[inductor] Fix shape mismatch in sdpa pattern matcher

test/inductor/test_cpu_repro.py

@@ -2463,6 +2463,55 @@ def fn(x1, x2):
         )
         self.assertEqual(metrics.generated_kernel_count, 1)
 
+    def test_attention_size_mismatch(self):
+        class Attention(torch.nn.Module):
+            def __init__(self, hidden_size, num_heads):
+                super().__init__()
+                self.hidden_size = hidden_size
+                self.num_heads = num_heads
+                self.head_size = hidden_size // num_heads
+                self.query = torch.nn.Linear(hidden_size, hidden_size)
+                self.key = torch.nn.Linear(hidden_size, hidden_size)
+                self.value = torch.nn.Linear(hidden_size, hidden_size)
+                self.inv_scale = torch.nn.Parameter(
+                    torch.Tensor([1 / self.head_size**0.5]), requires_grad=False
+                )
+
+            def forward(self, x):
+                query = self.query(x)
+                key = self.key(x)
+                value = self.value(x)
+                (batch_size, seq_len, hidden_size) = query.size()
+                query = query.view(
+                    batch_size, seq_len, self.num_heads, self.head_size
+                ).permute(0, 2, 1, 3)
+                key = key.view(
+                    batch_size, seq_len, self.num_heads, self.head_size
+                ).permute(0, 2, 3, 1)
+                value = value.view(
+                    batch_size, seq_len, self.num_heads, self.head_size
+                ).permute(0, 2, 1, 3)
+                attention_weights = (
+                    torch.matmul(query, key).div(self.inv_scale).softmax(dim=-1)
+                )
+                output = torch.matmul(attention_weights, value)
+                return output
+
+        torch.manual_seed(123)
+        hidden_size = 16
+        num_heads = 1
+        seq_len = 4
+        batch_size = 1
+        x = torch.randn(batch_size, seq_len, hidden_size)
+
+        func = Attention(hidden_size, num_heads).to("cpu")
+
+        with torch.no_grad():
+            res1 = func(x)
+            jit_func = torch.compile(func)
+            res2 = jit_func(x)
+        self.assertEqual(res1, res2)
+
     def test_scalar_mul_bfloat16(self):
         def f(x):
             return torch.ops.aten.mul.Tensor(x, 1.7015043497085571)

torch/_inductor/pattern_matcher.py

@@ -919,7 +919,15 @@ def check_fn(match: Match):
                         device=args[i].device,
                         requires_grad=grad,
                     )
-            specific_graph = trace_fn(search_fn, args)
+            try:
+                specific_graph = trace_fn(search_fn, args)
+            except RuntimeError as e:
+                log.info(
+                    "Replacement pattern %s failed to apply due to shape mismatch: %s",
+                    search_fn.__name__,
+                    e,
+                )
+                return False
             specific_pattern = fx_to_pattern(
                 specific_graph,
                 argnames=argnames,