test_fake_tensor.py

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

import sys

from torch.testing._internal.common_utils import (
    TestCase, TEST_WITH_TORCHDYNAMO, run_tests, skipIfCrossRef, skipIfRocm, skipIfTorchDynamo, parametrize,
    instantiate_parametrized_tests, TemporaryFileName)
import torch
import torch._dynamo
import itertools
import numpy as np
from torch.testing._internal.jit_utils import RUN_CUDA
from torch._guards import tracing, TracingContext
from torch._subclasses.fake_tensor import (
    _ShapeEnvSettings,
    extract_tensor_metadata,
    FakeTensor,
    FakeTensorMode,
    FakeTensorConverter,
    DynamicOutputShapeException,
    UnsupportedOperatorException,
    unset_fake_temporarily,
)
from torch.fx.experimental.symbolic_shapes import ShapeEnv, DimDynamic, free_symbols, StatelessSymbolicContext
from torch.testing._internal.custom_op_db import custom_op_db
from torch.testing._internal.common_device_type import ops
from torch.testing._internal.common_device_type import instantiate_device_type_tests, OpDTypes
from torch.testing._internal.common_cuda import PLATFORM_SUPPORTS_FLASH_ATTENTION
from torch.fx.passes.fake_tensor_prop import FakeTensorProp
from torch._dynamo.testing import rand_strided
from torch._C._functorch import is_batchedtensor, _add_batch_dim, get_unwrapped
from torch.testing import FileCheck
import dataclasses
import inspect
import unittest
import torch._prims as prims
import contextlib
import weakref
import copy
import pickle
import torch._functorch.config
import torch.testing._internal.optests as optests
from unittest.mock import patch

from torch import distributed as dist
from torch.utils._mode_utils import no_dispatch
from torch.utils._python_dispatch import TorchDispatchMode
import torch.utils._pytree as pytree

aten = torch.ops.aten

torch._dynamo.config.fake_tensor_cache_enabled = True
torch._dynamo.config.fake_tensor_cache_crosscheck_enabled = True

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)


    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_cuda_initialized(self):
        # doesnt error
        with FakeTensorMode():
            p = torch.randn(4, 2, requires_grad=True, device='cuda')
            x = torch.randn(8, 4, device='cuda')
            y = torch.mm(x, p).square().sum()
            y.backward()

    def test_basic(self):
        x = torch.empty(2, 2, device="cpu")
        y = torch.empty(4, 2, 2, device="cpu")
        with FakeTensorMode() as 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))

    def test_custom_op_fallback(self):
        from torch.library import Library, impl

        test_lib = Library("my_test_op", "DEF")  # noqa: TOR901
        test_lib.define('foo(Tensor self) -> Tensor')

        @impl(test_lib, 'foo', 'CPU')
        def foo_impl(self):
            return self.cos()

        x = torch.empty(2, 2, device="cpu")
        with self.assertRaisesRegex(UnsupportedOperatorException, "my_test_op.foo.default"):
            with FakeTensorMode(allow_fallback_kernels=True) as mode:
                x = mode.from_tensor(x)
                torch.ops.my_test_op.foo(x)

    def test_parameter_instantiation(self):
        with FakeTensorMode():
            x = torch.rand([4])
            y = torch.nn.parameter.Parameter(x)
            self.assertTrue(isinstance(y, torch.nn.Parameter))

    @unittest.skipIf(not dist.is_available(), "requires distributed")
    def test_fsdp_flat_param(self):
        from torch.distributed.fsdp._flat_param import FlatParameter
        with FakeTensorMode() as m:
            data = torch.randn(2, 2)
            param = FlatParameter(data, requires_grad=True)
        self.assertIsInstance(param, FlatParameter)
        self.assertIsInstance(param, torch.nn.Parameter)
        self.assertIsInstance(param, FakeTensor)

    def test_non_parameter_grad(self):
        mode = FakeTensorMode()
        t = torch.rand([4], requires_grad=True)
        fake_t = mode.from_tensor(t)
        self.assertEqual(fake_t.requires_grad, t.requires_grad)

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_index_cuda_with_cpu(self):
        with FakeTensorMode():
            x = torch.rand([2048], device='cuda')
            out = x[torch.zeros([36], dtype=torch.int64)]
            self.checkType(out, "cuda", [36])

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_shape_take_not_device(self):
        with FakeTensorMode():
            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))

    def test_repr(self):
        with FakeTensorMode():
            x = torch.empty(2, 2, device="cpu")
            self.assertEqual(repr(x), 'FakeTensor(..., size=(2, 2))')
            x = torch.empty(2, 2, device="meta")
            self.assertEqual(repr(x), "FakeTensor(..., device='meta', size=(2, 2))")

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_zero_dim(self):
        with FakeTensorMode() as 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))

    def test_nan_to_num(self):
        with FakeTensorMode():
            for dtype in [torch.float16, torch.float32]:
                x = torch.rand([4], dtype=dtype)
                y = torch.nan_to_num(x, nan=None)
                z = torch.nan_to_num(x, 0.0)
                self.assertEqual(dtype, y.dtype)
                self.assertEqual(dtype, z.dtype)

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_throw(self):
        x = torch.tensor(0.)  # TODO: tensor() errors
        with FakeTensorMode() as 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 FakeTensorMode():
            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))

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_setitem(self):
        for device in ["cpu", "cuda"]:
            with FakeTensorMode():
                x = torch.rand([16, 1], device=device)
                x[..., 0] = 0

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_device_inplace_copy(self):
        with FakeTensorMode():
            x = torch.rand([8, 8], device="cpu")
            y = torch.rand([8, 8], device="cuda")
            assert x.copy_(y).device.type == "cpu"
            assert y.copy_(x).device.type == "cuda"

    def test_fake_dispatch_keys(self):
        with FakeTensorMode():
            x = torch.rand([4])
            f = FileCheck().check("CPU").check("ADInplaceOrView").check("AutogradCPU").check("AutocastCPU")
            f.run(torch._C._dispatch_key_set(x))

            with torch.inference_mode():
                x = torch.rand([4])
                y = x + x
                FileCheck().check("CPU").check("AutocastCPU").run(torch._C._dispatch_key_set(y))
                FileCheck().check_not("ADInplaceOrView").check_not("Autograd").run(torch._C._dispatch_key_set(y))

    def test_batch_tensor(self):
        x = torch.rand((3, 4, 5))
        b = _add_batch_dim(x, 0, 0)
        mode = FakeTensorMode()
        fake_b = mode.from_tensor(b)
        prims.utils.compare_tensor_meta(b, fake_b, check_strides=True)

        b1 = _add_batch_dim(x, 1, 1)
        b2 = _add_batch_dim(b1, 0, 2)
        fake_b2 = mode.from_tensor(b2)
        prims.utils.compare_tensor_meta(b2, fake_b2, check_strides=True)
        self.assertTrue(is_batchedtensor(fake_b2))
        fake_b1 = get_unwrapped(fake_b2)
        self.assertTrue(is_batchedtensor(fake_b1))
        fake_tensor = get_unwrapped(fake_b1)
        self.assertIsInstance(fake_tensor, FakeTensor)

    def test_constructor(self):
        with FakeTensorMode():
            x = torch.rand([4, 4], device="cpu")

