Fix attention dispatcher: use 4D tensors for SDPA, add math backend

SDPA with 3D xformers-BMK tensors cannot use Flash Attention and falls
back to efficient_attention/math kernels that miscompute on Ada Lovelace
GPUs (e.g. RTX 6000 Pro), producing brownish line artifacts.  Unsqueeze
to 4D (1, B*H, N, D) so Flash Attention is eligible.  Also add a naive
"math" backend (chunked bmm) as a guaranteed-correct diagnostic baseline.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

inference.py

@@ -114,6 +114,9 @@ for _name in _SAGE_VARIANTS:
     except (ImportError, AttributeError):
         pass
 
+# Manual attention (guaranteed correct, used as diagnostic baseline)
+_ATTN_BACKENDS["math"] = "math"
+
 _active_attn = "sdpa"
 
 
@@ -127,15 +130,31 @@ def _set_attn(backend: str):
 
 
 def _dispatched_mea(q, k, v, attn_bias=None, op=None):
-    if _active_attn == "sdpa":
-        return F.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
     if _active_attn == "xformers":
         return _real_xformers_mea(q, k, v, attn_bias=attn_bias, op=op)
+    if _active_attn == "math":
+        # Naive batched attention — slow but guaranteed correct.
+        scale = q.shape[-1] ** -0.5
+        cs = max(1, 2**28 // (q.shape[1] * q.shape[1] * max(q.element_size(), 1)))
+        outs = []
+        for i in range(0, q.shape[0], cs):
+            qi, ki, vi = q[i:i+cs], k[i:i+cs], v[i:i+cs]
+            a = torch.bmm(qi * scale, ki.transpose(1, 2))
+            if attn_bias is not None:
+                a = a + (attn_bias[i:i+cs] if attn_bias.shape[0] > 1 else attn_bias)
+            outs.append(torch.bmm(a.softmax(dim=-1), vi))
+        return torch.cat(outs)
+    # SDPA and SageAttention both need 4D (batch, heads, seq, dim).
+    # The model sends 3D xformers-BMK tensors (B*H, N, D); unsqueeze to
+    # (1, B*H, N, D) so SDPA can pick the Flash Attention kernel (the 3D
+    # path falls back to efficient_attention / math which can miscompute
+    # on newer Ada Lovelace GPUs).
+    q4, k4, v4 = q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0)
+    if _active_attn == "sdpa":
+        return F.scaled_dot_product_attention(q4, k4, v4, attn_mask=attn_bias).squeeze(0)
+    # SageAttention variants
     fn = _ATTN_BACKENDS[_active_attn]
-    return fn(
-        q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0),
-        tensor_layout="HND", is_causal=False,
-    ).squeeze(0)
+    return fn(q4, k4, v4, tensor_layout="HND", is_causal=False).squeeze(0)
 
 
 _dispatched_mea._is_star_dispatcher = True

nodes.py

@@ -78,6 +78,9 @@ for _name in _SAGE_VARIANTS:
     except (ImportError, AttributeError):
         pass
 
+# Manual attention (guaranteed correct, used as diagnostic baseline)
+_ATTN_BACKENDS["math"] = "math"
+
 _active_attn = "sdpa"
 
 
@@ -91,16 +94,31 @@ def _set_attn(backend: str):
 
 
 def _dispatched_mea(q, k, v, attn_bias=None, op=None):
-    if _active_attn == "sdpa":
-        return F.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
     if _active_attn == "xformers":
         return _real_xformers_mea(q, k, v, attn_bias=attn_bias, op=op)
-    # SageAttention variants: need 4D tensors (batch, heads, seq, dim)
+    if _active_attn == "math":
+        # Naive batched attention — slow but guaranteed correct.
+        scale = q.shape[-1] ** -0.5
+        cs = max(1, 2**28 // (q.shape[1] * q.shape[1] * max(q.element_size(), 1)))
+        outs = []
+        for i in range(0, q.shape[0], cs):
+            qi, ki, vi = q[i:i+cs], k[i:i+cs], v[i:i+cs]
+            a = torch.bmm(qi * scale, ki.transpose(1, 2))
+            if attn_bias is not None:
+                a = a + (attn_bias[i:i+cs] if attn_bias.shape[0] > 1 else attn_bias)
+            outs.append(torch.bmm(a.softmax(dim=-1), vi))
+        return torch.cat(outs)
+    # SDPA and SageAttention both need 4D (batch, heads, seq, dim).
+    # The model sends 3D xformers-BMK tensors (B*H, N, D); unsqueeze to
+    # (1, B*H, N, D) so SDPA can pick the Flash Attention kernel (the 3D
+    # path falls back to efficient_attention / math which can miscompute
+    # on newer Ada Lovelace GPUs).
+    q4, k4, v4 = q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0)
+    if _active_attn == "sdpa":
+        return F.scaled_dot_product_attention(q4, k4, v4, attn_mask=attn_bias).squeeze(0)
+    # SageAttention variants
     fn = _ATTN_BACKENDS[_active_attn]
-    return fn(
-        q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0),
-        tensor_layout="HND", is_causal=False,
-    ).squeeze(0)
+    return fn(q4, k4, v4, tensor_layout="HND", is_causal=False).squeeze(0)
 
 
 _dispatched_mea._is_star_dispatcher = True