chore: import upstream snapshot with attribution
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wehub-resource-sync
2026-07-13 13:36:55 +08:00
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#
# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
include(ShouldCompileKernel)
option(TRT_BUILD_INCLUDE_BERT_QKV_PLUGIN "Build the BERT QKV to Context Plugin and related plugins." ON)
# Create the main object library, which is shared between plugin, plugin_internal, and plugin_static.
add_library(trt_plugins OBJECT)
function(add_plugin_source)
target_sources(trt_plugins PRIVATE ${ARGN})
endfunction()
# Create the VC object lib, used by vc and vc_static.
add_library(trt_vc_plugins OBJECT)
function(add_vc_plugin_source)
target_sources(trt_vc_plugins PRIVATE ${ARGN})
endfunction()
set(TRT_PLUGIN_NAMES
cropAndResizePlugin
decodeBbox3DPlugin
detectionLayerPlugin
disentangledAttentionPlugin
efficientNMSPlugin
flattenConcat
generateDetectionPlugin
gridAnchorPlugin
groupNormalizationPlugin
instanceNormalizationPlugin
modulatedDeformConvPlugin
multilevelCropAndResizePlugin
multilevelProposeROI
multiscaleDeformableAttnPlugin
nvFasterRCNN
pillarScatterPlugin
priorBoxPlugin
proposalLayerPlugin
pyramidROIAlignPlugin
regionPlugin
reorgPlugin
resizeNearestPlugin
roiAlignPlugin
scatterElementsPlugin
scatterPlugin
topkLastDimPlugin
voxelGeneratorPlugin
)
if(${TRT_BUILD_INCLUDE_BERT_QKV_PLUGIN})
list(APPEND TRT_PLUGIN_NAMES
bertQKVToContextPlugin
embLayerNormPlugin
fcPlugin
skipLayerNormPlugin
)
endif()
add_subdirectory(api)
add_subdirectory(vc)
add_subdirectory(common)
foreach(PLUGIN_NAME IN LISTS TRT_PLUGIN_NAMES)
add_subdirectory(${PLUGIN_NAME})
endforeach()
set(trt_plugin_include_dirs
$<BUILD_LOCAL_INTERFACE:${TRT_EXTERNALS_DIR}>
$<BUILD_LOCAL_INTERFACE:${CMAKE_CURRENT_LIST_DIR}>
$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>
)
target_include_directories(trt_plugins PUBLIC ${trt_plugin_include_dirs})
target_include_directories(trt_vc_plugins PUBLIC ${trt_plugin_include_dirs})
# Use the compile-time dependencies of TRT when compiling the objects before the link stage.
# The final targets will be responsible for selecting the target TRT distribution to use.
target_link_libraries(trt_plugins PRIVATE $<COMPILE_ONLY:tensorrt>)
target_link_libraries(trt_vc_plugins PRIVATE $<COMPILE_ONLY:tensorrt>)
# Use true link dependencies on the global definitions and cudart.
target_link_libraries(trt_plugins PRIVATE trt_global_definitions TRT::cudart)
target_link_libraries(trt_vc_plugins PRIVATE trt_global_definitions TRT::cudart)
foreach(SM IN LISTS CMAKE_CUDA_ARCHITECTURES)
get_numeric_sm(${SM} "SM")
target_compile_definitions(trt_plugins PUBLIC "ENABLE_SM${SM}")
target_compile_definitions(trt_vc_plugins PUBLIC "ENABLE_SM${SM}")
endforeach()
target_compile_options(trt_plugins PUBLIC $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>)
target_compile_options(trt_vc_plugins PUBLIC $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>)
target_compile_definitions(trt_vc_plugins PRIVATE
COMPILE_VFC_PLUGIN=1
)
# Create all the library targets, reusing the objects we've compiled in the first step.
add_library(tensorrt_plugins SHARED)
target_link_libraries(tensorrt_plugins PRIVATE trt_plugins)
add_library(tensorrt_plugins_static STATIC)
target_bundle_libraries(tensorrt_plugins_static PRIVATE trt_plugins)
add_library(tensorrt_vc_plugins SHARED)
target_link_libraries(tensorrt_vc_plugins PRIVATE trt_vc_plugins)
add_library(tensorrt_vc_plugins_static STATIC)
target_bundle_libraries(tensorrt_vc_plugins_static PRIVATE trt_plugins)
if(NOT MSVC)
set(trt_plugins_link_options
"LINKER:-z,relro"
"LINKER:-Bsymbolic"
"LINKER:--no-undefined"
"LINKER:--no-as-needed"
"$<$<CONFIG:Release>:LINKER:--strip-all>"
)
else()
set(trt_plugins_link_options)
endif()
set(trt_plugin_dependencies
tensorrt
trt_global_definitions
)
if(NOT MSVC)
list(APPEND trt_plugin_dependencies
Threads::Threads
CUDA::culibos
)
endif()
### TRT Plugin Setup
target_link_libraries(tensorrt_plugins PRIVATE ${trt_plugin_dependencies})
target_link_options(tensorrt_plugins PRIVATE ${trt_plugins_link_options})
set_target_properties(
tensorrt_plugins
PROPERTIES CXX_VISIBILITY_PRESET hidden
VISIBILITY_INLINES_HIDDEN ON
OUTPUT_NAME nvinfer_plugin
VERSION ${TensorRT_VERSION}
SOVERSION ${TRT_MAJOR}
)
apply_export_map(
TARGETS tensorrt_plugins
)
update_windows_output_name(tensorrt_plugins ${TRT_MAJOR} ${TRT_MINOR})
### Static Plugin Setup
set(trt_plugin_static_dependencies
tensorrt_static
$<BUILD_LOCAL_INTERFACE:trt_global_definitions>
)
target_include_directories(tensorrt_plugins_static PRIVATE ${trt_plugin_include_dirs})
target_link_libraries(tensorrt_plugins_static PRIVATE ${trt_plugin_static_dependencies})
set_target_properties(
tensorrt_plugins_static
PROPERTIES CXX_VISIBILITY_PRESET hidden
VISIBILITY_INLINES_HIDDEN ON
OUTPUT_NAME nvinfer_plugin_static
VERSION ${TensorRT_VERSION}
SOVERSION ${TRT_MAJOR}
LINK_DEPENDS ${CMAKE_CURRENT_LIST_DIR}/exports/nvinfer_plugin.map)
if(NOT ${TRT_BUILD_ENABLE_STATIC_LIBS})
set_target_properties(tensorrt_plugins_static
PROPERTIES EXCLUDE_FROM_ALL ON
)
endif()
smoke_test_static_lib(tensorrt_plugins_static)
### VC Plugin Setup
if (NOT MSVC)
set(trt_vc_plugins_link_options
"LINKER:-z,relro"
"LINKER:-Bsymbolic"
"LINKER:--no-undefined"
"LINKER:--no-as-needed"
"$<$<CONFIG:Release>:LINKER:--strip-all>"
)
else()
set(trt_vc_plugins_link_options)
endif()
