trt_utils.hpp

/*
 * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a
 * copy of this software and associated documentation files (the "Software"),
 * to deal in the Software without restriction, including without limitation
 * the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons to whom the
 * Software is furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
 * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
 * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 * DEALINGS IN THE SOFTWARE.
 */

#pragma once

#include "onnx2trt.hpp"
#include "Status.hpp"
#include "TensorOrWeights.hpp"

#include <NvInfer.h>
#include <cassert>
#include <cmath>

namespace onnx2trt {

inline int get_dtype_size(nvinfer1::DataType trt_dtype) {
  switch( trt_dtype ) {
  case nvinfer1::DataType::kFLOAT: return 4;
  case nvinfer1::DataType::kINT8:  return 1;
  case nvinfer1::DataType::kHALF:  return 2;
    // TODO: Some sort of error handling
  default: return -1;
    //throw std::invalid_argument("Unsupported TRT data type: " +
    //                  std::to_string((int)trt_dtype));
  }
}

inline int64_t get_shape_size(nvinfer1::Dims shape) {
  // Returns total number of elements in shape
  if( shape.nbDims == 0 ) {
    return 0;
  }
  int64_t count = 1;
  for( int d=0; d<shape.nbDims; ++d ) {
    count *= shape.d[d];
  }
  return count;
}

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];
    new_dims.type[i] = dims.type[i];
  }
  for( int i=idx; i<new_dims.nbDims; ++i ) {
    new_dims.d[i]    = dims.d[i + 1];
    new_dims.type[i] = dims.type[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;
    new_dims.type[0] = nvinfer1::DimensionType::kCHANNEL;
  }
  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::Dims set_dims_CHW(nvinfer1::Dims const& dims) {
  nvinfer1::Dims new_dims = dims;
  assert(new_dims.nbDims > 0);
  new_dims.type[0] = nvinfer1::DimensionType::kCHANNEL;
  for( int i=1; i<new_dims.nbDims; ++i ) {
    new_dims.type[i] = nvinfer1::DimensionType::kSPATIAL;
  }
  return new_dims;
}

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 nvinfer1::DimsHW get_DimsHW_from_CHW(nvinfer1::Dims dims) {
  assert(dims.nbDims == 3);
  return nvinfer1::DimsHW(dims.d[1], dims.d[2]);
}

inline TensorOrWeights identity(IImporterContext* ctx,
                                TensorOrWeights input) {
  if( input.is_weights() ) {
    return input;
  } else {
    auto* layer = ctx->network()->addShuffle(input.tensor());
    if( !layer ) {
      return nullptr;
    }
    return layer->getOutput(0);
  }
}

namespace detail {

template<typename T, class Func>
void apply_unary_function(T const* idata,
                          T*       odata,
                          size_t   count,
                          Func     func) {
  for( size_t i=0; i<count; ++i ) {
    odata[i] = func(idata[i]);
  }
}

template<typename T>
Status apply_unary_function(T const* idata,
                           T*       odata,
                           size_t   count,
                           nvinfer1::UnaryOperation func) {
#define TRTUTILS_APPLY_UNARY_FUNC(func) \
  apply_unary_function(idata, odata, count, [](T x) { return func; })

  using namespace nvinfer1;
  switch( func ) {
  case UnaryOperation::kEXP:   TRTUTILS_APPLY_UNARY_FUNC(exp(x));   break;
  case UnaryOperation::kLOG:   TRTUTILS_APPLY_UNARY_FUNC(log(x));   break;
  case UnaryOperation::kSQRT:  TRTUTILS_APPLY_UNARY_FUNC(sqrt(x));  break;
  case UnaryOperation::kRECIP: TRTUTILS_APPLY_UNARY_FUNC(T(1) / x); break;
  case UnaryOperation::kABS:   TRTUTILS_APPLY_UNARY_FUNC(fabs(x));  break;
  case UnaryOperation::kNEG:   TRTUTILS_APPLY_UNARY_FUNC(-x);       break;
  default: return MAKE_ERROR("Unsupported unary function",
                             ErrorCode::kUNSUPPORTED_NODE);
  }
  return Status::success();

#undef TRTUTILS_APPLY_UNARY_FUNC
}

} // namespace detail

// TODO: This actually uses a combination of ONNX and TRT, so it may belong
//       in a different file.
inline Status apply_unary_function(ShapedWeights const& iweights,
                                   ShapedWeights*       oweights,
                                   nvinfer1::UnaryOperation func) {
  assert(iweights.type == oweights->type);
  assert(iweights.shape == oweights->shape);
  void const* idata = iweights.values;
  void* odata       = const_cast<void*>(oweights->values);
  size_t count = iweights.count();
  switch( iweights.type ) {
  case ::ONNX_NAMESPACE::TensorProto::FLOAT:
    return detail::apply_unary_function(
      (float*)idata, (float*)odata, count, func);
  default:
    return MAKE_ERROR("Unsupported weights data type for unary function",
                      ErrorCode::kUNSUPPORTED_NODE);
  }
}

ValueOrStatus<std::vector<TensorOrWeights>>
inline apply_unary_function(IImporterContext* ctx,
                            TensorOrWeights& input,
                            nvinfer1::UnaryOperation func) {
  if( input.is_weights() ) {
    ShapedWeights const& weights = input.weights();
    ShapedWeights new_weights =
      ctx->createTempWeights(weights.type, weights.shape);
    // TODO: This is a bit ugly (but safe because they share the same values
    //       pointer).
    TRT_CHECK(apply_unary_function(weights, &new_weights, func));
    return {{new_weights}};
  } else {
    auto* layer = ctx->network()->addUnary(
      input.tensor(), func);
    ASSERT(layer, ErrorCode::kUNSUPPORTED_NODE);
    return {{layer->getOutput(0)}};
  }
}

} // namespace onnx2trt