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trt_utils.hpp
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trt_utils.hpp
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/*
* SPDX-License-Identifier: Apache-2.0
*/
#pragma once
#include "Status.hpp"
#include "TensorOrWeights.hpp"
#include "onnx2trt.hpp"
#include <NvInfer.h>
#include <algorithm>
#include <cassert>
#include <cmath>
namespace onnx2trt
{
inline int getDtypeSize(nvinfer1::DataType trtDtype)
{
switch (trtDtype)
{
case nvinfer1::DataType::kFLOAT: return 4;
case nvinfer1::DataType::kUINT8:
case nvinfer1::DataType::kINT8: return 1;
case nvinfer1::DataType::kHALF: return 2;
case nvinfer1::DataType::kINT32:
return 4;
// TRT does not support booleans as a native type, so we treat them like int32 values.
case nvinfer1::DataType::kBOOL:
return 4;
// TODO: Some sort of error handling
default: return -1;
}
}
inline nvinfer1::Dims insert_dim(nvinfer1::Dims const& dims, int idx, int value)
{
assert(idx < dims.nbDims + 1);
nvinfer1::Dims new_dims;
new_dims.nbDims = dims.nbDims + 1;
for (int i = 0; i < idx; ++i)
{
new_dims.d[i] = dims.d[i];
}
new_dims.d[idx] = value;
for (int i = idx + 1; i < new_dims.nbDims; ++i)
{
new_dims.d[i] = dims.d[i - 1];
}
return new_dims;
}
inline nvinfer1::Dims remove_dim(nvinfer1::Dims const& dims, int idx)
{
assert(idx < dims.nbDims);
nvinfer1::Dims new_dims;
new_dims.nbDims = dims.nbDims - 1;
for (int i = 0; i < idx; ++i)
{
new_dims.d[i] = dims.d[i];
}
for (int i = idx; i < new_dims.nbDims; ++i)
{
new_dims.d[i] = dims.d[i + 1];
}
// Special case for scalar result (i.e., there was only one dim originally)
if (new_dims.nbDims == 0)
{
new_dims.nbDims = 1;
new_dims.d[0] = 1;
}
return new_dims;
}
// Adds unitary dimensions on the left
inline nvinfer1::Dims expand_dims(nvinfer1::Dims const& dims, int ndim_new)
{
assert(dims.nbDims <= ndim_new);
nvinfer1::Dims new_dims;
new_dims.nbDims = ndim_new;
int j = 0;
for (; j < ndim_new - dims.nbDims; ++j)
{
new_dims.d[j] = 1;
}
for (int i = 0; i < dims.nbDims; ++i, ++j)
{
new_dims.d[j] = dims.d[i];
}
return new_dims;
}
inline nvinfer1::Permutation remove_first_dim(nvinfer1::Permutation const& perm)
{
assert(perm.order[0] == 0);
nvinfer1::Permutation new_perm;
int ndim = nvinfer1::Dims::MAX_DIMS;
for (int i = 0; i < ndim - 1; ++i)
{
new_perm.order[i] = perm.order[i + 1] - 1;
}
return new_perm;
}
inline nvinfer1::DimsHW operator-(nvinfer1::DimsHW dims)
{
return nvinfer1::DimsHW(-dims.h(), -dims.w());
}
// Note: These are used for checking beg_padding == end_padding
inline bool operator==(nvinfer1::Dims const& a, nvinfer1::Dims const& b)
{
if (a.nbDims != b.nbDims)
{
return false;
}
for (int i = 0; i < a.nbDims; ++i)
{
if (a.d[i] != b.d[i])
{
return false;
}
}
return true;
}
inline bool operator!=(nvinfer1::Dims const& a, nvinfer1::Dims const& b)
{
return !(a == b);
}
inline TensorOrWeights identity(IImporterContext* ctx, TensorOrWeights input)
{
if (input.is_weights())
{
return input;
}
else
{
auto* layer = ctx->network()->addIdentity(input.tensor());
if (!layer)
{
return nullptr;
}
return layer->getOutput(0);
}
}
inline ::ONNX_NAMESPACE::TensorProto_DataType trtDataTypeToONNX(nvinfer1::DataType dt)
{
switch (dt)
{
case nvinfer1::DataType::kFLOAT: return ::ONNX_NAMESPACE::TensorProto::FLOAT;
case nvinfer1::DataType::kHALF: return ::ONNX_NAMESPACE::TensorProto::FLOAT16;
case nvinfer1::DataType::kINT32: return ::ONNX_NAMESPACE::TensorProto::INT32;
case nvinfer1::DataType::kINT8: return ::ONNX_NAMESPACE::TensorProto::INT8;
case nvinfer1::DataType::kBOOL: return ::ONNX_NAMESPACE::TensorProto::BOOL;
case nvinfer1::DataType::kUINT8: return ::ONNX_NAMESPACE::TensorProto::UINT8;
default: return ::ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED;
}
throw std::runtime_error{"Unreachable"};
}
} // namespace onnx2trt