[#8389][fix] Update group attention matching to first map to custom torch attention

tensorrt_llm/_torch/auto_deploy/config/default.yaml

@@ -34,9 +34,9 @@ transforms:
   match_eager_attention:
     stage: pattern_matcher
     requires_shape_prop: true
-  match_grouped_attention_with_repeat_kv:
+  match_sdpa_to_torch_attention:
     stage: pattern_matcher
-  match_grouped_attention_without_repeat_kv:
+  match_grouped_attention:
     stage: pattern_matcher
   match_attention_layout:
     stage: pattern_matcher

tensorrt_llm/_torch/auto_deploy/transform/library/attention.py

@@ -11,7 +11,6 @@
 
 from ...models.factory import ModelFactory
 from ...shim.interface import CachedSequenceInterface
-from ...utils.logger import ad_logger
 from ...utils.pattern_matcher import ADPatternMatcherPass, register_ad_pattern
 from ..interface import (
     BaseTransform,
@@ -374,28 +373,137 @@ def _call_attn(q, k, v, *, is_causal: bool, attn_mask=None, dropout_p=None, scal
     return torch.ops.auto_deploy.torch_attention.default(q, k, v, **kwargs)
 
 
-def make_grouped_attn_pair(
+def make_sdpa_to_torch_attn_pair(
     *,
-    repeat_kv: bool,
     is_causal: bool,
     has_scale: bool,
     enable_gqa: bool,
     has_attn_mask: bool,
     has_dropout: bool,
 ) -> Tuple[Callable, Callable, List[str]]:
     """
-    Returns (pattern_fn, replacement_fn, argnames) with exact positional parity.
-
-    Arg order rules:
-      Base: (q, k, v)
-      +repeat_kv -> insert n_rep after (q, k, v)
-      +attn_mask -> include attn_mask after n_rep if repeat_kv else after (q, k, v)
-      +dropout   -> include dropout_p after attn_mask or after n_rep/base if no attn_mask
-      +scale     -> include scale last
+    Returns (pattern_fn, replacement_fn, argnames) for matching SDPA to torch_attention.
+
+    Pattern: torch_attention_sdpa --> torch_attention
     """
     argnames: List[str] = ["q", "k", "v"]
-    if repeat_kv:
-        argnames.append("n_rep")
+    if has_attn_mask:
+        argnames.append("attn_mask")
+    if has_dropout:
+        argnames.append("dropout_p")
+    if has_scale:
+        argnames.append("scale")
+
+    def pattern_fn(*args):
+        if len(args) != len(argnames):
+            raise TypeError(f"Expected {len(argnames)} args {tuple(argnames)}, got {len(args)}")
+        m = dict(zip(argnames, args))
+        return _call_sdpa(
+            m["q"],
+            m["k"],
+            m["v"],
+            is_causal=is_causal,
+            enable_gqa=enable_gqa,
+            attn_mask=m.get("attn_mask"),
+            dropout_p=m.get("dropout_p"),
+            scale=m.get("scale"),
+        )
+
+    def replacement_fn(*args):
+        if len(args) != len(argnames):
+            raise TypeError(f"Expected {len(argnames)} args {tuple(argnames)}, got {len(args)}")
+        m = dict(zip(argnames, args))
+        return _call_attn(
+            m["q"],
+            m["k"],
+            m["v"],
+            is_causal=is_causal,
+            attn_mask=m.get("attn_mask"),
+            dropout_p=m.get("dropout_p"),
+            scale=m.get("scale"),
+        )
+
+    _attach_signature(pattern_fn, argnames)
+    _attach_signature(replacement_fn, argnames)
+    return pattern_fn, replacement_fn, argnames
+
+
+def generate_and_register_sdpa_to_torch_attn_patterns(patterns, register_ad_pattern: Callable):
+    """
+    Generate patterns for matching SDPA to torch_attention.
+    Enumerates combinations across:
+      - is_causal:        [False, True]
+      - has_scale:        [False, True]
+      - enable_gqa:       [False, True]
+      - has_attn_mask:    [False, True]
+      - has_dropout:      [False, True]
+    """
+    q = torch.randn(8, 8, 16, 64, device="cuda", dtype=torch.float16)
+    k = torch.randn(8, 8, 16, 64, device="cuda", dtype=torch.float16)
+    v = torch.randn(8, 8, 16, 64, device="cuda", dtype=torch.float16)
+    attn_mask_tensor = torch.randn(8, 1, 1, 16, device="cuda", dtype=torch.float16)
+
+    dropout_val = 0.12345
+    scale_val = 0.56789
+
+    total = 0
+    axes = ((False, True),) * 5
+    for is_causal, has_scale, enable_gqa, has_attn_mask, has_dropout in product(*axes):
+        pat_fn, rep_fn, argnames = make_sdpa_to_torch_attn_pair(
+            is_causal=is_causal,
+            has_scale=has_scale,
+            enable_gqa=enable_gqa,
+            has_attn_mask=has_attn_mask,
+            has_dropout=has_dropout,
+        )
+
+        value_map = {
+            "q": q,
+            "k": k,
+            "v": v,
+            "attn_mask": attn_mask_tensor,
+            "dropout_p": dropout_val,
+            "scale": scale_val,
+        }
+        dummy_args: List[object] = []
+        for name in argnames:
+            try:
+                dummy_args.append(value_map[name])
+            except KeyError:
+                raise RuntimeError(f"Unexpected arg name: {name}")
+
+        scalar_names = {"dropout_p", "scale"}
+        scalar_workaround: Dict[str, object] = {
+            n: value_map[n] for n in argnames if n in scalar_names
+        }
+        if not scalar_workaround:
+            scalar_workaround = None
+
+        register_ad_pattern(
+            search_fn=pat_fn,
+            replace_fn=rep_fn,
+            patterns=patterns,
+            dummy_args=dummy_args,
+            scalar_workaround=scalar_workaround,
+        )
+        total += 1
+    return total
+
+
+def make_repeat_kv_torch_attn_pair(
+    *,
+    is_causal: bool,
+    has_scale: bool,
+    has_attn_mask: bool,
+    has_dropout: bool,
+) -> Tuple[Callable, Callable, List[str]]:
+    """
+    Returns (pattern_fn, replacement_fn, argnames) for matching repeat_kv + torch_attention.
+
+    Pattern: repeat_kv(k, n_rep), repeat_kv(v, n_rep), torch_attention --> torch_attention
+    This handles GQA patterns where repeat_kv is explicitly applied before torch_attention.
+    """
+    argnames: List[str] = ["q", "k", "v", "n_rep"]
     if has_attn_mask:
         argnames.append("attn_mask")
     if has_dropout:
@@ -411,30 +519,27 @@ def pattern_fn(*args):
         q = m["q"]
         k = m["k"]
         v = m["v"]
+        n_rep = m["n_rep"]
 
