Spaces:
Running
Running
File size: 7,391 Bytes
c61ccee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
# mypy: ignore-errors
import functools
import warnings
from typing import Callable, Union
import torch
import torch.utils._pytree as pytree
from torch._ops import OpOverload
from torch._subclasses.fake_tensor import (
FakeTensorMode,
tree_flatten_only,
UnsupportedFakeTensorException,
)
from torch.utils._python_dispatch import TorchDispatchMode
aten = torch._ops.ops.aten
def outputs_alias_inputs(outputs, inputs):
input_storages = {
inp._typed_storage()._cdata
for inp in tree_flatten_only(torch.Tensor, inputs)
if torch._C._has_storage(inp)
}
return any(
torch._C._has_storage(out) and out._typed_storage()._cdata in input_storages
for out in tree_flatten_only(torch.Tensor, outputs)
)
def outputs_are_inputs(outputs, inputs):
input_ids = {id(inp) for inp in tree_flatten_only(torch.Tensor, inputs)}
return any(id(out) in input_ids for out in tree_flatten_only(torch.Tensor, outputs))
def output_alias_each_other(outputs):
storages = set()
for out in tree_flatten_only(torch.Tensor, outputs):
if not torch._C._has_storage(out):
continue
stor = out._typed_storage()._cdata
if stor in storages:
return True
storages.add(stor)
return False
def is_sdpa_error(func, idx, e):
if (
(
func is aten._scaled_dot_product_flash_attention.default
or func is aten._flash_attention_forward.default
)
and idx in (6, 7)
and "Devices" in repr(e)
):
return True
if (
(
func is aten._scaled_dot_product_efficient_attention.default
or func is aten._efficient_attention_forward.default
)
and idx in (2, 3)
and "Devices" in repr(e)
):
return True
return False
class CrossRefFakeMode(TorchDispatchMode):
def __init__(
self,
ignore_op_fn: Union[Callable[[OpOverload], bool], None] = None,
*,
check_strides=True,
check_aliasing=True,
):
self.ignore_op_fn = (
ignore_op_fn if ignore_op_fn is not None else lambda fn: False
)
self.check_strides = check_strides
self.check_aliasing = check_aliasing
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
kwargs = kwargs or {}
fake_r = None
# empty_like excluded for now due to sparse complex
# aten._to_dense.default this one is getting called with csc
if (
func
not in (
aten.lift_fresh.default,
aten.lift_fresh_copy.default,
aten.set_.source_Storage_storage_offset,
)
and not self.ignore_op_fn(func)
and torch.Tag.dynamic_output_shape not in func.tags
and torch.Tag.inplace_view not in func.tags
and torch.Tag.data_dependent_output not in func.tags
):
# Do not import symbolic_shapes at the top of the module as it imports sympy and that's slow
from torch.fx.experimental.symbolic_shapes import ShapeEnv
try:
# TODO: enable_python_dispatcher() here
with FakeTensorMode(shape_env=ShapeEnv()) as fake_mode:
fake_args, fake_kwargs = pytree.tree_map_only(
torch.Tensor,
functools.partial(fake_mode.from_tensor, static_shapes=True),
(args, kwargs),
)
with warnings.catch_warnings():
fake_r = func(*fake_args, **fake_kwargs)
except UnsupportedFakeTensorException:
pass
context = (
f"When comparing the output of {func} on FakeTensor and concrete Tensors, "
f"found"
)
r = func(*args, **kwargs)
if fake_r is not None:
r_flat = pytree.tree_leaves(r)
f_flat = pytree.tree_leaves(fake_r)
assert len(f_flat) == len(
r_flat
), f"{context} mismatch in number of returns {len(f_flat)} != {len(r_flat)}"
if self.check_aliasing:
r_aliasing = outputs_alias_inputs(r, (args, kwargs))
f_aliasing = outputs_alias_inputs(fake_r, (fake_args, fake_kwargs))
assert (
r_aliasing == f_aliasing
), f"{context} mismatch in outputs_alias_inputs check {f_aliasing} != {r_aliasing}"
r_identity_eq = outputs_are_inputs(r, (args, kwargs))
f_identity_eq = outputs_are_inputs(fake_r, (fake_args, fake_kwargs))
assert (
r_identity_eq == f_identity_eq
), f"{context} mismatch in outputs_are_inputs check {f_identity_eq} != {r_identity_eq}"
r_output_alias_each_other = output_alias_each_other(r)
f_output_alias_each_other = output_alias_each_other(fake_r)
assert r_output_alias_each_other == f_output_alias_each_other, (
f"{context} mismatch in outputs_alias_each_other check "
f"{f_output_alias_each_other} != {r_output_alias_each_other}"
)
for idx, (r_out, fake_out) in enumerate(
zip(pytree.tree_leaves(r), pytree.tree_leaves(fake_r))
):
r_is_ten = isinstance(r_out, torch.Tensor)
assert r_is_ten == isinstance(
fake_out, torch.Tensor
), f"{context} mismatched number of tensor outputs"
if r_is_ten:
assert r_out.requires_grad == fake_out.requires_grad, (
f"{context} mismatched requires_grad-ness of outputs. "
f"This usually means that you have added autograd support "
f"for your operator at a dispatch key other than Autograd, "
f"which will lead to problems"
)
if torch._C._has_storage(r_out):
r_offset = r_out.storage_offset()
f_offset = fake_out.storage_offset()
assert (
r_offset == f_offset
), f"{context} mismatched storage offset"
try:
torch._prims.utils.compare_tensor_meta(
r_out,
fake_out,
check_strides=self.check_strides,
allow_rhs_unbacked=True,
)
except Exception as e:
if is_sdpa_error(func, idx, e):
continue
error_message = (
f"{context} mismatched tensor metadata: {e}"
if len(r_flat) == 1
else f"{context} mismatched tensor metadata for output[{idx}]: {e}"
)
raise RuntimeError(error_message) from e
return r
|