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onnxOpCheckers.cpp
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/*
* SPDX-License-Identifier: Apache-2.0
*/
#include "onnxOpCheckers.hpp"
#include "ConditionalHelpers.hpp"
#include "LoopHelpers.hpp"
#include "ModelImporter.hpp"
#include "NvInfer.h"
#include "NvInferPlugin.h"
#include "NvInferRuntime.h"
#include "OnnxAttrs.hpp"
#include "RNNHelpers.hpp"
#include "ShapeTensor.hpp"
#include "bfloat16.hpp"
#include "half.h"
#include "importerUtils.hpp"
#include <array>
#include <iostream>
#include <iterator>
#include <tuple>
namespace onnx2trt
{
StringMap<OpStaticErrorChecker>& getOpStaticErrorCheckerMap()
{
static StringMap<OpStaticErrorChecker> error_checkers;
return error_checkers;
}
namespace
{
using nvinfer1::DataType;
#define IGNORE_UNUSED_GLOBAL(x) \
static void _ignore_unused2_##x(); \
static void _ignore_unused1_##x() \
{ \
(void) _ignore_unused2_##x; \
(void) x; \
} \
static void _ignore_unused2_##x() \
{ \
(void) _ignore_unused1_##x; \
} \
struct SwallowSemicolon##x \
{ \
}
#define DECLARE_OP_CHECKER(op) \
void check##op(ImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<Status>& errors, \
size_t const nodeIndex)
#define DEFINE_OP_CHECKER(op) \
void check##op(ImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<Status>& errors, \
size_t const nodeIndex); \
static bool const op##_registered_op_checker = registerOpStaticErrorChecker(#op, check##op); \
IGNORE_UNUSED_GLOBAL(op##_registered_op_checker); \
void check##op(ImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<Status>& errors, \
size_t const nodeIndex)
#define DEFINE_OP_EMPTY_CHECKER(op) \
DEFINE_OP_CHECKER(op) {}
bool registerOpStaticErrorChecker(std::string op, OpStaticErrorChecker const& checker)
{
bool inserted = getOpStaticErrorCheckerMap().insert({op, checker}).second;
assert(inserted);
return inserted;
}
void argMinMaxCheckHelper(
ImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node, std::vector<Status>& errors, size_t const nodeIndex)
{
OnnxAttrs attrs(node, ctx);
int32_t selectLastIndex = attrs.get<int32_t>("select_last_index", 0);
STATIC_CHECK((!selectLastIndex || (selectLastIndex && ctx->getOpsetVersion() >= 12))
&& "Per-opset 12 ONNX does not support the select_last_index attribute.",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
void poolingCheckHelper(
ImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node, std::vector<Status>& errors, size_t const nodeIndex)
{
OnnxAttrs attrs(node, ctx);
if (ctx->getOpsetVersion() >= 10)
{
auto const dilations = attrs.get<std::vector<int32_t>>("dilations", std::vector<int32_t>(2, 1));
for (size_t i = 0; i < dilations.size(); i++)
STATIC_CHECK((dilations[i] == 1) && "This version of TensorRT does not support dilations other than 1.",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
}
void randomUniformCheckHelper(
ImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<Status>& errors, size_t const nodeIndex)
{
OnnxAttrs attrs(node, ctx);
// Set datatype of output:
// RandomUniform: dype is required and defaults to 1
// RandomUniformLike: dtype is optional and defaults to the same type as the input
if (attrs.count("dtype"))
{
auto dtype = attrs.get<int32_t>("dtype", 1);
if (dtype != ::ONNX_NAMESPACE::TensorProto::FLOAT && dtype != ::ONNX_NAMESPACE::TensorProto::FLOAT16)
{
ADD_STATIC_ERROR(
"Unsupported data type in randomUniform", ErrorCode::kINVALID_VALUE, node, nodeIndex, errors);
}
}
}
void randomNormalCheckHelper(
ImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<Status>& errors, size_t const nodeIndex)
{
OnnxAttrs attrs(node, ctx);
// Set datatype of output:
// RandomNormal: dype is required and defaults to 1
// RandomNormalLike: dtype is optional and defaults to the same type as the input
if (attrs.count("dtype"))
{
auto dtype = attrs.get<int32_t>("dtype", 1);
if (dtype != ::ONNX_NAMESPACE::TensorProto::FLOAT && dtype != ::ONNX_NAMESPACE::TensorProto::FLOAT16)
