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