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qkvToContextPlugin.cpp
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/*
* 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.
*/
// Need 10.1 for cublasGemmStridedBatchedEx
#include <cuda.h>
#if CUDA_VERSION >= 10010
#include "NvInfer.h"
#include "bertQKVToContextPlugin/fused_multihead_attention/include/fused_multihead_attention.h"
#include "bertQKVToContextPlugin/fused_multihead_attention_v2/include/fused_multihead_attention_v2.h"
#include "common/bertCommon.h"
#include "common/serialize.hpp"
#include "qkvToContextPlugin.h"
#include <cstdint>
#include <cstring>
#include <iostream>
#include <tuple>
#include <vector>
using namespace nvinfer1;
using namespace nvinfer1::plugin;
using namespace nvinfer1::plugin::bert;
using namespace nvinfer1::pluginInternal;
namespace
{
char const* const kQKV_TO_CONTEXT_PLUGIN_VERSION{"1"};
char const* const kQKV_TO_CONTEXT_VAR_SEQLEN_PLUGIN_VERSION{"2"};
char const* const kQKV_TO_CONTEXT_PLUGIN_NAME{"CustomQKVToContextPluginDynamic"};
} // namespace
// Static class fields initialization
PluginFieldCollection QKVToContextPluginDynamicCreator::mFC{};
std::vector<PluginField> QKVToContextPluginDynamicCreator::mPluginAttributes;
REGISTER_TENSORRT_PLUGIN(QKVToContextPluginDynamicCreator);
constexpr uint32_t IIDX = 0; // index of the input tensor
constexpr uint32_t MIDX = 1; // index of the mask
// Static class fields initialization
PluginFieldCollection QKVToContextVarSeqlenPluginCreator::mFC{};
std::vector<PluginField> QKVToContextVarSeqlenPluginCreator::mPluginAttributes;
REGISTER_TENSORRT_PLUGIN(QKVToContextVarSeqlenPluginCreator);
QKVToContextPluginDynamic::QKVToContextPluginDynamic(const std::string name, const DataType type,
const int32_t hiddenSize, const int32_t numHeads, float const dqProbs, bool hasImask)
: mLayerName(name)
, mS(0)
, mB(0)
, mHeadSize(hiddenSize / numHeads)
, mHiddenSize(hiddenSize)
, mNumHeads(numHeads)
, mHasImask(hasImask)
, mType(type)
, mDqProbs(dqProbs)
{
mSM = getSMVersion();
}
QKVToContextPluginDynamic::QKVToContextPluginDynamic(const std::string name, void const* data, size_t length)
: mLayerName(name)
{
BERT_DEBUG_MSG("QKV Deser Start");
deserialize_value(&data, &length, &mType);
deserialize_value(&data, &length, &mNumHeads);
deserialize_value(&data, &length, &mHeadSize);
deserialize_value(&data, &length, &mHasImask);
deserialize_value(&data, &length, &mHiddenSize);
deserialize_value(&data, &length, &mSM);
deserialize_value(&data, &length, &mS);
deserialize_value(&data, &length, &mB);
deserialize_value(&data, &length, &mDqProbs);
createMHARunner();
int32_t hasUnfusedRunner = 0;
deserialize_value(&data, &length, &hasUnfusedRunner);
if (hasUnfusedRunner)
{
PLUGIN_ASSERT(unfusedDispatcher.get());
unfusedDispatcher->deserialize(data, length);
}
BERT_DEBUG_MSG("QKV Deser done");
}
void QKVToContextPluginDynamic::createMHARunner()
{
if (!fusedDispatcher.get())
{
if (mType == DataType::kHALF)
{
fusedDispatcher.reset(new FusedMHARunnerFP16(mNumHeads, mSM));
}
else if (mType == DataType::kINT8)
{
fusedDispatcher.reset(new FusedMHARunnerInt8(mNumHeads, mSM, mDqProbs));
}
}
if (!unfusedDispatcher.get())
{
unfusedDispatcher.reset(new UnfusedMHARunner(mType, mNumHeads, mSM));
}
}
// IPluginV2DynamicExt Methods
nvinfer1::IPluginV2DynamicExt* QKVToContextPluginDynamic::clone() const noexcept
{
BERT_DEBUG_MSG("QKV Clone");
QKVToContextPluginDynamic* ret = nullptr;
// the workspacesize is 0 if we have not call setup the dispatcher yet.