{
ADD_STATIC_ERROR(
"Unsupported data type in randomNormal", ErrorCode::kINVALID_VALUE, node, nodeIndex, errors);
}
}
}
void emptyOutputChecker(ImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<Status>& errors,
size_t const nodeIndex, int32_t numSupportedOutputs)
{
int32_t numOutputs = node.output().size();
for (int32_t i = 0; i < numOutputs; i++)
{
if (i < numSupportedOutputs)
{
continue;
}
if (!node.output(i).empty())
{
std::ostringstream ssMsg{};
ssMsg << "This version of TensorRT doesn't support mode than " << numSupportedOutputs << " outputs for "
<< node.op_type() << " nodes!";
ADD_STATIC_ERROR(ssMsg.str(), ErrorCode::kUNSUPPORTED_NODE, node, nodeIndex, errors);
}
}
}
DEFINE_OP_EMPTY_CHECKER(Abs)
DEFINE_OP_EMPTY_CHECKER(Acos)
DEFINE_OP_EMPTY_CHECKER(Acosh)
DEFINE_OP_EMPTY_CHECKER(And)
DEFINE_OP_EMPTY_CHECKER(Asin)
DEFINE_OP_EMPTY_CHECKER(Asinh)
DEFINE_OP_EMPTY_CHECKER(Atan)
DEFINE_OP_EMPTY_CHECKER(Atanh)
DEFINE_OP_EMPTY_CHECKER(Add)
DEFINE_OP_CHECKER(ArgMax)
{
argMinMaxCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(ArgMin)
{
argMinMaxCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(AveragePool)
{
poolingCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(BatchNormalization)
{
OnnxAttrs attrs(node, ctx);
auto const isTraining = attrs.get<int32_t>("training_mode", 0);
STATIC_CHECK(!isTraining && "This version of TensorRT does not support training_mode == 1 in BatchNormalization.",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
// Also check the number of outputs. TRT only supports the first output.
emptyOutputChecker(ctx, node, errors, nodeIndex, 1);
}
DEFINE_OP_EMPTY_CHECKER(BlackmanWindow)
DEFINE_OP_CHECKER(Cast)
{
OnnxAttrs attrs(node, ctx);
// Get data type to cast to.
auto onnxType = attrs.get<int32_t>("to");
DataType newType{DataType::kFLOAT};
STATIC_CHECK(convertDtype(onnxType, &newType) && "Unsupported data type for the Cast operator!",
ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(CastLike)
DEFINE_OP_EMPTY_CHECKER(Ceil)
DEFINE_OP_EMPTY_CHECKER(Celu)
DEFINE_OP_EMPTY_CHECKER(Clip)
DEFINE_OP_EMPTY_CHECKER(Concat)
DEFINE_OP_CHECKER(Constant)
{
OnnxAttrs attrs(node, ctx);
// Having the trt_outputs_range_min attributes means it's from
// serialized iNetworkDefinition which does not have this check.
if (attrs.get<std::vector<float>>("trt_outputs_range_min", {}).empty())
{
STATIC_CHECK((!attrs.count("sparse_value")) && (!attrs.count("value_string")) && (!attrs.count("value_strings"))
&& "This version of TensorRT does not support the sparse_value, value_string and value_strings attributes.",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
}
DEFINE_OP_CHECKER(ConstantOfShape)
{
OnnxAttrs attrs(node, ctx);
ShapedWeights zeroWeights
= ctx->createNamedTempWeights(::ONNX_NAMESPACE::TensorProto_DataType_FLOAT, nvinfer1::Dims{1, {1}});
static_cast<float*>(zeroWeights.values)[0] = 0.f;
auto valueWeights = TensorOrWeights{attrs.get("value", zeroWeights)};
STATIC_CHECK(notInvalidType(valueWeights, {"UINT8"}, node, nodeIndex).is_success()
&& "Invalid input type for ConstantOfShape",
ErrorCode::kUNSUPPORTED_NODE_DATATYPE, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(Conv)
DEFINE_OP_EMPTY_CHECKER(ConvTranspose)
DEFINE_OP_EMPTY_CHECKER(Cos)
DEFINE_OP_EMPTY_CHECKER(Cosh)
DEFINE_OP_EMPTY_CHECKER(CumSum)
DEFINE_OP_EMPTY_CHECKER(DeformConv)
DEFINE_OP_EMPTY_CHECKER(DepthToSpace)
DEFINE_OP_EMPTY_CHECKER(QuantizeLinear)
DEFINE_OP_EMPTY_CHECKER(DequantizeLinear)
DEFINE_OP_EMPTY_CHECKER(TRT_FP8QuantizeLinear)
DEFINE_OP_EMPTY_CHECKER(TRT_FP8DequantizeLinear)
DEFINE_OP_EMPTY_CHECKER(TRT_INT4QuantizeLinear)
DEFINE_OP_EMPTY_CHECKER(TRT_INT4DequantizeLinear)
DEFINE_OP_EMPTY_CHECKER(TRT_FP4DynamicQuantize)
DECLARE_OP_CHECKER(Mul);
DEFINE_OP_EMPTY_CHECKER(Div)
DEFINE_OP_CHECKER(Dropout)
{
// TensorRT does not support the Dropout operator with training mode.
