[Compilation] Refactor SiluMul activation+quant Fusion Pass (#39684)

Signed-off-by: BadrBasowid <badr.basowid@gmail.com>

tests/compile/fusions_e2e/conftest.py

@@ -189,7 +189,7 @@ def run_e2e_fusion_test(monkeypatch, caplog_mp_spawn):
             # TODO: Remove log counting in unit tests
             # once all matchers implement VllmFusionPatternMatcherPass
             n_expected = tp_size * num_ranges_activated
-            if match_name != "attn_quant_fusion":
+            if match_name not in ("attn_quant_fusion", "act_quant_fusion"):
                 assert len(log_matches) == n_expected, (
                     f"Could not find {n_expected} {match_name} "
                     f"(found {len(log_matches)}) in:\n {log_holder.text}"
@@ -250,6 +250,12 @@ def run_e2e_fusion_test(monkeypatch, caplog_mp_spawn):
                     f"entries (SP took precedence), found: {log_matches}"
                 )
 
+            elif match_name == "act_quant_fusion":
+                actual_match = match_table.get("activation_quant_fusion_pass", 0)
+                assert actual_match == expected_matches * n_expected, (
+                    f"Could not find {expected_matches * n_expected} "
+                    f"{match_name} (found {actual_match})."
+                )
             elif match_name == "attn_quant_fusion":
                 actual_match = match_table.get(
                     "attn_quant_fusion", 0

tests/compile/passes/test_silu_mul_quant_fusion.py

@@ -168,7 +168,7 @@ class TestSiluMulGroupFp8QuantModel(torch.nn.Module):
 
     def forward(self, x):
         y = self.silu_and_mul(x)
-        x2 = self.w8a8_block_fp8_linear(y, self.w, self.wscale)
+        x2 = self.w8a8_block_fp8_linear(y)
         return x2
 
     def ops_in_model_before(self):

vllm/compilation/passes/fusion/act_quant_fusion.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from abc import ABC, abstractmethod
+import itertools
 from typing import Any
 
 import torch
 from torch._higher_order_ops.auto_functionalize import auto_functionalized
-from torch._inductor.pattern_matcher import (
-    PatternMatcherPass,
-    fwd_only,
-    register_replacement,
-)
 from torch._ops import OpOverload
 
 from vllm.config import VllmConfig
@@ -24,8 +19,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 )
 from vllm.platforms import current_platform
 
-from ..inductor_pass import enable_fake_mode
-from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
+from ..vllm_inductor_pass import VllmFusionPatternMatcherPass, VllmPatternReplacement
 from .matcher_utils import MatcherQuantFP8, MatcherSiluAndMul
 from .rms_quant_fusion import QUANT_OPS, empty_bf16, empty_fp32, empty_i32
 
@@ -50,9 +44,9 @@ if current_platform.is_cuda_alike():
     FUSED_OPS[kFp8Dynamic64Sym] = torch.ops._C.silu_and_mul_per_block_quant.default
 
 
-class ActivationQuantPattern(ABC):
+class ActivationQuantPattern(VllmPatternReplacement):
     """
-    The base class for Activation+Quant fusions.
+    Base class for Activation+Quant fusions.
     Should not be used directly.
     """
 
@@ -79,10 +73,6 @@ class ActivationQuantPattern(ABC):
         kwargs = {"dtype": self.quant_dtype, "device": "cuda", **kwargs}
         return torch.empty(*args, **kwargs)
 
-    @abstractmethod
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        raise NotImplementedError
-
 
 class SiluMulFp8StaticQuantPattern(ActivationQuantPattern):
     """
@@ -100,8 +90,9 @@ class SiluMulFp8StaticQuantPattern(ActivationQuantPattern):
             scale,
         ]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> torch.Tensor:
@@ -109,7 +100,11 @@ class SiluMulFp8StaticQuantPattern(ActivationQuantPattern):
             result_quant = self.quant_matcher(result_silu_mul, scale)
             return result_quant[0]
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> torch.Tensor:
@@ -123,10 +118,7 @@ class SiluMulFp8StaticQuantPattern(ActivationQuantPattern):
             )
             return at[1]
 