-        if repeat_kv:
-            n_rep = m["n_rep"]
-            k = torch.ops.auto_deploy.torch_attention_repeat_kv(k, n_rep)
-            v = torch.ops.auto_deploy.torch_attention_repeat_kv(v, n_rep)
+        # Apply repeat_kv to k and v
+        k = torch.ops.auto_deploy.torch_attention_repeat_kv(k, n_rep)
+        v = torch.ops.auto_deploy.torch_attention_repeat_kv(v, n_rep)
 
-        return _call_sdpa(
+        return _call_attn(
             q,
             k,
             v,
             is_causal=is_causal,
-            enable_gqa=enable_gqa,
             attn_mask=m.get("attn_mask"),
             dropout_p=m.get("dropout_p"),
             scale=m.get("scale"),
         )
 
-    # Replacement: torch_attention.default mirroring the positional signature exactly.
-    # We do NOT pass enable_gqa here (it’s SDPA-only). We accept n_rep to mirror signature,
-    # but we don’t need to use it in the replacement graph.
     def replacement_fn(*args):
         if len(args) != len(argnames):
             raise TypeError(f"Expected {len(argnames)} args {tuple(argnames)}, got {len(args)}")
         m = dict(zip(argnames, args))
+        # Replacement: just call torch_attention directly (no repeat_kv needed)
         return _call_attn(
             m["q"],
             m["k"],
@@ -445,37 +550,19 @@ def replacement_fn(*args):
             scale=m.get("scale"),
         )
 
