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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
|
# mypy: ignore-errors
import functools
import warnings
from typing import Any, Callable, List, Union
import torch
import torch.utils._pytree as pytree
from torch._ops import OpOverload
from torch._subclasses.fake_tensor import (
FakeTensor,
FakeTensorMode,
MetadataMismatchError,
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 _check_alias_info(context, real_out, real_in, fake_out, fake_in):
r_aliasing = outputs_alias_inputs(real_out, real_in)
f_aliasing = outputs_alias_inputs(fake_out, fake_in)
if r_aliasing != f_aliasing:
raise MetadataMismatchError(
f"{context} mismatch in outputs_alias_inputs check {f_aliasing} != {r_aliasing}"
)
r_identity_eq = outputs_are_inputs(real_out, real_in)
f_identity_eq = outputs_are_inputs(fake_out, fake_in)
if r_identity_eq != f_identity_eq:
raise MetadataMismatchError(
f"{context} mismatch in outputs_are_inputs check {f_identity_eq} != {r_identity_eq}"
)
r_output_alias_each_other = output_alias_each_other(real_out)
f_output_alias_each_other = output_alias_each_other(fake_out)
if r_output_alias_each_other != f_output_alias_each_other:
raise MetadataMismatchError(
f"{context} mismatch in outputs_alias_each_other check "
f"{f_output_alias_each_other} != {r_output_alias_each_other}"
)
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
if (
func is aten._scaled_dot_product_cudnn_attention.default
and idx in (6, 7)
and "Devices" in repr(e)
):
return True
return False
def try_convert_fake_to_real(
ten_list: List[Union[FakeTensor, Any]]
) -> List[Union[FakeTensor, torch.Tensor, Any]]:
"""
Attempt to convert fake tensors to a corresponding real tensor with the correct underlying storage by looking up
the FakeTensorMode meta to real storage mapping. On failure to find the storage mapping, the FakeTensor will
remain in the list.
Note: this is not currently optimized (makes copies of the meta converter internal dictionaries)
"""
fake_tensor = next(
(item for item in ten_list if isinstance(item, FakeTensor)), None
)
if fake_tensor is None:
return ten_list
fake_mode = fake_tensor.fake_mode
meta_converter = fake_mode.fake_tensor_converter.meta_converter
desc = meta_converter.describer
storage_to_key = {v: k for k, v in meta_converter.storage_memo.items()}
key_to_real_storage = {v: k for k, v in desc.lookup_storage.items()}
out = []
for t in ten_list:
if not isinstance(t, FakeTensor) or not t.layout == torch.strided:
out.append(t)
continue
key = storage_to_key.get(t.untyped_storage())
real_storage = None if key is None else key_to_real_storage.get(key)
if real_storage is None:
out.append(t)
continue
unhinted = False
def map_symint(s):
nonlocal unhinted
if not isinstance(s, torch.SymInt):
return s
unhinted = unhinted if not unhinted else s.node.has_hint()
return s.node.hint
stor_offset = map_symint(t.storage_offset())
size = [map_symint(s) for s in t.shape]
stride = [map_symint(s) for s in t.stride()]
if unhinted:
out.append(t)
continue
new_tensor = torch.empty(
[],
dtype=t.dtype,
device=t.device,
)
new_tensor.set_(
real_storage,
storage_offset=stor_offset,
size=size,
stride=stride,
)
out.append(new_tensor.clone())
return out
def _check_fake_real_tensors(
real_out: torch.Tensor,
fake_out: FakeTensor,
context="",
sizes=True,
strides=False,
storage_offset=True,
requires_grad=True,
):
if requires_grad:
if real_out.requires_grad != fake_out.requires_grad:
raise MetadataMismatchError(
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(real_out):
r_offset = real_out.storage_offset()
f_offset = fake_out.storage_offset()
if r_offset != f_offset:
raise MetadataMismatchError(f"{context} mismatched storage offset")
torch._prims.utils.compare_tensor_meta(
real_out,
fake_out,
check_sizes=sizes,
check_strides=strides,
allow_rhs_unbacked=True,
)
class CrossRefFakeMode(TorchDispatchMode):
def __init__(
self,
ignore_op_fn: Union[Callable[[OpOverload], bool], None] = None,
*,
check_strides=True,
check_aliasing=True,
only_check_ops_with_meta=True,
):
super().__init__()
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
self.only_check_ops_with_meta = only_check_ops_with_meta
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 (
not self.only_check_ops_with_meta
or torch._subclasses.fake_impls.has_meta(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:
_check_alias_info(
context, r, (args, kwargs), fake_r, (fake_args, fake_kwargs)
)
for idx, (r_out, f_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(
f_out, torch.Tensor
), f"{context} mismatched number of tensor outputs"
if r_is_ten:
try:
_check_fake_real_tensors(
r_out,
f_out,
sizes=True,
strides=self.check_strides,
storage_offset=True,
requires_grad=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 MetadataMismatchError(error_message) from e
return r
|