111 lines
4.2 KiB
Python
111 lines
4.2 KiB
Python
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import threading
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import torch
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from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
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from ..modules import Module
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from torch.cuda._utils import _get_device_index
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from torch.cuda.amp import autocast
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from torch._utils import ExceptionWrapper
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__all__ = ['get_a_var', 'parallel_apply']
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def get_a_var(obj: Union[torch.Tensor, List[Any], Tuple[Any, ...], Dict[Any, Any]]) -> Optional[torch.Tensor]:
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if isinstance(obj, torch.Tensor):
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return obj
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if isinstance(obj, (list, tuple)):
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for result in map(get_a_var, obj):
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if isinstance(result, torch.Tensor):
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return result
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if isinstance(obj, dict):
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for result in map(get_a_var, obj.items()):
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if isinstance(result, torch.Tensor):
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return result
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return None
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def parallel_apply(
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modules: Sequence[Module],
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inputs: Sequence[Any],
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kwargs_tup: Optional[Sequence[Dict[str, Any]]] = None,
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devices: Optional[Sequence[Optional[Union[int, torch.device]]]] = None,
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) -> List[Any]:
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r"""Apply each `module` in :attr:`modules` in parallel on each of :attr:`devices`.
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Args:
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modules (Module): modules to be parallelized
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inputs (tensor): inputs to the modules
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devices (list of int or torch.device): CUDA devices
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:attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and
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:attr:`devices` (if given) should all have same length. Moreover, each
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element of :attr:`inputs` can either be a single object as the only argument
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to a module, or a collection of positional arguments.
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"""
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assert len(modules) == len(inputs), f'The number of modules {len(modules)} is not equal to the number of inputs {len(inputs)}'
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if kwargs_tup is not None:
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assert len(modules) == len(kwargs_tup)
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else:
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kwargs_tup = (cast(Dict[str, Any], {}),) * len(modules)
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if devices is not None:
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assert len(modules) == len(devices)
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else:
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devices = [None] * len(modules)
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devices = [_get_device_index(x, True) for x in devices]
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streams = [torch.cuda.current_stream(x) for x in devices]
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lock = threading.Lock()
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results = {}
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grad_enabled, autocast_enabled = torch.is_grad_enabled(), torch.is_autocast_enabled()
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def _worker(
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i: int,
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module: Module,
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input: Any,
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kwargs: Dict[str, Any],
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device: Optional[Union[int, torch.device]] = None,
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stream: Optional[torch.cuda.Stream] = None,
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) -> None:
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torch.set_grad_enabled(grad_enabled)
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if device is None:
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t = get_a_var(input)
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if t is None:
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with lock:
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results[i] = ExceptionWrapper(
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where=f"in replica {i}, no device was provided and no tensor input was found; "
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"device cannot be resolved")
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return
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device = t.get_device()
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if stream is None:
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stream = torch.cuda.current_stream(device)
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try:
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with torch.cuda.device(device), torch.cuda.stream(stream), autocast(enabled=autocast_enabled):
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# this also avoids accidental slicing of `input` if it is a Tensor
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if not isinstance(input, (list, tuple)):
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input = (input,)
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output = module(*input, **kwargs)
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with lock:
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results[i] = output
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except Exception:
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with lock:
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results[i] = ExceptionWrapper(
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where=f"in replica {i} on device {device}")
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if len(modules) > 1:
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threads = [threading.Thread(target=_worker,
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args=(i, module, input, kwargs, device, stream))
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for i, (module, input, kwargs, device, stream) in
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enumerate(zip(modules, inputs, kwargs_tup, devices, streams))]
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for thread in threads:
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thread.start()
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for thread in threads:
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thread.join()
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else:
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_worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0], streams[0])
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outputs = []
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for i in range(len(inputs)):
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output = results[i]
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if isinstance(output, ExceptionWrapper):
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output.reraise()
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outputs.append(output)
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return outputs
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