torch/ao/quantization/fx/tracer.py

import torch
from torch.fx._symbolic_trace import Tracer
from torch.fx.node import Target, Node, Argument
from torch.nn.intrinsic import _FusedModule
from typing import List, Callable, Tuple, Any, Dict, Optional

__all__ = [
    "QuantizationTracer",
]

class Scope(object):
    """ Scope object that records the module path and the module type
    of a module. Scope is used to track the information of the module
    that contains a Node in a Graph of GraphModule. For example::

        class Sub(torch.nn.Module):
            def forward(self, x):
                # This will be a call_method Node in GraphModule,
                # scope for this would be (module_path="sub", module_type=Sub)
                return x.transpose(1, 2)

        class M(torch.nn.Module):
            def __init__(self):
                self.sub = Sub()

            def forward(self, x):
                # This will be a call_method Node as well,
                # scope for this would be (module_path="", None)
                x = x.transpose(1, 2)
                x = self.sub(x)
                return x

    """

    def __init__(self, module_path: str, module_type: Any):
        super().__init__()
        self.module_path = module_path
        self.module_type = module_type


class ScopeContextManager(object):
    """ A context manager to track the Scope of Node during symbolic tracing.
    When entering a forward function of a Module, we'll update the scope information of
    the current module, and when we exit, we'll restore the previous scope information.
    """

    def __init__(
        self, scope: Scope, current_module: torch.nn.Module, current_module_path: str
    ):
        super().__init__()
        self.prev_module_type = scope.module_type
        self.prev_module_path = scope.module_path
        self.scope = scope
        self.scope.module_path = current_module_path
        self.scope.module_type = type(current_module)

    def __enter__(self):
        return

    def __exit__(self, *args):
        self.scope.module_path = self.prev_module_path
        self.scope.module_type = self.prev_module_type
        return

class QuantizationTracer(Tracer):
    def __init__(
        self, skipped_module_names: List[str], skipped_module_classes: List[Callable]
    ):
        super().__init__()
        self.skipped_module_names = skipped_module_names
        self.skipped_module_classes = skipped_module_classes
        # NB: initialized the module_type of top level module to None
        # we are assuming people won't configure the model with the type of top level
        # module here, since people can use "" for global config
        # We can change this if there is a use case that configures
        # qconfig using top level module type
        self.scope = Scope("", None)
        self.node_name_to_scope: Dict[str, Tuple[str, type]] = {}
        self.record_stack_traces = True

    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
        return (
            (
                (m.__module__.startswith("torch.nn") or m.__module__.startswith("torch.ao.nn"))
                and not isinstance(m, torch.nn.Sequential)
            )
            or module_qualified_name in self.skipped_module_names
            or type(m) in self.skipped_module_classes
            or isinstance(m, _FusedModule)
        )

    def call_module(
        self,
        m: torch.nn.Module,
        forward: Callable[..., Any],
        args: Tuple[Any, ...],
        kwargs: Dict[str, Any],
    ) -> Any:
        module_qualified_name = self.path_of_module(m)
        # Creating scope with information of current module
        # scope will be restored automatically upon exit
        with ScopeContextManager(self.scope, m, module_qualified_name):
            return super().call_module(m, forward, args, kwargs)

    def create_node(
        self,
        kind: str,
        target: Target,
        args: Tuple[Argument, ...],
        kwargs: Dict[str, Argument],
        name: Optional[str] = None,
        type_expr: Optional[Any] = None,
    ) -> Node:
        node = super().create_node(kind, target, args, kwargs, name, type_expr)
        self.node_name_to_scope[node.name] = (
            self.scope.module_path,
            self.scope.module_type,
        )
        return node