-        inps = self.get_inputs()
-        pattern(*inps)
-
-        register_replacement(pattern, replacement, inps, fwd_only, pm_pass)
+        return _replacement
 
 
 class SiluMulNvfp4QuantPattern(ActivationQuantPattern):
@@ -144,8 +136,9 @@ class SiluMulNvfp4QuantPattern(ActivationQuantPattern):
         scale = empty_fp32(1, 1)
         return [result, output_scale, input_, scale]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             result: torch.Tensor,
             output_scale: torch.Tensor,
             input: torch.Tensor,
@@ -162,7 +155,11 @@ class SiluMulNvfp4QuantPattern(ActivationQuantPattern):
             )
             return at[1], at[2]
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             result: torch.Tensor,
             output_scale: torch.Tensor,
             input: torch.Tensor,
@@ -177,7 +174,7 @@ class SiluMulNvfp4QuantPattern(ActivationQuantPattern):
             )
             return at[1], at[2]
 
-        register_replacement(pattern, replacement, self.get_inputs(), fwd_only, pm_pass)
+        return _replacement
 
 
 class SiluMulBlockQuantPattern(ActivationQuantPattern):
@@ -210,10 +207,9 @@ class SiluMulBlockQuantPattern(ActivationQuantPattern):
         scale = self.quant_matcher.empty_f32(1, 1)
         return self.silu_and_mul_matcher.inputs() + [scale]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        is_scale_transposed = self.is_scale_transposed
-
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -235,12 +231,16 @@ class SiluMulBlockQuantPattern(ActivationQuantPattern):
                 fp8_min=finfo.min,
                 fp8_max=finfo.max,
                 scale_ue8m0=self.is_e8m0,
-                dummy_is_scale_transposed=is_scale_transposed,
+                dummy_is_scale_transposed=self.is_scale_transposed,
                 dummy_is_tma_aligned=self.is_tma_aligned,
             )
             return result, scale
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             input: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -249,7 +249,7 @@ class SiluMulBlockQuantPattern(ActivationQuantPattern):
             result = torch.empty(
                 output_shape, device=input.device, dtype=self.quant_dtype
             )
-            if is_scale_transposed:
+            if self.is_scale_transposed:
                 scale = torch.empty(
                     (d // self.group_size, input.shape[0]),
                     device=input.device,
@@ -268,15 +268,14 @@ class SiluMulBlockQuantPattern(ActivationQuantPattern):
                 scales=scale,
                 group_size=self.group_size,
                 scale_ub=None,
-                is_scale_transposed=is_scale_transposed,
+                is_scale_transposed=self.is_scale_transposed,
             )
             return at[1], at[2]
 
-        inps = self.get_inputs()
-        register_replacement(pattern, replacement, inps, fwd_only, pm_pass)
+        return _replacement
 
 
-class ActivationQuantFusionPass(VllmPatternMatcherPass):
+class ActivationQuantFusionPass(VllmFusionPatternMatcherPass):
     """
     This pass fuses a pre-defined set of custom ops into fused ops.
     It uses the torch pattern matcher to find the patterns and replace them.
@@ -286,45 +285,33 @@ class ActivationQuantFusionPass(VllmPatternMatcherPass):
     https://github.com/pytorch/pytorch/pull/139321#issuecomment-2452354980
     """
 
-    @enable_fake_mode
     def __init__(self, config: VllmConfig) -> None:
-        super().__init__(config)
+        super().__init__(config, "activation_quant_fusion_pass")
 
