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https://github.com/zebrajr/pytorch.git
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cdc56d0b6c
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37700 Certain autograd functions can have optional Tensor arguments. For this purpose it would be nice to support c10::optional<Tensor> as an argument for C++ autograd functions. I've added the appropriate overload to ExtractVariables to ensure this works. For an example, you can look at D21272807 in terms of how this is used. ghstack-source-id: 103541789 Test Plan: waitforbuildbot Differential Revision: D21363491 fbshipit-source-id: 0c8665e9bfe279e6b9ab84a889524fea11fa971c
78 lines
2.4 KiB
C++
78 lines
2.4 KiB
C++
#include <torch/script.h>
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#include "op.h"
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#include <cstddef>
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#include <string>
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torch::List<torch::Tensor> custom_op(
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torch::Tensor tensor,
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double scalar,
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int64_t repeat) {
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torch::List<torch::Tensor> output;
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output.reserve(repeat);
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for (int64_t i = 0; i < repeat; ++i) {
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output.push_back(tensor * scalar);
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}
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return output;
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}
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int64_t custom_op2(std::string s1, std::string s2) {
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return s1.compare(s2);
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}
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struct CustomOpAutogradFunction : public torch::autograd::Function<CustomOpAutogradFunction> {
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static torch::Tensor forward(
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torch::autograd::AutogradContext* ctx,
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torch::Tensor var1,
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int64_t mul,
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torch::Tensor var2,
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c10::optional<torch::Tensor> var3) {
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ctx->saved_data["mul"] = mul;
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ctx->saved_data["var3_has_value"] = var3.has_value();
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ctx->save_for_backward({var1, var2});
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if (var3) {
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return var1 + mul * var2 + var1 * var2 + var3.value();
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}
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return var1 + mul*var2 + var1*var2;
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}
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static torch::autograd::variable_list backward(torch::autograd::AutogradContext *ctx, torch::autograd::variable_list grad_output) {
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int mul = ctx->saved_data["mul"].toInt();
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bool var3_has_value = ctx->saved_data["var3_has_value"].toBool();
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auto saved = ctx->get_saved_variables();
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auto var1 = saved[0];
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auto var2 = saved[1];
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auto var3_grad = var3_has_value ? grad_output[0] : torch::Tensor();
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torch::autograd::variable_list output = {
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grad_output[0] + grad_output[0] * var2,
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torch::Tensor(),
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grad_output[0] * mul + grad_output[0] * var1,
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var3_grad};
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return output;
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}
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};
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torch::Tensor custom_op_with_autograd(
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torch::Tensor var1,
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int64_t mul,
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torch::Tensor var2,
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c10::optional<torch::Tensor> var3) {
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return CustomOpAutogradFunction::apply(var1, mul, var2, var3);
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}
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static auto registry =
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torch::RegisterOperators()
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// We parse the schema for the user.
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.op("custom::op", &custom_op)
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.op("custom::op2", &custom_op2)
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// User provided schema. Among other things, allows defaulting values,
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// because we cannot infer default values from the signature. It also
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// gives arguments meaningful names.
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.op("custom::op_with_defaults(Tensor tensor, float scalar = 1, int repeat = 1) -> Tensor[]",
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&custom_op)
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.op("custom::op_with_autograd(Tensor var1, int mul, Tensor var2, Tensor? var3=None) -> Tensor",
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&custom_op_with_autograd);
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