if (ctx->getOpsetVersion() <= 6)
{
OnnxAttrs attrs(node, ctx);
int32_t isTestingMode = attrs.get<int32_t>("is_test", 1);
STATIC_CHECK(isTestingMode && "TensorRT does not support the Droupout operator with training mode.",
ErrorCode::kUNSUPPORTED_NODE_ATTR, node, errors, nodeIndex);
}
}
DEFINE_OP_CHECKER(Einsum)
{
OnnxAttrs attrs(node, ctx);
std::string equation = attrs.get<std::string>("equation");
std::string invalidCharacters;
for (char c : equation)
{
if ((c < 'a' || c > 'z') && c != '-' && c != '>' && c != '.' && c != ',' && c != ' ')
{
invalidCharacters.push_back(c);
invalidCharacters.push_back(',');
}
}
if (!invalidCharacters.empty())
{
invalidCharacters.pop_back();
ADD_STATIC_ERROR("Invalid character(s) in Einsum equation: " + invalidCharacters, ErrorCode::kINVALID_NODE,
node, nodeIndex, errors);
}
}
DEFINE_OP_EMPTY_CHECKER(Elu)
DEFINE_OP_EMPTY_CHECKER(Equal)
DEFINE_OP_EMPTY_CHECKER(Erf)
DEFINE_OP_EMPTY_CHECKER(Exp)
DEFINE_OP_EMPTY_CHECKER(Expand)
DEFINE_OP_EMPTY_CHECKER(EyeLike)
DEFINE_OP_EMPTY_CHECKER(Flatten)
DEFINE_OP_EMPTY_CHECKER(Floor)
DEFINE_OP_EMPTY_CHECKER(Gather)
DEFINE_OP_EMPTY_CHECKER(GatherElements)
DEFINE_OP_EMPTY_CHECKER(GatherND)
DEFINE_OP_EMPTY_CHECKER(Gelu)
DEFINE_OP_EMPTY_CHECKER(Gemm)
DEFINE_OP_EMPTY_CHECKER(GlobalAveragePool)
DEFINE_OP_EMPTY_CHECKER(GlobalLpPool)
DEFINE_OP_EMPTY_CHECKER(GlobalMaxPool)
DEFINE_OP_EMPTY_CHECKER(Greater)
DEFINE_OP_EMPTY_CHECKER(GreaterOrEqual)
DEFINE_OP_EMPTY_CHECKER(GroupNormalization)
DEFINE_OP_CHECKER(GRU)
{
using trtAct = nvinfer1::ActivationType;
OnnxAttrs attrs{node, ctx};
std::string const direction = attrs.get<std::string>("direction", "forward");
int32_t const numDirections = (direction == "bidirectional") ? 2 : 1;
constexpr int32_t NUM_ACTIVATIONS = 2;
std::vector<trtAct> defaultActs{trtAct::kSIGMOID, trtAct::kTANH};
if (numDirections == 2)
{
defaultActs.insert(defaultActs.end(), {trtAct::kSIGMOID, trtAct::kTANH});
}
std::vector<trtAct> activations = attrs.get<std::vector<trtAct>>("activations", defaultActs);
std::vector<float> activationAlphas = attrs.get<std::vector<float>>("activation_alpha", std::vector<float>{});
std::transform(activations.begin() + activationAlphas.size(), activations.end(),
std::back_inserter(activationAlphas), &getActivationDefaultAlpha);
std::vector<float> activationBetas = attrs.get<std::vector<float>>("activation_beta", std::vector<float>{});
std::transform(activations.begin() + activationBetas.size(), activations.end(), std::back_inserter(activationBetas),
&getActivationDefaultBeta);
// TODO: Support cases where in bidirectional GRUs, activations of reverse iteration do not match forward pass.
