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| #include "../sampled.h" | ||
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| #include <torch/autograd.h> | ||
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| namespace pyg { | ||
| namespace ops { | ||
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| namespace { | ||
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| using torch::autograd::Variable; | ||
| using torch::autograd::variable_list; | ||
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| class SampledOp : public torch::autograd::Function<SampledOp> { | ||
| public: | ||
| static variable_list forward(torch::autograd::AutogradContext* ctx, | ||
| const Variable& left, | ||
| const Variable& right, | ||
| const at::optional<at::Tensor> left_index, | ||
| const at::optional<at::Tensor> right_index, | ||
| const std::string fn) { | ||
| at::AutoDispatchBelowADInplaceOrView g; | ||
| Variable out = sampled_op(left, right, left_index, right_index, fn); | ||
| ctx->saved_data["has_left_index"] = left_index.has_value(); | ||
| ctx->saved_data["has_right_index"] = right_index.has_value(); | ||
| ctx->saved_data["fn"] = fn; | ||
| ctx->save_for_backward({ | ||
| left, right, | ||
| left_index.has_value() ? left_index.value() : left, // dummy | ||
| right_index.has_value() ? right_index.value() : right, // dummy | ||
| }); | ||
| return {out}; | ||
| } | ||
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| static variable_list backward(torch::autograd::AutogradContext* ctx, | ||
| variable_list grad_outs) { | ||
| auto grad_out = grad_outs[0]; | ||
| auto saved = ctx->get_saved_variables(); | ||
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| auto left = saved[0]; | ||
| auto right = saved[1]; | ||
| at::optional<at::Tensor> left_index = at::nullopt; | ||
| if (ctx->saved_data["has_left_index"].toBool()) { | ||
| left_index = saved[2]; | ||
| } | ||
| at::optional<at::Tensor> right_index = at::nullopt; | ||
| if (ctx->saved_data["has_right_index"].toBool()) { | ||
| right_index = saved[3]; | ||
| } | ||
| auto fn = ctx->saved_data["fn"].toStringRef(); | ||
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| auto grad_left = Variable(); | ||
| if (torch::autograd::any_variable_requires_grad({left})) { | ||
| grad_left = grad_out; | ||
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| if (fn == "mul") { | ||
| grad_left = | ||
| sampled_op(grad_left, right, at::nullopt, right_index, "mul"); | ||
| } else if (fn == "div") { | ||
| grad_left = | ||
| sampled_op(grad_left, right, at::nullopt, right_index, "div"); | ||
| } | ||
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| if (left_index.has_value()) { | ||
| grad_left = at::index_select_backward(grad_left, left.sizes(), 0, | ||
| left_index.value()); | ||
| } | ||
| } | ||
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| auto grad_right = Variable(); | ||
| if (torch::autograd::any_variable_requires_grad({right})) { | ||
| grad_right = grad_out; | ||
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| if (fn == "sub" && grad_out.size(0) <= right.size(0)) { | ||
| grad_right = -grad_right; | ||
| } else if (fn == "mul") { | ||
| grad_right = | ||
| sampled_op(grad_right, left, at::nullopt, left_index, "mul"); | ||
| } else if (fn == "div") { | ||
| auto tmp = sampled_op(left, right, left_index, right_index, "div"); | ||
| tmp = sampled_op(tmp, right, at::nullopt, right_index, "div"); | ||
| grad_right = -grad_right * tmp; | ||
| } | ||
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| if (right_index.has_value()) { | ||
| grad_right = at::index_select_backward(grad_right, right.sizes(), 0, | ||
| right_index.value()); | ||
| } | ||
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| if (fn == "sub" && grad_out.size(0) > right.size(0)) { | ||
| grad_right = -grad_right; | ||
| } | ||
| } | ||
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| return {grad_left, grad_right, Variable(), Variable(), Variable()}; | ||
| } | ||
| }; | ||
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| at::Tensor sampled_op_autograd(const at::Tensor& left, | ||
| const at::Tensor& right, | ||
| const at::optional<at::Tensor> left_index, | ||
| const at::optional<at::Tensor> right_index, | ||
| const std::string fn) { | ||
| return SampledOp::apply(left, right, left_index, right_index, fn)[0]; | ||
| } | ||
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| } // namespace | ||
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| TORCH_LIBRARY_IMPL(pyg, Autograd, m) { | ||
| m.impl(TORCH_SELECTIVE_NAME("pyg::sampled_op"), | ||
| TORCH_FN(sampled_op_autograd)); | ||
| } | ||
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| } // namespace ops | ||
| } // namespace pyg |
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