pytorch/test/package/test_package_fx.py

# Owner(s): ["oncall: package/deploy"]

from io import BytesIO

import torch
from torch.fx import Graph, GraphModule, symbolic_trace
from torch.package import (
    ObjMismatchError,
    PackageExporter,
    PackageImporter,
    sys_importer,
)
from torch.testing._internal.common_utils import run_tests

try:
    from .common import PackageTestCase
except ImportError:
    # Support the case where we run this file directly.
    from common import PackageTestCase

torch.fx.wrap("len")
# Do it twice to make sure it doesn't affect anything
torch.fx.wrap("len")

class TestPackageFX(PackageTestCase):
    """Tests for compatibility with FX."""

    def test_package_fx_simple(self):
        class SimpleTest(torch.nn.Module):
            def forward(self, x):
                return torch.relu(x + 3.0)

        st = SimpleTest()
        traced = symbolic_trace(st)

        f = BytesIO()
        with PackageExporter(f) as pe:
            pe.save_pickle("model", "model.pkl", traced)

        f.seek(0)
        pi = PackageImporter(f)
        loaded_traced = pi.load_pickle("model", "model.pkl")
        input = torch.rand(2, 3)
        self.assertEqual(loaded_traced(input), traced(input))

    def test_package_then_fx(self):
        from package_a.test_module import SimpleTest

        model = SimpleTest()
        f = BytesIO()
        with PackageExporter(f) as pe:
            pe.intern("**")
            pe.save_pickle("model", "model.pkl", model)

        f.seek(0)
        pi = PackageImporter(f)
        loaded = pi.load_pickle("model", "model.pkl")
        traced = symbolic_trace(loaded)
        input = torch.rand(2, 3)
        self.assertEqual(loaded(input), traced(input))

    def test_package_fx_package(self):
        from package_a.test_module import SimpleTest

        model = SimpleTest()
        f = BytesIO()
        with PackageExporter(f) as pe:
            pe.intern("**")
            pe.save_pickle("model", "model.pkl", model)

        f.seek(0)
        pi = PackageImporter(f)
        loaded = pi.load_pickle("model", "model.pkl")
        traced = symbolic_trace(loaded)

        # re-save the package exporter
        f2 = BytesIO()
        # This should fail, because we are referencing some globals that are
        # only in the package.
        with self.assertRaises(ObjMismatchError):
            with PackageExporter(f2) as pe:
                pe.intern("**")
                pe.save_pickle("model", "model.pkl", traced)

        f2.seek(0)
        with PackageExporter(f2, importer=(pi, sys_importer)) as pe:
            # Make the package available to the exporter's environment.
            pe.intern("**")
            pe.save_pickle("model", "model.pkl", traced)
        f2.seek(0)
        pi2 = PackageImporter(f2)
        loaded2 = pi2.load_pickle("model", "model.pkl")

        input = torch.rand(2, 3)
        self.assertEqual(loaded(input), loaded2(input))

    def test_package_fx_with_imports(self):
        import package_a.subpackage

        # Manually construct a graph that invokes a leaf function
        graph = Graph()
        a = graph.placeholder("x")
        b = graph.placeholder("y")
        c = graph.call_function(package_a.subpackage.leaf_function, (a, b))
        d = graph.call_function(torch.sin, (c,))
        graph.output(d)
        gm = GraphModule(torch.nn.Module(), graph)

        f = BytesIO()
        with PackageExporter(f) as pe:
            pe.intern("**")
            pe.save_pickle("model", "model.pkl", gm)
        f.seek(0)

        pi = PackageImporter(f)
        loaded_gm = pi.load_pickle("model", "model.pkl")
        input_x = torch.rand(2, 3)
        input_y = torch.rand(2, 3)

        self.assertTrue(
            torch.allclose(loaded_gm(input_x, input_y), gm(input_x, input_y))
        )

        # Check that the packaged version of the leaf_function dependency is
        # not the same as in the outer env.
        packaged_dependency = pi.import_module("package_a.subpackage")
        self.assertTrue(packaged_dependency is not package_a.subpackage)

    def test_package_fx_custom_tracer(self):
        from package_a.test_all_leaf_modules_tracer import TestAllLeafModulesTracer
        from package_a.test_module import ModWithTwoSubmodsAndTensor, SimpleTest

        class SpecialGraphModule(torch.fx.GraphModule):
            def __init__(self, root, graph, info):
                super().__init__(root, graph)
                self.info = info

        sub_module = SimpleTest()
        module = ModWithTwoSubmodsAndTensor(
            torch.ones(3),
            sub_module,
            sub_module,
        )
        tracer = TestAllLeafModulesTracer()
        graph = tracer.trace(module)

        self.assertEqual(graph._tracer_cls, TestAllLeafModulesTracer)

        gm = SpecialGraphModule(module, graph, "secret")
        self.assertEqual(gm._tracer_cls, TestAllLeafModulesTracer)

        f = BytesIO()
        with PackageExporter(f) as pe:
            pe.intern("**")
            pe.save_pickle("model", "model.pkl", gm)
        f.seek(0)

        pi = PackageImporter(f)
        loaded_gm = pi.load_pickle("model", "model.pkl")
        self.assertEqual(
            type(loaded_gm).__class__.__name__, SpecialGraphModule.__class__.__name__
        )
        self.assertEqual(loaded_gm.info, "secret")

        input_x = torch.randn(3)
        self.assertEqual(loaded_gm(input_x), gm(input_x))

    def test_package_fx_wrap(self):
        class TestModule(torch.nn.Module):
            def __init__(self):
                super().__init__()

            def forward(self, a):
                return len(a)

        traced = torch.fx.symbolic_trace(TestModule())

        f = BytesIO()
        with torch.package.PackageExporter(f) as pe:
            pe.save_pickle("model", "model.pkl", traced)
        f.seek(0)

        pi = PackageImporter(f)
        loaded_traced = pi.load_pickle("model", "model.pkl")
        input = torch.rand(2, 3)
        self.assertEqual(loaded_traced(input), traced(input))



if __name__ == "__main__":
    run_tests()