// TODO: This will require splitting the input tensor in the loop when applying activations.
if (numDirections == 2)
{
STATIC_CHECK(std::equal(activations.begin(), activations.begin() + NUM_ACTIVATIONS, activations.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activations for the reverse pass of the GRU do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
STATIC_CHECK(std::equal(activationAlphas.begin(), activationAlphas.begin() + NUM_ACTIVATIONS, activationAlphas.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activations for the reverse pass of the GRU do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
STATIC_CHECK(std::equal(activationBetas.begin(), activationBetas.begin() + NUM_ACTIVATIONS, activationBetas.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activations for the reverse pass of the GRU do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
}
DEFINE_OP_EMPTY_CHECKER(HammingWindow)
DEFINE_OP_EMPTY_CHECKER(HannWindow)
DEFINE_OP_EMPTY_CHECKER(Hardmax)
DEFINE_OP_EMPTY_CHECKER(HardSigmoid)
DEFINE_OP_EMPTY_CHECKER(Identity)
DEFINE_OP_CHECKER(If)
{
OnnxAttrs attrs(node, ctx);
::ONNX_NAMESPACE::GraphProto const& thenGraph = attrs.get<::ONNX_NAMESPACE::GraphProto const&>("then_branch");
::ONNX_NAMESPACE::GraphProto const& elseGraph = attrs.get<::ONNX_NAMESPACE::GraphProto const&>("else_branch");
// Number of outputs are the same between the two branches.
STATIC_CHECK(thenGraph.output_size() == elseGraph.output_size()
&& "then/else subgraphs should have the same number of outputs.",
ErrorCode::kUNSUPPORTED_NODE_ATTR, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(ImageScaler)
DEFINE_OP_EMPTY_CHECKER(InstanceNormalization)
DEFINE_OP_EMPTY_CHECKER(IsInf)
DEFINE_OP_EMPTY_CHECKER(IsNaN)
DEFINE_OP_CHECKER(LayerNormalization)
{
// TRT only expects one valid output. Other outputs are training artifacts that should've been removed for inference graphs.
emptyOutputChecker(ctx, node, errors, nodeIndex, 1);
}
DEFINE_OP_EMPTY_CHECKER(LeakyRelu)
DEFINE_OP_EMPTY_CHECKER(Less)
DEFINE_OP_EMPTY_CHECKER(LessOrEqual)
DEFINE_OP_EMPTY_CHECKER(Log)
DEFINE_OP_EMPTY_CHECKER(LogSoftmax)
DEFINE_OP_EMPTY_CHECKER(Loop)
DEFINE_OP_EMPTY_CHECKER(LRN)
DEFINE_OP_CHECKER(LSTM)
{
using trtAct = nvinfer1::ActivationType;
OnnxAttrs attrs{node, ctx};
std::string const direction = attrs.get<std::string>("direction", "forward");
int32_t const numDirections = (direction == "bidirectional") ? 2 : 1;
int32_t const inputForget = attrs.get("input_forget", 0);
STATIC_CHECK(inputForget == 0 && "Coupled input/forget is unsupported in the LSTM converter",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
constexpr int32_t NUM_ACTIVATIONS = 3;
std::vector<trtAct> defaultActs{trtAct::kSIGMOID, trtAct::kTANH, trtAct::kTANH};
if (numDirections == 2)
{
defaultActs.insert(defaultActs.end(), {trtAct::kSIGMOID, trtAct::kTANH, trtAct::kTANH});
}
std::vector<trtAct> activations = attrs.get<std::vector<trtAct>>("activations", defaultActs);
std::vector<float> activationAlphas = attrs.get<std::vector<float>>("activation_alpha", std::vector<float>{});
activationAlphas = parseLSTMActivationValues(activations, activationAlphas, true);
std::vector<float> activationBetas = attrs.get<std::vector<float>>("activation_beta", std::vector<float>{});
activationBetas = parseLSTMActivationValues(activations, activationBetas, false);
// TODO: Support cases where in bidirectional LSTMs, activations of reverse iteration do not match forward pass.
