105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
from torch.nn import DataParallel
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import threading
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import torch
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from torch.cuda._utils import _get_device_index
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import pdb
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def get_a_var(obj):
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if isinstance(obj, torch.Tensor):
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return obj
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if isinstance(obj, list) or isinstance(obj, 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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class LightningDataParallel(DataParallel):
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"""
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Override the forward call in lightning so it goes to training and validation step respectively
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"""
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def parallel_apply(self, replicas, inputs, kwargs):
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return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
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def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):
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r"""Applies each `module` in :attr:`modules` in parallel on arguments
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contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
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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)
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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 = ({},) * 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 = list(map(lambda x: _get_device_index(x, True), devices))
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lock = threading.Lock()
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results = {}
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grad_enabled = torch.is_grad_enabled()
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def _worker(i, module, input, kwargs, device=None):
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torch.set_grad_enabled(grad_enabled)
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if device is None:
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device = get_a_var(input).get_device()
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try:
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with torch.cuda.device(device):
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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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# ---------------
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# CHANGE
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if module.training:
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output = module.training_step(*input, **kwargs)
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else:
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output = module.validation_step(*input, **kwargs)
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# ---------------
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with lock:
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results[i] = output
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except Exception as e:
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with lock:
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results[i] = e
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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))
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for i, (module, input, kwargs, device) in
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enumerate(zip(modules, inputs, kwargs_tup, devices))]
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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])
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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, Exception):
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raise output
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outputs.append(output)
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return outputs |