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

    def test_mode(self):
        with FakeTensorMode():
            y = torch.rand([4], device="cpu")
            out = y + y

        self.assertTrue(isinstance(out, FakeTensor))

    def test_full(self):
        # Test torch.full returns tensor with correct dtype
        with torch._subclasses.CrossRefFakeMode():
            y = torch.full((4, 4), 1)

    def check_function_with_fake(self, fn):
        out = fn()
        with torch._subclasses.FakeTensorMode():
            out_fake = fn()

        for a, b in zip(pytree.tree_leaves(out), pytree.tree_leaves(out_fake)):
            if not isinstance(a, torch.Tensor):
                self.assertTrue(not isinstance(b, torch.Tensor))
                continue

            prims.utils.compare_tensor_meta(a, b, check_strides=True)

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_non_kwarg_device(self):
        with FakeTensorMode():
            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_non_overlapping_stride_zero(self):
        def foo():
            x = torch.empty_strided([1, 3, 427, 640], (0, 1, 1920, 3))
            return x.half()

        self.check_function_with_fake(foo)

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

        with self.assertRaisesRegex(Exception, "Please convert all Tensors"):
            with FakeTensorMode():
                y = x[0]

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    def test_fake_grad_copy(self):
        x = torch.rand([4, 4], requires_grad=True)
        x.grad = torch.rand([4, 4])
        mode = FakeTensorMode()
        fake_x = mode.from_tensor(x)
        prims.utils.compare_tensor_meta(fake_x, x)
        prims.utils.compare_tensor_meta(fake_x.grad, x.grad)

        self.assertTrue(isinstance(fake_x.grad, FakeTensor))

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_index_put_error(self):
        mode = FakeTensorMode()
        for context in [contextlib.nullcontext, lambda: mode]:
            with context():
                y = torch.randn(2, 2, 3)
                x = torch.randn(2, 2, 3).to('cuda')
                with self.assertRaises(RuntimeError):
                    x[[1, 1]] = y

                with self.assertRaises(RuntimeError):
                    torch.ops.aten.index_put(x, torch.tensor([1, 1], device="cuda"), y)

                # no error
                torch.ops.aten.index_put(x, torch.tensor([1, 1], device="cuda"), torch.tensor(5.))
                torch.ops.aten.index_put_(x, torch.tensor([1, 1], device="cuda"), torch.tensor(5.))



    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_like_constructor(self):
        with FakeTensorMode():
            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 FakeTensorMode():
            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(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    def test_from_numpy(self):
        with FakeTensorMode():
            x = torch.tensor(np.zeros([4, 4]))
            self.checkType(x, "cpu", [4, 4])

    def test_randperm(self):
        x = torch.randperm(10)
        y = torch.randperm(5, device="cpu")
        with FakeTensorMode():
            x1 = torch.randperm(10)
            prims.utils.compare_tensor_meta(x, x1)
            y1 = torch.randperm(5, device="cpu")
            prims.utils.compare_tensor_meta(y, y1)

    def test_print_in_fake_mode(self):
        x = torch.zeros(2)
        # does not fail
        with FakeTensorMode():
            out = str(x)
        assert "FakeTensor" not in out

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_upsample_bilinear_small_channels(self):
        out = []
        mode = FakeTensorMode()
        for i, context in enumerate([contextlib.nullcontext, lambda: mode]):
            with context():
                arg0_1 = torch.empty_strided((3, 427, 640), (1, 1920, 3), dtype=torch.float32, device='cuda')
                unsqueeze = torch.ops.aten.unsqueeze.default(arg0_1, 0)
                out.append(torch.ops.aten.upsample_bilinear2d.default(unsqueeze, [800, 1199], False))

        self.assertTrue(out[1].is_contiguous())
        self.checkMetaProps(out[0], out[1])

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_cpu_fallback(self):
        with FakeTensorMode(allow_fallback_kernels=False):
            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])

        with FakeTensorMode(allow_fallback_kernels=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 FakeTensorMode(allow_fallback_kernels=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])

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_out_multi_device(self):
        with FakeTensorMode():
            x = torch.rand([4])
            y = torch.rand([4], device="cuda")

            with self.assertRaisesRegex(Exception, "found two different devices"):
                torch.sin(x, out=y)

            with self.assertRaisesRegex(Exception, "found two different devices"):
                x.add_(y)


    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_normalize_device(self):
        with FakeTensorMode():
            x = torch.empty(1, device="cuda")
            y = torch.empty(1, device=f"cuda:{torch.cuda.current_device()}")
            out = x + y
        self.checkType(out, "cuda", [1])

    def test_recursive_invocation(self):
        mode = FakeTensorMode()
        with mode:
            x = torch.tensor(2)
            mode.in_kernel_invocation = True
            y = x + x
            self.assertTrue(mode.in_kernel_invocation)

