pytorch/test/fx/test_fx_split.py

# Owner(s): ["module: fx"]

from collections import defaultdict
from typing import List, Tuple, Dict

import torch
from torch.fx.passes.split_utils import split_by_tags

from torch.testing._internal.common_utils import TestCase


class TestFXSplit(TestCase):
    def test_split_preserve_node_meta(self):
        class TestModule(torch.nn.Module):
            def forward(self, x, y):
                x = x + x
                y = y * y
                return x - y

        gm = torch.fx.symbolic_trace(TestModule())
        for node in gm.graph.nodes:
            node.meta["name"] = node.name
            if node.name == "add":
                node.tag = "a"
            elif node.name == "mul":
                node.tag = "b"
            elif node.name == "sub":
                node.tag = "c"

        split_gm = split_by_tags(gm, ["a", "b", "c"])
        for m in split_gm.children():
            for n in m.graph.nodes:
                if n.op != "output":
                    self.assertIn("name", n.meta)
                    self.assertEqual(n.meta["name"], n.name)

        # Validate that metadata is copied correctly for graph placeholder nodes
        for node in split_gm.graph.nodes:
            if node.op == "placeholder":
                self.assertIn("name", node.meta)
                self.assertEqual(node.meta["name"], node.name)

class TestSplitByTags(TestCase):
    class TestModule(torch.nn.Module):
        def __init__(self) -> None:
            super().__init__()
            self.linear1 = torch.nn.Linear(2, 3)
            self.linear2 = torch.nn.Linear(4, 5)
            self.linear3 = torch.nn.Linear(6, 7)
            self.linear4 = torch.nn.Linear(8, 6)

        def forward(
            self,
            x1: torch.Tensor,
            x2: torch.Tensor,
            x3: torch.Tensor,
        ) -> torch.Tensor:
            v1 = self.linear1(x1)
            v2 = self.linear2(x2)
            v3 = self.linear3(x3)
            v4 = torch.cat([v1, v2, v3])
            return self.linear4(v4)

    @staticmethod
    def trace_and_tag(
        module: torch.nn.Module, tags: List[str]
    ) -> Tuple[torch.fx.GraphModule, Dict[str, List[str]]]:
        """
        Test simple gm consists of nodes with tag (only show call_module nodes here):
            linear1 - tag: "red"
            linear2 - tag: "blue"
            linear3, linear4 - tag: "green"

        At the beginning we have:
            gm:
                linear1
                linear2
                linear3
                linear4

        split_gm = split_by_tags(gm, tags)

        Then we have:
            split_gm:
                red:
                    linear1
                blue:
                    linear2
                green:
                    linear3
                    linear4
        """
        tag_node = defaultdict(list)
        gm: torch.fx.GraphModule = torch.fx.symbolic_trace(module)

        # Add tag to all nodes and build dictionary record tag to call_module nodes
        for node in gm.graph.nodes:
            if "linear1" in node.name:
                node.tag = tags[0]
                tag_node[tags[0]].append(node.name)
            elif "linear2" in node.name:
                node.tag = tags[1]
                tag_node[tags[1]].append(node.name)
            else:
                node.tag = tags[2]
                if node.op == "call_module":
                    tag_node[tags[2]].append(node.name)
        return gm, tag_node

    def test_split_by_tags(self) -> None:
        tags = ["red", "blue", "green"]
        module = TestSplitByTags.TestModule()
        gm, tag_node = TestSplitByTags.trace_and_tag(module, tags)
        split_gm, orig_to_split_fqn_mapping = split_by_tags(
            gm, tags, return_fqn_mapping=True
        )
        # Ensure split_gm has (and only has) ordered submodules named
        # red_0, blue_1, green_2
        for idx, (name, _) in enumerate(split_gm.named_children()):
            if idx < len(tags):
                self.assertTrue(
                    name == tags[idx],
                    f"split_gm has an incorrect submodule named {name}",
                )

        # Ensure each submodule has expected (ordered) call_module node(s).
        # For example, a submodule named split_gm.red_0 has (and only has) linear1;
        # split_gm.green_2 has (and only has) linear3 and linear4 with order
        sub_graph_idx = 0
        for sub_name, sub_graph_module in split_gm.named_children():
            node_idx = 0
            for node in sub_graph_module.graph.nodes:
                if node.op != "call_module":
                    continue
                self.assertTrue(
                    node.name == tag_node[f"{sub_name}"][node_idx],
                    # pyre-fixme[61]: `name` is undefined, or not always defined.
                    f"{sub_name} has incorrectly include {node.name}",
                )
                node_idx += 1
            sub_graph_idx += 1

        self.assertEqual(
            orig_to_split_fqn_mapping,
            {
                "linear1": "red.linear1",
                "linear2": "blue.linear2",
                "linear3": "green.linear3",
                "linear4": "green.linear4",
            },
            f"{orig_to_split_fqn_mapping=}",
        )