numb3r3 jemfu commited on
Commit
8bb94e2
1 Parent(s): 6949f0a

fix: sagemaker import issue (#6)

Browse files

- fix: sagemaker import issue (6adb9db6f1b60fe291e168c81d8a7b8226351908)


Co-authored-by: Jie Fu <jemfu@users.noreply.huggingface.co>

Files changed (1) hide show
  1. block.py +59 -0
block.py CHANGED
@@ -7,6 +7,7 @@ from functools import partial
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  from typing import Optional
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  import torch
 
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  import torch.nn as nn
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  import torch.nn.functional as F
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  from torch import Tensor
@@ -21,6 +22,64 @@ except ImportError:
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  layer_norm_fn, RMSNorm = None, None
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  class Block(nn.Module):
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  def __init__(
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  self,
 
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  from typing import Optional
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  import torch
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+ import torch.fx
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  import torch.nn as nn
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  import torch.nn.functional as F
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  from torch import Tensor
 
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  layer_norm_fn, RMSNorm = None, None
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+ def stochastic_depth(
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+ input: Tensor, p: float, mode: str, training: bool = True
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+ ) -> Tensor:
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+ """
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+ Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth"
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+ <https://arxiv.org/abs/1603.09382>`_ used for randomly dropping residual
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+ branches of residual architectures.
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+ Args:
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+ input (Tensor[N, ...]): The input tensor or arbitrary dimensions with the first one
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+ being its batch i.e. a batch with ``N`` rows.
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+ p (float): probability of the input to be zeroed.
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+ mode (str): ``"batch"`` or ``"row"``.
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+ ``"batch"`` randomly zeroes the entire input, ``"row"`` zeroes
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+ randomly selected rows from the batch.
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+ training: apply stochastic depth if is ``True``. Default: ``True``
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+ Returns:
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+ Tensor[N, ...]: The randomly zeroed tensor.
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+ """
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+ if p < 0.0 or p > 1.0:
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+ raise ValueError(f"drop probability has to be between 0 and 1, but got {p}")
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+ if mode not in ["batch", "row"]:
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+ raise ValueError(f"mode has to be either 'batch' or 'row', but got {mode}")
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+ if not training or p == 0.0:
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+ return input
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+
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+ survival_rate = 1.0 - p
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+ if mode == "row":
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+ size = [input.shape[0]] + [1] * (input.ndim - 1)
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+ else:
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+ size = [1] * input.ndim
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+ noise = torch.empty(size, dtype=input.dtype, device=input.device)
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+ noise = noise.bernoulli_(survival_rate)
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+ if survival_rate > 0.0:
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+ noise.div_(survival_rate)
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+ return input * noise
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+
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+
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+ torch.fx.wrap("stochastic_depth")
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+
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+
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+ class StochasticDepth(nn.Module):
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+ """
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+ See :func:`stochastic_depth`.
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+ """
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+
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+ def __init__(self, p: float, mode: str) -> None:
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+ super().__init__()
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+ self.p = p
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+ self.mode = mode
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+
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+ def forward(self, input: Tensor) -> Tensor:
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+ return stochastic_depth(input, self.p, self.mode, self.training)
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+
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+ def __repr__(self) -> str:
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+ s = f"{self.__class__.__name__}(p={self.p}, mode={self.mode})"
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+ return s
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+
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+
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  class Block(nn.Module):
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  def __init__(
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  self,