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https://github.com/zebrajr/pytorch.git
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1290e586fb
Variable is now a subclass of at::Tensor backed by a VariableImpl* pImpl. The implementation of the ATen functions is defined in the auto-generated VariableType.h/cpp file. Currently, only functions which fall through to the base type, such as sizes() and isCuda() are implemented. Differentiable ops like add() and mul() will be added in a subsequent PR.
66 lines
2.0 KiB
C++
66 lines
2.0 KiB
C++
#include "basic_ops.h"
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#include "torch/csrc/autograd/variable.h"
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#include "torch/csrc/autograd/functions/utils.h"
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#include "torch/csrc/utils/auto_gpu.h"
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namespace torch { namespace autograd {
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auto Error::apply(const variable_list& grad_outputs) -> variable_list {
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throw std::runtime_error(msg);
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};
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auto DelayedError::apply(const variable_list& inputs) -> variable_list {
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tensor_list outputs;
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outputs.reserve(inputs.size());
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for (auto& var : inputs) {
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// FIXME: share version counters
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outputs.emplace_back(var.defined() ? var.data() : Tensor());
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}
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return wrap_outputs(inputs, std::move(outputs), [&](FunctionFlags f) {
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return std::make_shared<Error>(msg, std::move(f));
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});
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};
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auto Add::apply(const variable_list& inputs) -> variable_list {
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check_input_variables("Add", inputs, 2);
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auto& input1 = inputs[0].data();
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auto& input2 = inputs[1].data();
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AutoGPU guard(input1);
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at::Tensor output;
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if (input1.type().isSparse()) {
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output = input2 + input1;
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} else {
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output = input1 + input2;
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}
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return wrap_outputs(inputs, as_tensor_list(std::move(output)), [&](FunctionFlags f) {
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return std::make_shared<AddBackward>(std::move(f));
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});
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};
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auto AddBackward::apply(const variable_list& grad_outputs) -> variable_list {
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check_input_variables("AddBackward", grad_outputs, 1);
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return {grad_outputs[0], grad_outputs[0]};
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};
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auto Mul::apply(const variable_list& inputs) -> variable_list {
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check_input_variables("Mul", inputs, 2);
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AutoGPU guard(inputs[0]);
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auto& input1 = inputs[0].data();
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auto& input2 = inputs[1].data();
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auto output = input1 * input2;
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return wrap_outputs(inputs, as_tensor_list(std::move(output)), [&](FunctionFlags f) {
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return std::make_shared<MulBackward>(std::move(f));
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});
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};
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auto MulBackward::apply(const variable_list& grad_outputs) -> variable_list {
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check_input_variables("MulBackward", grad_outputs, 1);
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throw std::runtime_error("MulBackward::apply not implemented");
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};
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}} // namespace torch::autograd
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