mirror of https://github.com/hpcaitech/ColossalAI
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
from typing import Dict, List, Tuple, Union
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import torch
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from torch.fx import Node
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from .._compatibility import compatibility, is_compatible_with_meta
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__all__ = ["activation_size", "parameter_size", "is_inplace"]
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@compatibility(is_backward_compatible=True)
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def activation_size(out: Union[torch.Tensor, Dict, List, Tuple, int]) -> int:
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"""Calculate activation size of a node.
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Args:
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activation (Union[torch.Tensor, Dict, List, Tuple, int]): The activation of a `torch.nn.Module` or `torch.nn.functional`.
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Returns:
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int: The activation size, unit is byte.
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"""
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act_size = 0
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if isinstance(out, torch.Tensor):
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if out.is_quantized:
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act_size += out.numel() * torch._empty_affine_quantized([], dtype=out.dtype).element_size()
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else:
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act_size += out.numel() * torch.tensor([], dtype=out.dtype).element_size()
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elif isinstance(out, dict):
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value_list = [v for _, v in out.items()]
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act_size += activation_size(value_list)
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elif isinstance(out, tuple) or isinstance(out, list) or isinstance(out, set):
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for element in out:
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act_size += activation_size(element)
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return act_size
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@compatibility(is_backward_compatible=True)
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def parameter_size(mod: torch.nn.Module) -> int:
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"""Calculate parameter size of a node.
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Args:
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mod (torch.nn.Module): The target `torch.nn.Module`.
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Returns:
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int: The parameter size, unit is byte.
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"""
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param_size = 0
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for param in mod.parameters():
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param_size += param.numel() * torch.tensor([], dtype=param.dtype).element_size()
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return param_size
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def is_inplace(n: Node):
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"""Get the inplace argument from torch.fx.Node
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Args:
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node (Node): torch.fx.Node
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Returns:
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bool: indicates whether this op is inplace
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"""
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inplace = False
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if n.op == "call_function":
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inplace = n.kwargs.get("inplace", False)
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if is_compatible_with_meta():
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from .constants import ALIAS_ATEN
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if n.target in ALIAS_ATEN:
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inplace = True
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elif n.op == "call_module":
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inplace = getattr(n.graph.owning_module.get_submodule(n.target), "inplace", False)
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return inplace
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