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Add instances for conv_scale with fp8@bf8->fp8 (#1220)

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* Update device op api to support BComputeType

* Add example

* Add instances

* Add profiler mode

* Add client example

* Update copyright year

* Add BComputeType check

* Fix compute types
This commit is contained in:
Rostyslav Geyyer
2024-04-03 09:08:08 -05:00
committed by GitHub
parent 9a194837af
commit a61e73bc56
17 changed files with 441 additions and 103 deletions
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15
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp
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@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
@@ -254,13 +254,14 @@ template <index_t NDimSpatial,
index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEBlockTransferScalarPerVector_NPerBlock,
typename ComputeDataType =
typename AComputeDataType =
decltype(UnpackDataType<is_detected<is_tuple, ADataType>::value,
Number<0>,
ADataType>()), // ComputeType is InputType by default (first
// in tuple for MultiAB), unpack if tuple was
// passed
LoopScheduler LoopSched = make_default_loop_scheduler()>
typename BComputeDataType = AComputeDataType,
LoopScheduler LoopSched = make_default_loop_scheduler()>
struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
: public DeviceGroupedConvFwdMultipleABD<NDimSpatial,
ALayout,
@@ -274,7 +275,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
ComputeDataType>
AComputeDataType,
BComputeDataType>
{
using DeviceOp = DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle;
@@ -386,7 +388,7 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
using GemmBDataType = std::conditional_t<!isMultiB && isMultiA, Tuple<BDataType>, BDataType>;
#define GridwiseGemmTemplateParameters \
GemmADataType, GemmBDataType, ComputeDataType, AccDataType, CShuffleDataType, DsDataType, \
GemmADataType, GemmBDataType, AComputeDataType, AccDataType, CShuffleDataType, DsDataType, \
EDataType, AElementwiseOperation, BElementwiseOperation, CDEElementwiseOperation, \
InMemoryDataOperationEnum::Set, NumGemmKPrefetchStage, BlockSize, MPerBlock, NPerBlock, \
KPerBlock, AK1, BK1, MPerXDL, NPerXDL, MXdlPerWave, NXdlPerWave, \
@@ -399,7 +401,8 @@ struct DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
BBlockTransferDstScalarPerVector_BK1, false, BBlockLdsExtraN, \
CShuffleMXdlPerWavePerShuffle, CShuffleNXdlPerWavePerShuffle, \
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock, \
CDEBlockTransferScalarPerVector_NPerBlock, LoopSched
CDEBlockTransferScalarPerVector_NPerBlock, LoopSched, PipelineVersion::v1, \
BComputeDataType
// Use appropriate gridwise gemm
using GridwiseGemm =
std::conditional_t<isMultiA || isMultiB,

10
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp
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@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
@@ -75,13 +75,14 @@ template <index_t NDimSpatial,
index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEBlockTransferScalarPerVector_NPerBlock,
typename ComputeDataType =
typename AComputeDataType =
decltype(UnpackDataType<is_detected<is_tuple, ADataType>::value,
Number<0>,
ADataType>()), // ComputeType is InputType by default (first
// in tuple for MultiAB), unpack if tuple was
// passed
LoopScheduler LoopSched = make_default_loop_scheduler()>
typename BComputeDataType = AComputeDataType,
LoopScheduler LoopSched = make_default_loop_scheduler()>
using DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle = DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
NDimSpatial,
ALayout,
@@ -128,7 +129,8 @@ using DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle = DeviceGroupedConvFwdMultipl
CShuffleNXdlPerWavePerShuffle,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
CDEBlockTransferScalarPerVector_NPerBlock,
ComputeDataType,
AComputeDataType,
BComputeDataType,
LoopSched>;
} // namespace device