pytorch/torch/ao/quantization/fx/match_utils.py

import sys
import torch
from torch.fx.graph import (
    Graph,
    Node,
)
from torch.ao.quantization.utils import Pattern
from .quantize_handler import (
    QuantizeHandler,
)
from ..qconfig import (
    QConfigAny,
)
from ..utils import (
    MatchAllNode
)
from .graph_module import (
    is_observed_standalone_module,
)
from torch.nn.utils.parametrize import type_before_parametrizations
from typing import Any, Dict, List, Callable, Optional, Tuple, Type, Set, Iterable


__all__: List[str] = []

# TODO(future PR): the 1st argument is typed as `List[Node]`, but a better type
# would be a recursive `List[Union[Node, Tuple[Union[Node, ...]]]]`
_MatchResult = Tuple[Node, List[Node], Optional[Pattern], QuantizeHandler]

_MatchResultWithQConfig = Tuple[Node, List[Node], Optional[Pattern], QuantizeHandler,
                                QConfigAny]

# Note: The order of patterns is important! match function will take whatever is matched first, so we'll
# need to put the fusion patterns before single patterns. For example, add_relu should be registered come before relu.
# decorators are applied in the reverse order we see. Also when we match the nodes in the graph with these patterns,
# we'll start from the last node of the graph and traverse back.
def _is_match(modules, node, pattern, max_uses=sys.maxsize):
    """ Matches a node in fx against a pattern
    """
    if isinstance(pattern, tuple):
        self_match, *arg_matches = pattern
        if self_match is getattr:
            assert len(pattern) == 2, 'Expecting getattr pattern to have two elements'
            arg_matches = []
    else:
        self_match = pattern
        arg_matches = []

    if isinstance(self_match, type) and issubclass(self_match, MatchAllNode):
        return True

    if node == pattern:
        return True

    if not isinstance(node, Node) or len(node.users) > max_uses:
        return False

    if isinstance(self_match, type) and issubclass(self_match, torch.nn.Module):
        if node.op != 'call_module':
            return False
        if not type_before_parametrizations(modules[node.target]) == self_match:
            return False
    elif callable(self_match):
        if node.op != 'call_function' or node.target is not self_match:
            return False
        elif node.target is getattr:
            if node.args[1] != pattern[1]:
                return False
    elif isinstance(self_match, str):
        if node.op != 'call_method' or node.target != self_match:
            return False
    elif node.target != self_match:
        return False

    if not arg_matches:
        return True

    if len(arg_matches) != len(node.args):
        return False

    return all(_is_match(modules, node, arg_match, max_uses=1) for node, arg_match in zip(node.args, arg_matches))

def _find_matches(
        graph: Graph,
        modules: Dict[str, torch.nn.Module],
        patterns: Dict[Pattern, QuantizeHandler],
        root_node_getter_mapping: Dict[Pattern, Callable],
        standalone_module_names: List[str] = None,
        standalone_module_classes: List[Type] = None,
        custom_module_classes: List[Any] = None) -> Dict[str, _MatchResult]:
    """
    Matches the nodes in the input graph to quantization patterns, and
    outputs the information needed to quantize them in future steps.

    Inputs:
      - graph: an fx.Graph object
      - modules: a mapping of fully qualified module name to instance,
          for example, {'foo': ModuleFoo, ...}
      - patterns: a mapping from a tuple of nodes in reverse order to
          uninitialized QuantizeHandler subclass.

    Outputs a map of
      node_name ->
        (node, matched_values, matched_pattern, QuantizeHandler instance,
         qconfig)

    For example, {
      'relu_1': (relu_1, [relu_1], torch.nn.functional.relu,
                 <CopyNodeQuantizeHandler instance>, QConfig(...)),
      ...
    }
    """
    if custom_module_classes is None:
        custom_module_classes = []

    if standalone_module_classes is None:
        standalone_module_classes = []

    if standalone_module_names is None:
        standalone_module_names = []

    match_map: Dict[str, _MatchResult] = {}
    all_matched : Set[str] = set()

    def _recursive_record_node_in_match_map(
            last_node,
            match_map,
            node_pattern,
            matched_node_pattern,
            pattern,
            match_value):
        if isinstance(node_pattern, Node):
            match_map[node_pattern.name] = (
                last_node, matched_node_pattern, pattern, match_value)
        elif not isinstance(node_pattern, Iterable):
            return
        else:
            for n in node_pattern:
                _recursive_record_node_in_match_map(last_node, match_map, n, matched_node_pattern, pattern, match_value)

    # TODO: 1. merge with fuse matcher 2. document the code
    def record_match(
            pattern,
            node,
            last_node,
            matched_node_pattern,
            match_map):
        if isinstance(pattern, tuple):
            s, *args = pattern
            is_single_arg = len(args) == 1
            current_node_pattern: List[Node] = []
            record_match(
                s,
                node,
                last_node,
                matched_node_pattern,
                match_map)
            if pattern[0] is not getattr:
                for subpattern, arg in zip(args, node.args):
                    record_match(
                        subpattern,
                        arg,
                        node,
                        current_node_pattern,
                        match_map)
            if len(current_node_pattern) > 1:
                # current_node_pattern is  the node pattern we get from matching
                # the subpattern with arguments of the node
                # we use is_single_arg to recover the original structure of the pattern
                # if the original pattern has a single argument, we will have
                # (original_op, (original_arg, ...))
                # otherwise, we'll have a list of arguments
                # (original_op, arg0, arg1, arg2, ...)
                if is_single_arg:
                    matched_node_pattern.append(tuple(current_node_pattern))
                else:
                    matched_node_pattern.extend(list(current_node_pattern))
            else:
                matched_node_pattern.append(current_node_pattern[0])
        else:
            matched_node_pattern.append(node)

    for node in reversed(graph.nodes):
        if node.name not in match_map and node.name not in all_matched:
            for pattern, quantize_handler_cls in patterns.items():
                root_node_getter = root_node_getter_mapping.get(pattern, None)
                if _is_match(modules, node, pattern) and node.name not in match_map:
                    matched_node_pattern: List[Node] = []
                    record_match(
                        pattern,
                        node,
                        node,
                        matched_node_pattern,
                        match_map)
                    quantize_handler = quantize_handler_cls(  # type: ignore[operator]
                        matched_node_pattern,
                        modules,
                        root_node_getter)
                    last_node = node
                    # record the match for all nodes in the pattern
                    _recursive_record_node_in_match_map(
                        last_node,
                        match_map,
                        # we need to record all nodes in the matched pattern in the match_map
                        matched_node_pattern,
                        # this is a part of the value corresponding to the node
                        matched_node_pattern,
                        pattern,
                        quantize_handler)
                    break

    # add custom module instances to the match result
    assert modules is not None
    for node in graph.nodes:
        if node.op == 'call_module' and \
           type(modules[node.target]) in custom_module_classes:
            match_map[node.name] = (
                node, node, None, QuantizeHandler(node, modules, is_custom_module=True))

    def is_standalone_module(node_target: str, modules: Dict[str, torch.nn.Module]):
        assert modules is not None
        return (
            node_target in standalone_module_names or  # type: ignore[operator]
            type(modules[node_target]) in standalone_module_classes  # type: ignore[operator]
        )

    # add standalone modules to the match
    for node in graph.nodes:
        if node.op == 'call_module' and \
           (is_standalone_module(node.target, modules) or
                is_observed_standalone_module(modules[node.target])):
            # add node to matched nodes
            match_map[node.name] = (
                node, node, None,
                QuantizeHandler(node, modules, is_standalone_module=True))

    return match_map