Ensure rule ordering in MHA fusion

onnxscript/rewriter/ort_fusions/_core.py

@@ -20,7 +20,7 @@
 from onnxscript.rewriter.ort_fusions.fuse_packed_qkv_gqa import fuse_qkv_gqa
 from onnxscript.rewriter.ort_fusions.gelu import fuse_gelu
 from onnxscript.rewriter.ort_fusions.gqa import fuse_gqa
-from onnxscript.rewriter.ort_fusions.mha import fuse_mha
+from onnxscript.rewriter.ort_fusions.mha import fuse_mha1, fuse_mha2
 from onnxscript.rewriter.ort_fusions.rms_normalization import fuse_rms_normalization
 from onnxscript.rewriter.ort_fusions.rotary_embedding import (
     fuse_partial_rotary_embedding,
@@ -87,8 +87,9 @@ def fuse(func, apply_shape_inference: bool = False):
     # in the rewrite rule for certain patterns of SDPA.
     fusion_count["sdpa"] = fuse(fuse_sdpa, apply_shape_inference=True)
     # Optimize to avoid trying multiple attention-based fusions
-    fusion_count["mha"] = fuse(fuse_mha)
-    if fusion_count["mha"] == 0:
+    fusion_count["mha1"] = fuse(fuse_mha1)
+    fusion_count["mha2"] = fuse(fuse_mha2)
+    if (fusion_count["mha1"] == 0) and (fusion_count["mha2"] == 0):
         # If no MHA fusion was applied, we can try the GQA fusion.
         # and avoid trying the attention fusion.
         fusion_count["gqa"] = fuse(fuse_gqa)

onnxscript/rewriter/ort_fusions/attention_test.py

@@ -173,7 +173,8 @@ def test_whisper_encoder(self):
         sdpa_count = xformers.fuse_sdpa(model)
         self.assertGreater(sdpa_count, 0)
         model = shape_inference.infer_shapes(model)
-        mha_count = xformers.fuse_mha(model)
+        mha_count = xformers.fuse_mha1(model)
+        mha_count += xformers.fuse_mha2(model)
         self.assertGreater(mha_count, 0)
         fused_mha_bias_count = xformers.fuse_mha_bias(model)
         self.assertGreater(fused_mha_bias_count, 0)

onnxscript/rewriter/ort_fusions/fuse_xformers_test.py

@@ -27,7 +27,7 @@ def test_fuse_xformers(self):
         self.assertEqual(fusion_count["partial_rotary_embedding"], 0)
         self.assertEqual(fusion_count["cos_sin_cache"], 2)
         self.assertEqual(fusion_count["sdpa"], 1)
-        self.assertEqual(fusion_count["mha"], 1)
+        self.assertEqual(fusion_count["mha1"] + fusion_count["mha2"], 1)
         self.assertEqual(fusion_count["attention"], 0)
         self.assertEqual(fusion_count["gqa"], 0)
         self.assertEqual(fusion_count["gelu"], 0)

onnxscript/rewriter/ort_fusions/mha.py

@@ -376,45 +376,50 @@ def rewrite(
         )
 
