test_fake_tensor.py

# Owner(s): ["module: meta tensors"]

from torch.testing._internal.common_utils import TestCase, run_tests, skipIfCrossRef
import torch
import itertools
from torch.testing._internal.jit_utils import RUN_CUDA
from torch._subclasses.fake_tensor import (
    FakeTensor,
    FakeTensorMode,
    FakeTensorConverter,
    DynamicOutputShapeException,
)
from torch.utils._python_dispatch import enable_torch_dispatch_mode
import unittest
import torch._prims as prims
import copy

class FakeTensorTest(TestCase):
    def checkType(self, t, device_str, size):
        self.assertTrue(isinstance(t, FakeTensor))
        self.assertEqual(t.device.type, device_str)
        self.assertEqual(list(t.size()), size)

    def test_basic(self):
        mode = FakeTensorMode(inner=None)
        x = torch.empty(2, 2, device="cpu")
        y = torch.empty(4, 2, 2, device="cpu")
        with enable_torch_dispatch_mode(mode):
            x = mode.from_tensor(x)
            y = mode.from_tensor(y)
            z = x + y
            self.assertEqual(z.shape, (4, 2, 2))
            self.assertEqual(z.device, torch.device("cpu"))
            self.assertTrue(isinstance(z, FakeTensor))

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_shape_take_not_device(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            x = torch.empty(1, device="cpu")
            y = torch.empty(8, 8, device="cuda")
            out = x.resize_as_(y)
            self.assertEqual(out.shape, (8, 8))
            self.assertEqual(out.device.type, "cpu")
            self.assertTrue(isinstance(out, FakeTensor))

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_zero_dim(self):
        mode = FakeTensorMode(inner=None)
        with enable_torch_dispatch_mode(mode):
            x = torch.tensor(0.)
            y = torch.rand([4, 4], device="cuda")
            out = x + y
            self.assertEqual(out.shape, (4, 4))
            self.assertEqual(out.device, y.device)
            self.assertTrue(isinstance(out, FakeTensor))

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_throw(self):
        mode = FakeTensorMode(inner=None)
        x = torch.tensor(0.)  # TODO: tensor() errors
        with enable_torch_dispatch_mode(mode):
            x_conv = mode.from_tensor(x)
            y = torch.rand([4, 4], device="cuda")
            z = torch.rand([4, 4], device="cpu")
            self.assertRaises(Exception, lambda: torch.lerp(x_conv, y, z))

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_type_as(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            x = torch.rand([16, 1], device="cpu")
            y = torch.rand([4, 4], device="cuda")
            out = x.type_as(y)
            self.assertEqual(out.device.type, "cuda")
            self.assertTrue(isinstance(out, FakeTensor))

    def test_constructor(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            x = torch.rand([4, 4], device="cpu")

        self.assertTrue(isinstance(x, FakeTensor))
        self.assertTrue(x.device.type == "cpu")

    def test_mode(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            y = torch.rand([4], device="cpu")
            out = y + y

        self.assertTrue(isinstance(out, FakeTensor))

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_non_kwarg_device(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            x = torch.rand([16, 1], device="cpu")
            y = x.to(torch.device("cpu"))
            self.assertIs(x, y)
            z = x.to(torch.device("cuda"))
            self.assertEqual(z.device.type, "cuda")

    def test_fake_mode_error(self):
        x = torch.rand([4, 4])

        with self.assertRaisesRegex(Exception, "non-Fake Tensor inputs"):
            with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
                y = x[0]

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_like_constructor(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            x = torch.rand([4, 4])
            y = torch.ones_like(x)
            self.assertTrue(isinstance(y, FakeTensor))
            self.assertEqual(y.device.type, "cpu")
            z = torch.ones_like(x, device="cuda")
            self.assertTrue(isinstance(z, FakeTensor))
            self.assertEqual(z.device.type, "cuda")

    def test_binary_op_type_promotion(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            x = torch.empty([2, 2], dtype=torch.float)
            y = torch.empty([2, 2], dtype=torch.int64)
            out = x / y
            self.assertEqual(out.dtype, torch.float)
            self.assertEqual(out.device.type, "cpu")