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    @skipIfRocm
    @parametrize("allow_fallback_kernels", [False, True],
                 lambda a: 'with_fallback' if a else 'without_fallback')
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_cudnn_rnn(self, allow_fallback_kernels):
        def fn(
            a0,
            b0,
            b1,
            b2,
            b3,
            b4,
            b5,
            b6,
            b7,
            b8,
            b9,
            b10,
            b11,
            b12,
            b13,
            b14,
            b15,
            a3,
            a4,
            a5,
        ):
            a1 = [
                b0,
                b1,
                b2,
                b3,
                b4,
                b5,
                b6,
                b7,
                b8,
                b9,
                b10,
                b11,
                b12,
                b13,
                b14,
                b15,
            ]
            return torch.ops.aten._cudnn_rnn(
                a0,
                a1,
                4,
                a3,
                a4,
                a5,
                2,
                2048,
                0,
                2,
                False,
                0.0,
                False,
                True,
                [],
                None,
            )

        mode = FakeTensorMode(allow_fallback_kernels=allow_fallback_kernels)
        for i, context in enumerate([contextlib.nullcontext, lambda: mode]):
            with context():
                inps1 = [
                    torch.randn([92, 8, 2048]).cuda(),
                    torch.randn([8192, 2048]).cuda(),
                    torch.randn([8192, 2048]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([8192, 2048]).cuda(),
                    torch.randn([8192, 2048]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([8192, 4096]).cuda(),
                    torch.randn([8192, 2048]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([8192, 4096]).cuda(),
                    torch.randn([8192, 2048]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([8192]).cuda(),
                    torch.randn([167837696]).cuda(),
                    torch.randn([4, 8, 2048]).cuda(),
                    torch.randn([4, 8, 2048]).cuda(),
                ]
                inps2 = inps1
                inps2[len(inps2) - 1] = None  # argument `cx` can be None

                for inps in [inps1, inps2]:
                    out = fn(*inps)
                    self.assertIs(out[4], inps[-3])
                    for ten in out:
                        if i == 1:
                            self.assertTrue(isinstance(ten, FakeTensor))
                        self.assertEqual(ten.device.type, 'cuda')

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_cuda_lstm(self):
        # Ensure CUDA (non-cuDNN) impl succeeds with fake tensors.
        with torch.backends.cudnn.flags(enabled=False):
            fake_tensor_mode = FakeTensorMode(allow_fallback_kernels=False)
            with fake_tensor_mode:
                N = 5
                L = 4
                H_in = 2
                hidden_size = 3
                proj_size = 2
                num_layers = 2
                bidir = False
                D = 2 if bidir else 1
                H_out = proj_size if proj_size > 0 else hidden_size

                lstm = torch.nn.LSTM(input_size=H_in, hidden_size=hidden_size,
                                     num_layers=num_layers, proj_size=proj_size, batch_first=False,
                                     bias=True, bidirectional=bidir, device='cuda')

                h_0 = torch.randn((num_layers * D, N, H_out), device='cuda')
                c_0 = torch.randn((num_layers * D, N, hidden_size), device='cuda')
                inp = torch.randn((L, N, H_in), device='cuda')
                (output, (h_n, c_n)) = lstm(inp, (h_0, c_0))
                output.sum().backward()

                self.assertEqual(output.shape, (L, N, D * H_out))
                self.assertEqual(h_n.shape, (D * num_layers, N, H_out))
                self.assertEqual(c_n.shape, (D * num_layers, N, hidden_size))

    def test_data_dependent_operator(self):
        with FakeTensorMode(allow_fallback_kernels=False):
            x = torch.rand([10, 10])

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

    def test_tolist(self):
        shape_env = ShapeEnv()
        with FakeTensorMode(allow_fallback_kernels=False, shape_env=shape_env):
            x = torch.rand([10])
            x.tolist()

    def test_same_shape_env_preserved(self):
        shape_env = ShapeEnv()
        mode1 = FakeTensorMode(shape_env=shape_env)
        t1 = mode1.from_tensor(
            torch.randn(10),
            symbolic_context=StatelessSymbolicContext(
                dynamic_sizes=[DimDynamic.DYNAMIC],
                constraint_sizes=[None]
            )
        )
        mode2 = FakeTensorMode(shape_env=shape_env)
        t2 = mode2.from_tensor(t1)
        # t2.size(0) is still dynamic, even though we didn't pass DYNAMIC here
        self.assertIsNot(t2, t1)
        self.assertIs(t1.fake_mode, mode1)
        self.assertIs(t2.fake_mode, mode2)
        self.assertIs(t2.size(0).node.shape_env, t1.size(0).node.shape_env)
        self.assertEqual(str(t2.size(0)), str(t1.size(0)))

    def test_jagged_fake_to_fake_preserved(self):
        from torch.nested._internal.nested_tensor import jagged_from_list

        S0, S1, S2 = 3, 4, 5
        D = 4
        a = torch.randn(S0, D, requires_grad=True, dtype=torch.float64)
        b = torch.randn(S1, D, requires_grad=True, dtype=torch.float64)
        c = torch.randn(S2, D, requires_grad=True, dtype=torch.float64)
        offsets = None
        jt, _ = jagged_from_list([a, b, c], offsets)
        shape_env = ShapeEnv()
        mode1 = FakeTensorMode(shape_env=shape_env)
        t1 = mode1.from_tensor(jt)
        mode2 = FakeTensorMode(shape_env=shape_env)
        t2 = mode2.from_tensor(t1)
        # It's not obvious that the invocation above makes it dynamic but it
        # does!
        self.assertTrue(free_symbols(t1.size()))
        self.assertIsNot(t2, t1)
        self.assertIs(t1.offsets().fake_mode, mode1)
        self.assertIs(t2.offsets().fake_mode, mode2)
        self.assertIs(t2.size(1).node.shape_env, t1.size(1).node.shape_env)
        self.assertEqual(str(t2.size(1)), str(t1.size(1)))

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

    @skipIfCrossRef
    def test_deepcopy(self):
        with FakeTensorMode() as mode:
            pass
        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().__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)

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_new(self):
        with FakeTensorMode():
            a = torch.rand([16, 1])
            self.checkType(a.new(10, 10), "cpu", [10, 10])
            self.checkType(a.new([1, 2, 3, 4]), "cpu", [4])
            b = torch.rand([4, 4], device='cuda')
            self.checkType(b.new(device='cuda'), "cuda", [0])
            self.checkType(a.new(torch.rand([1])), "cpu", [1])

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    def test_scalar_inputs(self):
        with FakeTensorMode():
            self.checkType(torch.div(3, 2), "cpu", [])
            ten = torch.zeros(2, dtype=torch.int32) * 2.0
            self.assertEqual(ten.dtype, torch.float)
            self.checkType(ten, "cpu", [2])

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    def test_allow_meta(self):
        def run_meta():
            with FakeTensorMode():
                x = torch.rand([4], device="meta")
                return x + x

        self.checkType(run_meta(), "meta", [4])

        with patch.object(torch._functorch.config, "fake_tensor_allow_meta", False):
            self.assertRaises(Exception, run_meta)

    def test_embedding_bag_meta(self):
        def f():
            # This behavior was originally unintentional but we see people
            # relying on it
            embedding = torch.nn.EmbeddingBag(10, 3, mode='sum', device='meta')
            input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
            offsets = torch.tensor([0, 4], dtype=torch.long)
            return embedding(input, offsets)

        real_out = f()
        with FakeTensorMode():
            fake_out = f()

        for r, f in zip(real_out, fake_out):
            self.assertEqual(r.size(), f.size())
            self.assertEqual(r.device, f.device)