 
-parameter_combinations = [
-    {
-        "double_transpose": double_transpose,
-        "transpose_4d": transpose_4d,
-        "pre_scale_q": pre_scale_q,
-        "is_rotary": is_rotary,
-        "use_mask": use_mask,
-        "has_past_present": has_past_present,
-        "is_cross_attention": is_cross_attention,
-    }
-    for double_transpose in [False, True]
-    for transpose_4d in (
-        [False, True] if double_transpose else [False]
-    )  # Only generate patterns when double_transpose is True
-    for pre_scale_q in [True, False]
-    for is_rotary in [False, True]
-    for use_mask in [False, True]
-    for is_cross_attention in [False, True]
-    for has_past_present in ([False] if is_cross_attention else [True, False])
-    # Skip if both has_past_present and is_cross_attention are True
-    if not (has_past_present and is_cross_attention)
-]
-
-# Dynamically create the rules
-mha_rules = pattern.RewriteRuleSet(
-    [
-        MultiHeadAttention.rule(
-            f"MHA_{'4D' if params['transpose_4d'] else '3D'}_Transpose"
-            f"{'_Twice' if params['double_transpose'] else ''}"
-            f"{'_PreScaleQ' if params['pre_scale_q'] else ''}"
-            f"{'_Rotary' if params['is_rotary'] else ''}"
-            f"{'_Masked' if params['use_mask'] else ''}"
-            f"{'_Past' if params['has_past_present'] else ''}"
-            f"{'_CrossAttention' if params['is_cross_attention'] else ''}",
-            **params,
-        )
-        for params in parameter_combinations
+def _make_rule_set(has_past_present: bool):
+    parameter_combinations = [
+        {
+            "double_transpose": double_transpose,
+            "transpose_4d": transpose_4d,
+            "pre_scale_q": pre_scale_q,
+            "is_rotary": is_rotary,
+            "use_mask": use_mask,
+            "has_past_present": has_past_present,
+            "is_cross_attention": is_cross_attention,
+        }
+        for double_transpose in [False, True]
+        for transpose_4d in (
+            [False, True] if double_transpose else [False]
+        )  # Only generate patterns when double_transpose is True
+        for pre_scale_q in [True, False]
+        for is_rotary in [False, True]
+        for use_mask in [False, True]
+        for is_cross_attention in ([False] if has_past_present else [False, True])
     ]
-)
 
+    # Dynamically create the rules
+    mha_rules = pattern.RewriteRuleSet(
+        [
+            MultiHeadAttention.rule(
+                f"MHA_{'4D' if params['transpose_4d'] else '3D'}_Transpose"
+                f"{'_Twice' if params['double_transpose'] else ''}"
+                f"{'_PreScaleQ' if params['pre_scale_q'] else ''}"
+                f"{'_Rotary' if params['is_rotary'] else ''}"
+                f"{'_Masked' if params['use_mask'] else ''}"
+                f"{'_Past' if params['has_past_present'] else ''}"
+                f"{'_CrossAttention' if params['is_cross_attention'] else ''}",
+                **params,
+            )
+            for params in parameter_combinations
+        ]
+    )
+
+    return mha_rules
+
+
+mha_rules_no_past = _make_rule_set(has_past_present=False)
+mha_rules_with_past = _make_rule_set(has_past_present=True)
 
-fuse_mha = _fusion_utils.apply_fusion_rules(mha_rules)
+# Try rules with past first, and then rules without past.
+fuse_mha1 = _fusion_utils.apply_fusion_rules(mha_rules_with_past)
+fuse_mha2 = _fusion_utils.apply_fusion_rules(mha_rules_no_past)

onnxscript/rewriter/ort_fusions/mha_test.py

@@ -35,7 +35,8 @@ def test_smollm(self):
         # Fuse SDPA and MHA
         sdpa_count = xformers.fuse_sdpa(model)
         self.assertGreater(sdpa_count, 0)
-        mha_count = xformers.fuse_mha(model)
+        mha_count = xformers.fuse_mha1(model)
+        mha_count += xformers.fuse_mha2(model)
         self.assertGreater(mha_count, 0)
 
         if test_with_ort:
@@ -59,7 +60,8 @@ def test_whisper_encoder(self):
         sdpa_count = xformers.fuse_sdpa(model)
         self.assertGreater(sdpa_count, 0)
         model = shape_inference.infer_shapes(model)
-        mha_count = xformers.fuse_mha(model)
+        mha_count = xformers.fuse_mha1(model)
+        mha_count += xformers.fuse_mha2(model)
         self.assertGreater(mha_count, 0)
         onnxscript.optimizer.optimize(model)
 
@@ -84,7 +86,8 @@ def test_whisper_decoder(self):
         sdpa_count = xformers.fuse_sdpa(model)
         self.assertGreater(sdpa_count, 0)
         model = shape_inference.infer_shapes(model)
-        mha_count = xformers.fuse_mha(model)
+        mha_count = xformers.fuse_mha1(model)
+        mha_count += xformers.fuse_mha2(model)
         self.assertGreater(mha_count, 0)
         onnxscript.optimizer.optimize(model)