if (unfusedDispatcher.get() && unfusedDispatcher->getWorkspaceSize())
{
std::vector<char> buff;
buff.resize(getSerializationSize());
serialize(buff.data());
ret = new QKVToContextPluginDynamic(mLayerName, buff.data(), buff.size());
}
else
{
ret = new QKVToContextPluginDynamic(mLayerName, mType, mHiddenSize, mNumHeads, mDqProbs, mHasImask);
}
ret->setPluginNamespace(mNamespace.c_str());
BERT_DEBUG_MSG("QKV Clone done");
return ret;
}
DimsExprs QKVToContextPluginDynamic::getOutputDimensions(
int32_t outputIndex, DimsExprs const* inputs, int32_t /*nbInputs*/, IExprBuilder& exprBuilder) noexcept
{
// Input is BxSx3*N*H, output should be BxSxN*H
PLUGIN_ASSERT(outputIndex == 0);
// Copy over everything
DimsExprs output(inputs[IIDX]);
// Divide last dim by three
auto const* three = exprBuilder.constant(3);
output.d[HDIM] = exprBuilder.operation(DimensionOperation::kFLOOR_DIV, *inputs[IIDX].d[HDIM], *three);
return output;
}
bool QKVToContextPluginDynamic::supportsFormatCombination(
int32_t pos, PluginTensorDesc const* inOut, int32_t nbInputs, int32_t /*nbOutputs*/) noexcept
{
PLUGIN_ASSERT(pos >= 0);
PLUGIN_ASSERT(pos < 2 + mHasImask);
PLUGIN_ASSERT(nbInputs == 1 + mHasImask);
auto const* in = inOut;
auto const* out = inOut + nbInputs;
int32_t packedSize = getMHAMaskPackedSize(mSM, mType, in->dims.d[SDIM]);
// we only support int8 IO in fused mha runner, and we only support fused mha runner on Xavier, Turing and Ampere
if (mType == DataType::kINT8)
{
if (mSM != kSM_75 && mSM != kSM_80 && mSM != kSM_86 && mSM != kSM_87 && mSM != kSM_89 && mSM != kSM_90)
{
gLogError << "INT8 IO is only supported on Turing, Ampere and Hopper for plugin "
<< kQKV_TO_CONTEXT_PLUGIN_NAME << std::endl;
return false;
}
if (in->dims.d[SDIM] == -1)
{
gLogError << "INT8 IO not support dynamic shape in sequence dimension for plugin "
<< kQKV_TO_CONTEXT_PLUGIN_NAME << std::endl;
return false;
}
if (packedSize == unfusedMaskSize)
{
gLogError << "INT8 IO only support sequence length 128,384 for plugin " << kQKV_TO_CONTEXT_PLUGIN_NAME
<< std::endl;
return false;
}
}
if (mType == DataType::kHALF)
{
if (mSM < kSM_53)
{
gLogError
<< "Half-precision floating-point is only supported on compute capability 5.3 and later for plugin "
<< kQKV_TO_CONTEXT_PLUGIN_NAME << std::endl;
return false;
}
}
if (pos == 0)
{
bool isFormatSupported = in->format == TensorFormat::kLINEAR;
if (mType == DataType::kINT8)
{
if (in->dims.d[HDIM] % 32U == 0)
{
isFormatSupported = in->format == TensorFormat::kCHW32;
}
else
{
isFormatSupported = in->format == TensorFormat::kCHW4;
}
}
// must not check descriptions > pos
return (in->type == mType) && // precision
isFormatSupported && // format
(in->dims.nbDims == 5) && // num dims
((in->dims.d[HDIM] % 3U) == 0) && // see getOutputDimensions
((in->dims.d[3]) == 1) && // for fc
((in->dims.d[4]) == 1) // for fc
;
}
// pos==1
if ((mHasImask && pos == 1)) // pos 1 is the mask
{
auto const* inMask = &inOut[1];
if (inMask->dims.d[1] != -1 && inMask->dims.d[1] != packedSize)
{
gLogError << "CustomEmbLayerNormPluginDynamic returned mask with pack size " << inMask->dims.d[1]
<< ", but " << kQKV_TO_CONTEXT_PLUGIN_NAME << " expects mask pack size " << packedSize
<< std::endl;
return false;
}
// detect full mask and check that it was produced
return (inMask->type == DataType::kINT32) && // precision
(inMask->format == TensorFormat::kLINEAR) && // format
(inMask->dims.nbDims == 2) && // Bx2*maskSize
(inMask->dims.d[0] == in->dims.d[BDIM]);
}
if (!mHasImask || pos == 2) // output pos
{
bool isFormatSupported = out->format == TensorFormat::kLINEAR;
if (mType == DataType::kINT8)
{
if (out->dims.d[HDIM] % 32U == 0)
{
isFormatSupported = out->format == TensorFormat::kCHW32;
}
else
{