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    def test_mixed_real_and_fake_inputs(self):
        class _TestPattern(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.conv = torch.nn.Conv2d(1, 1, 1)
                self.bn = torch.nn.BatchNorm2d(1)

            def forward(self, input):
                running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
                scale_factor = self.bn.weight / running_std
                weight_shape = [1] * len(self.conv.weight.shape)
                weight_shape[0] = -1
                bias_shape = [1] * len(self.conv.weight.shape)
                bias_shape[1] = -1
                scaled_weight = self.conv.weight * scale_factor.reshape(weight_shape)
                zero_bias = torch.zeros_like(self.conv.bias, dtype=input.dtype)
                conv = self.conv._conv_forward(input, scaled_weight, zero_bias)
                conv_orig = conv / scale_factor.reshape(bias_shape)
                conv_orig = conv_orig + self.conv.bias.reshape(bias_shape)
                conv = self.bn(conv_orig)
                return conv

        example_inputs = (torch.randn(1, 1, 3, 3),)
        mod = _TestPattern()
        with FakeTensorMode(allow_non_fake_inputs=True):
            out = mod(torch.randn(1, 1, 3, 3))
        self.checkType(out, "cpu", (1, 1, 3, 3))

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_aten_copy_multi_device(self):
        with FakeTensorMode():
            x1 = torch.rand(4, device="cpu")
            x2 = torch.rand(4, device="cuda")
            copy1 = torch.ops.aten.copy.default(x1, x2)
            copy2 = torch.ops.aten.copy.default(x2, x1)
            out = torch.empty(4, device="cpu")
            torch.ops.aten.copy.out(x1, x2, out=out)
        self.checkType(copy1, "cpu", (4,))
        self.checkType(copy2, "cuda", (4,))
        self.checkType(out, "cpu", (4,))

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_aten_index_multi_device(self):
        with FakeTensorMode():
            x1 = torch.rand(4, 4, device="cpu")
            x2 = torch.rand(4, 4, device="cuda")
            i1 = torch.tensor([0, 1], device="cuda")
            i2 = torch.tensor([0, 1], device="cpu")
            r1 = torch.ops.aten.index(x1, i1)
            r2 = torch.ops.aten.index(x2, i2)

            y1 = torch.rand(4, device="cpu")
            y2 = torch.rand(4, device="cuda")
            j1 = torch.tensor([2], device="cuda")
            j2 = torch.tensor([2], device="cpu")
            r3 = torch.ops.aten.index_put.default(x1, j1, y1)
            r4 = torch.ops.aten.index_put.default(x2, j2, y2)
        self.checkType(r1, "cpu", ())
        self.checkType(r2, "cuda", ())
        self.checkType(r3, "cpu", (4, 4))
        self.checkType(r4, "cuda", (4, 4))

    @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "isinstance check for FakeTensor won't work with compile")
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_aten_slice_scatter_multi_device(self):
        with FakeTensorMode():
            x1 = torch.rand(4, 4, device="cpu")
            y1 = torch.rand(2, 4, device="cuda")
            x2 = torch.rand(4, 4, device="cuda")
            y2 = torch.rand(2, 4, device="cpu")
            out = torch.empty(4, 4, device="cpu")
            r1 = torch.ops.aten.slice_scatter.default(x1, y1, start=2)
            r2 = torch.ops.aten.slice_scatter.default(x2, y2, start=2)
            r3 = torch.ops.aten.slice_scatter.out(x1, y1, out=out, start=2)
        self.checkType(r1, "cpu", (4, 4))
        self.checkType(r2, "cuda", (4, 4))
        self.checkType(r3, "cpu", (4, 4))
        self.checkType(out, "cpu", (4, 4))

    def test__adaptive_avg_pool2d_backward(self):
        with FakeTensorMode():
            grad_out = torch.rand(2, 3, 4, 4)
            inp = torch.rand(2, 3, 4, 4).to(memory_format=torch.channels_last)
            grad_in = torch.ops.aten._adaptive_avg_pool2d_backward(grad_out, inp)
            self.assertTrue(torch._prims_common.suggest_memory_format(grad_in) == torch.channels_last)

    @unittest.skipIf(
        sys.version_info >= (3, 12), "torch.compile is not supported on python 3.12+"
    )
    def test_export_numpy(self):
        class MyNumpyModel(torch.nn.Module):
            def forward(self, input):
                input = input.numpy()
                return input + np.random.randn(*input.shape)

        with FakeTensorMode():
            ep = torch.export.export(MyNumpyModel(), args=(torch.randn(1000),))
            self.assertTrue(isinstance(ep, torch.export.ExportedProgram))


class FakeTensorConstHandling(TestCase):
    def assertConst(self, *args):
        for arg in args:
            self.assertTrue(arg.constant is not None)

    def assertNotConst(self, *args):
        for arg in args:
            self.assertTrue(arg.constant is None)

    def test_simple(self):
        with FakeTensorMode():
            x = torch.tensor(4.)
            self.assertEqual(x.item(), 4.)

    def test_inplace_add(self):
        with FakeTensorMode():
            x = torch.tensor(4.)
            y = x.add_(1)
            self.assertEqual(x.item(), 5.)
            self.assertEqual(y.item(), 5.)
            self.assertConst(x, y)

    def test_shared_storages(self):
        with FakeTensorMode():
            x = torch.tensor([4.])
            y = x[:]

            self.assertEqual(x.storage()._cdata, y.storage()._cdata)
            self.assertEqual(x.constant.storage()._cdata, y.constant.storage()._cdata)

    def test_constant_invalidation(self):
        with FakeTensorMode():
            x = torch.tensor([1.])
            self.assertConst(x)
            y = torch.rand([1])
            x.add_(y)
            self.assertNotConst(x)

    def test_inplace_view_invalidation(self):
        with FakeTensorMode():
            x = torch.tensor([1])
            self.assertConst(x)
            x.resize_([2])
            self.assertEqual(x.size(0), 2)
            self.assertNotConst(x)

    def test_fake_tensor_in_intlist_repro(self):

        def fn(tensors):
            max_size = torch.tensor([800, 1216], dtype=torch.int64)
            batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size)
            return tensors[0].new_full(batch_shape, 0.0)