# Target properties for tensorrt_vc_plugins
# This library includes a minimal subset of the plugins used for version compatibility.
target_include_directories(tensorrt_vc_plugins PRIVATE ${trt_plugin_include_dirs})
target_link_libraries(tensorrt_vc_plugins PRIVATE trt_global_definitions)
target_link_options(tensorrt_vc_plugins PRIVATE ${trt_vc_plugins_link_options})
set_target_properties(
tensorrt_vc_plugins
PROPERTIES CXX_VISIBILITY_PRESET hidden
VISIBILITY_INLINES_HIDDEN ON
OUTPUT_NAME nvinfer_vc_plugin
VERSION ${TensorRT_VERSION}
SOVERSION ${TRT_MAJOR}
)
apply_export_map(
TARGETS tensorrt_vc_plugins
)
update_windows_output_name(tensorrt_vc_plugins ${TRT_MAJOR} ${TRT_MINOR})
### VC Plugin Static Setup
target_include_directories(tensorrt_vc_plugins_static PRIVATE ${trt_plugin_include_dirs})
target_link_libraries(tensorrt_vc_plugins_static PRIVATE ${trt_plugin_static_dependencies})
set_target_properties(
tensorrt_vc_plugins_static
PROPERTIES CXX_VISIBILITY_PRESET hidden
VISIBILITY_INLINES_HIDDEN ON
OUTPUT_NAME nvinfer_vc_plugin_static
VERSION ${TensorRT_VERSION}
SOVERSION ${TRT_MAJOR}
LINK_DEPENDS ${CMAKE_CURRENT_LIST_DIR}/exports/nvinfer_vc_plugin.map)
if(NOT ${TRT_BUILD_ENABLE_STATIC_LIBS})
set_target_properties(tensorrt_vc_plugins_static
PROPERTIES EXCLUDE_FROM_ALL ON
)
endif()
smoke_test_static_lib(tensorrt_vc_plugins_static)
if(${TRT_BUILD_STUB_LIBS} AND NOT MSVC)
create_stub_lib(tensorrt_plugins)
create_stub_lib(tensorrt_vc_plugins)
endif()
installLibraries(
TARGETS tensorrt_plugins tensorrt_vc_plugins
OPTIONAL
COMPONENT external
EXPORT TensorRT
)
installLibraries(
TARGETS tensorrt_plugins_static tensorrt_vc_plugins_static
OPTIONAL
COMPONENT external
EXPORT TensorRT_Static
)
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# TensorRT Plugins
## Contents
| Plugin | Name | Versions |
|---|---|---|
| [bertQKVToContextPlugin](bertQKVToContextPlugin) [DEPRECATED] | CustomQKVToContextPluginDynamic | 1, 2, 3, 4, 5, 6 |
| [cropAndResizePlugin](cropAndResizePlugin) | CropAndResizeDynamic | 2 |
| [decodeBbox3DPlugin](decodeBbox3DPlugin) [DEPRECATED] | DecodeBbox3DPlugin | 1 |
| [detectionLayerPlugin](detectionLayerPlugin) [DEPRECATED] | DetectionLayer_TRT | 1 |
| [disentangledAttentionPlugin](disentangledAttentionPlugin) [DEPRECATED] | DisentangledAttention_TRT | 1 |
| [disentangledAttentionPlugin](disentangledAttentionPlugin) | DisentangledAttention_TRT | 2 |
| [efficientNMSPlugin](efficientNMSPlugin) [DEPRECATED] | EfficientNMS_TRT | 1 |
| [efficientNMSExplicitTFTRTPlugin](efficientNMSPlugin/tftrt) [DEPRECATED] | EfficientNMS_Explicit_TF_TRT | 1 |
| [efficientNMSImplicitTFTRTPlugin](efficientNMSPlugin/tftrt) [DEPRECATED] | EfficientNMS_Implicit_TF_TRT | 1 |
| [embLayerNormPlugin](embLayerNormPlugin) [DEPRECATED]| CustomEmbLayerNormPluginDynamic | 1, 2, 3 |
| [embLayerNormPlugin](embLayerNormPlugin) | CustomEmbLayerNormPluginDynamic | 4, 5, 6 |
| [fcPlugin](fcPlugin) [DEPRECATED] | CustomFCPluginDynamic | 1 |
| [flattenConcat](flattenConcat) [DEPRECATED] | FlattenConcat_TRT | 1 |
| [generateDetectionPlugin](generateDetectionPlugin) [DEPRECATED] | GenerateDetection_TRT | 1 |
| [gridAnchorPlugin](gridAnchorPlugin) [DEPRECATED] | GridAnchor_TRT | 1 |
| [gridAnchorRectPlugin](gridAnchorPlugin) [DEPRECATED] | GridAnchorRect_TRT | 1 |
| [groupNormalizationPlugin](groupNormalizationPlugin) [DEPRECATED] | GroupNormalizationPlugin | 1 |
| [instanceNormalizationPlugin](instanceNormalizationPlugin) [DEPRECATED] | InstanceNormalization_TRT | 1, 2 |
| [instanceNormalizationPlugin](instanceNormalizationPlugin) | InstanceNormalization_TRT | 3 |
| [modulatedDeformConvPlugin](modulatedDeformConvPlugin) [DEPRECATED] | ModulatedDeformConv2d | 1 |
| [modulatedDeformConvPlugin](modulatedDeformConvPlugin) | ModulatedDeformConv2d | 2 |
| [multilevelCropAndResizePlugin](multilevelCropAndResizePlugin) [DEPRECATED] | MultilevelCropAndResize_TRT | 1 |
| [multilevelProposeROI](multilevelProposeROI) [DEPRECATED] | MultilevelProposeROI_TRT | 1 |
| [multiscaleDeformableAttnPlugin](multiscaleDeformableAttnPlugin) [DEPRECATED] | MultiscaleDeformableAttnPlugin_TRT | 1 |
| [multiscaleDeformableAttnPlugin](multiscaleDeformableAttnPlugin) | MultiscaleDeformableAttnPlugin_TRT | 2 |
| [nvFasterRCNN](nvFasterRCNN) [DEPRECATED] | RPROI_TRT | 1 |
| [pillarScatterPlugin](pillarScatterPlugin) [DEPRECATED] | PillarScatterPlugin | 1 |
| [priorBoxPlugin](priorBoxPlugin) [DEPRECATED] | PriorBox_TRT | 1 |
| [proposalLayerPlugin](proposalLayerPlugin) [DEPRECATED] | ProposalLayer_TRT | 1 |
| [pyramidROIAlignPlugin](pyramidROIAlignPlugin) [DEPRECATED] | PyramidROIAlign_TRT | 1 |
| [regionPlugin](regionPlugin) [DEPRECATED] | Region_TRT | 1 |
| [reorgPlugin](reorgPlugin) [DEPRECATED] | Reorg_TRT | 2 |
| [roiAlignPlugin](roiAlignPlugin) [DEPRECATED] | ROIAlign_TRT | 1 |
| [roiAlignPlugin](roiAlignPlugin) | ROIAlign_TRT | 2 |
| [resizeNearestPlugin](resizeNearestPlugin) [DEPRECATED] | ResizeNearest_TRT | 1 |
| [scatterElementsPlugin](scatterElementsPlugin) [DEPRECATED] | ScatterElements | 1 |
| [scatterElementsPlugin](scatterElementsPlugin) | ScatterElements | 2 |
| [scatterPlugin](scatterPlugin) [DEPRECATED] | ScatterND | 1 |
| [skipLayerNormPlugin](skipLayerNormPlugin) [DEPRECATED] | CustomSkipLayerNormPluginDynamic | 1, 2, 3, 4 |
| [skipLayerNormPlugin](skipLayerNormPlugin) | CustomSkipLayerNormPluginDynamic | 5, 6, 7, 8 |
| [topkLastDimPlugin](topkLastDimPlugin) | TopkLastDim | 1 |
| [voxelGeneratorPlugin](voxelGeneratorPlugin) [DEPRECATED] | VoxelGeneratorPlugin | 1 |
## Known Limitations
- None
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#
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
add_plugin_source(inferPlugin.cpp)
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/*
* SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "NvInfer.h"
#include "NvInferPlugin.h"
#include "common/checkMacrosPlugin.h"
#include "common/plugin.h"
#include "roiAlignPlugin/roiAlignPlugin.h"
#include "cropAndResizePlugin/cropAndResizePlugin.h"
#include "decodeBbox3DPlugin/decodeBbox3D.h"
#include "detectionLayerPlugin/detectionLayerPlugin.h"
#include "efficientNMSPlugin/efficientNMSPlugin.h"
#include "efficientNMSPlugin/tftrt/efficientNMSExplicitTFTRTPlugin.h"
#include "efficientNMSPlugin/tftrt/efficientNMSImplicitTFTRTPlugin.h"
#include "flattenConcat/flattenConcat.h"
#include "generateDetectionPlugin/generateDetectionPlugin.h"
#include "gridAnchorPlugin/gridAnchorPlugin.h"
#include "instanceNormalizationPlugin/instanceNormalizationPlugin.h"
#include "instanceNormalizationPlugin/instanceNormalizationPluginLegacy.h"
#include "modulatedDeformConvPlugin/modulatedDeformConvPlugin.h"
#include "modulatedDeformConvPlugin/modulatedDeformConvPluginLegacy.h"
#include "multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h"
#include "multilevelProposeROI/multilevelProposeROIPlugin.h"
#include "multiscaleDeformableAttnPlugin/multiscaleDeformableAttnPlugin.h"
#include "multiscaleDeformableAttnPlugin/multiscaleDeformableAttnPluginLegacy.h"
#include "nvFasterRCNN/nvFasterRCNNPlugin.h"
#include "pillarScatterPlugin/pillarScatter.h"
#include "priorBoxPlugin/priorBoxPlugin.h"
#include "proposalLayerPlugin/proposalLayerPlugin.h"
#include "pyramidROIAlignPlugin/pyramidROIAlignPlugin.h"
#include "regionPlugin/regionPlugin.h"
#include "reorgPlugin/reorgPlugin.h"
#include "resizeNearestPlugin/resizeNearestPlugin.h"
#include "roiAlignPlugin/roiAlignPlugin.h"
#include "roiAlignPlugin/roiAlignPluginLegacy.h"
#include "scatterElementsPlugin/scatterElementsPlugin.h"
#include "scatterElementsPlugin/scatterElementsPluginLegacy.h"
#include "scatterPlugin/scatterPlugin.h"
#include "topkLastDimPlugin/topkLastDimPlugin.h"
#include "voxelGeneratorPlugin/voxelGenerator.h"
#include <algorithm>
#include <array>
#include <iostream>
#include <memory>
#include <mutex>
#include <stack>
#include <unordered_set>
using namespace nvinfer1;
using namespace nvinfer1::plugin;
using namespace nvinfer1::pluginInternal;
using nvinfer1::plugin::RPROIParams;
namespace nvinfer1::plugin
{
extern ILogger* gLogger;
} // namespace nvinfer1::plugin
namespace
{
// This singleton ensures that each plugin is only registered once for a given
// namespace and type, and attempts of duplicate registration are ignored.
