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https://github.com/zebrajr/pytorch.git
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d605058212
This removes volatile from Variable. The functionality is mostly replaced by a global (thread-local) flag, which is controlled by torch.set_grad_enabled() and the context manager torch.no_grad(). In C++, the flag is exposed through GradMode::is_enabled() and GradMode::set_enabled() Fixes #3627
58 lines
1.5 KiB
C++
58 lines
1.5 KiB
C++
#include "torch/csrc/autograd/functions/utils.h"
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#include "torch/csrc/utils/functional.h"
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#include "torch/csrc/jit/tracer.h"
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#include "torch/csrc/autograd/variable.h"
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#include <sstream>
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namespace torch { namespace autograd {
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variable_list wrap_outputs(const variable_list& inputs, tensor_list&& outputs,
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function_constructor ctr) {
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auto flags = Function::flags(inputs);
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variable_list result;
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result.reserve(outputs.size());
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if (!flags.is_executable) {
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for (auto& output : outputs) {
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if (output.defined()) {
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result.emplace_back(make_variable(output, false));
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} else {
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result.emplace_back();
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}
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}
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} else {
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auto grad_fn = ctr(std::move(flags));
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for (auto& output : outputs) {
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if (output.defined()) {
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result.emplace_back(make_variable(output, grad_fn));
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} else {
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++grad_fn->num_inputs;
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result.emplace_back();
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}
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}
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}
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return result;
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}
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void check_input_variables(const char* name, const variable_list& inputs, int args, int required_args) {
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if (required_args == -1) {
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required_args = args;
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}
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if (inputs.size() != (size_t)args) {
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std::stringstream ss;
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ss << name << ": expected " << args << " arguments (got " << inputs.size();
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ss << ")";
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throw std::runtime_error(ss.str());
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}
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for (int i = 0; i < required_args; ++i) {
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if (!inputs[i].defined()) {
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std::stringstream ss;
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ss << name << ": expected Variable at argument " << i << " (got None)";
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throw std::runtime_error(ss.str());
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}
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}
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}
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}}
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