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#pragma once
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/distributed/autograd/context/context.h>
#include <torch/csrc/distributed/autograd/rpc_messages/autograd_metadata.h>
#include <torch/csrc/distributed/rpc/rpc_agent.h>
namespace torch {
namespace distributed {
namespace autograd {
// Forward declarations.
class DistAutogradContext;
// As part of our distributed autograd implementation, whenever we receive an
// RPC from a node, we add a 'RecvRpcBackward' autograd function to the
// autograd graph. This is more or less a placeholder function that is used to
// pass gradients to the remote host during the backward pass. The inputs to the
// RPC function are the inputs to this autograd function.
class TORCH_API RecvRpcBackward : public torch::autograd::Node {
public:
explicit RecvRpcBackward(
const AutogradMetadata& autogradMetadata,
std::shared_ptr<DistAutogradContext> autogradContext,
rpc::worker_id_t fromWorkerId,
rpc::DeviceMap deviceMap);
torch::autograd::variable_list apply(
torch::autograd::variable_list&& grads) override;
private:
const AutogradMetadata autogradMetadata_;
// Hold a weak reference to the autograd context to avoid circular
// dependencies with the context (since it holds a reference to
// RecvRpcBackward).
std::weak_ptr<DistAutogradContext> autogradContext_;
// The worker id from which the RPC was received. During the backward pass,
// we need to propagate the gradients to this workerId.
rpc::worker_id_t fromWorkerId_;
// Device mapping for tensors sent over RPC.
const rpc::DeviceMap deviceMap_;
};
} // namespace autograd
} // namespace distributed
} // namespace torch
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