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1433160a36
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: malfet Differential Revision: D31705366 fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
93 lines
2.9 KiB
C++
93 lines
2.9 KiB
C++
#pragma once
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#include "caffe2/core/context.h"
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#include "caffe2/core/logging.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/operators/filler_op.h"
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#include "caffe2/utils/cast.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <typename T, class Context>
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class GivenTensorFillOp final : public FillerOp<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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explicit GivenTensorFillOp(const OperatorDef& operator_def, Workspace* ws)
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: FillerOp<Context>(operator_def, ws) {
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const ArgumentHelper helper(operator_def);
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// GivenTensorFillOp can be provided with a "dtype" arg if float is
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// is specified as T. Otherwise, "dtype" is ignored.
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// In the ideal world, we would get rid of templating of T at all, but we
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// need to provide backwards compatibility.
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if (!std::is_same<T, float>::value || !helper.HasArgument("dtype")) {
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ExtractValues<T>();
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} else {
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auto dtype = cast::GetCastDataType(helper, "dtype");
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switch (dtype) {
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case TensorProto_DataType_FLOAT:
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ExtractValues<float>();
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break;
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case TensorProto_DataType_DOUBLE:
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ExtractValues<double>();
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break;
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case TensorProto_DataType_BOOL:
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ExtractValues<bool>();
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break;
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case TensorProto_DataType_INT16:
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ExtractValues<int16_t>();
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break;
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case TensorProto_DataType_INT32:
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ExtractValues<int>();
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break;
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case TensorProto_DataType_INT64:
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ExtractValues<int64_t>();
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break;
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case TensorProto_DataType_STRING:
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ExtractValues<std::string>();
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break;
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case TensorProto_DataType_UNDEFINED:
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CAFFE_THROW("Cannot have undefined 'dtype' argument");
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default:
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CAFFE_THROW("Unexpected 'dtype' argument value: ", dtype);
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}
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}
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}
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bool Fill(Tensor* output) override {
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return (this->*body_)(output);
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}
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private:
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template <typename Type>
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void ExtractValues() {
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auto source_values = this->template GetRepeatedArgument<Type>("values");
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ReinitializeTensor(
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&values_,
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{static_cast<int64_t>(source_values.size())},
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at::dtype<Type>().device(CPU));
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Type* values_data = values_.template mutable_data<Type>();
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// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
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for (const auto i : c10::irange(source_values.size())) {
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values_data[i] = static_cast<Type>(source_values[i]);
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}
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body_ = &GivenTensorFillOp::FillWithType<Type>;
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}
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template <typename Type>
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bool FillWithType(Tensor* output) {
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CAFFE_ENFORCE_EQ(output->numel(), values_.numel());
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auto* data = output->template mutable_data<Type>();
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const Type* values_data = values_.template data<Type>();
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if (output->numel()) {
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context_.CopyItemsFromCPU(
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TypeMeta::Make<Type>(), output->numel(), values_data, data);
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}
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return true;
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}
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bool (GivenTensorFillOp::*body_)(Tensor* output);
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Tensor values_;
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};
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} // namespace caffe2
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