||
InSrcVectorSize,
|
||||
OutDstVectorSize,
|
||||
true>;
|
||||
|
||||
struct Argument : public Reduction::Argument
|
||||
{
|
||||
Argument(const std::vector<index_t> inLengths,
|
||||
const std::vector<index_t> inStrides,
|
||||
const std::vector<index_t> reduceDims,
|
||||
AccDataType alpha,
|
||||
AccDataType beta,
|
||||
const InDataType* in_dev,
|
||||
OutDataType* out_dev)
|
||||
: Reduction::Argument(inLengths,
|
||||
inStrides,
|
||||
{},
|
||||
{},
|
||||
reduceDims,
|
||||
0.0f, // alpha
|
||||
0.0f, // beta
|
||||
in_dev,
|
||||
nullptr,
|
||||
out_dev,
|
||||
nullptr,
|
||||
PassThrough{},
|
||||
PassThrough{}),
|
||||
// FIXME: The base class DeviceReduceMultiBlock::Argument only supports alpha/beta of
|
||||
// float32 precision. Make it support any data type so the fields can be removed.
|
||||
alpha_(alpha),
|
||||
beta_(beta)
|
||||
{
|
||||
// std::cout << "blkGroupSize= " << this->blkGroupSize
|
||||
// << ", numBlockTileIteration= " << this->numBlockTileIteration
|
||||
// << ", gridSize=" << this->gridSize
|
||||
// << ", invariant_total_length=" << this->invariant_total_length <<
|
||||
// std::endl;
|
||||
}
|
||||
|
||||
AccDataType alpha_;
|
||||
AccDataType beta_;
|
||||
};
|
||||
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
const auto in_grid_desc_m_k = Reduction::MakeSrc2dDescriptor(
|
||||
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
|
||||
const auto out_grid_desc_m_k = Reduction::MakeSrc2dDescriptor(
|
||||
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
|
||||
|
||||
bool sweep_once =
|
||||
in_grid_desc_m_k.GetLength(Number<1>{}) <= KThreadClusterSize * KThreadSliceSize;
|
||||
|
||||
const auto kernel_main = sweep_once ? kernel_softmax<GridwiseSoftmaxSweepOnce,
|
||||
InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K>
|
||||
: kernel_softmax<GridwiseSoftmaxGeneric,
|
||||
InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K>;
|
||||
|
||||
float avg_time = 0;
|
||||
|
||||
avg_time += launch_and_time_kernel(stream_config,
|
||||
kernel_main,
|
||||
dim3(arg.gridSize),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
in_grid_desc_m_k,
|
||||
out_grid_desc_m_k,
|
||||
arg.blkGroupSize,
|
||||
arg.numBlockTileIteration,
|
||||
arg.alpha_,
|
||||
arg.in_dev_,
|
||||
arg.beta_,
|
||||
arg.out_dev_);
|
||||
|
||||
return (avg_time);
|
||||
};
|
||||
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
};
|
||||
};
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
const Argument* p_arg_ = dynamic_cast<const Argument*>(p_arg);
|
||||
|
||||
if(!Reduction::IsSupportedArgument(p_arg_))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
if(p_arg_->inLengths_[Rank - 1] % OutDstVectorSize != 0)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
// inLengths: input tensor extent(s) from high to low dimension
|
||||
// inStrides: input tensor stride(s) from high to low dimension
|
||||
// reduceDims: the dimension(s) the softmax normalization operate on
|
||||
// alpha: typeless pointer in host memory storing the alpha scaling value as type AccDataType
|
||||
// beta: typeless pointer in host memory storing the beta scaling value as type AccDataType
|
||||
// in_dev: typeless const pointer in device memory storing the input tensor
|
||||
// out_dev: typeless pointer in device memory storing the output tensor
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> inLengths,
|
||||
const std::vector<index_t> inStrides,
|
||||
const std::vector<int> reduceDims,
|
||||
const void* alpha,
|
||||
const void* beta,
|
||||
const void* in_dev,
|
||||
void* out_dev) override
|
||||
{
|
||||
return std::make_unique<Argument>(inLengths,
|
||||
inStrides,
|
||||
reduceDims,
|
||||
*static_cast<const AccDataType*>(alpha),
|
||||
*static_cast<const AccDataType*>(beta),
|
||||
static_cast<const InDataType*>(in_dev),
|
||||
static_cast<OutDataType*>(out_dev));
|
||||
};
|
||||
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>();
|
||||
};
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceReduceSoftmax<" << BlockSize << ",";
|
||||
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
|
||||
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
|
||||
