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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.nn.parallel._functions import _get_stream
def scatter(input, devices, streams=None):
"""Scatters tensor across multiple GPUs."""
if streams is None:
streams = [None] * len(devices)
if isinstance(input, list):
chunk_size = (len(input) - 1) // len(devices) + 1
outputs = [
scatter(input[i], [devices[i // chunk_size]],
[streams[i // chunk_size]]) for i in range(len(input))
]
return outputs
elif isinstance(input, torch.Tensor):
output = input.contiguous()
# TODO: copy to a pinned buffer first (if copying from CPU)
stream = streams[0] if output.numel() > 0 else None
if devices != [-1]:
with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
output = output.cuda(devices[0], non_blocking=True)
else:
# unsqueeze the first dimension thus the tensor's shape is the
# same as those scattered with GPU.
output = output.unsqueeze(0)
return output
else:
raise Exception(f'Unknown type {type(input)}.')
def synchronize_stream(output, devices, streams):
if isinstance(output, list):
chunk_size = len(output) // len(devices)
for i in range(len(devices)):
for j in range(chunk_size):
synchronize_stream(output[i * chunk_size + j], [devices[i]],
[streams[i]])
elif isinstance(output, torch.Tensor):
if output.numel() != 0:
with torch.cuda.device(devices[0]):
main_stream = torch.cuda.current_stream()
main_stream.wait_stream(streams[0])
output.record_stream(main_stream)
else:
raise Exception(f'Unknown type {type(output)}.')
def get_input_device(input):
if isinstance(input, list):
for item in input:
input_device = get_input_device(item)
if input_device != -1:
return input_device
return -1
elif isinstance(input, torch.Tensor):
return input.get_device() if input.is_cuda else -1
else:
raise Exception(f'Unknown type {type(input)}.')
class Scatter:
@staticmethod
def forward(target_gpus, input):
input_device = get_input_device(input)
streams = None
if input_device == -1 and target_gpus != [-1]:
# Perform CPU to GPU copies in a background stream
streams = [_get_stream(device) for device in target_gpus]
outputs = scatter(input, target_gpus, streams)
# Synchronize with the copy stream
if streams is not None:
synchronize_stream(outputs, target_gpus, streams)
return tuple(outputs)
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