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62 lines
1.9 KiB
62 lines
1.9 KiB
import torch
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from colossalai.zero.sharded_param import ShardedParamV2
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from colossalai.utils import get_current_device
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from typing import List
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class BucketizedTensorCopy(object):
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def __init__(
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self,
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chunk_size: int,
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):
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r"""
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torch.nn.Parameter CPU (fp32) -> ShardedParam GPU (fp16)
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TODO(jiaruifang) The class is a little bit hardcoded
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I will make it more general later.
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"""
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self.chunk_size = chunk_size
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self._offset = 0
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self._cpu_buffer = torch.empty(chunk_size, dtype=torch.float, device=torch.device("cpu:0"), pin_memory=True)
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self._cuda_buffer = torch.empty(chunk_size,
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dtype=torch.half,
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device=torch.device(f"cuda:{get_current_device()}"))
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self._buffered_param_list: List[ShardedParamV2] = []
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self._numel_list = []
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def copy(self, src_param: torch.nn.Parameter, target_param: ShardedParamV2):
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assert isinstance(target_param, ShardedParamV2)
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assert isinstance(src_param, torch.nn.Parameter)
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numel = src_param.numel()
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if self._offset + numel > self.chunk_size:
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self.flush()
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assert src_param.data.device.type == 'cpu'
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self._cpu_buffer.narrow(0, self._offset, numel).copy_(src_param.data.view(-1))
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self._buffered_param_list.append(target_param)
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self._numel_list.append(numel)
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self._offset += numel
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def flush(self):
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"""
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flush to cuda memory
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"""
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self._cuda_buffer.copy_(self._cpu_buffer)
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flush_offset = 0
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for sparam, numel in zip(self._buffered_param_list, self._numel_list):
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sparam.sharded_data_tensor.copy_payload(self._cpu_buffer.narrow(0, flush_offset, numel))
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flush_offset += numel
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self.reset()
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def reset(self):
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self._buffered_param_list = []
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self._numel_list = []
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self._offset = 0
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