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_cpu_fallback(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None, allow_cpu_fallback=False)):
            filters = torch.randn(8, 4, 3, 3).cuda()
            inputs = torch.randn(1, 4, 5, 5).cuda()
            with self.assertRaises(NotImplementedError):
                torch.nn.functional.conv2d(inputs, filters, padding=1)

        with enable_torch_dispatch_mode(FakeTensorMode(inner=None, allow_cpu_fallback=True)):
            # intentionally bad inputs
            filters = torch.randn(8, 20, 3, 3).cuda()
            inputs = torch.randn(1, 7, 10, 5).cuda()
            with self.assertRaises(RuntimeError):
                torch.nn.functional.conv2d(inputs, filters, padding=1)

        with enable_torch_dispatch_mode(FakeTensorMode(inner=None, allow_cpu_fallback=True)):
            filters = torch.randn(8, 4, 3, 3).cuda()
            inputs = torch.randn(1, 4, 5, 5).cuda()

            out = torch.nn.functional.conv2d(inputs, filters, padding=1)
            self.assertEqual(out.device.type, "cuda")
            self.assertEqual(list(out.size()), [1, 8, 5, 5])

    def test_data_dependent_operator(self):
        with enable_torch_dispatch_mode(
            FakeTensorMode(inner=None, allow_cpu_fallback=False)
        ):
            x = torch.rand([10, 10])

            self.assertRaises(DynamicOutputShapeException, lambda: torch.nonzero(x))

    def checkMetaProps(self, t1, t2):
        prims.utils.compare_tensor_meta(t1, t2)

    @skipIfCrossRef
    def test_deepcopy(self):
        mode = FakeTensorMode(inner=None)
        mod = torch.nn.BatchNorm2d(10)
        with torch._subclasses.fake_tensor.FakeCopyMode(mode):
            mod_copied = copy.deepcopy(mod)

        def check_copy(mod, mod_copied):
            for name, param in itertools.chain(mod.named_parameters(), mod.named_buffers()):
                param_copied = getattr(mod_copied, name)
                self.checkMetaProps(param, param_copied)
                self.assertTrue(isinstance(param_copied, FakeTensor))
                self.assertEqual(isinstance(param, torch.nn.Parameter), isinstance(param_copied, torch.nn.Parameter))
                self.assertEqual(param.requires_grad, param_copied.requires_grad)

        check_copy(mod, mod_copied)

        class ModuleNew(torch.nn.Module):
            def __init__(self):
                super(ModuleNew, self).__init__()
                self.a = torch.rand([10, 2])
                self.b = self.a
                self.c = self.a[0]

        mod = ModuleNew()
        with torch._subclasses.fake_tensor.FakeCopyMode(mode):
            mod_copied = copy.deepcopy(mod)

        self.assertIs(mod_copied.a, mod_copied.b)
        self.assertEqual(mod_copied.b.storage()._cdata, mod_copied.a.storage()._cdata)

def contains_type(type: torch._C.Type, maybe_contained_type: torch._C.Type):
    return maybe_contained_type.isSubtypeOf(type) or any(
        contains_type(e, maybe_contained_type) for e in type.containedTypes()
    )


class FakeTensorConverterTest(TestCase):
    def test_memoized_conversion_to_meta(self):
        x = torch.rand(2, 2, 2)
        mode = FakeTensorMode(inner=None)
        self.assertTrue(mode.from_tensor(x) is mode.from_tensor(x))

    def test_memoized_conversion_from_meta(self):
        x = torch.rand(2, 2).to(device="meta")
        mode = FakeTensorMode(inner=None)
        converter = mode.fake_tensor_converter
        self.assertTrue(converter(mode, x, "cpu") is converter(mode, x, "cpu"))