        with self.assertRaises(torch._subclasses.fake_tensor.DataDependentOutputException):
            with torch._subclasses.fake_tensor.FakeTensorMode():
                a = torch.randn(3, 800, 1199)
                b = torch.randn(3, 800, 800)
                inputs = [a, b]
                ref = fn(inputs)

    def test_fake_tensor_batch_norm_cpu(self):
        with torch._subclasses.CrossRefFakeMode():
            m = torch.nn.Sequential(
                torch.nn.BatchNorm2d(10),
                torch.nn.ReLU(),
            )
            m.eval()
            out = m(torch.randn([2, 10, 8, 8]))

    def test_shared_storage_invalidation(self):
        with FakeTensorMode():
            x = torch.tensor([1.])
            y = x[:]
            self.assertConst(x, y)
            y.add_(torch.rand([1]))
            self.assertNotConst(x, y)

    def test_aliased_const_write(self):
        with FakeTensorMode():
            x = torch.tensor([1])
            y = x.expand([4])
            self.assertNotConst(y)
            y[0] = 1
            self.assertNotConst(x)

    def test_constant_propagate_through_functions(self):
        with FakeTensorMode():
            y = torch.div(4, 4, rounding_mode='trunc')
            self.assertConst(y)

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 FakeTensorOpInfoTest(TestCase):
    @ops(custom_op_db, dtypes=OpDTypes.any_one)
    def test_fake(self, device, dtype, op):
        sample_inputs_itr = op.sample_inputs(device, dtype, requires_grad=False)
        for sample_input in sample_inputs_itr:
            args = (sample_input.input,) + sample_input.args
            kwargs = sample_input.kwargs
            optests.fake_check(op, args, kwargs)


class FakeTensorConverterTest(TestCase):
    def test_memoized_conversion_to_meta(self):
        x = torch.rand(2, 2, 2)
        mode = FakeTensorMode()
        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()
        converter = mode.fake_tensor_converter
        self.assertTrue(converter.from_meta_and_device(mode, x, "cpu") is converter.from_meta_and_device(mode, x, "cpu"))

    def test_separate_tensor_storages_view(self):
        x = torch.rand(2, 2, 2)
        y = x[0]
        mode = FakeTensorMode()
        converter = mode.fake_tensor_converter
        x_conv = converter.from_real_tensor(mode, x)
        y_conv = converter.from_real_tensor(mode, y)
        self.assertEqual(torch._C._storage_id(x_conv), torch._C._storage_id(y_conv))

    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
    def test_separate_tensor_storages_non_view(self):
        x = torch.rand(2, 2, 2)
        y = torch.rand(4, 2)
        y.set_(x.storage())
        mode = FakeTensorMode()
        converter = mode.fake_tensor_converter
        x_conv = converter.from_real_tensor(mode, x)
        y_conv = converter.from_real_tensor(mode, y)
        stor_id = torch._C._storage_id(x_conv)
        self.assertEqual(stor_id, torch._C._storage_id(y_conv))
        del x
        del x_conv
        self.assertEqual(len(converter.tensor_memo), 1)
        self.assertEqual(len(converter.meta_converter.storage_memo), 1)
        del y
        del y_conv
        self.assertEqual(len(converter.tensor_memo), 0)
        self.assertEqual(len(converter.meta_converter.storage_memo), 0)


    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
    def test_dead_weak_ref(self):
        x = torch.rand(2, 2, 2)
        y = x[0]
        mode = FakeTensorMode()
        converter = FakeTensorConverter()
        x_conv = converter.from_real_tensor(mode, x)
        x_conv_storage = x_conv.untyped_storage()
        del x_conv
        self.assertFalse(x in converter.tensor_memo)
        y_conv = converter.from_real_tensor(mode, y)
        self.assertIs(x_conv_storage, y_conv.untyped_storage())

    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
    def test_dead_key(self):
        x = torch.rand(2, 2, 2)
        mode = FakeTensorMode()
        converter = FakeTensorConverter()
        x_conv = converter.from_real_tensor(mode, x)
        self.assertEqual(len(converter.tensor_memo), 1)
        x_conv2 = converter.from_real_tensor(mode, x)
        assert x_conv2 is x_conv
        del x
        del x_conv
        del x_conv2
        self.assertEqual(len(converter.tensor_memo), 0)

    def test_no_active_mode(self):
        with FakeTensorMode() as 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_multiple_modes(self):
        t = torch.rand([4])
        t2 = torch.rand([4])
        with FakeTensorMode() as m:
            with FakeTensorMode() as m2:
                t_fake = m.from_tensor(t)
                t2_fake = m2.from_tensor(t2)

                with self.assertRaisesRegex(Exception, "Mixing fake modes"):
                    t_fake + t2_fake

    def test_separate_mode_error(self):
        with FakeTensorMode():
            x = torch.empty(2, 2, device="cpu")
        with FakeTensorMode():
            y = torch.empty(2, 2, device="cpu")
        self.assertRaises(Exception, lambda: x, y)

    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
    def test_no_ref_cycle(self):
        x = torch.rand([4])
        mode = FakeTensorMode()
        y = mode.from_tensor(x)
        self.assertEqual(len(mode.fake_tensor_converter.tensor_memo), 1)
        mode_weak = weakref.ref(mode)
        y_weak = weakref.ref(mode)
        del mode
        del y
        assert mode_weak() is None
        assert y_weak() is None


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
            )

    @unittest.expectedFailure
    def test_sparse_new(self):
        with FakeTensorMode():
            indices = torch.randn(1, 1, dtype=torch.int64)
            values = torch.randn(1)
            extra = (2,)
            sparse = torch.randn(1).to_sparse()
            # This used to segfault, now it does not, but it still raises an
            # error
            sparse2 = sparse.new(indices, values, extra)

    def test_tensor_new(self):
        with FakeTensorMode():
            x = torch.Tensor([1, 2, 3])
        self.assertIsInstance(x, FakeTensor)

    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.assertIn(op, torch._subclasses.fake_tensor._like_tensor_constructors)

    def test_str_storage(self):
        x = torch.zeros(3)
        with FakeTensorMode() as m:
            y = m.from_tensor(x)
            self.assertExpectedInline(str(x.storage()), '''\
 0.0
 0.0
 0.0
[torch.storage.TypedStorage(dtype=torch.float32, device=cpu) of size 3]''')
            self.assertExpectedInline(str(y.storage()), '''\
...
[torch.storage.TypedStorage(dtype=torch.float32, device=meta) of size 3]''')

        self.assertExpectedInline(str(y.storage()), '''\
...
[torch.storage.TypedStorage(dtype=torch.float32, device=meta) of size 3]''')