str << "InSrcVectorDim_" << InSrcVectorDim << "_InSrcVectorSize_" << InSrcVectorSize << "_OutDstVectorSize_" << OutDstVectorSize << ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
|
||||
virtual index_t GetRank() const = 0;
|
||||
virtual index_t GetNumReduceDim() const = 0;
|
||||
};
|
||||
|
||||
template <typename InDataType,
|
||||
typename AccDataType,
|
||||
typename OutDataType,
|
||||
typename InElementwiseOp,
|
||||
typename AccElementwiseOp,
|
||||
index_t Rank>
|
||||
using DeviceSoftmaxPtr = std::unique_ptr<
|
||||
DeviceSoftmax<InDataType, AccDataType, OutDataType, InElementwiseOp, AccElementwiseOp, Rank>>;
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
|
||||
@@ -0,0 +1,272 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
|
||||
#include "ck/utility/reduction_operator.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_base.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_softmax.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_reduce_multiblock.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_reduce_common.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_softmax.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_set_buffer_value.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <typename InDataType,
|
||||
typename AccDataType,
|
||||
typename OutDataType,
|
||||
typename InElementwiseOp,
|
||||
typename AccElementwiseOp,
|
||||
index_t Rank,
|
||||
index_t NumReduceDim,
|
||||
index_t BlockSize,
|
||||
index_t MThreadClusterSize,
|
||||
index_t KThreadClusterSize,
|
||||
index_t MThreadSliceSize,
|
||||
index_t KThreadSliceSize,
|
||||
index_t InSrcVectorDim,
|
||||
index_t InSrcVectorSize,
|
||||
index_t OutDstVectorSize>
|
||||
struct DeviceSoftmaxImpl : public DeviceSoftmax<InDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
InElementwiseOp,
|
||||
AccElementwiseOp,
|
||||
Rank>
|
||||
{
|
||||
static constexpr index_t kRank = Rank;
|
||||
static constexpr index_t kNumReduceDim = NumReduceDim;
|
||||
|
||||
virtual index_t GetRank() const override { return kRank; }
|
||||
|
||||
virtual index_t GetNumReduceDim() const override { return kNumReduceDim; }
|
||||
|
||||
// Used for freeloading of some handy functions from DeviceReduceMultiBlock
|
||||
using Reduction = DeviceReduceMultiBlock<InDataType,
|
||||
AccDataType,
|
||||
OutDataType,
|
||||
Rank,
|
||||
NumReduceDim,
|
||||
reduce::Add,
|
||||
InElementwiseOp,
|
||||
AccElementwiseOp,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
false, // PropagateNan
|
||||
false, // OutputIndex
|
||||
false, // HaveIndexInputIfOutputIndex
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
InSrcVectorDim,
|
||||
InSrcVectorSize,
|
||||
1>; // OutDstVectorSize
|
||||
|
||||
using GridDesc_M_K = decltype(Reduction::MakeSrc2dDescriptor({1}, {1}, 1, 1));
|
||||
|
||||
using GridwiseSoftmaxGeneric = GridwiseSoftmax_mk_to_mk<InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K,
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
InSrcVectorDim,
|
||||
InSrcVectorSize,
|
||||
OutDstVectorSize,
|
||||
false>;
|
||||
|
||||
using GridwiseSoftmaxSweepOnce = GridwiseSoftmax_mk_to_mk<InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K,
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
InSrcVectorDim,
|
||||
InSrcVectorSize,
|
||||
OutDstVectorSize,
|
||||
true>;
|
||||
|
||||
struct Argument : public Reduction::Argument
|
||||
{
|
||||
Argument(const std::vector<index_t> inLengths,
|
||||
const std::vector<index_t> inStrides,
|
||||
const std::vector<index_t> reduceDims,
|
||||
AccDataType alpha,
|
||||
AccDataType beta,
|
||||
const InDataType* in_dev,
|
||||
OutDataType* out_dev,
|
||||
InElementwiseOp in_elementwise_op,
|
||||
AccElementwiseOp acc_elementwise_op)
|
||||
: Reduction::Argument(inLengths,
|
||||
inStrides,
|
||||
{},
|
||||
{},
|
||||
reduceDims,
|
||||
0.0f, // alpha
|
||||
0.0f, // beta
|
||||
in_dev,
|
||||
nullptr,
|
||||
out_dev,
|
||||
nullptr,
|
||||
in_elementwise_op,
|
||||
acc_elementwise_op),
|
||||
// FIXME: The base class DeviceReduceMultiBlock::Argument only supports alpha/beta of
|
||||
// float32 precision. Make it support any data type so the fields can be removed.
