mirror of
https://github.com/zebrajr/pytorch.git
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d1c092ae1b
Summary: We've made the following changes: - The new way to use the API is `m.impl_abstract_pystub(module, context)`. Every subsequent m.def of an op inside the TORCH_LIBRARY block gives the op the `impl_abstract_pystub`. - Added a mechanism to determine if an operator was defined in Python or C++. Library.define in Python appends the op to a global set, which is analogous to what we do for tracking Library.impl. - If someone does `torch.library.impl_abstract` in Python for an operator, then we require that it has an `impl_abstract_pystub` specified and we also check that the module in the `impl_abstract_pystub` is the same as the module where the call to `torch.library.impl_abstract` exists. - Unfortunately we can't check the "context" (which is the buck target on buck-based systems) because buck sits above us. bypass-github-export-checks Test Plan: - existing tests Differential Revision: D51080493 Pull Request resolved: https://github.com/pytorch/pytorch/pull/113182 Approved by: https://github.com/ezyang
98 lines
2.7 KiB
C++
98 lines
2.7 KiB
C++
#include <c10/util/irange.h>
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#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 (const auto i : c10::irange(repeat)) {
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(void)i; // Suppress unused variable warning
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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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torch::Tensor custom_nonzero(torch::Tensor x) {
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return x.nonzero();
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}
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torch::Tensor custom_sin(torch::Tensor x) {
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return x.sin();
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}
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TORCH_LIBRARY_FRAGMENT(custom, m) {
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m.impl_abstract_pystub("my_custom_ops2");
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m.def("op", custom_op);
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m.def("op2", custom_op2);
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m.def("op_with_defaults(Tensor tensor, float scalar = 1, int repeat = 1) -> Tensor[]", custom_op);
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m.def("op_with_autograd(Tensor var1, int mul, Tensor var2, Tensor? var3=None) -> Tensor", custom_op_with_autograd);
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m.def("sin(Tensor x) -> Tensor");
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m.def("cos(Tensor x) -> Tensor");
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}
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TORCH_LIBRARY_FRAGMENT(custom, m) {
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m.impl_abstract_pystub("my_custom_ops");
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m.def("nonzero(Tensor x) -> Tensor");
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}
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TORCH_LIBRARY_FRAGMENT(custom, m) {
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m.def("tan(Tensor x) -> Tensor");
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}
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TORCH_LIBRARY_IMPL(custom, CPU, m) {
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m.impl("nonzero", &custom_nonzero);
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m.impl("sin", &custom_sin);
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}
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