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#include <torch/csrc/distributed/rpc/python_call.h>
#include <c10/util/C++17.h>
namespace torch {
namespace distributed {
namespace rpc {
PythonCall::PythonCall(SerializedPyObj&& serializedPyObj, bool isAsyncExecution)
: serializedPyObj_(std::move(serializedPyObj)),
isAsyncExecution_(isAsyncExecution) {}
c10::intrusive_ptr<Message> PythonCall::toMessageImpl() && {
std::vector<char> payload;
payload.reserve(serializedPyObj_.payload_.length() + 1);
payload.push_back(isAsyncExecution_ ? 1 : 0);
payload.insert(
payload.end(),
serializedPyObj_.payload_.begin(),
serializedPyObj_.payload_.end());
return c10::make_intrusive<Message>(
std::move(payload),
std::move(serializedPyObj_.tensors_),
MessageType::PYTHON_CALL);
}
std::unique_ptr<PythonCall> PythonCall::fromMessage(const Message& message) {
TORCH_INTERNAL_ASSERT(
message.payload().size() >= 1,
"Failed to convert an RPC message to PythonCall, the payload should at "
"least contain one byte indicating whether this is an async function, "
"but got payload of size ",
message.payload().size());
const char& c = message.payload()[0];
TORCH_INTERNAL_ASSERT(c == 0 || c == 1);
bool isAsyncExecution = (c == 1);
std::string payload(message.payload().begin() + 1, message.payload().end());
std::vector<Tensor> tensors = message.tensors();
SerializedPyObj serializedPyObj(std::move(payload), std::move(tensors));
return std::make_unique<PythonCall>(
std::move(serializedPyObj), isAsyncExecution);
}
const SerializedPyObj& PythonCall::serializedPyObj() const {
return serializedPyObj_;
}
} // namespace rpc
} // namespace distributed
} // namespace torch
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