|
||||
alpha_(alpha),
|
||||
beta_(beta)
|
||||
{
|
||||
// std::cout << "blkGroupSize= " << this->blkGroupSize
|
||||
// << ", numBlockTileIteration= " << this->numBlockTileIteration
|
||||
// << ", gridSize=" << this->gridSize
|
||||
// << ", invariant_total_length=" << this->invariant_total_length <<
|
||||
// std::endl;
|
||||
}
|
||||
|
||||
AccDataType alpha_;
|
||||
AccDataType beta_;
|
||||
};
|
||||
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
const auto in_grid_desc_m_k = Reduction::MakeSrc2dDescriptor(
|
||||
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
|
||||
const auto out_grid_desc_m_k = Reduction::MakeSrc2dDescriptor(
|
||||
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.numBlockTileIteration);
|
||||
|
||||
bool sweep_once =
|
||||
in_grid_desc_m_k.GetLength(Number<1>{}) <= KThreadClusterSize * KThreadSliceSize;
|
||||
|
||||
const auto kernel_main = sweep_once ? kernel_softmax<GridwiseSoftmaxSweepOnce,
|
||||
InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K>
|
||||
: kernel_softmax<GridwiseSoftmaxGeneric,
|
||||
InDataType,
|
||||
OutDataType,
|
||||
AccDataType,
|
||||
GridDesc_M_K>;
|
||||
|
||||
float avg_time = 0;
|
||||
|
||||
avg_time += launch_and_time_kernel(stream_config,
|
||||
kernel_main,
|
||||
dim3(arg.gridSize),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
in_grid_desc_m_k,
|
||||
out_grid_desc_m_k,
|
||||
arg.blkGroupSize,
|
||||
arg.numBlockTileIteration,
|
||||
arg.alpha_,
|
||||
arg.in_dev_,
|
||||
arg.beta_,
|
||||
arg.out_dev_);
|
||||
|
||||
return (avg_time);
|
||||
};
|
||||
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
};
|
||||
};
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
const Argument* p_arg_ = dynamic_cast<const Argument*>(p_arg);
|
||||
|
||||
if(!Reduction::IsSupportedArgument(p_arg_))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
if(p_arg_->inLengths_[Rank - 1] % OutDstVectorSize != 0)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
//
|
||||
// @brief Makes a pointer to Argument class.
|
||||
//
|
||||
// @param[in] inLengths Input tensor extent(s) from high to low dimension
|
||||
// @param[in] inStrides Input tensor stride(s) from high to low dimension
|
||||
// @param[in] reduceDims The dimension(s) the normalization operation is applied
|
||||
// @param[in] alpha Typeless pointer in host memory storing the alpha scaling
|
||||
// value as type AccDataType
|
||||
// @param[in] beta Typeless pointer in host memory storing the beta scaling
|
||||
// value as type AccDataType
|
||||
// @param[in] in_dev Typeless const pointer in device memory storing the input
|
||||
// tensor
|
||||
// @param out_dev Typeless pointer in device memory storing the output tensor
|
||||
// @param[in] in_elementwise_op The input elementwise operation.
|
||||
// @param[in] acc_elementwise_op The accumulation elementwise operation.
|
||||
//
|
||||
// @return Unique pointer to the Argument class.
|
||||
//
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> inLengths,
|
||||
const std::vector<index_t> inStrides,
|
||||
const std::vector<int> reduceDims,
|
||||
const void* alpha,
|
||||
const void* beta,
|
||||
const void* in_dev,
|
||||
void* out_dev,
|
||||
InElementwiseOp in_elementwise_op,
|
||||
AccElementwiseOp acc_elementwise_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(inLengths,
|
||||
inStrides,
|
||||
reduceDims,
|
||||
*static_cast<const AccDataType*>(alpha),
|
||||
*static_cast<const AccDataType*>(beta),
|
||||
static_cast<const InDataType*>(in_dev),
|
||||
static_cast<OutDataType*>(out_dev),
|
||||
in_elementwise_op,
|
||||
acc_elementwise_op);
|
||||
};
|
||||
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>();
|
||||
};
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceReduceSoftmax<" << BlockSize << ",";
|
||||
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
|
||||
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
|
||||
str << "InSrcVectorDim_" << InSrcVectorDim << "_InSrcVectorSize_" << InSrcVectorSize << "_OutDstVectorSize_" << OutDstVectorSize << ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user