188 lines
6.9 KiB
Python
188 lines
6.9 KiB
Python
from torch.nn import DataParallel
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from torch.nn.parallel import DistributedDataParallel
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import itertools
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from itertools import chain
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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 _find_tensors(obj): # pragma: no cover
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r"""
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Recursively find all tensors contained in the specified object.
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"""
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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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return itertools.chain(*map(_find_tensors, obj))
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if isinstance(obj, dict):
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return itertools.chain(*map(_find_tensors, obj.values()))
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return []
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def get_a_var(obj): # pragma: no cover
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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 forward(self, *inputs, **kwargs):
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if not self.device_ids:
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return self.module(*inputs, **kwargs)
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for t in chain(self.module.parameters(), self.module.buffers()):
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if t.device != self.src_device_obj:
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raise RuntimeError("module must have its parameters and buffers "
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"on device {} (device_ids[0]) but found one of "
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"them on device: {}".format(self.src_device_obj, t.device))
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inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
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if len(self.device_ids) == 1:
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# lightning
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if self.module.training:
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return self.module.training_step(*inputs[0], **kwargs[0])
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else:
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return self.module.validation_step(*inputs[0], **kwargs[0])
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replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
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outputs = self.parallel_apply(replicas, inputs, kwargs)
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return self.gather(outputs, self.output_device)
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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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class LightningDistributedDataParallel(DistributedDataParallel):
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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 forward(self, *inputs, **kwargs): # pragma: no cover
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self._sync_params()
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if self.device_ids:
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inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
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if len(self.device_ids) == 1:
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# --------------
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# LIGHTNING MOD
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# --------------
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# normal
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# output = self.module(*inputs[0], **kwargs[0])
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# lightning
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if self.module.training:
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output = self.module.training_step(*inputs[0], **kwargs[0])
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else:
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output = self.module.validation_step(*inputs[0], **kwargs[0])
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else:
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outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)
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output = self.gather(outputs, self.output_device)
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else:
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output = self.module(*inputs, **kwargs)
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if torch.is_grad_enabled():
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# We'll return the output object verbatim since it is a freeform
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# object. We need to find any tensors in this object, though,
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# because we need to figure out which parameters were used during
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# this forward pass, to ensure we short circuit reduction for any
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# unused parameters. Only if `find_unused_parameters` is set.
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if self.find_unused_parameters:
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self.reducer.prepare_for_backward(list(_find_tensors(output)))
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else:
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self.reducer.prepare_for_backward([])
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return output
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def parallel_apply(modules, inputs, kwargs_tup=None, devices=None): # pragma: no cover
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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
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