    # at::_embedding_bag has no op info,
    # and returns extra tensors that at::embedding bag throws away
    def test_embedding_bag_private(self):
        args = [
            torch.ones(6, 1),
            torch.ones(6, dtype=torch.int64),
            torch.arange(2, dtype=torch.int64),
            False,
            2,  # mode = max
        ]

        ref_out = torch.ops.aten._embedding_bag(*args)
        with FakeTensorMode() as m:
            meta_args = [m.from_tensor(a) if isinstance(a, torch.Tensor) else a for a in args]
            meta_out = torch.ops.aten._embedding_bag(*meta_args)

        self.assertEqual(len(ref_out), len(meta_out))
        for ref_o, meta_o in zip(ref_out, meta_out):
            self.assertEqual(ref_o.size(), meta_o.size())

    def test_cross_entropy_loss(self):
        inp = torch.randn(3, 5)
        target = torch.randint(5, (3,), dtype=torch.long)
        weight = torch.rand(5)
        fn = torch.nn.functional.cross_entropy
        for w in (weight, None):
            args = (inp, target, w)
            ref = fn(*args)
            with FakeTensorMode() as m:
                meta_args = [m.from_tensor(a) if isinstance(a, torch.Tensor) else a for a in args]
                meta_out = torch.nn.functional.cross_entropy(*meta_args, label_smoothing=0.5)

            self.assertEqual(ref.size(), meta_out.size())

    @skipIfRocm
    @unittest.skipIf(not PLATFORM_SUPPORTS_FLASH_ATTENTION, "Does not support SDPA or pre-SM80 hardware")
    def test_flash_attention(self):
        class Repro(torch.nn.Module):
            def __init__(self):
                super().__init__()

            def forward(self, arg1, arg2, arg3):
                torch.ops.aten._scaled_dot_product_flash_attention(arg1, arg2, arg3, scale=0.17677669529663687)

        args_new = [
            [
                ((1, 48, 64, 64), (0, 4096, 64, 1), torch.float16, "cuda"),
                ((1, 48, 64, 64), (0, 4096, 64, 1), torch.float16, "cuda"),
                ((1, 48, 64, 64), (0, 4096, 64, 1), torch.float16, "cuda"),
            ],
            [
                ((4, 2, 16, 32), (1024, 512, 32, 1), torch.float16, "cuda"),
                ((4, 2, 16, 32), (1024, 512, 32, 1), torch.float16, "cuda"),
                ((4, 2, 16, 32), (1024, 512, 32, 1), torch.float16, "cuda"),
            ]
        ]
        for args_list in args_new:
            args = [rand_strided(bsz, num_heads, seq_len, head_dim) for
                    (bsz, num_heads, seq_len, head_dim) in args_list]
            try:
                with torch._subclasses.CrossRefFakeMode():
                    Repro()(*args)
            except RuntimeError as e:
                # We expect the cross ref to succed for the first output to fail
                # for the rng state, see Note [Seed and Offset]
                self.assertTrue("output[0]" not in str(e))
                self.assertTrue("found mismatched tensor metadata for output[6]: Devices cpu and cuda:0 are not equal!" in str(e))

    @skipIfRocm
    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_conv_c1_backward(self):
        class Repro(torch.nn.Module):
            def __init__(self):
                super().__init__()

            def forward(self, arg1, arg2, arg3):
                torch.ops.aten.convolution_backward.default(
                    arg1,
                    arg2,
                    arg3,
                    [1],
                    [1, 1],
                    [1, 1],
                    [1, 1],
                    False,
                    [0, 0],
                    1,
                    [True, True, False],
                )

        args_new = [
            ((16, 1, 128, 128), (16384, 16384, 128, 1), torch.float16, "cuda"),
            ((16, 64, 128, 128), (1048576, 1, 8192, 64), torch.float16, "cuda"),
            ((1, 64, 3, 3), (576, 9, 3, 1), torch.float16, "cuda"),
        ]
        args = [rand_strided(sh, st, dt, dev) for (sh, st, dt, dev) in args_new]

        with torch._subclasses.CrossRefFakeMode():
            Repro()(*args)

    def test_no_dispatch_with_like_function(self):
        class CountingMode(TorchDispatchMode):
            def __init__(self):
                self.count = 0

            def __torch_dispatch__(self, func, types, args=(), kwargs=None):
                self.count += 1
                return func(*args, **kwargs)

        with FakeTensorMode():
            x = torch.randn(2)
            with CountingMode() as mode:
                with no_dispatch():
                    torch.zeros_like(x)

        self.assertEqual(mode.count, 0)


class FakeTensorPropTest(TestCase):
    def test_fake_tensor_prop_on_nn_module(self):
        class ToyNnModuleWithParameters(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.layer1 = torch.nn.Linear(4, 3)
                self.layer2 = torch.nn.Linear(3, 2)

            def forward(self, value):
                value = self.layer1(value)
                value = torch.relu(value)
                value = self.layer2(value)
                return value

        model = ToyNnModuleWithParameters()
        value = torch.randn(5, 4)
        # Convert nn.Module to GraphModule so that FakeTensorProp runs.
        graph_model = torch.fx.symbolic_trace(model, (value,))
        # The following block runs FakeTensorProp on graph_module w/to the same FakeTensorMode
        #
        # TODO(wschin): there should be an API to run FakeTensorProp for GraphModule
        # with parameters and buffers.
        with FakeTensorMode() as fake_tensor_mode:

            def to_fake_tensor(x):
                if isinstance(x, torch.Tensor) and not isinstance(x, FakeTensor):
                    return fake_tensor_mode.from_tensor(x)
                return x

            fake_parameters_and_buffers = {
                k: to_fake_tensor(v)
                for k, v in itertools.chain(
                    graph_model.named_parameters(), graph_model.named_buffers()
                )
            }
            with torch.nn.utils.stateless._reparametrize_module(
                graph_model, fake_parameters_and_buffers
            ):
                # This case uses the **same** fake tensor mode to
                #  1. create fake parameters and fake buffers, and
                #  2. run FakeTensorProp
                # The result should be correct.
                result = FakeTensorProp(graph_model, fake_tensor_mode).propagate(value)
                self.assertTrue(isinstance(result, FakeTensor))
                self.assertEqual(result.shape, (5, 2))
                # This case uses the **different** fake tensor modes to
                #  1. create fake parameters and fake buffers, and
                #  2. run FakeTensorProp
                # The following code should fail.
                failed = False
                try:
                    FakeTensorProp(graph_model).propagate(value)
                except AssertionError:
                    # AssertionError: tensor's device must be `meta`, got cpu instead
                    failed = True
                self.assertTrue(failed)