// TODO: This will require splitting the input tensor in the loop when applying activations.
if (numDirections == 2)
{
STATIC_CHECK(std::equal(activations.begin(), activations.begin() + NUM_ACTIVATIONS, activations.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activations for the reverse pass of the LSTM do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
STATIC_CHECK(std::equal(activationAlphas.begin(), activationAlphas.begin() + NUM_ACTIVATIONS, activationAlphas.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activation alphas for the reverse pass of the LSTM do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
STATIC_CHECK(std::equal(activationBetas.begin(), activationBetas.begin() + NUM_ACTIVATIONS, activationBetas.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activation betas for the reverse pass of the LSTM do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
}
DEFINE_OP_CHECKER(LpNormalization)
{
OnnxAttrs attrs(node, ctx);
int32_t p = attrs.get<int32_t>("p", 2);
STATIC_CHECK((p == 1 || p == 2) && "Only L1 and L2 normalization are supported.", ErrorCode::kUNSUPPORTED_NODE,
node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(LpPool)
{
OnnxAttrs attrs(node, ctx);
int32_t p = attrs.get<int32_t>("p", 2);
STATIC_CHECK((p == 1 || p == 2) && "Only L1 and L2 normalization are supported.", ErrorCode::kUNSUPPORTED_NODE,
node, errors, nodeIndex);
poolingCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(MatMul)
DEFINE_OP_EMPTY_CHECKER(Max)
DEFINE_OP_CHECKER(MaxPool)
{
// TRT only expects one valid output. `Indices` output is unsupported.
emptyOutputChecker(ctx, node, errors, nodeIndex, 1);
poolingCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(Mean)
DEFINE_OP_EMPTY_CHECKER(MeanVarianceNormalization)
DEFINE_OP_EMPTY_CHECKER(Min)
DEFINE_OP_EMPTY_CHECKER(Mul)
DEFINE_OP_EMPTY_CHECKER(Mod)
DEFINE_OP_EMPTY_CHECKER(Neg)
DEFINE_OP_EMPTY_CHECKER(NonMaxSuppression)
DEFINE_OP_EMPTY_CHECKER(Not)
DEFINE_OP_EMPTY_CHECKER(OneHot)
DEFINE_OP_EMPTY_CHECKER(Or)
DEFINE_OP_EMPTY_CHECKER(Pad)
DEFINE_OP_EMPTY_CHECKER(ParametricSoftplus)
DEFINE_OP_CHECKER(Pow)
{
int32_t const nbInputs = node.input().size();
STATIC_CHECK(
nbInputs == 2 && "POW operator expects two inputs!", ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(PRelu)
DEFINE_OP_CHECKER(RandomUniform)
{
randomUniformCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(RandomUniformLike)
{
randomUniformCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(RandomNormal)
{
randomNormalCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(RandomNormalLike)
{
randomNormalCheckHelper(ctx, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(Range)
DEFINE_OP_EMPTY_CHECKER(Reciprocal)
DEFINE_OP_EMPTY_CHECKER(ReduceL1)
DECLARE_OP_CHECKER(ReduceSum);
DEFINE_OP_EMPTY_CHECKER(ReduceLogSum)
DEFINE_OP_EMPTY_CHECKER(ReduceLogSumExp)
DECLARE_OP_CHECKER(ReduceSumSquare);
DEFINE_OP_EMPTY_CHECKER(ReduceL2)
DEFINE_OP_EMPTY_CHECKER(ReduceMax)
DEFINE_OP_EMPTY_CHECKER(ReduceMean)
DEFINE_OP_EMPTY_CHECKER(ReduceMin)
DEFINE_OP_EMPTY_CHECKER(ReduceProd)
DEFINE_OP_EMPTY_CHECKER(ReduceSum)
DEFINE_OP_EMPTY_CHECKER(ReduceSumSquare)
DEFINE_OP_EMPTY_CHECKER(Relu)
DEFINE_OP_EMPTY_CHECKER(Sign)
DEFINE_OP_EMPTY_CHECKER(Round)
DEFINE_OP_CHECKER(Resize)
{
OnnxAttrs attrs(node, ctx);
auto mode = attrs.get<std::string>("mode", "nearest");
if (mode != "cubic" || mode != "linear")
{
STATIC_CHECK((mode == "cubic" || mode == "linear" || mode == "nearest") && "Invalid Resize mode",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