-    # Pattern matcher needs to see explicit arg names
     _attach_signature(pattern_fn, argnames)
     _attach_signature(replacement_fn, argnames)
-
     return pattern_fn, replacement_fn, argnames
 
 
-def generate_and_register_grouped_attn_patterns(
-    patterns, register_ad_pattern: Callable, only_repeat_kv: bool = None
-):
+def generate_and_register_repeat_kv_torch_attn_patterns(patterns, register_ad_pattern: Callable):
     """
-    Auto-generate all grouped attention patterns across these axes:
-      1) repeat_kv:        [False, True]
-      2) is_causal:        [False, True]
-      3) has_scale:        [False, True]
-      4) enable_gqa:       [False, True]   (only a kwarg to SDPA side)
-      5) has_attn_mask:    [False, True]
-      6) has_dropout:      [False, True]
-
-    Args:
-        patterns: The ADPatternMatcherPass instance to register patterns to
-        register_ad_pattern: The function to call to register each pattern
-        only_repeat_kv: If True, only register patterns with repeat_kv=True.
-                        If False, only register patterns with repeat_kv=False.
-                        If None, register all patterns.
-
-    For each valid combo, we:
-      - build pattern/replacement functions with exact-arg parity
-      - build dummy args matching the signature (with CUDA fp16 tensors etc.)
-      - build scalar_workaround dict for any scalars/n_rep present
-      - call register_ad_pattern(...)
+    Generate patterns for matching repeat_kv + torch_attention.
+    Enumerates combinations across:
+      - is_causal:        [False, True]
+      - has_scale:        [False, True]
+      - has_attn_mask:    [False, True]
+      - has_dropout:      [False, True]
     """
     q = torch.randn(8, 8, 16, 64, device="cuda", dtype=torch.float16)
     k1 = torch.randn(8, 1, 16, 64, device="cuda", dtype=torch.float16)
@@ -487,24 +574,15 @@ def generate_and_register_grouped_attn_patterns(
     n_rep_val = 7
 
     total = 0
-    axes = ((False, True),) * 6
-    for repeat_kv, is_causal, has_scale, enable_gqa, has_attn_mask, has_dropout in product(*axes):
-        if only_repeat_kv is not None:
-            if only_repeat_kv and not repeat_kv:
-                continue  # Skip patterns without repeat_kv
-            if not only_repeat_kv and repeat_kv:
-                continue  # Skip patterns with repeat_kv
-
-        pat_fn, rep_fn, argnames = make_grouped_attn_pair(
-            repeat_kv=repeat_kv,
+    axes = ((False, True),) * 4
+    for is_causal, has_scale, has_attn_mask, has_dropout in product(*axes):
+        pat_fn, rep_fn, argnames = make_repeat_kv_torch_attn_pair(
             is_causal=is_causal,
             has_scale=has_scale,
-            enable_gqa=enable_gqa,
             has_attn_mask=has_attn_mask,
             has_dropout=has_dropout,
         )
 
-        # Build dummy args in the same positional order
         value_map = {
             "q": q,
             "k": k1,
@@ -539,12 +617,17 @@ def generate_and_register_grouped_attn_patterns(
     return total
 
 
-@TransformRegistry.register("match_grouped_attention_with_repeat_kv")
-class MatchGroupedAttentionWithRepeatKV(BaseTransform):
+@TransformRegistry.register("match_sdpa_to_torch_attention")
+class MatchSDPAToTorchAttention(BaseTransform):
     """
-    Match and replace grouped attention patterns WITH repeat_kv to
-    torch.ops.auto_deploy.torch_attention.
+    Match and replace SDPA patterns to torch.ops.auto_deploy.torch_attention.
 