    def test_fake_tensor_prop_on_nn_module_with_optional_args(self):
        class OptionalArgumentInBetween(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.layer1 = torch.nn.Linear(4, 3)
                self.layer2 = torch.nn.Linear(3, 2)

            def forward(self, value, another_value=None, another_optional_value=None):
                # Mimic huggingface's `forward` methods which have several optional arguments.
                # For example, GPT accepts forward(self, input_ids, None, attention_mask, ...).
                # To apply FakeTensorProp, its from_real_tensor(...) needs to accept None.
                if another_value is None:
                    another_value = torch.rand_like(value)
                if another_optional_value is None:
                    another_optional_value = torch.rand_like(value)
                value = value + another_value + another_optional_value
                return value * value

        fake_mode = FakeTensorMode(allow_non_fake_inputs=True, allow_fallback_kernels=False)
        with fake_mode:
            model = OptionalArgumentInBetween()
            value = torch.randn(5, 4)
            another_optional_value = torch.randn(5, 4)
            graph_model = torch.fx.symbolic_trace(model, (value, None, another_optional_value))
            FakeTensorProp(graph_model, fake_mode).propagate(value, None, another_optional_value)


    def test_torch_load_with_fake_mode(self):

        class TheModelClass(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.fc1 = torch.nn.Linear(5, 10)

            def forward(self, x):
                return self.fc1(x)

        with TemporaryFileName() as state_dict_file:
            # Create state_dict to be loaded later
            model = TheModelClass()
            torch.save(model.state_dict(), state_dict_file)

            fake_mode = FakeTensorMode()
            with fake_mode:
                torch.load(state_dict_file)  # scenario 1
                torch.load(state_dict_file, map_location="cpu")  # scenario 2


class FakeTensorSerialization(TestCase):
    def test_serialization(self):
        x = torch.tensor([0], device="cpu")
        with FakeTensorMode():
            y = pickle.loads(pickle.dumps(x))
            self.assertEqual(type(y), FakeTensor)
            self.assertEqual(y.device.type, "meta")

            with unset_fake_temporarily():
                y = pickle.loads(pickle.dumps(x))
                self.assertEqual(x.device, y.device)

    def test_serialization_with_tracing(self):
        x = torch.tensor([0], device="cpu")
        with tracing(TracingContext(FakeTensorMode())):
            y = pickle.loads(pickle.dumps(x))
            self.assertEqual(x.device, y.device)


class FakeTensorDispatchCache(TestCase):
    def test_shape_env_settings(self):
        """
        Validation that any boolean settings in ShapeEnv are present in the
        _ShapeEnvSettings. We hope to ensure that any new settings that might
        affect FakeTensor dispatch are included in the cache key calculation.
        If this test fails, consider updating _ShapeEnvSettings or change this
        test to omit checking for the new field.
        """
        init_sig = inspect.signature(ShapeEnv._init)
        args = [
            name for name, param in init_sig.parameters.items()
            if type(param.default) is bool
        ]

        settings = [f.name for f in dataclasses.fields(_ShapeEnvSettings)]
        for arg in args:
            self.assertTrue(arg in settings)

    def _test_cache_key(self, fm, x, y, z):
        """
        Helper for all test_cache_key_* tests below. Assert that the
        cache keys for inputs x and y are the same, but z is different.
        """
        func = aten.add.Tensor
        key_x = fm._cache_key(func, [x], {})
        key_y = fm._cache_key(func, [y], {})
        key_z = fm._cache_key(func, [z], {})

        self.assertEqual(key_x, key_y)
        self.assertNotEqual(key_x, key_z)

    def test_cache_key_dtype(self):
        with FakeTensorMode() as fm:
            x = torch.randn(4, 3, dtype=torch.float16)
            y = torch.randn(4, 3, dtype=torch.float16)
            z = x.to(dtype=torch.float32)
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_shape(self):
        with FakeTensorMode() as fm:
            x = torch.randn(4, 3)
            y = torch.randn(4, 3)
            z = torch.randn(4, 2)
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_stride(self):
        with FakeTensorMode() as fm:
            x = torch.randn(4, 2)
            y = torch.randn(4, 2)
            z = x.as_strided((4, 2), (1, 2))
            self._test_cache_key(fm, x, y, z)

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_cache_key_device(self):
        with FakeTensorMode() as fm:
            x = torch.randn(4, 3)
            y = torch.randn(4, 3)
            z = x.to(device="cuda")
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_memory_format(self):
        with FakeTensorMode() as fm:
            x = torch.randn(1, 2, 3, 4)
            y = torch.randn(1, 2, 3, 4)
            z = x.to(memory_format=torch.channels_last)
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_storage_offset(self):
        with FakeTensorMode() as fm:
            x = torch.randn(3)[1:]
            y = torch.randn(3)[1:]
            z = torch.randn(2)
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_requires_grad(self):
        with FakeTensorMode() as fm:
            x = torch.randn(4, 3)
            y = torch.randn(4, 3)
            z = torch.randn(4, 3, requires_grad=True)
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_is_conj(self):
        with FakeTensorMode() as fm:
            x = torch.randn(4, 3, dtype=torch.complex64)
            y = torch.randn(4, 3, dtype=torch.complex64)
            z = torch.randn(4, 3, dtype=torch.complex64)
            torch._C._set_conj(z, not z.is_conj())
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_is_neg(self):
        with FakeTensorMode() as fm:
            x = torch.randn(4, 3, dtype=torch.complex64)
            y = torch.randn(4, 3, dtype=torch.complex64)
            z = torch.randn(4, 3, dtype=torch.complex64)
            torch._C._set_neg(z, not z.is_neg())
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_is_inference(self):
        with torch.inference_mode(True):
            t = torch.randn(4, 3)
        with FakeTensorMode() as fm:
            x = torch.randn(4, 3)
            y = torch.randn(4, 3)
            z = fm.from_tensor(t)
            self._test_cache_key(fm, x, y, z)

    def test_cache_key_constants(self):
        with FakeTensorMode() as fm:
            # Python hashes 1.0 to the same value as 1. Make sure the
            # cache key calculation differentiates them.
            self._test_cache_key(fm, 1.0, 1.0, 1)
            self._test_cache_key(fm, 0.0, 0.0, 0)

    def assertHitsMisses(self, hits, misses):
        """
        Helper to assert on the number of recorded hits and misses.
        """
        info = FakeTensorMode.cache_info()
        self.assertEqual(info.hits, hits)
        self.assertEqual(info.misses, misses)

    def assertBypasses(self, reason, count):
        """
        Helper to assert on the number of recorded bypasses.
        """
        info = FakeTensorMode.cache_info()
        if count > 0:
            self.assertIn(reason, info.bypasses)
            self.assertEqual(info.bypasses[reason], count)
        else:
            self.assertNotIn(reason, info.bypasses)

    def test_cache_hit(self):
        """
        Test that cache hit/miss counters are updated correctly.
        """
        with FakeTensorMode():
            x = torch.randn(4, 3)
            y = torch.randn(4, 3)

