/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators/half_float_ops.h (2732B)
#ifndef CAFFE2_OPERATORS_HALF_FLOAT_OPS_H_
#define CAFFE2_OPERATORS_HALF_FLOAT_OPS_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
namespace caffe2 {
template
class FloatToHalfOp : public Operator {
public:
explicit FloatToHalfOp(const OperatorDef& operator_def, Workspace* ws)
: Operator(operator_def, ws),
clip_(this->template GetSingleArgument("clip", false)) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
private:
bool clip_;
};
template
class HalfToFloatOp : public Operator {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
USE_SIMPLE_CTOR_DTOR(HalfToFloatOp);
bool RunOnDevice() override;
};
class Float16ConstantFillOp : public Operator {
public:
template
explicit Float16ConstantFillOp(Args&&... args)
: Operator(std::forward(args)...),
shape_(this->template GetRepeatedArgument("shape")) {}
USE_OPERATOR_FUNCTIONS(CPUContext);
virtual ~Float16ConstantFillOp() {}
bool RunOnDevice() override;
private:
vector shape_;
};
template
class Float16UniformFillOp : public Operator {
public:
template
explicit Float16UniformFillOp(Args&&... args)
: Operator(std::forward(args)...),
shape_(this->template GetRepeatedArgument("shape")),
min_(this->template GetSingleArgument("min", 0)),
max_(this->template GetSingleArgument("max", 1)) {
if (InputSize() == 3) {
CAFFE_ENFORCE(
!this->template HasSingleArgumentOfType("min"),
"Cannot set both min arg and min input blob");
CAFFE_ENFORCE(
!this->template HasSingleArgumentOfType("max"),
"Cannot set both max arg and max input blob");
} else {
CAFFE_ENFORCE_LT(
min_, max_, "Max value should be bigger than min value.");
}
}
USE_OPERATOR_CONTEXT_FUNCTIONS;
virtual ~Float16UniformFillOp() {}
bool RunOnDevice() override;
private:
vector shape_;
float min_;
float max_;
Tensor temp_data_buffer_;
};
inline std::vector Float16FillerTensorInference(
const OperatorDef& def,
const vector& in) {
vector out(1);
ArgumentHelper helper(def);
out[0].set_data_type(static_cast(
helper.GetSingleArgument("dtype", TensorProto_DataType_FLOAT16)));
auto shape = helper.GetRepeatedArgument("shape");
for (int d : shape) {
out[0].add_dims(d);
}
return out;
}
} // namespace caffe2
#endif // CAFFE2_OPERATORS_HALF_FLOAT_OPS_H_