class PluginCreatorRegistry
{
public:
static PluginCreatorRegistry& getInstance()
{
static PluginCreatorRegistry instance;
return instance;
}
template <typename CreatorType>
void addPluginCreator(void* logger, char const* libNamespace)
{
// Make accesses to the plugin creator registry thread safe
std::lock_guard<std::mutex> lock(mRegistryLock);
std::string errorMsg;
std::string verboseMsg;
std::unique_ptr<CreatorType> pluginCreator{new CreatorType{}};
pluginCreator->setPluginNamespace(libNamespace);
nvinfer1::plugin::gLogger = static_cast<nvinfer1::ILogger*>(logger);
std::string pluginType = std::string{pluginCreator->getPluginNamespace()}
+ "::" + std::string{pluginCreator->getPluginName()} + " version "
+ std::string{pluginCreator->getPluginVersion()};
if (mRegistryList.find(pluginType) == mRegistryList.end())
{
bool status = getPluginRegistry()->registerCreator(*pluginCreator, libNamespace);
if (status)
{
mRegistry.push(std::move(pluginCreator));
mRegistryList.insert(pluginType);
verboseMsg = "Registered plugin creator - " + pluginType;
}
else
{
errorMsg = "Could not register plugin creator - " + pluginType;
}
}
else
{
verboseMsg = "Plugin creator already registered - " + pluginType;
}
if (logger)
{
if (!errorMsg.empty())
{
nvinfer1::plugin::gLogger->log(ILogger::Severity::kERROR, errorMsg.c_str());
}
if (!verboseMsg.empty())
{
nvinfer1::plugin::gLogger->log(ILogger::Severity::kVERBOSE, verboseMsg.c_str());
}
}
}
~PluginCreatorRegistry()
{
std::lock_guard<std::mutex> lock(mRegistryLock);
// Release pluginCreators in LIFO order of registration.
while (!mRegistry.empty())
{
mRegistry.pop();
}
mRegistryList.clear();
}
private:
PluginCreatorRegistry() {}
std::mutex mRegistryLock;
std::stack<std::unique_ptr<IPluginCreatorInterface>> mRegistry;
std::unordered_set<std::string> mRegistryList;
public:
PluginCreatorRegistry(PluginCreatorRegistry const&) = delete;
void operator=(PluginCreatorRegistry const&) = delete;
};
template <typename CreatorType>
void initializePlugin(void* logger, char const* libNamespace)
{
PluginCreatorRegistry::getInstance().addPluginCreator<CreatorType>(logger, libNamespace);
}
} // namespace
// New Plugin APIs
extern "C"
{
bool initLibNvInferPlugins(void* logger, char const* libNamespace)
{
initializePlugin<nvinfer1::plugin::ROIAlignV3PluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::CropAndResizeDynamicPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::InstanceNormalizationV3PluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ScatterElementsPluginV3Creator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::MultiscaleDeformableAttnPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ModulatedDeformableConvPluginDynamicCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::DecodeBbox3DPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::DetectionLayerPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::EfficientNMSExplicitTFTRTPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::EfficientNMSImplicitTFTRTPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::EfficientNMSPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::FlattenConcatPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::GenerateDetectionPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::GridAnchorPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::GridAnchorRectPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::InstanceNormalizationPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::InstanceNormalizationPluginCreatorV2>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ModulatedDeformableConvPluginDynamicLegacyCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::MultilevelCropAndResizePluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::MultilevelProposeROIPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::MultiscaleDeformableAttnPluginCreatorLegacy>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::PillarScatterPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::PriorBoxPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ProposalLayerPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::PyramidROIAlignPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::RegionPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ReorgDynamicPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ReorgStaticPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ResizeNearestPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ROIAlignPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::RPROIPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ScatterElementsPluginV2Creator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::ScatterNDPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::TopkLastDimPluginCreator>(logger, libNamespace);
initializePlugin<nvinfer1::plugin::VoxelGeneratorPluginCreator>(logger, libNamespace);
return true;
}
} // extern "C"
@@ -0,0 +1,46 @@
#
# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
add_plugin_source(
mhaRunner.cu
mhaRunner.h
qkvToContextInt8InterleavedPlugin.cpp
qkvToContextInt8InterleavedPlugin.h
qkvToContextInt8InterleavedPluginLegacy.cpp
qkvToContextInt8InterleavedPluginLegacy.h
qkvToContextPlugin.cpp
qkvToContextPlugin.h
qkvToContextPluginLegacy.cpp
qkvToContextPluginLegacy.h
zeroPadding2d.cu
zeroPadding2d.h
)
set(BERT_QKV_SUPPORTED_SMS
75
80
86
87
89
90
100
120
)
add_subdirectory(fused_multihead_attention)
add_subdirectory(fused_multihead_attention_v2)
@@ -0,0 +1,216 @@
#
# SPDX-FileCopyrightText: Copyright (c) 2022-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
---
name: CustomQKVToContextPluginDynamic
interface: "IPluginV3"
versions:
"4":
inputs:
- input
outputs:
- output
input_dims:
input: 5
output_dims:
output: "input_0, input_1, hidden_size_0, 1, 1"
attributes:
- type_id
- hidden_size
- num_heads
- has_mask
- dq_probs
attribute_types:
type_id: int32
hidden_size: int32
num_heads: int32
has_mask: int32
dq_probs: float32
attribute_length:
type_id: 1
hidden_size: 1
num_heads: 1
has_mask: 1
dq_probs: 1
attribute_options:
type_id:
- 0
- 1
- 2
hidden_size:
min: "0"
max: "=pinf"
num_heads:
min: "0"
max: "=pinf"
has_mask:
- 0
- 1
dq_probs:
min: "0"
max: "=pinf"
attribute_constraints:
- "num_heads * k == hidden_size"
attributes_required:
- type_id
- hidden_size
- num_heads
- has_mask
golden_io_path: "plugin/CustomQKVToContextPluginDynamic_PluginGoldenIO.json"
abs_tol: 1e-5
rel_tol: 1e-5
fp16_atol: 1e-2
fp16_rtol: 1e-2
configs:
config1:
input_types:
input: float16
attribute_options:
type_id:
value: 1
shape: "1"
hidden_size:
value: 768
shape: "1"
num_heads:
value: 12
shape: "1"
has_mask:
value: 0
shape: "1"
output_types:
output: float16
config2:
input_types:
input: float16
attribute_options:
type_id:
value: 1
shape: "1"
hidden_size:
value: 1024
shape: "1"
num_heads:
value: 16
shape: "1"
has_mask:
value: 0
shape: "1"
output_types:
output: float16
config5:
input_types:
input: float16
attribute_options:
type_id:
value: 1
shape: "1"
hidden_size:
value: 384
shape: "1"
num_heads:
value: 12
shape: "1"
has_mask:
value: 0
shape: "1"
output_types:
output: float16
has_mask:
input_types:
input: float32
input_mask: int32
attribute_options:
type_id:
value: 0
shape: "1"
hidden_size:
value: 3
shape: "1"
num_heads:
value: 3
shape: "1"
has_mask:
value: 1
shape: "1"
output_types:
output: float32
"5":
inputs:
- input
- input_mask
outputs:
- output
input_dims:
input: 5
input_mask: 1
output_dims:
output: "input_0, input_mask_0, hidden_size_0, 1, 1"
attributes:
- type_id
- hidden_size
- num_heads
- has_mask
- dq_probs
- var_seqlen
- use_int8_scale_max
attribute_types:
type_id: int32
hidden_size: int32
num_heads: int32
has_mask: int32
dq_probs: float32
var_seqlen: int32
use_int8_scale_max: int32
attribute_length:
type_id: 1
hidden_size: 1
num_heads: 1
has_mask: 1
dq_probs: 1
var_seqlen: 1
use_int8_scale_max: 1
attribute_options:
type_id:
- 0
- 1
- 2
hidden_size:
min: "0"
max: "=pinf"
num_heads:
min: "0"
max: "=pinf"
has_mask:
- 0
- 1
dq_probs:
min: "0"
max: "=pinf"
use_int8_scale_max:
- 0
- 1
var_seqlen:
- 0
# - 1 # Disabled since SM specific tests are not supported yet
attributes_required:
- type_id
- hidden_size
- num_heads
- has_mask
# "3": #version3 - Not supported yet
...
+211
View File
@@ -0,0 +1,211 @@
# bertQKVToContextPlugin [DEPRECATED]
**This plugin has been deprecated since TensorRT 10.15 and will be removed in a future release. Use `IAttention` (via `INetworkDefinition::addAttention` / `addAttentionV2`) as the out-of-the-box replacement. See [Migration to IAttention](#migration-to-iattention) for the steps to adapt.**
**Table Of Contents**
- [Description](#description)
* [Structure](#structure)
- [Migration to IAttention](#migration-to-iattention)
- [Parameters](#parameters)
- [Additional resources](#additional-resources)
- [License](#license)
- [Changelog](#changelog)
- [Known issues](#known-issues)
## Description
Takes query, key and value tensors and computes scaled multi-head attention - computes scaled dot product attention scores `softmax(K'Q/sqrt(HeadSize))` and returns values weighted by these attention scores.
### Structure
The `bertQKVToContextPlugin` takes two inputs; `input`, and optionally `input_mask`.
`input`
input is a tensor with shape `[S, B, 3 * E, 1, 1]` where `B` is the batch size and `E` is the hidden size. The input has two trailing dimensions in order to generate an output with the same trailing dimensions for a FC optimiazation further down the network.
This plugin makes strong assumptions about its input:
- The input tensor contains all 3 matrices Q, K, V
- This input tensor is computed by multiplying a tensor of size `[S, B, E]` with the weights `W_qkv` of size `[E, 3 * E]`
- The weight matrix W_qkv is NOT just the vertical concatenation of individual matrices `W_tmp = [W_q', W_k', W_v']'`, but to start with `W_tmp`, reshaping it into `[E, 3, N, H]` (where `N * H = E` and `N` is number of heads, `H` is head size) transposing it into `[E, N, 3, H]` and reshaping it back to `[E, 3 * E]`. The interpretation is to layout the k-th heads of Q, K and V next to each other, instead of first all N heads of Q, then all N heads of K, then all heads of V
`input_mask`
The input mask is encoded in the `maskIdx` format described in `embLayerNormPlugin`, and contains the number of valid elements from the start of the sequence.