            FakeTensorMode.cache_clear()
            self.assertHitsMisses(0, 0)
            res1 = x + y
            self.assertHitsMisses(0, 1)
            res2 = x + y
            self.assertHitsMisses(1, 1)

            self.assertEqual(
                extract_tensor_metadata(res1),
                extract_tensor_metadata(res2),
            )

    def test_cache_bypass(self):
        """
        Test that cache bypass counters are updated correctly.
        """
        with FakeTensorMode():
            x = torch.randn(1, 2)

            FakeTensorMode.cache_clear()
            self.assertBypasses("inplace view", 0)

            x.unsqueeze_(0)
            self.assertBypasses("inplace view", 1)

    def test_cache_default_dtype(self):
        """
        Test that the default dtype is respected when serving cached results.
        """
        with FakeTensorMode():
            x = torch.tensor([1, 2], dtype=torch.int32)
            torch.set_default_dtype(torch.float32)

            FakeTensorMode.cache_clear()
            self.assertHitsMisses(0, 0)

            y = x + 1.0
            self.assertEqual(y.dtype, torch.float32)
            self.assertHitsMisses(0, 1)

            torch.set_default_dtype(torch.float16)
            y = x + 1.0
            self.assertEqual(y.dtype, torch.float16)
            self.assertHitsMisses(0, 2)

            torch.set_default_dtype(torch.float32)
            y = x + 1.0
            self.assertEqual(y.dtype, torch.float32)
            self.assertHitsMisses(1, 2)

    @unittest.skipIf(not RUN_CUDA, "requires cuda")
    def test_cache_default_device(self):
        """
        Test that the default device is respected when serving cached results.
        """
        with FakeTensorMode():
            FakeTensorMode.cache_clear()
            self.assertHitsMisses(0, 0)

            torch.set_default_device("cpu")
            x = torch.tensor([1, 2])
            y = x + 1.0
            self.assertEqual(y.device.type, "cpu")
            self.assertHitsMisses(0, 1)

            torch.set_default_device("cuda")
            x = torch.tensor([1, 2])
            y = x + 1.0
            self.assertEqual(y.device.type, "cuda")
            self.assertHitsMisses(0, 2)

            torch.set_default_device("cpu")
            x = torch.tensor([1, 2])
            y = x + 1.0
            self.assertEqual(y.device.type, "cpu")
            self.assertHitsMisses(1, 2)

    def test_cache_inplace_op(self):
        """
        Test that inplace ops served from the cache correctly reference the
        input parameter.
        """
        with FakeTensorMode():
            x = torch.randn(1, 2)
            y = torch.randn(1, 2)

            FakeTensorMode.cache_clear()
            self.assertHitsMisses(0, 0)

            z = x.add_(y)
            self.assertHitsMisses(0, 1)
            self.assertEqual(id(x), id(z))

            w = x.add_(y)
            self.assertHitsMisses(1, 1)
            self.assertEqual(id(x), id(w))

    def test_cache_view_op(self):
        """
        Test that view ops are handled correctly when served from the cache.
        """
        with FakeTensorMode():
            x1 = torch.ones(2, requires_grad=True).clone()
            x2 = torch.ones(2, requires_grad=True).clone()
            y2 = x2.view(-1)

            # Test operating on a non-view tensor, then the same operation
            # on a view tensor. Assert that the view property is set correctly.
            z1 = x1.mul_(2)
            self.assertFalse(z1._is_view())

            z2 = y2.mul_(2)
            self.assertTrue(z2._is_view())

            # Now the other way around: first operate on a view tensor, then
            # the same operation on a non-view tensor.
            z2 = y2.mul_(2)
            self.assertTrue(z2._is_view())

            z1 = x1.mul_(2)
            self.assertFalse(z1._is_view())

    def test_cache_dispatch_key_set(self):
        """
        Test that operations that change the dispatch key set bypass caching.
        """
        with FakeTensorMode():
            FakeTensorMode.cache_clear()
            self.assertBypasses("dispatch_key_set mismatch", 0)

            x = torch._efficientzerotensor(3)
            self.assertTrue(x._is_zerotensor())
            self.assertBypasses("dispatch_key_set mismatch", 1)

            y = torch._efficientzerotensor(3)
            self.assertTrue(y._is_zerotensor())
            self.assertBypasses("dispatch_key_set mismatch", 2)

    def test_inference_mode(self):
        """
        Test that caching handles inference mode correctly.
        """
        with FakeTensorMode():
            x = torch.randn(4, 3)
            y = torch.randn(4, 3)

            FakeTensorMode.cache_clear()
            self.assertHitsMisses(0, 0)

            # Expect a miss when the inference mode is different
            res1 = x + y
            with torch.inference_mode():
                res2 = x + y

            self.assertHitsMisses(0, 2)
            self.assertFalse(res1.is_inference())
            self.assertTrue(res2.is_inference())

            # Second tries should see hits
            res3 = x + y

            self.assertHitsMisses(1, 2)
            self.assertFalse(res3.is_inference())
            self.assertEqual(
                extract_tensor_metadata(res1),
                extract_tensor_metadata(res3),
            )

            with torch.inference_mode():
                res4 = x + y

            self.assertHitsMisses(2, 2)
            self.assertTrue(res4.is_inference())
            self.assertEqual(
                extract_tensor_metadata(res2),
                extract_tensor_metadata(res4),
            )


instantiate_parametrized_tests(FakeTensorTest)

only_for = ("cpu", "cuda")
instantiate_device_type_tests(FakeTensorOpInfoTest, globals(), only_for=only_for)

if __name__ == "__main__":
    run_tests()