// set transformation
std::string transformationMode = "half_pixel";
if (ctx->getOpsetVersion() >= 11)
{
// Check for TRT-supported resize attributes
transformationMode = attrs.get<std::string>("coordinate_transformation_mode", "half_pixel");
auto const nearest_mode = attrs.get<std::string>("nearest_mode", "round_prefer_floor");
STATIC_CHECK((transformationMode != "tf_half_pixel_for_nn" || nearest_mode == "round_prefer_floor")
&& "This version of TensorRT only support round_prefer_floor nearest mode in tf_half_pixel_for_nn!",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
STATIC_CHECK((transformationMode == "align_corners" || transformationMode == "tf_half_pixel_for_nn" || transformationMode == "pytorch_half_pixel"
|| transformationMode == "half_pixel" || transformationMode == "asymmetric")
&& "TensorRT only supports half_pixel, pytorch_half_pixel, tf_half_pixel_for_nn, asymmetric and "
"align_corners transformation modes!", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
// Antialiasing is not supported currently.
auto const antialias = attrs.get<int32_t>("antialias", 0);
STATIC_CHECK((antialias == 0) && "Antialiasing is not supported currently.", ErrorCode::kUNSUPPORTED_NODE_ATTR,
node, errors, nodeIndex);
// Only stretch keep_aspect_ratio_policy is supported currently.
auto const keep_aspect_ratio_policy = attrs.get<std::string>("keep_aspect_ratio_policy", "stretch");
STATIC_CHECK((keep_aspect_ratio_policy == "stretch")
&& "Only `stretch` is supported currently as `keep_aspect_ratio_policy`.",
ErrorCode::kUNSUPPORTED_NODE_ATTR, node, errors, nodeIndex);
// Axes provided must be unique.
auto const resizeAxes = attrs.get<std::vector<int32_t>>("axes", std::vector<int32_t>());
STATIC_CHECK((std::unordered_set<int32_t>(resizeAxes.begin(), resizeAxes.end()).size() == resizeAxes.size())
&& "The input axes must have unique elements.",
ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(Reshape)
DEFINE_OP_CHECKER(ReverseSequence)
{
OnnxAttrs attrs{node, ctx};
int32_t const batchAxis = attrs.get<int32_t>("batch_axis", 1);
int32_t const sequenceAxis = attrs.get<int32_t>("time_axis", 0);
STATIC_CHECK((batchAxis != sequenceAxis) && "batch_axis and time_axis cannot be the same",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(RNN)
{
OnnxAttrs attrs{node, ctx};
const std::string direction = attrs.get<std::string>("direction", "forward");
const int32_t numDirections = (direction == "bidirectional") ? 2 : 1;
constexpr int32_t NUM_ACTIVATIONS = 1;
std::vector<nvinfer1::ActivationType> defaultActs{nvinfer1::ActivationType::kTANH};
if (numDirections == 2)
{
defaultActs.insert(defaultActs.end(), {nvinfer1::ActivationType::kTANH});
}
std::vector<nvinfer1::ActivationType> activations
= attrs.get<std::vector<nvinfer1::ActivationType>>("activations", defaultActs);
std::vector<float> activationAlphas = attrs.get<std::vector<float>>("activation_alpha", std::vector<float>{});
std::transform(activations.begin() + activationAlphas.size(), activations.end(),
std::back_inserter(activationAlphas), &getActivationDefaultAlpha);
std::vector<float> activationBetas = attrs.get<std::vector<float>>("activation_beta", std::vector<float>{});
std::transform(activations.begin() + activationBetas.size(), activations.end(), std::back_inserter(activationBetas),
&getActivationDefaultBeta);
// TODO: Support cases where in bidirectional RNNs, activations of reverse iteration do not match forward pass.