If provided, the attention scores, i.e. the softmax distribution, are only computed over the elements designated as valid by the input mask
The `bertQKVToContextPlugin` generates the following output:
`output`
output is a tensor with shape `[S, B, E, 1, 1]` where `B` is the batch size.
## Migration to [IAttention](https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/operators/Attention.html)
`IAttention` is the out-of-the-box attention layer in TensorRT and is functionally equivalent to this plugin for the multi-head attention computation, but it is not a drop-in replacement. Adapter steps:
1. **Split the fused QKV input.** This plugin consumes one `[S, B, 3*E, 1, 1]` tensor with the K-th heads of Q, K, V interleaved. `IAttention` takes three separate tensors with shape `[B, N, S, H]`. Reshape the fused QKV to `[S, B, N, 3, H]`, slice along the size-3 axis, then transpose each slice to `[B, N, S, H]`. If the upstream FC weight `W_qkv` was packed using the head-interleaved layout described in the Structure section, no weight repacking is needed. Otherwise, repack `W_qkv` to produce three contiguous `[B, N, S, H]` tensors directly.
```cpp
// [S, B, 3*E, 1, 1] -> [B, N, S, 3, H], then slice axis 3.
auto* shuf = network->addShuffle(*fusedQKV);
shuf->setReshapeDimensions(Dims{5, {S, B, N, 3, H}});
shuf->setSecondTranspose(Permutation{{1, 2, 0, 3, 4}});
ITensor* t = shuf->getOutput(0);
auto pick = [&](int32_t i) -> ITensor* {
auto* sl = network->addSlice(*t, Dims{5, {0,0,0,i,0}},
Dims{5, {B,N,S,1,H}}, Dims{5, {1,1,1,1,1}});
auto* sq = network->addShuffle(*sl->getOutput(0));
sq->setReshapeDimensions(Dims{4, {B, N, S, H}});
return sq->getOutput(0);
};
ITensor *q = pick(0), *k = pick(1), *v = pick(2);
```
2. **Convert the mask.** This plugin uses the `maskIdx` format (count of valid tokens from the start of each sequence). `IAttention` takes a 4D boolean or additive mask of shape `[B, N, S_q, S_kv]` (broadcastable over `B` and `N`). For non-causal BERT-style self-attention, materialize a boolean mask from the valid-token counts. For causal generation, use `IAttention::setCausalKind(CausalMaskKind)` instead of a mask.
```cpp
// Boolean mask from maskIdx ([B] int32). Broadcasts to [B, NQ, S_q, S_kv].
auto* idx = network->addFill(Dims{1, {S}}, FillOperation::kLINSPACE, DataType::kINT32);
idx->setAlpha(0.0); idx->setBeta(1.0);
auto* less = network->addElementWise(*idx->getOutput(0), *maskIdx,
ElementWiseOperation::kLESS);
attn->setMask(*less->getOutput(0));
// For causal autoregressive decode, skip the mask and use:
// attn->setCausalKind(CausalMaskKind::kLOWER_RIGHT);
```
3. **Reshape the output.** This plugin produces `[S, B, E, 1, 1]`. `IAttention` produces `[B, N, S_q, H]`. Transpose and reshape as needed for downstream layers.
```cpp
// [B, N, S_q, H] -> [S, B, E, 1, 1] for downstream layers expecting the plugin layout.
auto* out = network->addShuffle(*attn->getOutput(0));
out->setFirstTranspose(Permutation{{2, 0, 1, 3}}); // -> [S, B, N, H]
out->setReshapeDimensions(Dims{5, {S, B, E, 1, 1}});
```
4. **INT8/FP8 quantization.** The plugin folded the entire INT8 attention pipeline into a single op, exposing only `type_id==2` (implicit per-tensor scales) or the explicit-INT8 attribute set. With `IAttention`, you express the QDQ pattern in the network so the builder can fuse it into the FP8/INT8 attention kernel. See also [Explicit Quantization](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/work-quantized-types.html#explicit-quantization).
```cpp
// QDQ Q, K, V (input_qkv_scale).
auto qdq = [&](ITensor* x) -> ITensor* {
auto* qn = network->addQuantize(*x, *qkvScale);
qn->setOutputType(0, DataType::kINT8);
auto* dq = network->addDequantize(*qn->getOutput(0), *qkvScale);
return dq->getOutput(0);
};
ITensor *qq = qdq(q), *kq = qdq(k), *vq = qdq(v);
auto* attn = network->addAttentionV2(*qq, *kq, *vq,
AttentionNormalizationOp::kSOFTMAX,
CausalMaskKind::kNONE);
// dq_probs -> NormalizationQuantizeScale.
attn->setNormalizationQuantizeScale(*dqProbsScale);
// QDQ attention output (output_ctx_scale).
auto* qOut = network->addQuantize(*attn->getOutput(0), *outScale);
qOut->setOutputType(0, DataType::kINT8);
auto* dqOut = network->addDequantize(*qOut->getOutput(0), *outScale);
```
5. **Variable sequence length.** This plugin accepts a `cu_seqlen` packed layout. `IAttention` does not natively consume packed cu-seqlen input; users relying on this path must either pad to a fixed sequence length or implement their own packing/unpacking around `IAttention`. See below for an example.
```cpp
// `scatterIdx`, `gatherIdx` are application-specific and are typically built from `cu_seqlen`
// Packed input: [total_tokens, E]; cu_seqlen: [B+1] int32 prefix sums.
// Scatter packed -> padded [B, S, E] (pad slots are filled with zeros at a sentinel index).
auto* padded = network->addGather(*packedInput, *scatterIdx, /*axis=*/0); // [B*S, E]
auto* padBSE = network->addShuffle(*padded->getOutput(0));
padBSE->setReshapeDimensions(Dims{3, {B, S, E}});
// ...split into Q/K/V (step 1), build mask from validLen = cu_seqlen[b+1]-cu_seqlen[b] (step 2),
// call addAttentionV2 (step 4 if quantized)...
// Gather attention output [B, N, S_q, H] -> [B, S, E] -> packed [total_tokens, E].
auto* outBSE = network->addShuffle(*attn->getOutput(0));
outBSE->setFirstTranspose(Permutation{{0, 2, 1, 3}});
outBSE->setReshapeDimensions(Dims{2, {B * S, E}});
auto* repacked = network->addGather(*outBSE->getOutput(0), *gatherIdx, /*axis=*/0);
```
## Parameters
`bertQKVToContextPlugin` has plugin creator class `QKVToContextPluginDynamicCreator` and plugin class `CustomQKVToContextPluginDynamic`.
The parameters are defined below and consists of the following attributes:
| Type | Parameter | Version | Description
|----------|-----------------------------------------|-----------------------------------|-------------------------------------------------------------------
|`int` |`type_id` | 1, 2, 4, 5 |Integer encoding the DataType (0: FP32, 1: FP16, 2: INT8)
|`int` |`hidden_size` | 1, 2, 3, 4, 5, 6 |The hidden size, denoted by `E` above.
|`int` |`num_heads` | 1, 2, 3, 4, 5, 6 |The number of self-attention heads.
|`bool` |`has_mask` | 1, 2, 4, 5 |Whether to use the input_mask input.
|`float` |`dq_probs` | 1, 2, 3, 4, 5, 6 |inner layer scale factor when run in int8 precision, default 1.f/127.f.
|`int` |`var_seqlen` | 2, 5 |Whether to use variable sequence length (0: disable, 1: enable), default 0.
|`int` |`use_int8_scale_max` | 2, 3, 5, 6 |Whether to use INT8 scale factors to optimize softmax MAX reduction. Only active when `type_id==2`. (0: disable, 1: enable), default 1.
|`int` |`use_explicit_int8` | 3, 6 |Whether to use explicit INT8, (0: disable, 1: enable), default 0.
|`float` |`input_qkv_scale` | 3, 6 |The int8 scale for the input qkv tensor when explicit precision is used, default 1.f.
|`float` |`output_ctx_scale` | 3, 6 |The int8 scale for the output context tensor when explicit precision is used, default 1.f.
## Additional resources
**Networks:**
- [Transformer](https://arxiv.org/abs/1706.03762)
## License
For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html)
documentation.
## Changelog
April 2026
Provide `IAttention` as the out-of-the-box replacement.
Oct 2025
Deprecated this plugin. No alternatives are planned to be provided.
Jan 2025
Added precompiled kernel cubins for gb100 (compute capability 10.0) and gb20x (compute capability 12.0) platforms.
Aug 2024
Ported v3 variant (IPluginV2 design) to v6 (IpluginV3 design), but preserves identical attributes and I/O to v3.
Ported v2 variant (IPluginV2 design) to v5 (IpluginV3 design), but preserves identical attributes and I/O to v2.
Ported v1 variant (IPluginV2 design) to v4 (IpluginV3 design), but preserves identical attributes and I/O to v1.
Feb 2024
The issue of the V2 plugin not supporting head sizes of 32 or less and variable sequences of 64, 96, and 384 has been resolved.
Oct 2023
Support explicit int8.
April 2023
Optimize the GPU memory usage by using the cublas handle from attachToContext.
