FM / passes

/usr/local/lib64/python3.6/site-packages/torch/fx/passes/

/usr/local/lib64/python3.6/site-packages/torch/fx/passes

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
graph_drawer.py	9333	0644	edit dl rm
graph_manipulation.py	18204	0644	edit dl rm
net_min_base.py	19563	0644	edit dl rm
operator_support.py	3196	0644	edit dl rm
param_fetch.py	3434	0644	edit dl rm
shape_prop.py	4289	0644	edit dl rm
splitter_base.py	28013	0644	edit dl rm
split_module.py	8620	0644	edit dl rm
split_utils.py	11155	0644	edit dl rm
tools_common.py	7085	0644	edit dl rm
__init__.py	277	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/fx/passes/operator_support.py (3196B)

from typing import Dict

import torch
import torch.fx
from torch.fx._compatibility import compatibility

from .tools_common import get_node_target, CALLABLE_NODE_OPS

@compatibility(is_backward_compatible=False)
class OperatorSupport:
    """
    `_support_dict` maps node.target to supported inputs dtypes.

node.target is retrived using helper function `get_node_target()`

If supported inputs dtypes is None, it means any dtype is supported, else
    we should see a tuple like (([dtypes], ...), {"name":[dtypes], ...}).

The first tuple ([dtypes], ...) indicates what dtypes are supported for
    inputs in node.args and the second dict {"name": [dtypes], ...} indicates
    what dtypes are supported for inputs in node.kwargs.

For inputs in args, if we don't want to check it, we can put None there,
    e.g. (None, [torch.float]) indicates that we don't care about the type of
    the first input in args. And for inputs in kwargs, if not listed, will not
    be checked.
    """

_support_dict: Dict = {}

def is_node_supported(
        self, submodules: Dict[str, torch.nn.Module], node: torch.fx.Node
    ) -> bool:
        """
        Args:
            `sumodules`: mapping from module name to the module. This can be
                         retrieved by calling model.named_modules().

`node`: a Fx node that we want to determine whether it's supported.

Returns:
            `is_supported`: whether the arg `node` is supported.
        """
        if node.op not in CALLABLE_NODE_OPS:
            return True

target = get_node_target(submodules, node)

# Target not found in _support_dict meaning that we don't support this op at all
        if target not in self._support_dict:
            return False

# The rule for target is None meaning that we accept any dtype
        if self._support_dict[target] is None:
            return True

args_dtypes, kwargs_dtypes = self._support_dict[target]

# Check args dtypes
        for i, dtypes in enumerate(args_dtypes):
            if len(node.args) <= i:
                break

# None indicates we don't care about the dtype of args[i]
            if dtypes is None:
                continue

# If arg is not a node then we don't check it
            if not isinstance(node.args[i], torch.fx.Node):
                continue

arg_tensor_meta = node.args[i].meta.get("tensor_meta")  # type: ignore[union-attr]
            arg_dtype = arg_tensor_meta.dtype if arg_tensor_meta else None

if arg_dtype not in dtypes:
                return False

# Check kwargs dtypes
        for k, dtypes in kwargs_dtypes.items():
            if k not in node.kwargs:
                continue

# If arg is not a node then we don't check it
            if not isinstance(node.kwargs[k], torch.fx.Node):
                continue

kwarg_tensor_meta = node.kwargs[k].meta.get("tensor_meta")  # type: ignore[union-attr]
            kwarg_dtype = kwarg_tensor_meta.dtype if kwarg_tensor_meta else None

if kwarg_dtype not in dtypes:
                return False

return True

from typing import Dict import torch import torch.fx from torch.fx._compatibility import compatibility from .tools_common import get_node_target, CALLABLE_NODE_OPS @compatibility(is_backward_compatible=False) class OperatorSupport: """ `_support_dict` maps node.target to supported inputs dtypes. node.target is retrived using helper function `get_node_target()` If supported inputs dtypes is None, it means any dtype is supported, else we should see a tuple like (([dtypes], ...), {"name":[dtypes], ...}). The first tuple ([dtypes], ...) indicates what dtypes are supported for inputs in node.args and the second dict {"name": [dtypes], ...} indicates what dtypes are supported for inputs in node.kwargs. For inputs in args, if we don't want to check it, we can put None there, e.g. (None, [torch.float]) indicates that we don't care about the type of the first input in args. And for inputs in kwargs, if not listed, will not be checked. """ _support_dict: Dict = {} def is_node_supported( self, submodules: Dict[str, torch.nn.Module], node: torch.fx.Node ) -> bool: """ Args: `sumodules`: mapping from module name to the module. This can be retrieved by calling model.named_modules(). `node`: a Fx node that we want to determine whether it's supported. Returns: `is_supported`: whether the arg `node` is supported. """ if node.op not in CALLABLE_NODE_OPS: return True target = get_node_target(submodules, node) # Target not found in _support_dict meaning that we don't support this op at all if target not in self._support_dict: return False # The rule for target is None meaning that we accept any dtype if self._support_dict[target] is None: return True args_dtypes, kwargs_dtypes = self._support_dict[target] # Check args dtypes for i, dtypes in enumerate(args_dtypes): if len(node.args) <= i: break # None indicates we don't care about the dtype of args[i] if dtypes is None: continue # If arg is not a node then we don't check it if not isinstance(node.args[i], torch.fx.Node): continue arg_tensor_meta = node.args[i].meta.get("tensor_meta") # type: ignore[union-attr] arg_dtype = arg_tensor_meta.dtype if arg_tensor_meta else None if arg_dtype not in dtypes: return False # Check kwargs dtypes for k, dtypes in kwargs_dtypes.items(): if k not in node.kwargs: continue # If arg is not a node then we don't check it if not isinstance(node.kwargs[k], torch.fx.Node): continue kwarg_tensor_meta = node.kwargs[k].meta.get("tensor_meta") # type: ignore[union-attr] kwarg_dtype = kwarg_tensor_meta.dtype if kwarg_tensor_meta else None if kwarg_dtype not in dtypes: return False return True