// TODO: This will require splitting the input tensor in the loop when applying activations.
if (numDirections == 2)
{
STATIC_CHECK(std::equal(activations.begin(), activations.begin() + NUM_ACTIVATIONS, activations.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activations for the reverse pass of the RNN do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
STATIC_CHECK(std::equal(activationAlphas.begin(), activationAlphas.begin() + NUM_ACTIVATIONS, activationAlphas.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activations for the reverse pass of the RNN do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
STATIC_CHECK(std::equal(activationBetas.begin(), activationBetas.begin() + NUM_ACTIVATIONS, activationBetas.begin() + NUM_ACTIVATIONS)
&& "The parser does not currently support cases where activations for the reverse pass of the RNN do not match the forward pass.", ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
}
DEFINE_OP_CHECKER(RoiAlign)
{
OnnxAttrs attrs(node, ctx);
auto mode = attrs.get("mode", std::string("avg"));
STATIC_CHECK((mode == "avg" || mode == "max") && "Mode must be avg or max!", ErrorCode::kINVALID_NODE, node, errors,
nodeIndex);
auto samplingRatio = attrs.get<int32_t>("sampling_ratio", 0);
STATIC_CHECK(
samplingRatio >= 0 && "Sampling ratio cannot be negative!", ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
// Opset 16 attributes
if (ctx->getOpsetVersion() >= 16)
{
auto ctm = attrs.get("coordinate_transformation_mode", std::string("half_pixel"));
STATIC_CHECK((ctm == "half_pixel" || ctm == "output_half_pixel")
&& "Coordinate transformation mode must be half_pixel or output_half_pixel!",
ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
}
}
DEFINE_OP_EMPTY_CHECKER(ScaledTanh)
DEFINE_OP_EMPTY_CHECKER(Scan)
DEFINE_OP_EMPTY_CHECKER(GridSample)
DEFINE_OP_EMPTY_CHECKER(ScatterND)
DEFINE_OP_EMPTY_CHECKER(ScatterElements)
DEFINE_OP_EMPTY_CHECKER(Scatter)
DEFINE_OP_EMPTY_CHECKER(Selu)
DEFINE_OP_EMPTY_CHECKER(Shape)
DEFINE_OP_EMPTY_CHECKER(Sigmoid)
DEFINE_OP_EMPTY_CHECKER(Sin)
DEFINE_OP_EMPTY_CHECKER(Sinh)
DEFINE_OP_EMPTY_CHECKER(Size)
DEFINE_OP_CHECKER(Slice)
{
int32_t const nbInputs = node.input().size();
if (ctx->getOpsetVersion() >= 10)
{
STATIC_CHECK((nbInputs >= 3 && nbInputs <= 5) && "Post-opset 10 Slice operator requires 3 - 5 inputs.",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
}
DEFINE_OP_EMPTY_CHECKER(Softmax)
DEFINE_OP_EMPTY_CHECKER(Softsign)
DEFINE_OP_EMPTY_CHECKER(Softplus)
DEFINE_OP_EMPTY_CHECKER(SpaceToDepth)
DEFINE_OP_EMPTY_CHECKER(Split)
DEFINE_OP_EMPTY_CHECKER(Sqrt)
DEFINE_OP_EMPTY_CHECKER(Squeeze)
DEFINE_OP_EMPTY_CHECKER(Sub)
DEFINE_OP_EMPTY_CHECKER(Sum)
DEFINE_OP_EMPTY_CHECKER(Tan)
DEFINE_OP_EMPTY_CHECKER(Tanh)
DEFINE_OP_EMPTY_CHECKER(ThresholdedRelu)
DEFINE_OP_EMPTY_CHECKER(Tile)
DEFINE_OP_CHECKER(TopK)
{
OnnxAttrs attrs(node, ctx);
if (ctx->getOpsetVersion() < 10)
{
STATIC_CHECK(
(attrs.count("k")) && "Attribute k is missing.", ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
}
}
DEFINE_OP_EMPTY_CHECKER(Transpose)
DEFINE_OP_EMPTY_CHECKER(Trilu)
DEFINE_OP_EMPTY_CHECKER(Unsqueeze)
DEFINE_OP_CHECKER(Upsample)
{
OnnxAttrs attrs(node, ctx);
auto mode = attrs.get<std::string>("mode", "nearest");
STATIC_CHECK((mode == "nearest" || mode == "linear" || mode == "bilinear")
&& "The attribute mode can only be nearest, linear, or bilinear.",
ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_EMPTY_CHECKER(Where)
DEFINE_OP_EMPTY_CHECKER(Xor)
DEFINE_OP_EMPTY_CHECKER(Shrink)
DEFINE_OP_EMPTY_CHECKER(HardSwish)
DEFINE_OP_EMPTY_CHECKER(NonZero)
DEFINE_OP_EMPTY_CHECKER(Mish)
// Any ops that are not supported will attempt to import as plugins.
