67 lines
3.4 KiB
Python
67 lines
3.4 KiB
Python
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from torch.fx.graph_module import GraphModule
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from typing import Any, Callable, Dict, List, Tuple, Type
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import torch
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import torch.nn as nn
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from torch.fx._compatibility import compatibility
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__all__ = ['default_matching', 'extract_attrs_for_lowering', 'lift_lowering_attrs_to_nodes']
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# Matching method matches the attribute name of current version to the attribute name of `target_version`
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@compatibility(is_backward_compatible=False)
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def default_matching(name: str, target_version: int) -> str:
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"""Default matching method
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"""
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return name
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# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering.
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# The first integer in the tuple is the version number of the nn.Module class when we create the parameter list.
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# If there's a version mismatch then it means the parameter names in the book might be mismatched with nn.Module.
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module_fetch_book: Dict[Type, Tuple[int, List[str], Callable[[str, int], str]]] = {
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torch.nn.modules.linear.Linear: (1, ["weight", "bias"], default_matching),
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torch.nn.modules.conv.Conv2d: (
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1, ["weight", "bias", "kernel_size", "stride", "padding", "dilation", "groups", "padding_mode"], default_matching
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),
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torch.nn.modules.batchnorm.BatchNorm2d: (2, ["weight", "bias", "running_mean", "running_var", "eps"], default_matching),
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torch.nn.modules.pooling.AdaptiveAvgPool2d: (1, [], default_matching),
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torch.nn.modules.pooling.MaxPool2d: (
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1, ["kernel_size", "stride", "padding", "dilation", "return_indices", "ceil_mode"], default_matching
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),
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torch.nn.modules.activation.ReLU: (1, ["inplace"], default_matching),
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}
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@compatibility(is_backward_compatible=False)
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def extract_attrs_for_lowering(mod: nn.Module) -> Dict[str, Any]:
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"""If `mod` is in `module_fetch_book`, fetch the mod's attributes that in the `module_fetch_book`
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after checking module's version is compatible with the `module_fetch_book`.
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"""
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attrs_for_lowering: Dict[str, Any] = {}
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attrs_for_lowering["name"] = torch.typename(mod)
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if type(mod) in module_fetch_book:
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version, param_to_fetch, matching_method = module_fetch_book[type(mod)]
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if version < mod._version:
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raise RuntimeError(f"Fetcher version {version} try to fetch {torch.typename(mod)} version {mod._version}, "
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"please upgrade the module_fetch_book, open an issue and @842974287 "
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"or report a bug to AIACC team directly.")
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for attr in param_to_fetch:
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attrs_for_lowering[attr] = getattr(mod, matching_method(attr, mod._version))
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else:
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raise RuntimeError(f"{torch.typename(mod)} is not in the module_fetch_book yet, "
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"please add it to the module_fetch_book, open an issue and @842974287 "
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"or report a bug to AIACC team directly.")
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return attrs_for_lowering
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@compatibility(is_backward_compatible=False)
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def lift_lowering_attrs_to_nodes(fx_module: GraphModule) -> None:
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"""Recursively traverse all `fx_module` nodes and fetch the module's attributes if the node is a leaf module.
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"""
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submodules = dict(fx_module.named_modules())
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for node in fx_module.graph.nodes:
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if node.op == "call_module":
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if isinstance(submodules[node.target], GraphModule):
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lift_lowering_attrs_to_nodes(submodules[node.target])
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else:
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node.attrs_for_lowering = extract_attrs_for_lowering(submodules[node.target])
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