isFormatSupported = out->format == TensorFormat::kCHW4;
}
}
return (in->type == out->type) && // precision
isFormatSupported && // format
(out->dims.nbDims == 5) && // num dims
((in->dims.d[HDIM] / 3) == (out->dims.d[HDIM])) && // div 3
((out->dims.d[3]) == 1) && // for fc
((out->dims.d[4]) == 1) && // for fc
((out->dims.d[BDIM]) == in->dims.d[BDIM]) && // check B
((out->dims.d[SDIM]) == in->dims.d[SDIM]) // check S
;
}
return false;
}
void QKVToContextPluginDynamic::configurePlugin(
DynamicPluginTensorDesc const* in, int32_t nbInputs, DynamicPluginTensorDesc const* out, int32_t nbOutputs) noexcept
{
PLUGIN_ASSERT(nbInputs == 1 + mHasImask);
PLUGIN_ASSERT(nbOutputs == 1);
PluginTensorDesc const& inDesc = in[IIDX].desc;
TRT_UNUSED inDesc;
PluginTensorDesc const& outDesc = out->desc;
TRT_UNUSED outDesc;
PLUGIN_ASSERT(mType == inDesc.type);
PLUGIN_ASSERT(mType == outDesc.type);
PLUGIN_ASSERT(inDesc.dims.d[BDIM] == outDesc.dims.d[BDIM]);
PLUGIN_ASSERT(inDesc.dims.d[SDIM] == outDesc.dims.d[SDIM]);
PLUGIN_ASSERT(inDesc.dims.d[HDIM] == 3 * outDesc.dims.d[HDIM]);
if (mHasImask)
{
PluginTensorDesc const& maskDesc = in[MIDX].desc;
TRT_UNUSED maskDesc;
PLUGIN_ASSERT(maskDesc.dims.d[0] == inDesc.dims.d[BDIM]);
}
createMHARunner();
const int32_t S = inDesc.dims.d[SDIM];
const int32_t B = inDesc.dims.d[BDIM] <= 0 ? in->max.d[BDIM] : inDesc.dims.d[BDIM];
if (S <= 0)
{
// in dynamic shape build stage, we setup with max sequence that cannot fused
const int32_t Smin = in->min.d[SDIM];
const int32_t Smax = in->max.d[SDIM];
if (fusedDispatcher.get())
{
for (int32_t i = Smax; i >= Smin; --i)
{
if (!fusedDispatcher->isValid(mHeadSize, i))
{
unfusedDispatcher->setup(i, B, mHeadSize);
mS = i;
mB = B;
break;
}
}
}
else
{
unfusedDispatcher->setup(Smax, B, mHeadSize);
mS = Smax;
mB = B;
}
}
else
{
// in inference stage or in static shape build stage
if (fusedDispatcher.get() && fusedDispatcher->isValid(mHeadSize, S))
{
fusedDispatcher->setup(S, B, mHeadSize);
}
else
{
unfusedDispatcher->setup(S, B, mHeadSize);
}
mS = S;
mB = B;
}
}
size_t QKVToContextPluginDynamic::getWorkspaceSize(PluginTensorDesc const* /*inputs*/, int32_t /*nbInputs*/,
PluginTensorDesc const* /*outputs*/, int32_t /*nbOutputs*/) const noexcept
{
// only unfused kernel need workspace, and we need larger workspace for larger sequence length
// we have already setup unfusedDispatcher with max sequence in configurePlugin
// if unfusedDispatcher is not initialized in configurePlugin
PLUGIN_ASSERT(unfusedDispatcher.get());
return unfusedDispatcher->getWorkspaceSize();
}
// IPluginV2Ext Methods
DataType QKVToContextPluginDynamic::getOutputDataType(
int32_t index, nvinfer1::DataType const* inputTypes, int32_t /*nbInputs*/) const noexcept
{
PLUGIN_ASSERT(index == 0);
PLUGIN_ASSERT(
inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF || inputTypes[0] == DataType::kINT8);
return inputTypes[0];
}
void QKVToContextPluginDynamic::attachToContext(
cudnnContext* cudnn, cublasContext* cublas, nvinfer1::IGpuAllocator* allocator) noexcept
{
try
{
mCublasWrapper = createPluginCublasWrapper(allocator);
mCublas = mCublasWrapper->getCublasHandle();
PLUGIN_VALIDATE(mCublas != nullptr);
}
catch (const std::exception& e)
{
caughtError(e);
}
}
// IPluginV2 Methods
char const* QKVToContextPluginDynamic::getPluginType() const noexcept
{
return kQKV_TO_CONTEXT_PLUGIN_NAME;
}
char const* QKVToContextPluginDynamic::getPluginVersion() const noexcept
{
return kQKV_TO_CONTEXT_PLUGIN_VERSION;
}
int32_t QKVToContextPluginDynamic::getNbOutputs() const noexcept
{
return 1;
}
int32_t QKVToContextPluginDynamic::initialize() noexcept
{
return 0;
}
void QKVToContextPluginDynamic::terminate() noexcept {}
size_t QKVToContextPluginDynamic::getSerializationSize() const noexcept
{
PLUGIN_ASSERT(unfusedDispatcher.get());
return sizeof(mNumHeads) + sizeof(mHeadSize) + sizeof(DataType) + sizeof(mHasImask) + sizeof(mHiddenSize)
+ sizeof(mSM) + sizeof(mS) + sizeof(mB) + sizeof(mDqProbs) + sizeof(int32_t)
+ unfusedDispatcher->getSerializationSize();
}
void QKVToContextPluginDynamic::serialize(void* buffer) const noexcept
{
serialize_value(&buffer, mType);
serialize_value(&buffer, mNumHeads);
serialize_value(&buffer, mHeadSize);
serialize_value(&buffer, mHasImask);
serialize_value(&buffer, mHiddenSize);
serialize_value(&buffer, mSM);
serialize_value(&buffer, mS);
serialize_value(&buffer, mB);
serialize_value(&buffer, mDqProbs);
if (unfusedDispatcher.get() && unfusedDispatcher->getWorkspaceSize())
{
int32_t hasUnfusedRunner = 1;
serialize_value(&buffer, hasUnfusedRunner);
unfusedDispatcher->serialize(buffer);
}
else
{
int32_t hasUnfusedRunner = 0;
serialize_value(&buffer, hasUnfusedRunner);
}
}
void QKVToContextPluginDynamic::destroy() noexcept
{
delete this;
}
void QKVToContextPluginDynamic::setPluginNamespace(char const* libNamespace) noexcept
{
mNamespace = libNamespace;
}
char const* QKVToContextPluginDynamic::getPluginNamespace() const noexcept
{
return mNamespace.c_str();
}
int32_t QKVToContextPluginDynamic::enqueue(PluginTensorDesc const* inputDesc, PluginTensorDesc const* outputDesc,
void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept
{
PLUGIN_VALIDATE(inputDesc != nullptr && outputDesc != nullptr && inputs != nullptr && outputs != nullptr);
PLUGIN_ASSERT(mS == inputDesc->dims.d[SDIM]);
PLUGIN_ASSERT(mB == inputDesc->dims.d[BDIM]);
try
{
void const* const maskPtr = mHasImask ? inputs[1] : nullptr;
if (mHasImask && fusedDispatcher.get() && fusedDispatcher->isValid(mHeadSize, inputDesc->dims.d[SDIM]))
{
fusedDispatcher->run(
inputDesc[0], outputDesc[0], inputs[0], maskPtr, outputs[0], workspace, stream, mCublas);
}
else
{
PLUGIN_VALIDATE(unfusedDispatcher.get(), "The Unfused MHARunner is uninitialized, no MHARunner available!");
PLUGIN_VALIDATE(mType != DataType::kINT8, "The Unfused MHARunner does not support INT8!");
unfusedDispatcher->run(
inputDesc[0], outputDesc[0], inputs[0], maskPtr, outputs[0], workspace, stream, mCublas);
}
}
catch (std::exception const& e)
{
caughtError(e);
return -1;
}
return 0;
}
QKVToContextPluginDynamicCreator::QKVToContextPluginDynamicCreator()
{
mPluginAttributes.emplace_back(PluginField("type_id", nullptr, PluginFieldType::kINT32, 1));
mPluginAttributes.emplace_back(PluginField("hidden_size", nullptr, PluginFieldType::kINT32, 1));
mPluginAttributes.emplace_back(PluginField("num_heads", nullptr, PluginFieldType::kINT32, 1));
mPluginAttributes.emplace_back(PluginField("has_mask", nullptr, PluginFieldType::kINT32, 1));
mPluginAttributes.emplace_back(PluginField("dq_probs", nullptr, PluginFieldType::kFLOAT32, 1));
mFC.nbFields = mPluginAttributes.size();
mFC.fields = mPluginAttributes.data();
}
char const* QKVToContextPluginDynamicCreator::getPluginName() const noexcept
{
return kQKV_TO_CONTEXT_PLUGIN_NAME;
}
char const* QKVToContextPluginDynamicCreator::getPluginVersion() const noexcept
{
return kQKV_TO_CONTEXT_PLUGIN_VERSION;
}
PluginFieldCollection const* QKVToContextPluginDynamicCreator::getFieldNames() noexcept
{
return &mFC;
}
IPluginV2* QKVToContextPluginDynamicCreator::createPlugin(char const* name, PluginFieldCollection const* fc) noexcept
{
try
{
BERT_DEBUG_MSG("Creating QKV2ContextPlugin...");
PLUGIN_VALIDATE(fc != nullptr);
int32_t hiddenSize = 0;
// Since numHeads must always exist or validateRequiredAttributes will fail,
// we can set numHeads to -1 so that static analysis tools don't warn about
// a division by zero in QKVToContextPluginDynamic constructor.
int32_t numHeads{-1};
bool hasMask = false;
int32_t typeId = -1;
float dqProbs = -1;
PLUGIN_VALIDATE(fc->fields != nullptr);
plugin::validateRequiredAttributesExist({"type_id", "hidden_size", "num_heads", "has_mask"}, fc);
for (int32_t i = 0; i < fc->nbFields; i++)
{
PLUGIN_VALIDATE(fc->fields[i].name != nullptr);
PLUGIN_VALIDATE(fc->fields[i].data != nullptr);
std::string field_name(fc->fields[i].name);
if (field_name.compare("type_id") == 0)
{
typeId = *static_cast<int32_t const*>(fc->fields[i].data);
PLUGIN_VALIDATE(typeId >= 0 && typeId <= 2, ("QKV: Invalid TypeId " + std::to_string(typeId)).c_str());
BERT_DEBUG_VALUE("Building typeId: ", typeId);
}
if (field_name.compare("hidden_size") == 0)
{
hiddenSize = *static_cast<int32_t const*>(fc->fields[i].data);
PLUGIN_VALIDATE(hiddenSize > 0, ("QKV: Invalid hiddenSize " + std::to_string(hiddenSize)).c_str());
BERT_DEBUG_VALUE("Building hiddenSize: ", hiddenSize);
}
if (field_name.compare("num_heads") == 0)
{
numHeads = *static_cast<int32_t const*>(fc->fields[i].data);
PLUGIN_VALIDATE(numHeads > 0, ("QKV: Invalid numHeads " + std::to_string(numHeads)).c_str());
BERT_DEBUG_VALUE("Building numHeads: ", numHeads);
}
if (field_name.compare("has_mask") == 0)
{
auto hasMaskValue = *static_cast<int32_t const*>(fc->fields[i].data);
PLUGIN_VALIDATE(hasMaskValue == 0 || hasMaskValue == 1,
("QKV: Invalid hasMask " + std::to_string(hasMaskValue)).c_str());
hasMask = static_cast<bool>(hasMaskValue);
BERT_DEBUG_VALUE("Building hasMask: ", hasMask);
}
if (field_name.compare("dq_probs") == 0)
{
dqProbs = *static_cast<float const*>(fc->fields[i].data);
PLUGIN_VALIDATE(dqProbs > 0.0F, ("QKV: Invalid dqProbs " + std::to_string(dqProbs)).c_str());
BERT_DEBUG_VALUE("Building dqProbs: ", dqProbs);
}
}
BERT_DEBUG_MSG("Building the Plugin...");
auto type = static_cast<DataType>(typeId);
if (type == DataType::kINT8 && dqProbs < 0)
{
BERT_DEBUG_MSG("Using default scale factor");
dqProbs = 1.F / 127.F;
}
auto* p = new QKVToContextPluginDynamic(name, type, hiddenSize, numHeads, dqProbs, hasMask);
return p;
}
catch (std::exception const& e)
{
caughtError(e);
}
return nullptr;
}
IPluginV2* QKVToContextPluginDynamicCreator::deserializePlugin(
char const* name, void const* serialData, size_t serialLength) noexcept
{
// This object will be deleted when the network is destroyed, which will
// call QKVToContextPluginDynamic::destroy()
return new QKVToContextPluginDynamic(name, serialData, serialLength);
}
void QKVToContextPluginDynamicCreator::setPluginNamespace(char const* libNamespace) noexcept
{
mNamespace = libNamespace;
}
char const* QKVToContextPluginDynamicCreator::getPluginNamespace() const noexcept
{
return mNamespace.c_str();
}
QKVToContextVarSeqlenPlugin::QKVToContextVarSeqlenPlugin(std::string const name, DataType const type,
int32_t const hiddenSize, int32_t const numHeads, float const dqProbs, bool hasImask, bool varSeqlen,
bool useInt8ScaleMax)
: mLayerName(name)
, mS(0)
, mB(0)
, mHeadSize(hiddenSize / numHeads)
, mHiddenSize(hiddenSize)
, mNumHeads(numHeads)
, mHasImask(hasImask)
, mType(type)
, mDqProbs(dqProbs)
, mHdim(HDIM)
, mUseVarSeqlen(varSeqlen)
, mUseInt8ScaleMax(useInt8ScaleMax)
{
mSM = getSMVersion();
if (varSeqlen)
{
// variable sequence length is only supported with the fused MHA kernels
// we should not override mS!
PLUGIN_ASSERT((mSM == kSM_90 || mSM == kSM_87 || mSM == kSM_86 || mSM == kSM_89 || mSM == kSM_80
|| mSM == kSM_75 || mSM == kSM_72)
&& (type == DataType::kINT8 || type == DataType::kHALF)
&& "requesting maxSeqlen not compatible with GPU arch");
// the layout changes: SxB will be a combined \sum_i s_i and hdim will be the 2nd dimension instead of the third
mHdim = 1;
}
}
QKVToContextVarSeqlenPlugin::QKVToContextVarSeqlenPlugin(const std::string name, void const* data, size_t length)
: mLayerName(name)
{
BERT_DEBUG_MSG("QKV Deser Start");
deserialize_value(&data, &length, &mType);
deserialize_value(&data, &length, &mNumHeads);
deserialize_value(&data, &length, &mHeadSize);
deserialize_value(&data, &length, &mHasImask);
deserialize_value(&data, &length, &mHiddenSize);
deserialize_value(&data, &length, &mSM);
deserialize_value(&data, &length, &mS);
deserialize_value(&data, &length, &mB);
deserialize_value(&data, &length, &mDqProbs);
deserialize_value(&data, &length, &mUseVarSeqlen);
deserialize_value(&data, &length, &mHdim);
deserialize_value(&data, &length, &mUseInt8ScaleMax);
createMHARunner();
mDispatcher->deserialize(data, length);
BERT_DEBUG_MSG("QKV Deser done");
}
void QKVToContextVarSeqlenPlugin::createMHARunner()
{
if (mDispatcher.get())
{
return;
}
if (mUseVarSeqlen)
{
PLUGIN_ASSERT(mHeadSize <= 64);
{
if (mHeadSize != 64)
{
mPatcher.reset(new QkvPaddingRunner(mType));
}
if (mType == DataType::kHALF)
{
mDispatcher.reset(new FusedMHARunnerFP16v2(mNumHeads, mSM));
}
else if (mType == DataType::kINT8)
{
mDispatcher.reset(new FusedMHARunnerInt8v2(mNumHeads, mSM, mDqProbs, mUseInt8ScaleMax));
}
}
}
else
{
PLUGIN_ASSERT(mType != DataType::kINT8);
mDispatcher.reset(new UnfusedMHARunner(mType, mNumHeads, mSM));
}
}
// IPluginV2DynamicExt Methods
nvinfer1::IPluginV2DynamicExt* QKVToContextVarSeqlenPlugin::clone() const noexcept
{
BERT_DEBUG_MSG("QKV Clone");
QKVToContextVarSeqlenPlugin* ret = nullptr;
if (mDispatcher.get())
{
std::vector<char> buff;
buff.resize(getSerializationSize());
serialize(buff.data());
ret = new QKVToContextVarSeqlenPlugin(mLayerName, buff.data(), buff.size());
}
else
{
ret = new QKVToContextVarSeqlenPlugin(
mLayerName, mType, mHiddenSize, mNumHeads, mDqProbs, mHasImask, mUseVarSeqlen, mUseInt8ScaleMax);
}
ret->setPluginNamespace(mNamespace.c_str());
BERT_DEBUG_MSG("QKV Clone done");
return ret;
}
DimsExprs QKVToContextVarSeqlenPlugin::getOutputDimensions(
int32_t outputIndex, DimsExprs const* inputs, int32_t /*nbInputs*/, IExprBuilder& exprBuilder) noexcept
{
// Input is BxSx3*N*H, output should be BxSxN*H
PLUGIN_ASSERT(outputIndex == 0);
// Copy over everything
DimsExprs output(inputs[IIDX]);
// Divide last dim by three
auto const* three = exprBuilder.constant(3);
output.d[mHdim] = exprBuilder.operation(DimensionOperation::kFLOOR_DIV, *inputs[IIDX].d[mHdim], *three);
return output;
}
bool QKVToContextVarSeqlenPlugin::supportsFormatCombination(
int32_t pos, PluginTensorDesc const* inOut, int32_t nbInputs, int32_t nbOutputs) noexcept
{
// we only support variable sequence and int8 IO in fused mha runner, and we only support fused mha runner on
// Turing, Ampere and Hopper
bool const hasV2Kernels = (mSM == kSM_90 || mSM == kSM_89 || mSM == kSM_87 || mSM == kSM_86 || mSM == kSM_80
|| mSM == kSM_75 || mSM == kSM_72);
PLUGIN_ASSERT(
(mType != DataType::kINT8 || hasV2Kernels) && "INT8 IO is only supported on Xavier, Turing, Ampere and Hopper");
PLUGIN_ASSERT(
(!mUseVarSeqlen || hasV2Kernels) && "Variable sequence is only supported on Xavier, Turing, Ampere and Hopper");
PLUGIN_ASSERT(pos >= 0);
PLUGIN_ASSERT(pos < 2 + mHasImask + 2 * mUseVarSeqlen);
PLUGIN_ASSERT(nbInputs == 1 + mHasImask + 2 * mUseVarSeqlen);
PLUGIN_ASSERT(nbOutputs == 1);
auto const* in = inOut;
auto const* out = inOut + nbInputs;
if (mUseVarSeqlen)
{
PLUGIN_ASSERT((mType == DataType::kHALF || mType == DataType::kINT8)
&& "Conditions for variable seqlen support not fulfilled");
// qkv, mask, cu_seqlens, dummy
PLUGIN_ASSERT(nbInputs == 4 && "for varseqlen, expected 4 inputs");
}
auto const inDims = in->dims;
// const auto inType = in->type;
// const auto inFmt = in->format;
// const auto outType = out->type;
auto const outDims = out->dims;
// const auto outFmt = out->format;
auto supportedFormat = TensorFormat::kLINEAR;
if (mType == DataType::kINT8)
{
supportedFormat = (inDims.d[mHdim] % 32U == 0) ? TensorFormat::kCHW32 : TensorFormat::kCHW4;
}
int32_t supportedNbDims = 5;
if (mUseVarSeqlen)
{
supportedNbDims = 4;
}
bool supportedHdim = (pos == 0) ? (inDims.d[mHdim] % 3U == 0) : (inDims.d[mHdim] / 3 == outDims.d[mHdim]);
if (pos == 0 || pos == nbInputs)
{ // check input and output
auto const& desc = inOut[pos];
return (desc.type == mType) && // check type
(desc.format == supportedFormat) && // check format
(desc.dims.nbDims == supportedNbDims) && // check dims:
(supportedHdim) && // - hidden dims multiple of 3 for qkv
(desc.dims.d[mHdim + 1] == 1) && // - dummy 1 or h
(desc.dims.d[mHdim + 2] == 1) // - dummy 1 or w
;
}
PLUGIN_ASSERT(mHasImask);
if (pos == 1)
{ // must be input mask
auto const* mask = &inOut[pos];
if (mUseVarSeqlen)
{
// dummy input
return true;
}
return mask->format == TensorFormat::kLINEAR && (mask->type == DataType::kINT32) && // precision
(mask->dims.nbDims == 1) // num dims
;
}
PLUGIN_ASSERT(mUseVarSeqlen);
if (pos == 2)
{ // must be cuSeqlens
// cuSeqlens is a int32_t array of size B+1
auto const* seqlens = &inOut[pos];
return (seqlens->type == DataType::kINT32) && (seqlens->format == TensorFormat::kLINEAR);
}
if (pos == 3)
{
// this is the dummy input
return inOut[pos].dims.nbDims == 1;
}
return false;
}
void QKVToContextVarSeqlenPlugin::configurePlugin(
DynamicPluginTensorDesc const* in, int32_t nbInputs, DynamicPluginTensorDesc const* out, int32_t nbOutputs) noexcept
{
PLUGIN_ASSERT(nbInputs == 1 + mHasImask + 2 * mUseVarSeqlen);
PLUGIN_ASSERT(nbOutputs == 1);
PluginTensorDesc const& inDesc = in[IIDX].desc;
TRT_UNUSED inDesc;
PluginTensorDesc const& outDesc = out->desc;
TRT_UNUSED outDesc;
PLUGIN_ASSERT(mType == inDesc.type);
PLUGIN_ASSERT(mType == outDesc.type);
if (!mUseVarSeqlen)
{
PLUGIN_ASSERT(inDesc.dims.d[BDIM] == outDesc.dims.d[BDIM]);
PLUGIN_ASSERT(inDesc.dims.d[SDIM] == outDesc.dims.d[SDIM]);
PLUGIN_ASSERT(inDesc.dims.d[mHdim] == 3 * outDesc.dims.d[mHdim]);
if (mHasImask)
{
PluginTensorDesc const& maskDesc = in[MIDX].desc;
TRT_UNUSED maskDesc;
PLUGIN_ASSERT(maskDesc.dims.d[0] == inDesc.dims.d[BDIM]);
}
const int32_t S = inDesc.dims.d[SDIM] <= 0 ? in->max.d[SDIM] : inDesc.dims.d[SDIM];
const int32_t B = inDesc.dims.d[BDIM] <= 0 ? in->max.d[BDIM] : inDesc.dims.d[BDIM];
if (S != mS || B != mB)
{
BERT_DEBUG_MSG("setting up MHA runner for single sequence length");
createMHARunner();
this->mDispatcher->setup(S, B, mHeadSize);
mS = S;
mB = B;
}
}
else
{
BERT_DEBUG_MSG("setting up MHA runner for variable sequence length");
createMHARunner();
// need to initialize S and B with somewhat useful values, they will be reset at enqueue for the actual
// batchsize
this->mDispatcher->setup(256, 1, mHeadSize);
}
}
size_t QKVToContextVarSeqlenPlugin::getWorkspaceSize(PluginTensorDesc const* inputs, int32_t /* nbInputs */,
PluginTensorDesc const* /* outputs */, int32_t /* nbOutputs */) const noexcept
{
size_t paddingWorkpaceSize = mPatcher ? mPatcher->getWorkspaceSize(inputs[0].dims.d[0], mNumHeads) : 0;
return mDispatcher->getWorkspaceSize() + paddingWorkpaceSize;
}
// IPluginV2Ext Methods
DataType QKVToContextVarSeqlenPlugin::getOutputDataType(
int32_t index, nvinfer1::DataType const* inputTypes, int32_t /*nbInputs*/) const noexcept
{
PLUGIN_ASSERT(index == 0);
PLUGIN_ASSERT(
inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF || inputTypes[0] == DataType::kINT8);
return inputTypes[0];
}
void QKVToContextVarSeqlenPlugin::attachToContext(
cudnnContext* cudnn, cublasContext* cublas, nvinfer1::IGpuAllocator* allocator) noexcept
{
try
{
mCublasWrapper = createPluginCublasWrapper(allocator);
mCublas = mCublasWrapper->getCublasHandle();
PLUGIN_VALIDATE(mCublas != nullptr);
}
catch (const std::exception& e)
{
caughtError(e);
}
}
// IPluginV2 Methods
char const* QKVToContextVarSeqlenPlugin::getPluginType() const noexcept
{
return kQKV_TO_CONTEXT_PLUGIN_NAME;
}
char const* QKVToContextVarSeqlenPlugin::getPluginVersion() const noexcept
{
return kQKV_TO_CONTEXT_VAR_SEQLEN_PLUGIN_VERSION;
}
int32_t QKVToContextVarSeqlenPlugin::getNbOutputs() const noexcept
{
return 1;
}
int32_t QKVToContextVarSeqlenPlugin::initialize() noexcept
{
return 0;
}
void QKVToContextVarSeqlenPlugin::terminate() noexcept {}
size_t QKVToContextVarSeqlenPlugin::getSerializationSize() const noexcept
{
return sizeof(mNumHeads) + sizeof(mHeadSize) + sizeof(DataType) + sizeof(mHasImask) + sizeof(mHiddenSize)
+ sizeof(mSM) + sizeof(mS) + sizeof(mB) + sizeof(mDqProbs) + mDispatcher->getSerializationSize()
+ sizeof(mUseVarSeqlen) + sizeof(mHdim) + sizeof(mUseInt8ScaleMax);
}
void QKVToContextVarSeqlenPlugin::serialize(void* buffer) const noexcept
{
serialize_value(&buffer, mType);
serialize_value(&buffer, mNumHeads);
serialize_value(&buffer, mHeadSize);
serialize_value(&buffer, mHasImask);
serialize_value(&buffer, mHiddenSize);
serialize_value(&buffer, mSM);
serialize_value(&buffer, mS);
serialize_value(&buffer, mB);
serialize_value(&buffer, mDqProbs);
serialize_value(&buffer, mUseVarSeqlen);
serialize_value(&buffer, mHdim);
serialize_value(&buffer, mUseInt8ScaleMax);
mDispatcher->serialize(buffer);
}
void QKVToContextVarSeqlenPlugin::destroy() noexcept
{
delete this;
}
void QKVToContextVarSeqlenPlugin::setPluginNamespace(char const* libNamespace) noexcept
{
mNamespace = libNamespace;
}
char const* QKVToContextVarSeqlenPlugin::getPluginNamespace() const noexcept
{
return mNamespace.c_str();
}
int32_t QKVToContextVarSeqlenPlugin::enqueue(nvinfer1::PluginTensorDesc const* inputDesc,
nvinfer1::PluginTensorDesc const* outputDesc, void const* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) noexcept
{
PLUGIN_VALIDATE(inputDesc != nullptr && outputDesc != nullptr && inputs != nullptr && outputs != nullptr);
if (mUseVarSeqlen)
{
const int32_t B = inputDesc[2].dims.d[0] - 1;
const int32_t maxS = inputDesc[3].dims.d[0];
PLUGIN_ASSERT((maxS <= 512)
&& "No implementation for variable sequence length multi-head attention plugin with sequence > 512.");
int32_t S = 512;
if (DataType::kHALF == mType && maxS <= 64)
{
S = 64;
}
else if (DataType::kHALF == mType && maxS <= 96)
{
S = 96;
}
else if (maxS <= 128)
{
S = 128;
}
else if (maxS <= 192)
{
S = 192;
if (mType == DataType::kHALF)
{
S = 256;
}
}
else if (maxS <= 256)
{
S = 256;
}
else if (maxS <= 384)
{
S = 384;
}