DEFINE_OP_CHECKER(FallbackPluginImporter)
{
OnnxAttrs attrs(node, ctx);
std::string const pluginName{node.op_type()};
std::string const pluginVersion{attrs.get<std::string>("plugin_version", "1")};
std::string const pluginNamespace{attrs.get<std::string>("plugin_namespace", "")};
nvinfer1::IPluginCreatorInterface* creator = importPluginCreator(ctx, pluginName, pluginVersion, pluginNamespace);
STATIC_CHECK(creator && "Plugin not found, are the plugin name, version, and namespace correct?",
nvonnxparser::ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(LocalFunctionImporter)
{
auto function = ctx->localFunctions().at(node.op_type());
STATIC_CHECK(node.input().size() == function.input().size(), ErrorCode::kINVALID_NODE, node, errors, nodeIndex);
// Create attribute map for the local function instance. Attributes can have default values (from the parent
// FunctionProto) or local values (from the NodeProto instance of the Function).
StringMap<::ONNX_NAMESPACE::AttributeProto const*> attrMap;
// Add local values first as they override any default values.
for (auto const& attr : node.attribute())
{
attrMap.insert({attr.name(), &attr});
}
// Add default values
for (auto const& attr : function.attribute_proto())
{
attrMap.insert({attr.name(), &attr});
}
// Push current function name to top of stack in order to properly set layer metadata and track attributes
ctx->localFunctionStack().push_back({node.op_type(), getNodeName(node), attrMap});
for (auto const& node : function.node())
{
onnx2trt::parseNodeStaticCheck(ctx, node, errors, nodeIndex);
}
// Pop the current function name from stack
ctx->localFunctionStack().pop_back();
}
DEFINE_OP_EMPTY_CHECKER(TRT_Scale)
DEFINE_OP_EMPTY_CHECKER(TRT_Shuffle)
DEFINE_OP_EMPTY_CHECKER(TRT_TopK_Min)
DEFINE_OP_EMPTY_CHECKER(TRT_MatMul)
DEFINE_OP_EMPTY_CHECKER(TRT_RNNv2)
DEFINE_OP_EMPTY_CHECKER(TRT_RaggedSoftmax)
DEFINE_OP_EMPTY_CHECKER(TRT_FullyConnected)
DEFINE_OP_EMPTY_CHECKER(TRT_MaxAverageBlendPool)
#if ENABLE_STD_PLUGIN
DEFINE_OP_EMPTY_CHECKER(TRT_PluginV2)
#endif // ENABLE_STD_PLUGIN
DEFINE_OP_EMPTY_CHECKER(TRT_Gather)
DEFINE_OP_EMPTY_CHECKER(TRT_Slice)
DEFINE_OP_EMPTY_CHECKER(TRT_Resize)
DEFINE_OP_EMPTY_CHECKER(TRT_FloorDiv)
DEFINE_OP_EMPTY_CHECKER(TRT_Conv)
DEFINE_OP_EMPTY_CHECKER(TRT_Deconv)
DEFINE_OP_CHECKER(TRT_MaxPool)
{
checkMaxPool(ctx, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(TRT_AveragePool)
{
checkAveragePool(ctx, node, errors, nodeIndex);
}
// Define unsupported node checkers
DEFINE_OP_CHECKER(BitShift)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(BitwiseAnd)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(BitwiseNot)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(BitwiseOr)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(BitwiseXor)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(Col2Im)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(Compress)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(ConcatFromSequence)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(ConvInteger)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(DFT)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(Det)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(ImageDecoder)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(MatMulInteger)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(MaxRoiPool)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(MaxUnpool)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(MelWeightMatrix)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(Multinomial)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(Optional)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(OptionalGetElement)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(OptionalHasElement)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}
DEFINE_OP_CHECKER(QLinearConv)
{
STATIC_CHECK(false, ErrorCode::kUNSUPPORTED_NODE, node, errors, nodeIndex);
}