October 2022
Add IGMMA/HGMMA sm90 fmha_v2 kernels
August 2022
Expose `use_int8_scale_max` plugin attribute (for versions 2 and 3 of the plugin). This allows users to enable/disable the usage of INT8 scale factors to optimize softmax MAX reduction.
This optimization is applied always when `type_id==2` by default.
September 2021
Add sequence length 512 support in v2 plugin
Add head size 32 support when sequence length is 128, 256 or 512 in v2 plugin
October 2020
Add v2 plugin that supports variable sequence length.
Add v3 plugin that supports int8 interleaved variable sequence length.
November 2019
This is the first release of this `README.md` file.
## Known issues
This plugin only supports GPUs with compute capability >= 7.0. For more information see the [CUDA GPU Compute Capability Support Matrix](https://developer.nvidia.com/cuda-gpus#compute)
@@ -0,0 +1,54 @@
#
# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This folder contains a bunch of source files holding cubins. Since the files are huge, we only want to include and compile them if used.
# Usage is indicated by the SM being set in CMAKE_CUDA_ARCHITECTURES.
function(add_plugin_source_if_exists)
foreach(SRC_FILE IN LISTS ARGN)
if(EXISTS ${CMAKE_CURRENT_LIST_DIR}/${SRC_FILE})
add_plugin_source(${SRC_FILE})
endif()
endforeach()
endfunction()
foreach(SM IN LISTS BERT_QKV_SUPPORTED_SMS)
should_compile_kernel(${SM} SHOULD_COMPILE)
if (${SHOULD_COMPILE})
# Not every file exists for each SM, so we list all of the candidates and add them if present.
add_plugin_source_if_exists(
fused_multihead_attention_fp16_64_64_kernel.sm${SM}.cpp
fused_multihead_attention_fp16_96_64_kernel.sm${SM}.cpp
fused_multihead_attention_fp16_128_64_kernel.sm${SM}.cpp
fused_multihead_attention_fp16_256_64_kernel.sm${SM}.cpp
fused_multihead_attention_fp16_384_64_kernel.sm${SM}.cpp
fused_multihead_attention_fp16_512_64_kernel.sm${SM}.cpp
fused_multihead_attention_int8_64_64_kernel.sm${SM}.cpp
fused_multihead_attention_int8_96_64_kernel.sm${SM}.cpp
fused_multihead_attention_int8_128_64_kernel.sm${SM}.cpp
fused_multihead_attention_int8_192_64_kernel.sm${SM}.cpp
fused_multihead_attention_int8_256_64_kernel.sm${SM}.cpp
fused_multihead_attention_int8_384_64_kernel.sm${SM}.cpp
fused_multihead_attention_int8_512_64_kernel.sm${SM}.cpp
)
endif()
endforeach()
add_plugin_source(
fused_multihead_attention_common.h
)
@@ -0,0 +1,652 @@
/*
* SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef _BERT_FMHA_FMHA
#define _BERT_FMHA_FMHA
#include "common/bertCommon.h"
#include "common/cudaDriverWrapper.h"
#include "common/plugin.h"
#include "cuda_runtime_api.h"
#include "fused_multihead_attention_common.h"
#include <memory>
#include <mutex>
#include <set>
#include <stdint.h>
#include <unordered_map>
#include <unordered_set>
#include <vector>
namespace nvinfer1
{
namespace pluginInternal
{
template <typename TKernelMeta, typename TKernelParam>
class TFusedMultiHeadAttentionXMMAKernel
{
public:
using KernelMeta = TKernelMeta;
using KernelParam = TKernelParam;
inline uint64_t hashID(uint32_t s, uint32_t d) const
{
return (uint64_t) s << 32 | d;
}
virtual uint64_t hashID(const KernelMeta& kernelMeta) const
{
return hashID(kernelMeta.mS, kernelMeta.mD);
}
TFusedMultiHeadAttentionXMMAKernel(
const TKernelMeta* pMetaStart, uint32_t nMetaCount, plugin::bert::Data_type type, uint32_t sm)
: mDataType(type)
, mKernelMeta(pMetaStart)
, mKernelMetaCount(nMetaCount)
, mSM(sm)
{
PLUGIN_ASSERT(mKernelMetaCount && "No kernels were loaded correctly.");
}
void loadXMMAKernels(uint32_t smVersion)
{
for (uint32_t i = 0; i < mKernelMetaCount; ++i)
{
const auto& kernelMeta = mKernelMeta[i];
const auto kernelKey = hashID(kernelMeta);
if (kernelMeta.mSM == smVersion && kernelMeta.mDataType == mDataType
&& mFunctions.find(kernelKey) == mFunctions.end())
{
const uint32_t DEFAULT_SMEM_SIZE{48 * 1024};
if (kernelMeta.mSharedMemBytes >= DEFAULT_SMEM_SIZE)
{
int32_t deviceID{0};
cudaGetDevice(&deviceID);
int32_t sharedMemPerMultiprocessor{0};
if (cudaDeviceGetAttribute(
&sharedMemPerMultiprocessor, cudaDevAttrMaxSharedMemoryPerBlockOptin, deviceID)
!= cudaSuccess
|| sharedMemPerMultiprocessor < static_cast<int32_t>(kernelMeta.mSharedMemBytes))
{
// skip load function because not enough shared memory to launch the kernel
continue;
}
}
CUmodule hmod{0};
auto findModuleIter = mModules.find(kernelMeta.mCubin);
if (findModuleIter != mModules.end())
{
hmod = findModuleIter->second;
}
else
{
cuErrCheck(mDriver.cuModuleLoadData(&hmod, kernelMeta.mCubin), mDriver);
mModules.insert(std::make_pair(kernelMeta.mCubin, hmod));
}
FusedMultiHeadAttentionKernelInfo funcInfo;
funcInfo.mMetaInfoIndex = i;
cuErrCheck(mDriver.cuModuleGetFunction(&funcInfo.mDeviceFunction, hmod, kernelMeta.mFuncName), mDriver);
if (kernelMeta.mSharedMemBytes >= DEFAULT_SMEM_SIZE)
{
if (mDriver.cuFuncSetAttribute(funcInfo.mDeviceFunction,
CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, kernelMeta.mSharedMemBytes)
!= CUDA_SUCCESS)
{
// some chip may not have enough shared memory to launch the kernel
continue;
}
}
mFunctions.insert({kernelKey, funcInfo});
uint64_t const s = kernelMeta.mS;
uint64_t const headSize = kernelMeta.mD;
uint64_t key = (headSize << 32 | s);
if (mValidSequences.find(key) == mValidSequences.end())
{
mValidSequences.insert(key);
}
}
}
}
void loadXMMAKernels()
{
if (!mFunctions.empty())
{
return;
}
loadXMMAKernels(mSM);
// sm_86 chips prefer sm_86 sass, but can also use sm_80 sass if sm_86 not exist.
// sm_87 cannot run sm_80 sass
if (mSM == kSM_86)
{
loadXMMAKernels(kSM_80);
}
// sm_89 will reuse sm_80 and sm_86 kernels
if (mSM == kSM_89)
{
loadXMMAKernels(kSM_86);
loadXMMAKernels(kSM_80);
}
}
bool isValid(int32_t headSize, int32_t s) const
{
uint64_t key = (static_cast<uint64_t>(headSize) << 32 | static_cast<uint64_t>(s));
return (mValidSequences.find(key) != mValidSequences.end());
}
virtual void run(TKernelParam& params, cudaStream_t ss) const
{
const auto findIter = mFunctions.find(hashID(params.s, params.d));
std::stringstream errMsg;
errMsg << "Could not find kernel for:\n"
<< "\t s: " << params.s << "\n"
<< "\t d: " << params.d << "\n"
<< "Was the plugin compiled on a compatible CUDA and SM version?\n"
<< "\t Compiled on CUDA " << CUDA_VERSION << "\n"
<< "\t Current SM version: " << mSM << "\n"
<< "\t SM versions enabled during compilation: "
#if defined(ENABLE_SM72)
<< "72 "
#endif
#if defined(ENABLE_SM75)
<< "75 "
#endif
#if defined(ENABLE_SM80)
<< "80 "
#endif
#if defined(ENABLE_SM86)
<< "86 "
#endif
#if defined(ENABLE_SM87)
<< "87 "
#endif
#if defined(ENABLE_SM89)
<< "89 "
#endif
#if defined(ENABLE_SM90)
<< "90 "
#endif
<< "\n";
PLUGIN_VALIDATE(findIter != mFunctions.end(), errMsg.str().c_str());
const auto& kernelMeta = mKernelMeta[findIter->second.mMetaInfoIndex];
const CUfunction func = findIter->second.mDeviceFunction;
void* kernelParams[] = {&params, nullptr};
cuErrCheck(mDriver.cuLaunchKernel(func, params.h, params.b, 1, kernelMeta.mThreadsPerCTA, 1, 1,
kernelMeta.mSharedMemBytes, ss, kernelParams, nullptr),
mDriver);
}
virtual ~TFusedMultiHeadAttentionXMMAKernel() = default;
protected:
nvinfer1::CUDADriverWrapper mDriver;
plugin::bert::Data_type mDataType;
const TKernelMeta* mKernelMeta;
uint32_t mKernelMetaCount;
uint32_t mSM;
std::unordered_map<const unsigned char*, CUmodule> mModules;
struct FusedMultiHeadAttentionKernelInfo
{
uint32_t mMetaInfoIndex;
CUfunction mDeviceFunction;
};
std::unordered_map<uint64_t, FusedMultiHeadAttentionKernelInfo> mFunctions;
// Set of valid sequence and head size combination. We use (headSize << 32 | sequence) as key here.
std::unordered_set<uint64_t> mValidSequences;
};
template <typename TFusedMHAKernelList>
class TFusedMHAKernelFactory
{
public:
TFusedMHAKernelList const* getXMMAKernels(typename TFusedMHAKernelList::KernelMeta const* pKernelList,
uint32_t nbKernels, plugin::bert::Data_type type, uint32_t sm)
{
static std::mutex s_mutex;
std::lock_guard<std::mutex> lg(s_mutex);
auto const id = hashID(type, sm);
auto it = mKernels.find(id);
if (it == mKernels.end())
{
auto newKernel = std::make_unique<TFusedMHAKernelList>(pKernelList, nbKernels, type, sm);
newKernel->loadXMMAKernels();
it = mKernels.emplace(id, std::move(newKernel)).first;
}
return it->second.get();
}
static TFusedMHAKernelFactory<TFusedMHAKernelList>& Get()
{
static TFusedMHAKernelFactory<TFusedMHAKernelList> s_factory;
return s_factory;
}
private:
TFusedMHAKernelFactory() = default;
inline uint64_t hashID(plugin::bert::Data_type type, uint32_t sm) const
{
// use deviceID in hasID for multi GPU support before driver support context-less loading of cubin
int32_t deviceID{0};
CSC(cudaGetDevice(&deviceID), STATUS_FAILURE);
PLUGIN_ASSERT((deviceID & 0xFFFF) == deviceID);
PLUGIN_ASSERT((type & 0xFFFF) == type);
PLUGIN_ASSERT((sm & 0xFFFFFFFF) == sm);
return (uint64_t) type << 48 | (uint64_t) deviceID << 32 | sm;
}
std::unordered_map<uint64_t, const std::unique_ptr<TFusedMHAKernelList>> mKernels;
};
} // namespace pluginInternal
namespace plugin
{
namespace bert
{
static inline size_t get_size_in_bytes(size_t n, Data_type dtype)
{
switch (dtype)
{
case DATA_TYPE_E8M10: return n * 4;
case DATA_TYPE_FP32: return n * 4;
case DATA_TYPE_FP16: return n * 2;
case DATA_TYPE_INT32: return n * 4;
case DATA_TYPE_INT8: return n;
case DATA_TYPE_INT4: return n / 2U;
case DATA_TYPE_BOOL: return n / 8U;
case DATA_TYPE_E8M7: return n * 2;
default: PLUGIN_ASSERT(false); return 0;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
struct Fused_multihead_attention_params
{
// The QKV matrices.
void* qkv_ptr{};
// The mask to implement drop-out.
void* packed_mask_ptr{};
// The O matrix (output).
void* o_ptr{};
// The stride between rows of the Q, K and V matrices.
int64_t qkv_stride_in_bytes{};
// The stride between matrices of packed mask.
int64_t packed_mask_stride_in_bytes{};
// The stride between rows of O.
int64_t o_stride_in_bytes{};
#if defined(STORE_P)
// The pointer to the P matrix (for debugging).
void* p_ptr{};
// The stride between rows of the P matrix (for debugging).
int64_t p_stride_in_bytes{};
#endif // defined(STORE_P)
#if defined(STORE_S)
// The pointer to the S matrix (for debugging).
void* s_ptr{};
// The stride between rows of the S matrix (for debugging).
int64_t s_stride_in_bytes{};
#endif // defined(STORE_S)
// The dimensions.
int32_t b{};
int32_t h{};
int32_t s{};
int32_t d{};
// The scaling factors for the kernel.
uint32_t scale_bmm1{};
uint32_t scale_softmax{};
uint32_t scale_bmm2{};
// Do we use Niall's trick to avoid I2F/F2I in the INT8 kernel.
// See https://confluence.nvidia.com/pages/viewpage.action?pageId=302779721 for details.
bool enable_i2f_trick{};
// The number of heads computed by one iteration of the wave.
int32_t heads_per_wave{};
// Buffers to perform a global sync and a critical section.
int32_t* counters{};
int32_t* max_barriers{};
int32_t* sum_barriers{};
int32_t* locks{};
// Scratch buffers to finalize softmax.
float* max_scratch_ptr{};
float* sum_scratch_ptr{};
// Scratch buffer to finalize the output (not needed for FP16).
int32_t* o_scratch_ptr{};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
#if defined(ENABLE_SM75)
extern unsigned char fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o[];
extern unsigned char fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o[];
extern unsigned char fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o[];
extern unsigned char fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o[];
extern unsigned char fused_multihead_attention_int8_128_64_kernel_sm75_cu_o[];
extern unsigned char fused_multihead_attention_int8_384_64_kernel_sm75_cu_o[];
#endif // defined(ENABLE_SM75)
#if defined(ENABLE_SM80) || defined(ENABLE_SM86) || defined(ENABLE_SM89)
extern unsigned char fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o[];
extern unsigned char fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o[];
extern unsigned char fused_multihead_attention_int8_384_64_kernel_sm80_cu_o[];
extern unsigned char fused_multihead_attention_int8_128_64_kernel_sm80_cu_o[];
extern unsigned char fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o[];
extern unsigned char fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o[];
extern unsigned char cubin_fmha_v1_int8_64_64_sm80_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_96_64_sm80_cu_cubin[];
#endif // defined(ENABLE_SM80) || defined(SM86) || defined(ENABLE_SM89)
#if defined(ENABLE_SM86) || defined(ENABLE_SM89)
extern unsigned char fused_multihead_attention_fp16_384_64_kernel_sm86_cu_o[];
#endif // defined(ENABLE_SM86) || defined(ENABLE_SM89)
#if defined(ENABLE_SM87)
extern unsigned char cubin_fmha_v1_int8_384_64_sm87_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_128_64_sm87_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_384_64_sm87_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_128_64_sm87_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_96_64_sm87_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_64_64_sm87_cu_cubin[];
#endif // defined(ENABLE_SM87)
#if defined(ENABLE_SM90)
extern unsigned char cubin_fmha_v1_int8_512_64_sm90_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_384_64_sm90_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_128_64_sm90_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_512_64_sm90_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_384_64_sm90_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_128_64_sm90_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_96_64_sm90_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_64_64_sm90_cu_cubin[];
#endif // defined(ENABLE_SM90)
#if defined(ENABLE_SM100)
extern unsigned char cubin_fmha_v1_int8_512_64_sm100_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_384_64_sm100_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_128_64_sm100_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_512_64_sm100_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_384_64_sm100_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_128_64_sm100_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_96_64_sm100_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_64_64_sm100_cu_cubin[];
#endif // defined(ENABLE_SM100)
#if defined(ENABLE_SM120)
extern unsigned char cubin_fmha_v1_int8_512_64_sm120_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_384_64_sm120_cu_cubin[];
extern unsigned char cubin_fmha_v1_int8_128_64_sm120_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_512_64_sm120_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_384_64_sm120_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_128_64_sm120_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_96_64_sm120_cu_cubin[];
extern unsigned char cubin_fmha_v1_fp16_64_64_sm120_cu_cubin[];
#endif // defined(ENABLE_SM120)
#if defined(ENABLE_SM75)
extern uint32_t fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o_len;
extern uint32_t fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o_len;
extern uint32_t fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o_len;
extern uint32_t fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o_len;
extern uint32_t fused_multihead_attention_int8_128_64_kernel_sm75_cu_o_len;
extern uint32_t fused_multihead_attention_int8_384_64_kernel_sm75_cu_o_len;
#endif // defined(ENABLE_SM75)
#if defined(ENABLE_SM80) || defined(ENABLE_SM86) || defined(ENABLE_SM89)
extern uint32_t fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len;
extern uint32_t fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o_len;
extern uint32_t fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len;
extern uint32_t fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len;
extern uint32_t fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len;
extern uint32_t fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o_len;
extern uint32_t cubin_fmha_v1_int8_64_64_sm80_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_96_64_sm80_cu_cubin_len;
#endif // defined(ENABLE_SM80) || defined(SM86) || defined(ENABLE_SM89)
#if defined(ENABLE_SM86) || defined(ENABLE_SM89)
extern uint32_t fused_multihead_attention_fp16_384_64_kernel_sm86_cu_o_len;
#endif // defined(ENABLE_SM86) || defined(ENABLE_SM89)
#if defined(ENABLE_SM87)
extern uint32_t cubin_fmha_v1_int8_384_64_sm87_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_128_64_sm87_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_384_64_sm87_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_128_64_sm87_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_96_64_sm87_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_64_64_sm87_cu_cubin_len;
#endif // defined(ENABLE_SM87)
#if defined(ENABLE_SM90)
extern uint32_t cubin_fmha_v1_int8_512_64_sm90_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_384_64_sm90_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_128_64_sm90_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_512_64_sm90_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_384_64_sm90_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_128_64_sm90_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_96_64_sm90_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_64_64_sm90_cu_cubin_len;
#endif // defined(ENABLE_SM90)
#if defined(ENABLE_SM100)
extern uint32_t cubin_fmha_v1_int8_512_64_sm100_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_384_64_sm100_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_128_64_sm100_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_512_64_sm100_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_384_64_sm100_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_128_64_sm100_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_96_64_sm100_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_64_64_sm100_cu_cubin_len;
#endif // defined(ENABLE_SM100)
#if defined(ENABLE_SM120)
extern uint32_t cubin_fmha_v1_int8_512_64_sm120_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_384_64_sm120_cu_cubin_len;
extern uint32_t cubin_fmha_v1_int8_128_64_sm120_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_512_64_sm120_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_384_64_sm120_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_128_64_sm120_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_96_64_sm120_cu_cubin_len;
extern uint32_t cubin_fmha_v1_fp16_64_64_sm120_cu_cubin_len;
#endif // defined(ENABLE_SM120)
#if !(defined(ENABLE_SM72) || defined(ENABLE_SM75) || defined(ENABLE_SM80) || defined(ENABLE_SM86) \
|| defined(ENABLE_SM87) || defined(ENABLE_SM89) || defined(ENABLE_SM90) || defined(ENABLE_SM100) \
|| defined(ENABLE_SM120))
#error This file can only be included if one of sm 72, 75, 80, 86, 87, 89, 90, 100 or 120 is defined.
