torch.fx.subgraph_rewriter.replace_pattern

torch.fx.subgraph_rewriter.replace_pattern(gm, pattern, replacement)

Matches all possible non-overlapping sets of operators and their data dependencies (pattern) in the Graph of a GraphModule (gm), then replaces each of these matched subgraphs with another subgraph (replacement).

Parameters:

• gm (GraphModule) – The GraphModule that wraps the Graph to operate on

• pattern (Callable[[...], Any] | GraphModule) – The subgraph to match in gm for replacement

• replacement (Callable[[...], Any] | GraphModule) – The subgraph to replace pattern with

Returns:

A list of Match objects representing the places in the original graph that pattern was matched to. The list is empty if there are no matches. Match is defined as:

class Match(NamedTuple):
    # Node from which the match was found
    anchor: Node
    # Maps nodes in the pattern subgraph to nodes in the larger graph
    nodes_map: Dict[Node, Node]

Return type:

List[Match]

Examples:

import torch
from torch.fx import symbolic_trace, subgraph_rewriter


class M(torch.nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(self, x, w1, w2):
        m1 = torch.cat([w1, w2]).sum()
        m2 = torch.cat([w1, w2]).sum()
        return x + torch.max(m1) + torch.max(m2)


def pattern(w1, w2):
    return torch.cat([w1, w2])


def replacement(w1, w2):
    return torch.stack([w1, w2])


traced_module = symbolic_trace(M())

subgraph_rewriter.replace_pattern(traced_module, pattern, replacement)

The above code will first match pattern in the forward method of traced_module. Pattern-matching is done based on use-def relationships, not node names. For example, if you had p = torch.cat([a, b]) in pattern, you could match m = torch.cat([a, b]) in the original forward function, despite the variable names being different (p vs m).

The return statement in pattern is matched based on its value only; it may or may not match to the return statement in the larger graph. In other words, the pattern doesn’t have to extend to the end of the larger graph.

When the pattern is matched, it will be removed from the larger function and replaced by replacement. If there are multiple matches for pattern in the larger function, each non-overlapping match will be replaced. In the case of a match overlap, the first found match in the set of overlapping matches will be replaced. (“First” here being defined as the first in a topological ordering of the Nodes’ use-def relationships. In most cases, the first Node is the parameter that appears directly after self, while the last Node is whatever the function returns.)

One important thing to note is that the parameters of the pattern Callable must be used in the Callable itself, and the parameters of the replacement Callable must match the pattern. The first rule is why, in the above code block, the forward function has parameters x, w1, w2, but the pattern function only has parameters w1, w2. pattern doesn’t use x, so it shouldn’t specify x as a parameter. As an example of the second rule, consider replacing

def pattern(x, y):
    return torch.neg(x) + torch.relu(y)

with

def replacement(x, y):
    return torch.relu(x)

In this case, replacement needs the same number of parameters as pattern (both x and y), even though the parameter y isn’t used in replacement.

After calling subgraph_rewriter.replace_pattern, the generated Python code looks like this:

def forward(self, x, w1, w2):
    stack_1 = torch.stack([w1, w2])
    sum_1 = stack_1.sum()
    stack_2 = torch.stack([w1, w2])
    sum_2 = stack_2.sum()
    max_1 = torch.max(sum_1)
    add_1 = x + max_1
    max_2 = torch.max(sum_2)
    add_2 = add_1 + max_2
    return add_2

Note

Backwards-compatibility for this API is guaranteed.