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Fix bug of layernorm ckProfiler and refine code (#448)

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* Fix bug of profiler for layernorm

* 1. Rename layernorm into normalization
2. Decouple softmax from normalization

* clang-format

[ROCm/composable_kernel commit: 1b62bfaa2a]
This commit is contained in:
rocking5566
2022-10-13 10:06:39 +08:00
committed by GitHub
parent f41edc84af
commit c6e8de46da
29 changed files with 423 additions and 461 deletions
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4
library/src/tensor_operation_instance/gpu/softmax/CMakeLists.txt Normal file
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add_instance_library(device_softmax_instance
device_softmax_f16_f16_instance.cpp
device_softmax_f32_f32_instance.cpp
)

58
library/src/tensor_operation_instance/gpu/softmax/device_softmax_f16_f16_instance.cpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_softmax_impl.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/utility/data_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
namespace {
using F16 = ck::half_t;
using F32 = float;
using Pass = ck::tensor_operation::element_wise::PassThrough;
} // namespace
template <index_t Rank, index_t Reduce>
using device_softmax_f16_f16_instances = std::tuple<
// clang-format off
// InDataType, AccDataType, OutDataType, InElementwiseOp, AccElementwiseOp, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, InSrcVectorDim, InSrcVectorSize, OutDstVectorSize>
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 8, 32, 1, 8, 1, 1, 1>, // fallback kernel
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 8, 32, 1, 8, 1, 8, 8>,
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 4, 64, 1, 8, 1, 8, 8>,
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 2, 128, 1, 8, 1, 8, 8>,
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 2, 128, 1, 16, 1, 8, 8>,
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 2, 128, 1, 32, 1, 8, 8>,
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 8>,
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 8>,
DeviceSoftmaxImpl< F16, F32, F16, Pass, Pass, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 8>
// clang-format on
>;
void add_device_softmax_f16_f16_rank3_instances(
std::vector<DeviceSoftmaxPtr<F16, F32, F16, Pass, Pass, 3>>& instances)
{
add_device_operation_instances(instances, device_softmax_f16_f16_instances<3, 1>{});
add_device_operation_instances(instances, device_softmax_f16_f16_instances<3, 2>{});
}
void add_device_softmax_f16_f16_rank4_instances(
std::vector<DeviceSoftmaxPtr<F16, F32, F16, Pass, Pass, 4>>& instances)
{
add_device_operation_instances(instances, device_softmax_f16_f16_instances<4, 1>{});
add_device_operation_instances(instances, device_softmax_f16_f16_instances<4, 2>{});
add_device_operation_instances(instances, device_softmax_f16_f16_instances<4, 3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

56
library/src/tensor_operation_instance/gpu/softmax/device_softmax_f32_f32_instance.cpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_softmax_impl.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
namespace {
using F32 = float;
using Pass = ck::tensor_operation::element_wise::PassThrough;
} // namespace
template <index_t Rank, index_t Reduce>
using device_softmax_f32_f32_instances = std::tuple<
// clang-format off
// InDataType, AccDataType, OutDataType, InElementwiseOp, AccElementwiseOp, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, InSrcVectorDim, InSrcVectorSize, OutDstVectorSize>
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 8, 32, 1, 8, 1, 1, 1>, // fallback kernel
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 8, 32, 1, 8, 1, 4, 4>,
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 4, 64, 1, 8, 1, 4, 4>,
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 2, 128, 1, 8, 1, 4, 4>,
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 2, 128, 1, 16, 1, 4, 4>,
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 2, 128, 1, 32, 1, 4, 4>,
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 4>,
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 4>,
DeviceSoftmaxImpl< F32, F32, F32, Pass, Pass, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 4>
// clang-format on
>;
void add_device_softmax_f32_f32_rank3_instances(
std::vector<DeviceSoftmaxPtr<F32, F32, F32, Pass, Pass, 3>>& instances)
{
add_device_operation_instances(instances, device_softmax_f32_f32_instances<3, 1>{});
add_device_operation_instances(instances, device_softmax_f32_f32_instances<3, 2>{});
}
void add_device_softmax_f32_f32_rank4_instances(
std::vector<DeviceSoftmaxPtr<F32, F32, F32, Pass, Pass, 4>>& instances)
{
add_device_operation_instances(instances, device_softmax_f32_f32_instances<4, 1>{});
add_device_operation_instances(instances, device_softmax_f32_f32_instances<4, 2>{});
add_device_operation_instances(instances, device_softmax_f32_f32_instances<4, 3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck