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RNNHelpers.cpp
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RNNHelpers.cpp
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/*
* SPDX-License-Identifier: Apache-2.0
*/
#include "RNNHelpers.hpp"
#include "LoopHelpers.hpp"
#include "importerUtils.hpp"
#include <array>
namespace onnx2trt
{
nvinfer1::ITensor* addRNNInput(ImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node, nvinfer1::ILoop* loop,
std::vector<TensorOrWeights>& inputs, const std::string& direction)
{
// In the forward/reverse cases, we only use a single iterator. In the bidirectional case, a forward and reverse
// iterator must be concatenated.
// Input dimensions: [1, B, E]
nvinfer1::ITensor* iterationInput{nullptr};
nvinfer1::ITensor* input = &convertToTensor(inputs.at(0), ctx);
const int sequenceLenIndex = 4;
bool isRagged = inputs.size() > sequenceLenIndex && inputs.at(sequenceLenIndex);
if (direction == "forward")
{
iterationInput = unsqueezeTensor(ctx, node, *N_CHECK(loop->addIterator(*input)->getOutput(0)), std::vector<int>{0});
if (isRagged)
{
nvinfer1::ITensor* seqLens = &convertToTensor(inputs.at(sequenceLenIndex), ctx);
auto maxLen = getAxisLength(ctx, input, 0);
iterationInput = clearMissingSequenceElements(ctx, node, loop, seqLens, iterationInput, maxLen);
}
}
else if (direction == "reverse")
{
nvinfer1::IIteratorLayer* reverseIterator = N_CHECK(loop->addIterator(*input));
reverseIterator->setReverse(true);
auto reverseIteratorOutput = N_CHECK(reverseIterator->getOutput(0));
iterationInput = unsqueezeTensor(ctx, node, *reverseIteratorOutput, std::vector<int>{0});
if (isRagged)
{
nvinfer1::ITensor* seqLens = &convertToTensor(inputs.at(sequenceLenIndex), ctx);
auto maxLen = getAxisLength(ctx, input, 0);
iterationInput = clearMissingSequenceElements(ctx, node, loop, seqLens, iterationInput, maxLen, true);
}
}
else if (direction == "bidirectional")
{
nvinfer1::IIteratorLayer* forward = N_CHECK(loop->addIterator(*input));
nvinfer1::IIteratorLayer* reverse = N_CHECK(loop->addIterator(*input));
reverse->setReverse(true);
auto forwardInput = unsqueezeTensor(ctx, node, *N_CHECK(forward->getOutput(0)), std::vector<int>{0});
auto reverseInput = unsqueezeTensor(ctx, node, *N_CHECK(reverse->getOutput(0)), std::vector<int>{0});
if (isRagged)
{
nvinfer1::ITensor* seqLens = &convertToTensor(inputs.at(sequenceLenIndex), ctx);
auto counter = addLoopCounter(ctx, loop);
auto maxLen = getAxisLength(ctx, input, 0);
forwardInput = clearMissingSequenceElements(ctx, node, loop, seqLens, forwardInput, maxLen, false, counter);
reverseInput = clearMissingSequenceElements(ctx, node, loop, seqLens, reverseInput, maxLen, true, counter);
}
// Stack on the 0th axis to create a (numDirections, B, E) tensor.
std::array<nvinfer1::ITensor*, 2> tensors{{forwardInput, reverseInput}};
nvinfer1::IConcatenationLayer* concat = N_CHECK(ctx->network()->addConcatenation(tensors.data(), 2));
concat->setAxis(0);
iterationInput = N_CHECK(concat->getOutput(0));
}
if (iterationInput)
{
LOG_VERBOSE("Input shape: " << iterationInput->getDimensions());
}
return iterationInput;
}
nvinfer1::ITensor* clearMissingSequenceElements(ImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node,
nvinfer1::ILoop* loop, nvinfer1::ITensor* seqLens, nvinfer1::ITensor* toMask, nvinfer1::ITensor* maxLen,
bool reverse, nvinfer1::ITensor* counter)
{
nvinfer1::ITensor* zero
= addConstantScalar(ctx, 0.f, ::ONNX_NAMESPACE::TensorProto::FLOAT, nvinfer1::Dims3(1, 1, 1))->getOutput(0);
nvinfer1::ITensor* seqMask = getRaggedMask(ctx, node, loop, seqLens, maxLen, reverse, counter);
auto selectLayer = N_CHECK(ctx->network()->addSelect(*seqMask, *toMask, *zero));
return N_CHECK(selectLayer->getOutput(0));
}
nvinfer1::ITensor* maskRNNHidden(ImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node, nvinfer1::ILoop* loop,
nvinfer1::ITensor* seqLens, nvinfer1::ITensor* prevH, nvinfer1::ITensor* Ht, nvinfer1::ITensor* maxLen,
bool reverse, nvinfer1::ITensor* counter)
{
// maxLen must be provided if reverse is true
// Forwards previous hidden state if invalid
nvinfer1::ITensor* valid = getRaggedMask(ctx, node, loop, seqLens, maxLen, reverse, counter);
auto selectLayer = N_CHECK(ctx->network()->addSelect(*valid, *Ht, *prevH));
return N_CHECK(selectLayer->getOutput(0));
}
nvinfer1::ITensor* maskBidirRNNHidden(ImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node,
nvinfer1::ILoop* loop, nvinfer1::ITensor* seqLens, nvinfer1::ITensor* maxLen, nvinfer1::ITensor* Ht1,
nvinfer1::ITensor* Ht, nvinfer1::ITensor* singlePassShape)
{
// Splits hidden state into forward and backward states, masks each accordingly, then concatenates
nvinfer1::ITensor* forwardStart = addConstant(ctx, std::vector<int32_t>{0, 0, 0},
::ONNX_NAMESPACE::TensorProto::INT32,
nvinfer1::Dims{1, {3}})->getOutput(0);
nvinfer1::ITensor* reverseStart = addConstant(ctx, std::vector<int32_t>{1, 0, 0},
::ONNX_NAMESPACE::TensorProto::INT32,
nvinfer1::Dims{1, {3}})->getOutput(0);
nvinfer1::ISliceLayer* HtForwardLayer
= N_CHECK(ctx->network()->addSlice(*Ht, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{1, 1, 1}));
HtForwardLayer->setInput(1, *forwardStart);
HtForwardLayer->setInput(2, *singlePassShape);
nvinfer1::ISliceLayer* HtBackwardLayer
= N_CHECK(ctx->network()->addSlice(*Ht, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{1, 1, 1}));
HtBackwardLayer->setInput(1, *reverseStart);
HtBackwardLayer->setInput(2, *singlePassShape);
nvinfer1::ISliceLayer* Ht1ForwardLayer
= N_CHECK(ctx->network()->addSlice(*Ht1, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{1, 1, 1}));
Ht1ForwardLayer->setInput(1, *forwardStart);
Ht1ForwardLayer->setInput(2, *singlePassShape);
nvinfer1::ISliceLayer* Ht1BackwardLayer
= N_CHECK(ctx->network()->addSlice(*Ht1, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{0, 0, 0}, nvinfer1::Dims3{1, 1, 1}));
Ht1BackwardLayer->setInput(1, *reverseStart);
Ht1BackwardLayer->setInput(2, *singlePassShape);
auto forwardHt = N_CHECK(HtForwardLayer->getOutput(0));
auto backwardHt = N_CHECK(HtBackwardLayer->getOutput(0));
auto forwardHt1 = N_CHECK(Ht1ForwardLayer->getOutput(0));
auto backwardHt1 = N_CHECK(Ht1BackwardLayer->getOutput(0));
auto counter = addLoopCounter(ctx, loop, 0);
forwardHt = maskRNNHidden(ctx, node, loop, seqLens, forwardHt1, forwardHt, maxLen, false, counter);
backwardHt = maskRNNHidden(ctx, node, loop, seqLens, backwardHt1, backwardHt, maxLen, true, counter);
std::array<nvinfer1::ITensor*, 2> tensors{{forwardHt, backwardHt}};
nvinfer1::IConcatenationLayer* concat = N_CHECK(ctx->network()->addConcatenation(tensors.data(), 2));
concat->setAxis(0);
return N_CHECK(concat->getOutput(0));
}
nvinfer1::ITensor* getRaggedMask(ImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node, nvinfer1::ILoop* loop,
nvinfer1::ITensor* seqLens, nvinfer1::ITensor* maxLen, bool reverse, nvinfer1::ITensor* counter)
{
// Returns a bool tensor which is true where the elements are valid (within the sequence) and false when outside the
// sequence.
// maxLen must be provided if reverse is true
assert(!reverse || maxLen);
if (!counter)
{
counter = addLoopCounter(ctx, loop, 0);
}
// ONNX spec currently requires seqLens to be int32
counter = castHelper(ctx, counter, nvinfer1::DataType::kINT32);
// Create Mask
nvinfer1::ITensor* seqMask;
if (reverse)
{
counter
= getElementWiseResult(ctx, *unsqueezeTensor(ctx, node, *maxLen, {0}), *counter, nvinfer1::ElementWiseOperation::kSUB);
seqMask = getElementWiseResult(ctx, *seqLens, *counter, nvinfer1::ElementWiseOperation::kLESS);
seqMask = getUnaryResult(ctx, *seqMask, nvinfer1::UnaryOperation::kNOT);
}
else
{
seqMask = getElementWiseResult(ctx, *counter, *seqLens, nvinfer1::ElementWiseOperation::kLESS);
}
return unsqueezeTensor(ctx, node, *seqMask, std::vector<int>{0, 2});
}
} // namespace onnx2trt