mirror of https://github.com/hpcaitech/ColossalAI
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
from typing import Any, Callable, List, Tuple, Union
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import torch
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import torch.nn.functional as F
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from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor
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def get_gradient_predivide_factor(world_size: int) -> float:
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factor: int = 1
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while world_size % factor == 0 and world_size / factor > factor:
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factor *= 2
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return float(factor)
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def free_storage(data: torch.Tensor) -> None:
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"""Free underlying storage of a Tensor."""
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if data.storage().size() > 0:
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# Since we're modifying the Tensor's Storage directly, make sure the Tensor
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# is the sole occupant of the Storage.
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assert data.storage_offset() == 0
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data.storage().resize_(0)
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@torch.no_grad()
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def alloc_storage(data: torch.Tensor, size: torch.Size) -> None:
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"""Allocate storage for a tensor."""
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if data.storage().size() == size.numel(): # no need to reallocate
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return
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assert data.storage().size() == 0
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data.storage().resize_(size.numel())
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def cast_tensor_to_fp16(tensor: torch.Tensor) -> torch.Tensor:
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if isinstance(tensor, StatefulTensor):
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tensor = tensor.payload
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if torch.is_floating_point(tensor) and tensor.dtype is torch.float32:
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return tensor.half()
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return tensor
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def cast_tensor_to_fp32(tensor: Union[torch.Tensor, StatefulTensor]) -> torch.Tensor:
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if isinstance(tensor, StatefulTensor):
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tensor = tensor.payload
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if torch.is_floating_point(tensor) and tensor.dtype is torch.float16:
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return tensor.float()
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return tensor
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def apply_to_tensors(x: Any, fn: Callable):
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if torch.is_tensor(x):
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return fn(x)
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elif isinstance(x, list):
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return [apply_to_tensors(t, fn) for t in x]
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elif isinstance(x, tuple):
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return tuple(apply_to_tensors(t, fn) for t in x)
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elif isinstance(x, dict):
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return {key: apply_to_tensors(val, fn) for key, val in x.items()}
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else:
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return x
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def cast_float_arguments(fn: Callable, *args: Any, **kwargs: Any) -> Tuple[Any, Any]:
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return apply_to_tensors(args, fn), apply_to_tensors(kwargs, fn)
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def chunk_and_pad(tensor: torch.Tensor, num_chunks: int) -> List[torch.Tensor]:
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"""Chunk a given Tensor into num_chunks parts and add any necessary padding."""
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chunks = list(torch.flatten(tensor).chunk(num_chunks))
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# torch.chunk may return fewer than num_chunks chunks, pad accordingly.
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num_pad_for_partial_chunk = chunks[0].numel() - chunks[-1].numel()
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if num_pad_for_partial_chunk > 0:
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chunks[-1] = F.pad(chunks[-1], [0, num_pad_for_partial_chunk])
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if len(chunks) < num_chunks:
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chunks.extend([torch.zeros_like(chunks[0]) for _ in range(num_chunks - len(chunks))])
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return chunks
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