[FakeTensor] Workaround FFT ops with incorrect meta strides

test/functorch/test_aotdispatch.py

@@ -2748,7 +2748,6 @@ def forward(self, x):
 aot_autograd_failures = {
     # data-dependent control flow
     xfail('cov'),
-    xfail('istft'),
     xfail('nn.functional.gaussian_nll_loss'),
     xfail('tensor_split'),
     xfail('corrcoef'),
@@ -2770,24 +2769,6 @@ def forward(self, x):
     xfail('_segment_reduce', 'lengths'),
     skip('nn.functional.nll_loss', ''),  # UBSAN failure!
 
-    # many complex operators incorrect striding, metadata
-    xfail('fft.fft', ''),
-    xfail('fft.hfft2', ''),
-    xfail('fft.hfft', ''),
-    xfail('fft.hfftn', ''),
-    xfail('fft.ifft', ''),
-    xfail('fft.ihfft2', ''),
-    xfail('fft.ihfft', ''),
-    xfail('fft.ihfftn', ''),
-    xfail('fft.irfft2', ''),
-    xfail('fft.irfft', ''),
-    xfail('fft.irfftn', ''),
-    xfail('fft.rfft2', ''),
-    xfail('fft.rfft', ''),
-    xfail('fft.rfftn', ''),
-
-    xfail('stft', ''),
-
     # Misc
     xfail('to_sparse'),
     xfail('corrcoef'),
@@ -2850,12 +2831,29 @@ def forward(self, x):
     xfail('_segment_reduce', 'offsets'),  # aten.segment_reduce.default - couldn't find symbolic meta functio...
     xfail('sgn', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
     xfail('special.i1', ''),  # aten.i0.default - couldn't find symbolic meta function/decomposition
-    xfail('stft', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
     xfail('take_along_dim', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
     xfail('trace', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
     xfail('triangular_solve', ''),  # aten.triangular_solve.default - couldn't find symbolic meta function/de...
     xfail('_upsample_bilinear2d_aa'),  # RuntimeError: isIntList() INTERNAL ASSERT FAILED  Expected IntList but got GenericList
     decorate('linalg.householder_product', decorator=unittest.skipIf(IS_MACOS and IS_X86, 'flaky')),
+
+    # many complex operators incorrect striding, metadata
+    xfail('fft.fft', ''),
+    xfail('fft.hfft2', ''),
+    xfail('fft.hfft', ''),
+    xfail('fft.hfftn', ''),
+    xfail('fft.ifft', ''),
+    xfail('fft.ihfft2', ''),
+    xfail('fft.ihfft', ''),
+    xfail('fft.ihfftn', ''),
+    xfail('fft.irfft2', ''),
+    xfail('fft.irfft', ''),
+    xfail('fft.irfftn', ''),
+    xfail('fft.rfft2', ''),
+    xfail('fft.rfft', ''),
+    xfail('fft.rfftn', ''),
+
+    xfail('stft', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
 }
 
 def _test_aot_autograd_helper(self, device, dtype, op, dynamic=False):

test/test_ops.py

@@ -2016,6 +2016,8 @@ def test_refs_are_in_decomp_table(self, op):
 
 fake_backward_xfails = {skip(s) for s in fake_backward_skips} | {
     xfail("_segment_reduce", "lengths"),
+    xfail("fft.ihfftn"),  # Mismatch in aten._conj_physical.default
+    xfail("fft.ihfft2"),  # Mismatch in aten._conj_physical.default
     skip('nn.functional.ctc_loss'),
 }
 

test/test_proxy_tensor.py

@@ -1488,22 +1488,6 @@ def f(t):
     xfail('nanquantile'),
     xfail('narrow'),
 