#endif
static const struct FusedMultiHeadAttentionKernelMetaInfoV1
{
Data_type mDataType;
uint32_t mS;
uint32_t mD;
uint32_t mSM;
const unsigned char* mCubin;
uint32_t mCubinSize;
const char* mFuncName;
uint32_t mSharedMemBytes;
uint32_t mThreadsPerCTA;
} sMhaKernelMetaInfos[] = {
#if defined(ENABLE_SM75)
// Turing
{DATA_TYPE_FP16, 64, 64, kSM_75, fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o,
fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o_len,
"fused_multihead_attention_v2_fp16_64_64_kernel_sm75", 24576, 128},
{DATA_TYPE_FP16, 96, 64, kSM_75, fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o,
fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o_len,
"fused_multihead_attention_v2_fp16_96_64_kernel_sm75", 24576, 128},
{DATA_TYPE_FP16, 128, 64, kSM_75, fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o,
fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm75",
32768, 128},
{DATA_TYPE_FP16, 384, 64, kSM_75, fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o,
fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o_len, "fused_multihead_attention_fp16_384_64_kernel_sm75",
57344, 256},
{DATA_TYPE_INT8, 128, 64, kSM_75, fused_multihead_attention_int8_128_64_kernel_sm75_cu_o,
fused_multihead_attention_int8_128_64_kernel_sm75_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm75",
16384, 128},
{DATA_TYPE_INT8, 384, 64, kSM_75, fused_multihead_attention_int8_384_64_kernel_sm75_cu_o,
fused_multihead_attention_int8_384_64_kernel_sm75_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm75",
53284, 256},
#endif // defined(ENABLE_SM75)
#if defined(ENABLE_SM80) || defined(ENABLE_SM86) || defined(ENABLE_SM89)
// Ampere
{DATA_TYPE_FP16, 64, 64, kSM_80, fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o,
fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len,
"fused_multihead_attention_v2_fp16_64_64_kernel_sm80", 32768, 128},
{DATA_TYPE_FP16, 96, 64, kSM_80, fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o,
fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o_len,
"fused_multihead_attention_v2_fp16_96_64_kernel_sm80", 49152, 128},
{DATA_TYPE_FP16, 128, 64, kSM_80, fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o,
fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm80",
49152, 128},
{DATA_TYPE_FP16, 384, 64, kSM_80, fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o,
fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_384_64_kernel_sm80",
114688, 256},
{DATA_TYPE_INT8, 64, 64, kSM_80, cubin_fmha_v1_int8_64_64_sm80_cu_cubin, cubin_fmha_v1_int8_64_64_sm80_cu_cubin_len,
"fmha_v1_int8_64_64_sm80_kernel", 24576, 128},
{DATA_TYPE_INT8, 96, 64, kSM_80, cubin_fmha_v1_int8_96_64_sm80_cu_cubin, cubin_fmha_v1_int8_96_64_sm80_cu_cubin_len,
"fmha_v1_int8_96_64_sm80_kernel", 28672, 128},
{DATA_TYPE_INT8, 128, 64, kSM_80, fused_multihead_attention_int8_128_64_kernel_sm80_cu_o,
fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm80",
24576, 128},
{DATA_TYPE_INT8, 384, 64, kSM_80, fused_multihead_attention_int8_384_64_kernel_sm80_cu_o,
fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm80",
57344, 256},
#endif // defined(ENABLE_SM80) || defined(SM86) || defined(ENABLE_SM89)
#if defined(ENABLE_SM86) || defined(ENABLE_SM89)
// GA10x
// Note: For GA10X keep only kernels whose sharedMemBytes < 100KiB
{DATA_TYPE_FP16, 64, 64, kSM_86, fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o,
fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len,
"fused_multihead_attention_v2_fp16_64_64_kernel_sm80", 32768, 128},
{DATA_TYPE_FP16, 96, 64, kSM_86, fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o,
fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o_len,
"fused_multihead_attention_v2_fp16_96_64_kernel_sm80", 49152, 128},
{DATA_TYPE_FP16, 128, 64, kSM_86, fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o,
fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm80",
49152, 128},
{DATA_TYPE_FP16, 384, 64, kSM_86, fused_multihead_attention_fp16_384_64_kernel_sm86_cu_o,
fused_multihead_attention_fp16_384_64_kernel_sm86_cu_o_len, "fused_multihead_attention_fp16_384_64_kernel_sm80",
65536, 256},
{DATA_TYPE_INT8, 128, 64, kSM_86, fused_multihead_attention_int8_128_64_kernel_sm80_cu_o,
fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm80",
24576, 128},
{DATA_TYPE_INT8, 384, 64, kSM_86, fused_multihead_attention_int8_384_64_kernel_sm80_cu_o,
fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm80",
57344, 256},
#endif // defined(ENABLE_SM86) || defined(ENABLE_SM89)
#if defined(ENABLE_SM87)
// GA10b (Orin-Auto)
{DATA_TYPE_INT8, 384, 64, kSM_87, cubin_fmha_v1_int8_384_64_sm87_cu_cubin,
cubin_fmha_v1_int8_384_64_sm87_cu_cubin_len, "fmha_v1_int8_384_64_sm87_kernel", 40960, 256},
{DATA_TYPE_INT8, 128, 64, kSM_87, cubin_fmha_v1_int8_128_64_sm87_cu_cubin,
cubin_fmha_v1_int8_128_64_sm87_cu_cubin_len, "fmha_v1_int8_128_64_sm87_kernel", 24576, 128},
{DATA_TYPE_FP16, 384, 64, kSM_87, cubin_fmha_v1_fp16_384_64_sm87_cu_cubin,
cubin_fmha_v1_fp16_384_64_sm87_cu_cubin_len, "fmha_v1_fp16_384_64_sm87_kernel", 65536, 256},
{DATA_TYPE_FP16, 128, 64, kSM_87, cubin_fmha_v1_fp16_128_64_sm87_cu_cubin,
cubin_fmha_v1_fp16_128_64_sm87_cu_cubin_len, "fmha_v1_fp16_128_64_sm87_kernel", 49152, 128},
{DATA_TYPE_FP16, 96, 64, kSM_87, cubin_fmha_v1_fp16_96_64_sm87_cu_cubin, cubin_fmha_v1_fp16_96_64_sm87_cu_cubin_len,
"fmha_v1_fp16_96_64_sm87_kernel", 49152, 128},
{DATA_TYPE_FP16, 64, 64, kSM_87, cubin_fmha_v1_fp16_64_64_sm87_cu_cubin, cubin_fmha_v1_fp16_64_64_sm87_cu_cubin_len,
"fmha_v1_fp16_64_64_sm87_kernel", 32768, 128},
#endif // defined(ENABLE_SM87)
#if defined(ENABLE_SM90)
// GH100 hopper
{DATA_TYPE_INT8, 512, 64, kSM_90, cubin_fmha_v1_int8_512_64_sm90_cu_cubin,
cubin_fmha_v1_int8_512_64_sm90_cu_cubin_len, "fmha_v1_int8_512_64_sm90_kernel", 73728, 256},
{DATA_TYPE_INT8, 384, 64, kSM_90, cubin_fmha_v1_int8_384_64_sm90_cu_cubin,
cubin_fmha_v1_int8_384_64_sm90_cu_cubin_len, "fmha_v1_int8_384_64_sm90_kernel", 73728, 256},
{DATA_TYPE_INT8, 128, 64, kSM_90, cubin_fmha_v1_int8_128_64_sm90_cu_cubin,
cubin_fmha_v1_int8_128_64_sm90_cu_cubin_len, "fmha_v1_int8_128_64_sm90_kernel", 24576, 128},
{DATA_TYPE_FP16, 512, 64, kSM_90, cubin_fmha_v1_fp16_512_64_sm90_cu_cubin,
cubin_fmha_v1_fp16_512_64_sm90_cu_cubin_len, "fmha_v1_fp16_512_64_sm90_kernel", 73728, 256},
{DATA_TYPE_FP16, 384, 64, kSM_90, cubin_fmha_v1_fp16_384_64_sm90_cu_cubin,
cubin_fmha_v1_fp16_384_64_sm90_cu_cubin_len, "fmha_v1_fp16_384_64_sm90_kernel", 65536, 256},
{DATA_TYPE_FP16, 128, 64, kSM_90, cubin_fmha_v1_fp16_128_64_sm90_cu_cubin,
cubin_fmha_v1_fp16_128_64_sm90_cu_cubin_len, "fmha_v1_fp16_128_64_sm90_kernel", 65536, 128},
{DATA_TYPE_FP16, 96, 64, kSM_90, cubin_fmha_v1_fp16_96_64_sm90_cu_cubin, cubin_fmha_v1_fp16_96_64_sm90_cu_cubin_len,
"fmha_v1_fp16_96_64_sm90_kernel", 49152, 128},
{DATA_TYPE_FP16, 64, 64, kSM_90, cubin_fmha_v1_fp16_64_64_sm90_cu_cubin, cubin_fmha_v1_fp16_64_64_sm90_cu_cubin_len,
"fmha_v1_fp16_64_64_sm90_kernel", 32768, 128},
#endif // defined(ENABLE_SM90)
#if defined(ENABLE_SM100)
{DATA_TYPE_INT8, 512, 64, kSM_100, cubin_fmha_v1_int8_512_64_sm100_cu_cubin,
