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https://github.com/ROCm/composable_kernel.git
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Fix bug of layernorm ckProfiler and refine code (#448)
* 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
@@ -1,109 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_normalization.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace instance {
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// FP16
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void add_device_layernorm_rank_2_1_f16_instances(
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std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, PassThrough, 2, 1>>>&);
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void add_device_layernorm_rank_4_3_f16_instances(
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std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, PassThrough, 4, 3>>>&);
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void add_device_layernorm_rank_5_3_f16_instances(
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std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, PassThrough, 5, 3>>>&);
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// FP32
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void add_device_layernorm_rank_2_1_f32_instances(
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std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, PassThrough, 2, 1>>>&);
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void add_device_layernorm_rank_4_3_f32_instances(
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std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, PassThrough, 4, 3>>>&);
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void add_device_layernorm_rank_5_3_f32_instances(
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std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, PassThrough, 5, 3>>>&);
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template <typename XDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename YDataType,
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index_t Rank,
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index_t NumReduceDim>
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struct DeviceOperationInstanceFactory<
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ck::tensor_operation::device::DeviceLayernorm<XDataType,
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GammaDataType,
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BetaDataType,
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F32,
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YDataType,
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ck::tensor_operation::element_wise::PassThrough,
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Rank,
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NumReduceDim>>
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{
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using DeviceOp = DeviceLayernorm<XDataType,
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GammaDataType,
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BetaDataType,
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F32,
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YDataType,
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ck::tensor_operation::element_wise::PassThrough,
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Rank,
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NumReduceDim>;
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static auto GetInstances()
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{
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std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
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if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> &&
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is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16>)
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{
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if constexpr(Rank == 2 && NumReduceDim == 1)
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{
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add_device_layernorm_rank_2_1_f16_instances(op_ptrs);
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}
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else if constexpr(Rank == 4 && NumReduceDim == 3)
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{
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add_device_layernorm_rank_4_3_f16_instances(op_ptrs);
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}
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else if constexpr(Rank == 5 && NumReduceDim == 3)
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{
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add_device_layernorm_rank_5_3_f16_instances(op_ptrs);
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}
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}
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else if constexpr(is_same_v<XDataType, F32> && is_same_v<GammaDataType, F32> &&
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is_same_v<BetaDataType, F32> && is_same_v<YDataType, F32>)
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{
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if constexpr(Rank == 2 && NumReduceDim == 1)
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{
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add_device_layernorm_rank_2_1_f32_instances(op_ptrs);
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}
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else if constexpr(Rank == 4 && NumReduceDim == 3)
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{
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add_device_layernorm_rank_4_3_f32_instances(op_ptrs);
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}
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else if constexpr(Rank == 5 && NumReduceDim == 3)
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{
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add_device_layernorm_rank_5_3_f32_instances(op_ptrs);
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}
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}
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return op_ptrs;
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}
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};
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} // namespace instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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@@ -0,0 +1,109 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_normalization.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace instance {
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// FP16
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void add_device_normalization_rank_2_1_f16_instances(
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std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 2, 1>>>&);
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void add_device_normalization_rank_4_3_f16_instances(
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std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 4, 3>>>&);
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void add_device_normalization_rank_5_3_f16_instances(
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std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 5, 3>>>&);
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// FP32
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void add_device_normalization_rank_2_1_f32_instances(
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std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, PassThrough, 2, 1>>>&);
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void add_device_normalization_rank_4_3_f32_instances(
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std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, PassThrough, 4, 3>>>&);
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void add_device_normalization_rank_5_3_f32_instances(
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std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, PassThrough, 5, 3>>>&);
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template <typename XDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename YDataType,
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index_t Rank,
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index_t NumReduceDim>
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struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormalization<
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XDataType,
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GammaDataType,
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BetaDataType,
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F32,
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YDataType,
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ck::tensor_operation::element_wise::PassThrough,
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Rank,
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NumReduceDim>>
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{
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using DeviceOp = DeviceNormalization<XDataType,
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GammaDataType,
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BetaDataType,
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F32,
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YDataType,
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ck::tensor_operation::element_wise::PassThrough,
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Rank,
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NumReduceDim>;
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static auto GetInstances()
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{
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std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
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if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> &&
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is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16>)
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{
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if constexpr(Rank == 2 && NumReduceDim == 1)
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{
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add_device_normalization_rank_2_1_f16_instances(op_ptrs);
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}
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else if constexpr(Rank == 4 && NumReduceDim == 3)
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{
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add_device_normalization_rank_4_3_f16_instances(op_ptrs);
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}
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else if constexpr(Rank == 5 && NumReduceDim == 3)
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{
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add_device_normalization_rank_5_3_f16_instances(op_ptrs);
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}
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}
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else if constexpr(is_same_v<XDataType, F32> && is_same_v<GammaDataType, F32> &&
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is_same_v<BetaDataType, F32> && is_same_v<YDataType, F32>)
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{
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if constexpr(Rank == 2 && NumReduceDim == 1)
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{
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add_device_normalization_rank_2_1_f32_instances(op_ptrs);
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}
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else if constexpr(Rank == 4 && NumReduceDim == 3)
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{
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add_device_normalization_rank_4_3_f32_instances(op_ptrs);
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}
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else if constexpr(Rank == 5 && NumReduceDim == 3)
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{
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add_device_normalization_rank_5_3_f32_instances(op_ptrs);
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}
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}
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return op_ptrs;
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}
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};
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} // namespace instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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