-    # many complex operators incorrect striding, metadata
-    skip('fft.fft', ''),
-    skip('fft.hfft2', ''),
-    skip('fft.hfft', ''),
-    skip('fft.hfftn', ''),
-    skip('fft.ifft', ''),
-    skip('fft.ihfft2', ''),
-    skip('fft.ihfft', ''),
-    skip('fft.ihfftn', ''),
-    skip('fft.irfft2', ''),
-    skip('fft.irfft', ''),
-    skip('fft.irfftn', ''),
-    skip('fft.rfft2', ''),
-    skip('fft.rfft', ''),
-    skip('fft.rfftn', ''),
-
     # Seems like it's creating a sparse tensor that isn't captured by tensor.is_sparse
     xfail('sparse.sampled_addmm'),
     xfail('sparse.mm', 'reduce'),
@@ -1524,8 +1508,6 @@ def f(t):
     xfail('repeat_interleave'),
     # ASAN failures due to divide by 0
     skip('nn.functional.nll_loss'),
-
-    xfail("stft"),
 }
 
 symbolic_tensor_failures = {
@@ -1583,6 +1565,23 @@ def f(t):
     xfail('triangular_solve', ''),  # aten.triangular_solve.default - couldn't find symbolic meta function/decomposition
     xfail('unique_consecutive', ''),  # aten.unique_consecutive.default - couldn't find symbolic meta function/decomposition
     xfail('unique', ''),  # aten._unique2.default - couldn't find symbolic meta function/decomposition
+
+    # many complex operators incorrect striding, metadata
+    xfail('fft.fft', ''),
+    xfail('fft.hfft2', ''),
+    xfail('fft.hfft', ''),
+    xfail('fft.hfftn', ''),
+    xfail('fft.ifft', ''),
+    xfail('fft.ihfft2', ''),
+    xfail('fft.ihfft', ''),
+    xfail('fft.ihfftn', ''),
+    xfail('fft.ihfft2', ''),
+    xfail('fft.irfft2', ''),
+    xfail('fft.irfft', ''),
+    xfail('fft.irfftn', ''),
+    xfail('fft.rfft2', ''),
+    xfail('fft.rfft', ''),
+    xfail('fft.rfftn', ''),
 }
 symbolic_tensor_segfaults = {
     skip('nn.functional.batch_norm')  # Segfault??

torch/_subclasses/fake_tensor.py

@@ -428,10 +428,7 @@ def non_kwarg_to(fake_mode, func, *args, **kwargs):
     )
 
 
-# Many of these operators mutate striding in place and output conj depending on input
-# that is not reflected in meta registration.
-# TODO: fix registrations, add all existing impls that are correct
-def unsupported_complex_op(op):
+def stride_incorrect_op(op):
     if op.namespace not in ("aten", "prims"):
         return False
     if op is aten._fft_c2c.default:
@@ -443,10 +440,26 @@ def unsupported_complex_op(op):
     return False
 
 
-# These operators mutate striding in place and output conj depending on input
-# that is not reflected in meta registration
-@register_op_impl(unsupported_complex_op)
-def unsupported_fft(fake_mode, func, *args, **kwargs):
+# These operators have meta implementations with incorrect strides
+@register_op_impl(stride_incorrect_op)
+def wordaround_stride_incorrect_op(fake_mode, func, *args, **kwargs):
+    # This is a workaround for meta implmentations with incorrect strides
+
+    def is_symbolic(x):
+        if isinstance(x, FakeTensor):
+            return x._has_symbolic_sizes_strides
+        if isinstance(x, (torch.SymInt, torch.SymFloat, torch.SymBool)):
+            return True
+        return False
+
+    # For static shapes, we can fall back to eager for the real strides
+    if fake_mode.allow_fallback_kernels:
+        require_dynamic = any(
+            is_symbolic(x) for x in itertools.chain(args, kwargs.values())
+        )
+        if not require_dynamic:
+            return run_fallback_kernel(fake_mode, func, args, kwargs, None)
+
     raise UnsupportedOperatorException(func)
 
 
@@ -1436,7 +1449,7 @@ def dispatch(self, func, types, args=(), kwargs=None):
         if (
             "prims::" in func._schema.name
             and hasattr(func, "prim_meta_impl")
-            and not unsupported_complex_op(func)
+            and not stride_incorrect_op(func)
         ):
             with self:
                 return func.prim_meta_impl(*args, **kwargs)

torch/testing/_internal/common_utils.py

@@ -1857,7 +1857,7 @@ def print_repro_on_failure(repro_str):
         # NB: Hacking the exception args is the cleanest way I've found to append
         # failure reproduction info without poisoning the stack trace.
         if len(e.args) >= 1:
-            e.args = (str(e.args[0]) + f"\n{repro_str}",) + e.args[1:]
+            e.args = (f"{e.args[0]}\n{repro_str}", *e.args[1:])
         raise
 
 #  "min_satisfying_examples" setting has been deprecated in hypothesis