+    This handles:
+    - sdpa --> torch_attention
+    - repeat_kv + sdpa --> torch_attention
+
+    This transform should run BEFORE match_repeat_kv_with_torch_attention to ensure
+    SDPA calls are converted first.
     """
 
     def _apply(
@@ -554,32 +637,33 @@ def _apply(
         factory: ModelFactory,
         shared_config: SharedConfig,
     ) -> Tuple[GraphModule, TransformInfo]:
-        def register_grouped_attention_with_repeat_kv(patterns: ADPatternMatcherPass):
-            return generate_and_register_grouped_attn_patterns(
-                patterns, register_ad_pattern, only_repeat_kv=True
-            )
+        def register_sdpa_to_torch_attention(patterns: ADPatternMatcherPass):
+            return generate_and_register_sdpa_to_torch_attn_patterns(patterns, register_ad_pattern)
 
-        num_grouped_patterns = _apply_pattern(
-            gm, "Grouped Attention (with repeat_kv)", register_grouped_attention_with_repeat_kv
+        num_patterns = _apply_pattern(
+            gm, "SDPA to Torch Attention", register_sdpa_to_torch_attention
         )
 
         info = TransformInfo(
             skipped=False,
-            num_matches=num_grouped_patterns,
+            num_matches=num_patterns,
             is_clean=False,
             has_valid_shapes=False,
         )
         return gm, info
 
 
-@TransformRegistry.register("match_grouped_attention_without_repeat_kv")
-class MatchGroupedAttentionWithoutRepeatKV(BaseTransform):
+@TransformRegistry.register("match_grouped_attention")
+class MatchRepeatKVWithTorchAttention(BaseTransform):
     """
-    Match and replace grouped attention patterns WITHOUT repeat_kv to
-    torch.ops.auto_deploy.torch_attention.
+    Match and replace repeat_kv + torch_attention patterns to torch_attention.
 
-    This transform should run AFTER match_grouped_attention_with_repeat_kv
-    to avoid incorrectly matching patterns that should have repeat_kv.
+    This handles:
+    - repeat_kv + torch_attention --> torch_attention (removes redundant repeat_kv)
+    - torch_attention --> torch_attention (identity, catches any remaining patterns)
+
+    This transform should run AFTER match_sdpa_to_torch_attention to ensure
+    we match the repeat_kv + torch_attention pattern correctly.
     """
 
     def _apply(
@@ -589,26 +673,18 @@ def _apply(
         factory: ModelFactory,
         shared_config: SharedConfig,
     ) -> Tuple[GraphModule, TransformInfo]:
-        def register_grouped_attention_without_repeat_kv(patterns: ADPatternMatcherPass):
-            return generate_and_register_grouped_attn_patterns(
-                patterns, register_ad_pattern, only_repeat_kv=False
+        def register_repeat_kv_with_torch_attention(patterns: ADPatternMatcherPass):
+            return generate_and_register_repeat_kv_torch_attn_patterns(
+                patterns, register_ad_pattern
             )
 
-        num_grouped_patterns = _apply_pattern(
-            gm,
-            "Grouped Attention (without repeat_kv)",
-            register_grouped_attention_without_repeat_kv,
+        num_patterns = _apply_pattern(
+            gm, "Repeat KV with Torch Attention", register_repeat_kv_with_torch_attention
         )
 
-        if num_grouped_patterns == 0:
-            ad_logger.warning(
-                "Fail to find any Group Attention Pattern (without repeat_kv), "
-                "output or performance may be incorrect"
-            )
-
         info = TransformInfo(
             skipped=False,
-            num_matches=num_grouped_patterns,
+            num_matches=num_patterns,
             is_clean=False,
             has_valid_shapes=False,
         )

tests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_attention_matcher.py

@@ -446,10 +446,10 @@ def _get_match_grouped_attention_optimizer() -> Callable:
         "cleanup_noop_slice": {
             "stage": "post_export",
         },
-        "match_grouped_attention_with_repeat_kv": {
+        "match_sdpa_to_torch_attention": {
             "stage": "pattern_matcher",
         },
-        "match_grouped_attention_without_repeat_kv": {
+        "match_grouped_attention": {
             "stage": "pattern_matcher",
         },
     }