cubin_fmha_v1_int8_512_64_sm100_cu_cubin_len, "fmha_v1_int8_512_64_sm100_kernel", 73728, 256},
{DATA_TYPE_INT8, 384, 64, kSM_100, cubin_fmha_v1_int8_384_64_sm100_cu_cubin,
cubin_fmha_v1_int8_384_64_sm100_cu_cubin_len, "fmha_v1_int8_384_64_sm100_kernel", 73728, 256},
{DATA_TYPE_INT8, 128, 64, kSM_100, cubin_fmha_v1_int8_128_64_sm100_cu_cubin,
cubin_fmha_v1_int8_128_64_sm100_cu_cubin_len, "fmha_v1_int8_128_64_sm100_kernel", 24576, 128},
{DATA_TYPE_FP16, 512, 64, kSM_100, cubin_fmha_v1_fp16_512_64_sm100_cu_cubin,
cubin_fmha_v1_fp16_512_64_sm100_cu_cubin_len, "fmha_v1_fp16_512_64_sm100_kernel", 73728, 256},
{DATA_TYPE_FP16, 384, 64, kSM_100, cubin_fmha_v1_fp16_384_64_sm100_cu_cubin,
cubin_fmha_v1_fp16_384_64_sm100_cu_cubin_len, "fmha_v1_fp16_384_64_sm100_kernel", 65536, 256},
{DATA_TYPE_FP16, 128, 64, kSM_100, cubin_fmha_v1_fp16_128_64_sm100_cu_cubin,
cubin_fmha_v1_fp16_128_64_sm100_cu_cubin_len, "fmha_v1_fp16_128_64_sm100_kernel", 65536, 128},
{DATA_TYPE_FP16, 96, 64, kSM_100, cubin_fmha_v1_fp16_96_64_sm100_cu_cubin,
cubin_fmha_v1_fp16_96_64_sm100_cu_cubin_len, "fmha_v1_fp16_96_64_sm100_kernel", 49152, 128},
{DATA_TYPE_FP16, 64, 64, kSM_100, cubin_fmha_v1_fp16_64_64_sm100_cu_cubin,
cubin_fmha_v1_fp16_64_64_sm100_cu_cubin_len, "fmha_v1_fp16_64_64_sm100_kernel", 32768, 128},
#endif // defined(ENABLE_SM100)
#if defined(ENABLE_SM120)
{DATA_TYPE_INT8, 512, 64, kSM_120, cubin_fmha_v1_int8_512_64_sm120_cu_cubin,
cubin_fmha_v1_int8_512_64_sm120_cu_cubin_len, "fmha_v1_int8_512_64_sm120_kernel", 73728, 256},
{DATA_TYPE_INT8, 384, 64, kSM_120, cubin_fmha_v1_int8_384_64_sm120_cu_cubin,
cubin_fmha_v1_int8_384_64_sm120_cu_cubin_len, "fmha_v1_int8_384_64_sm120_kernel", 73728, 256},
{DATA_TYPE_INT8, 128, 64, kSM_120, cubin_fmha_v1_int8_128_64_sm120_cu_cubin,
cubin_fmha_v1_int8_128_64_sm120_cu_cubin_len, "fmha_v1_int8_128_64_sm120_kernel", 24576, 128},
{DATA_TYPE_FP16, 512, 64, kSM_120, cubin_fmha_v1_fp16_512_64_sm120_cu_cubin,
cubin_fmha_v1_fp16_512_64_sm120_cu_cubin_len, "fmha_v1_fp16_512_64_sm120_kernel", 73728, 256},
{DATA_TYPE_FP16, 384, 64, kSM_120, cubin_fmha_v1_fp16_384_64_sm120_cu_cubin,
cubin_fmha_v1_fp16_384_64_sm120_cu_cubin_len, "fmha_v1_fp16_384_64_sm120_kernel", 65536, 256},
{DATA_TYPE_FP16, 128, 64, kSM_120, cubin_fmha_v1_fp16_128_64_sm120_cu_cubin,
cubin_fmha_v1_fp16_128_64_sm120_cu_cubin_len, "fmha_v1_fp16_128_64_sm120_kernel", 65536, 128},
{DATA_TYPE_FP16, 96, 64, kSM_120, cubin_fmha_v1_fp16_96_64_sm120_cu_cubin,
cubin_fmha_v1_fp16_96_64_sm120_cu_cubin_len, "fmha_v1_fp16_96_64_sm120_kernel", 49152, 128},
{DATA_TYPE_FP16, 64, 64, kSM_120, cubin_fmha_v1_fp16_64_64_sm120_cu_cubin,
cubin_fmha_v1_fp16_64_64_sm120_cu_cubin_len, "fmha_v1_fp16_64_64_sm120_kernel", 32768, 128},
#endif // defined(ENABLE_SM120)
};
using FusedMultiHeadAttentionXMMAKernel
= pluginInternal::TFusedMultiHeadAttentionXMMAKernel<FusedMultiHeadAttentionKernelMetaInfoV1,
Fused_multihead_attention_params>;
using FusedMHAKernelFactory = pluginInternal::TFusedMHAKernelFactory<FusedMultiHeadAttentionXMMAKernel>;
inline const FusedMultiHeadAttentionXMMAKernel* getXMMAKernels(Data_type type, uint32_t sm)
{
return FusedMHAKernelFactory::Get().getXMMAKernels(
sMhaKernelMetaInfos, sizeof(sMhaKernelMetaInfos) / sizeof(sMhaKernelMetaInfos[0]), type, sm);
}
} // namespace bert
} // namespace plugin
} // namespace nvinfer1
#endif // _BERT_FMHA_FMHA
@@ -0,0 +1,43 @@
/*
* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef TRT_FUSED_MULTIHEAD_ATTENTION_COMMON_H
#define TRT_FUSED_MULTIHEAD_ATTENTION_COMMON_H
#include "common/checkMacrosPlugin.h"
#include "common/plugin.h"
#include <cstdint>
namespace nvinfer1
{
namespace plugin
{
namespace bert
{
enum Data_type
{
DATA_TYPE_BOOL,
DATA_TYPE_E8M10,
DATA_TYPE_E8M7,
DATA_TYPE_FP16,
DATA_TYPE_FP32,
DATA_TYPE_INT4,
DATA_TYPE_INT8,
DATA_TYPE_INT32
};
} // namespace bert
} // namespace plugin
} // namespace nvinfer1
#endif // TRT_FUSED_MULTIHEAD_ATTENTION_COMMON_H
@@ -0,0 +1,924 @@
/*
* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
namespace nvinfer1
{
namespace plugin
{
namespace bert
{
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unsigned int fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o_len = 17000;
} // namespace bert
} // namespace plugin
} // namespace nvinfer1
@@ -0,0 +1,903 @@
/*
* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
namespace nvinfer1::plugin::bert
{
unsigned char fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33,
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unsigned int fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len = 16744;
} // namespace nvinfer1::plugin::bert
@@ -0,0 +1,68 @@
#
# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This folder contains a bunch of source files holding cubins. Since the files are huge, we only want to include and compile them if used.
# Usage is indicated by the SM being set in CMAKE_CUDA_ARCHITECTURES.
function(add_plugin_source_if_exists)
foreach(SRC_FILE IN LISTS ARGN)
if(EXISTS ${CMAKE_CURRENT_LIST_DIR}/${SRC_FILE})
add_plugin_source(${SRC_FILE})
endif()
endforeach()
endfunction()
foreach(SM IN LISTS BERT_QKV_SUPPORTED_SMS)
should_compile_kernel(${SM} SHOULD_COMPILE)
if (${SHOULD_COMPILE})
# Not every file exists for each SM, so we list all of the candidates and add them if present.
add_plugin_source_if_exists(
fused_multihead_attention_v2_fp16_64_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_96_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_128_32_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_128_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_256_32_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_256_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_384_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_512_32_kernel.sm${SM}.cpp
fused_multihead_attention_v2_fp16_512_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_il_int8_64_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_il_int8_96_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_il_int8_128_32_kernel.sm${SM}.cpp
fused_multihead_attention_v2_il_int8_128_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_il_int8_192_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_il_int8_256_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_il_int8_384_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_64_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_96_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_128_32_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_128_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_192_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_256_32_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_256_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_384_64_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_512_32_kernel.sm${SM}.cpp
fused_multihead_attention_v2_int8_512_64_kernel.sm${SM}.cpp
)
endif()
endforeach()
add_plugin_source(
fused_multihead_attention_v2.h
)

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