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# Owner(s): ["module: meta tensors"]
from torch.testing._internal.common_utils import TestCase, run_tests, skipIfCrossRef, skipIfRocm
import torch
import itertools
import numpy as np
from torch.testing._internal.jit_utils import RUN_CUDA
from torch._subclasses.fake_tensor import (
FakeTensor,
FakeTensorMode,
FakeTensorConverter,
DynamicOutputShapeException,
)
from torch.testing import FileCheck
from torch import nn
import unittest
import torch._prims as prims
import contextlib
import weakref
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):
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_parameter_instantiation(self):
with FakeTensorMode():
x = torch.rand([4])
y = torch.nn.parameter.Parameter(x)
self.assertTrue(isinstance(y, torch.nn.Parameter))
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(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))
@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
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_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))
@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_fake_mode_error(self):
x = torch.rand([4, 4])
with self.assertRaisesRegex(Exception, "non-Fake Tensor inputs"):
with FakeTensorMode():
y = x[0]
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_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")
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)
@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_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)
@skipIfRocm
@unittest.skipIf(not RUN_CUDA, "requires cuda")
def test_cudnn_rnn(self):
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()
for i, context in enumerate([contextlib.nullcontext, lambda: mode]):
with context():
inps = (
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(),
)
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')
@skipIfRocm
@unittest.skipIf(not RUN_CUDA, "requires cuda")
def test_fallback_memory_prop(self):
m = nn.Conv2d(16, 33, 3, stride=2, device="cuda", dtype=torch.half)
m = m.to(memory_format=torch.channels_last)
mode = FakeTensorMode()
# TODO: module.to() doesn't work because it assigns .data, which is ignored
with torch._subclasses.fake_tensor.FakeCopyMode(mode):
mod_copied = copy.deepcopy(m)
with mode:
input = torch.rand(20, 16, 50, 100, dtype=torch.half, device="cuda").to(memory_format=torch.channels_last)
out = mod_copied(input)
self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
self.checkType(out, "cuda", [20, 33, 24, 49])
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 checkMetaProps(self, t1, t2):
prims.utils.compare_tensor_meta(t1, t2)
@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(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)
@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])
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])
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(throw_on_data_dependent_ops=True):
a = torch.randn(3, 800, 1199)
b = torch.randn(3, 800, 800)
inputs = [a, b]
ref = fn(inputs)
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 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()
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(mode, x, "cpu") is converter(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(mode, x)
y_conv = converter(mode, y)
self.assertEqual(torch._C._storage_id(x_conv), torch._C._storage_id(y_conv))
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(mode, x)
y_conv = converter(mode, y)
stor_id = torch._C._storage_id(x_conv)
self.assertEqual(stor_id, torch._C._storage_id(y_conv))
del x
self.assertEqual(len(converter.tensor_memo), 1)
converter.meta_converter.check_for_expired_weak_storages()
self.assertEqual(len(converter.meta_converter.storage_memo), 1)
del y
self.assertEqual(len(converter.tensor_memo), 0)
converter.meta_converter.check_for_expired_weak_storages()
self.assertEqual(len(converter.meta_converter.storage_memo), 0)
def test_dead_weak_ref(self):
x = torch.rand(2, 2, 2)
y = x[0]
mode = FakeTensorMode()
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_dead_key(self):
x = torch.rand(2, 2, 2)
mode = FakeTensorMode()
converter = FakeTensorConverter()
x_conv = converter(mode, x)
self.assertEqual(len(converter.tensor_memo), 1)
self.assertEqual(len(converter.meta_converter.tensor_memo), 1)
del x
self.assertEqual(len(converter.tensor_memo), 0)
self.assertEqual(len(converter.meta_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_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)
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_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)
if __name__ == "__main__":
run_tests()
|