U Jºck ã@sedœdd„ZdZdZdZdS)©Z!enable_moving_cpu_tensors_to_cudacCst|r tntS)N)Ú_TEMPLATE_PREFIXÚ:_REMOTE_FORWARD_TEMPLATE_ENABLE_MOVING_CPU_TENSORS_TO_CUDAÚ_REMOTE_FORWARD_TEMPLATEr©rú]/tmp/pip-unpacked-wheel-gikjz4vx/torch/distributed/nn/jit/templates/remote_module_template.pyÚget_remote_module_templates ÿýra±from typing import * import torch import torch.distributed.rpc as rpc from torch import Tensor from torch._jit_internal import Future from torch.distributed.rpc import RRef from typing import Tuple # pyre-ignore: unused import {assign_module_interface_cls} def forward_async(self, {arg_types}){arrow_and_future_return_type}: args = (self.module_rref, self.device, self.is_device_map_set, {args}) kwargs = {{{kwargs}}} return rpc.rpc_async( self.module_rref.owner(), _remote_forward, args, kwargs, ) def forward(self, {arg_types}){arrow_and_return_type}: args = (self.module_rref, self.device, self.is_device_map_set, {args}) kwargs = {{{kwargs}}} ret_fut = rpc.rpc_async( self.module_rref.owner(), _remote_forward, args, kwargs, ) return ret_fut.wait() _generated_methods = [ forward_async, forward, ] {jit_script_decorator} aä def _remote_forward( module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, {arg_types}){arrow_and_return_type}: module = module_rref.local_value() device = torch.device(device) if device.type != "cuda": return module.forward({args}, {kwargs}) # If the module is on a cuda device, # move any CPU tensor in args or kwargs to the same cuda device. # Since torch script does not support generator expression, # have to use concatenation instead of # ``tuple(i.to(device) if isinstance(i, Tensor) else i for i in *args)``. args = ({args},) out_args: Tuple[()] = () for arg in args: arg = (arg.to(device),) if isinstance(arg, Tensor) else (arg,) out_args = out_args + arg kwargs = {{{kwargs}}} for k, v in kwargs.items(): if isinstance(v, Tensor): kwargs[k] = kwargs[k].to(device) if is_device_map_set: return module.forward(*out_args, {kwargs}) # If the device map is empty, then only CPU tensors are allowed to send over wire, # so have to move any GPU tensor to CPU in the output. # Since torch script does not support generator expression, # have to use concatenation instead of # ``tuple(i.cpu() if isinstance(i, Tensor) else i for i in module.forward(*out_args, {kwargs}))``. ret: Tuple[()] = () for i in module.forward(*out_args, {kwargs}): i = (i.cpu(),) if isinstance(i, Tensor) else (i,) ret = ret + i return ret zâ def _remote_forward( module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, {arg_types}){arrow_and_return_type}: module = module_rref.local_value() return module.forward({args}, {kwargs}) N)ÚboolrrrrrrrrÚs1(