    def test_separate_tensor_storages(self):
        x = torch.rand(2, 2, 2)
        y = x[0]
        mode = FakeTensorMode(inner=None)
        converter = mode.fake_tensor_converter
        x_conv = converter(mode, x)
        y_conv = converter(mode, y)
        self.assertEqual(torch._C._storage_id(x_conv), torch._C._storage_id(y_conv))

    def test_dead_weak_ref(self):
        x = torch.rand(2, 2, 2)
        y = x[0]
        mode = FakeTensorMode(inner=None)
        converter = FakeTensorConverter()
        x_conv = converter(mode, x)
        x_conv_storage = torch._C._storage_id(x_conv)
        del x_conv
        self.assertFalse(x in converter.tensor_memo)
        y_conv = converter(mode, y)
        self.assertEqual(x_conv_storage, torch._C._storage_id(y_conv))

    def test_no_active_mode(self):
        mode = FakeTensorMode(inner=None)
        with enable_torch_dispatch_mode(mode):
            x = torch.empty(2, 2, device="cpu")
            y = torch.empty(2, 2, device="cpu")

        out = x + y
        self.assertEqual(mode, out.fake_mode)
        self.assertTrue(isinstance(out, FakeTensor))
        self.assertEqual(out.device.type, "cpu")

    def test_separate_mode_error(self):
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            x = torch.empty(2, 2, device="cpu")
        with enable_torch_dispatch_mode(FakeTensorMode(inner=None)):
            y = torch.empty(2, 2, device="cpu")
        self.assertRaises(Exception, lambda: x, y)

    def test_no_ref_cycle(self):
        x = torch.rand([4])
        mode = torch._prims.utils.get_prim_fake_mode()
        y = mode.from_tensor(x)
        assert mode is torch._prims.utils.get_prim_fake_mode()
        self.assertEqual(len(mode.fake_tensor_converter.tensor_memo), 1)
        del mode
        del y
        new_mode = torch._prims.utils.get_prim_fake_mode()
        self.assertEqual(len(new_mode.fake_tensor_converter.tensor_memo), 0)


class FakeTensorOperatorInvariants(TestCase):
    @staticmethod
    def get_aten_op(schema):
        namespace, name = schema.name.split("::")
        overload = schema.overload_name if schema.overload_name else "default"
        assert namespace == "aten"
        return getattr(getattr(torch.ops.aten, name), overload)

    @staticmethod
    def get_all_aten_schemas():
        for schema in torch._C._jit_get_all_schemas():
            namespace = schema.name.split("::")[0]
            if namespace != "aten":
                continue
            yield schema

    def test_non_kwarg_only_device(self):
        for schema in self.get_all_aten_schemas():
            ten_type = torch._C.TensorType.get()
            if not any(
                contains_type(arg.type, ten_type)
                for arg in itertools.chain(schema.arguments, schema.returns)
            ):
                continue

            opt_device = torch._C.OptionalType(torch._C.DeviceObjType.get())
            has_non_kwarg_device = any(
                not arg.kwarg_only and arg.type.isSubtypeOf(opt_device)
                for arg in schema.arguments
            )
            if has_non_kwarg_device:
                self.assertTrue(
                    self.get_aten_op(schema) in torch._subclasses.fake_tensor._device_not_kwarg_ops
                )

    def test_tensor_constructors_all_have_kwarg_device(self):
        for schema in self.get_all_aten_schemas():
            op = self.get_aten_op(schema)
            if not torch._subclasses.fake_tensor._is_tensor_constructor(op):
                continue

            opt_device = torch._C.OptionalType(torch._C.DeviceObjType.get())
            has_kwarg_device = any(
                arg.kwarg_only and arg.type.isSubtypeOf(opt_device)
                for arg in schema.arguments
            )

            self.assertTrue(
                has_kwarg_device or op == torch.ops.aten._list_to_tensor.default
            )

    def test_like_ops(self):
        for schema in self.get_all_aten_schemas():
            if "_like" == schema.name[-5:]:
                op = self.get_aten_op(schema)
                self.assertTrue(op in torch._subclasses.fake_tensor._like_tensor_constructors)


if __name__ == "__main__":
    run_tests()