-        self.patterns: PatternMatcherPass = PatternMatcherPass(
-            pass_name="activation_quant_fusion_pass"
-        )
-
-        pattern_silu_mul_fp8 = SiluMulFp8StaticQuantPattern()
-        pattern_silu_mul_fp8.register(self.patterns)
+        self.register(SiluMulFp8StaticQuantPattern())
 
         if silu_and_mul_nvfp4_quant_supported:
-            pattern_silu_mul_nvfp4 = SiluMulNvfp4QuantPattern()
-            pattern_silu_mul_nvfp4.register(self.patterns)
+            self.register(SiluMulNvfp4QuantPattern())
 
         if current_platform.is_cuda():
-            for quant_key in [kFp8Dynamic128Sym, kFp8Dynamic64Sym]:
-                for is_scale_transposed in [False, True]:
-                    for is_e8m0 in [True, False]:
-                        for is_tma_aligned in [False, True]:
+            for (
+                quant_key,
+                is_scale_transposed,
+                is_e8m0,
+                is_tma_aligned,
+            ) in itertools.product(
+                [kFp8Dynamic128Sym, kFp8Dynamic64Sym],
+                [False, True],
+                [True, False],
+                [False, True],
+            ):
+                self.register(
                     SiluMulBlockQuantPattern(
                         quant_key,
                         is_scale_transposed=is_scale_transposed,
                         is_e8m0=is_e8m0,
                         is_tma_aligned=is_tma_aligned,
-                            ).register(self.patterns)
-
-        self.dump_patterns(config, self.patterns)
-
-    @VllmInductorPass.time_and_log
-    def __call__(self, graph: torch.fx.Graph) -> None:
-        self.matched_count = self.patterns.apply(graph)
-        logger.debug("Replaced %s patterns", self.matched_count)
-
-    def uuid(self) -> str:
-        return VllmInductorPass.hash_source(
-            self,
-            ActivationQuantPattern,
-            SiluMulFp8StaticQuantPattern,
-            SiluMulNvfp4QuantPattern,
-            SiluMulBlockQuantPattern,
                     )
+                )
+
+        self.dump_patterns(config, self.pm_pass)

vllm/compilation/passes/fusion/rocm_aiter_fusion.py

@@ -28,7 +28,6 @@ from ..vllm_inductor_pass import (
     VllmPatternMatcherPass,
     VllmPatternReplacement,
 )
-from .act_quant_fusion import ActivationQuantPattern
 from .matcher_utils import (
     MatcherFusedAddRMSNorm,
     MatcherQuantFP8,
@@ -345,7 +344,7 @@ class RocmAiterRMSNormQuantFusionPass(VllmPatternMatcherPass):
         return self.hash_source(self, *fusion_patterns)
 
 
-class AiterSiluMulFp8GroupQuantPattern(ActivationQuantPattern):
+class AiterSiluMulFp8GroupQuantPattern(VllmPatternReplacement):
     """
     This pattern fuses aiter silu_and_mul & group fp8 quant custom
     ops into an aiter silu_and_mul_group_fp8_quant op.
@@ -364,26 +363,29 @@ class AiterSiluMulFp8GroupQuantPattern(ActivationQuantPattern):
             self.silu_and_mul_matcher.inputs()[0],
         ]
 
-    def register(self, pm_pass: PatternMatcherPass) -> None:
-        def pattern(
+    @property
+    def pattern(self):
+        def _pattern(
             input: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             at1 = self.silu_and_mul_matcher(input)
             at2 = self.quant_matcher(at1)
             return at2[0], at2[1]
 
-        def replacement(
+        return _pattern
+
+    @property
+    def replacement(self):
+        def _replacement(
             input: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             at = self.FUSED_SILU_MUL_QUANT_OP(x=input, group_size=128)
             return at[0], at[1]
 
-        pm.register_replacement(
-            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
-        )
+        return _replacement
 
 
-class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmPatternMatcherPass):
+class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmFusionPatternMatcherPass):
     """
     This pass fuses a pre-defined set of custom ops into fused ops.
     It uses the torch pattern matcher to find the patterns and replace them.
@@ -393,29 +395,12 @@ class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmPatternMatcherPass):
     https://github.com/pytorch/pytorch/pull/139321#issuecomment-2452354980
     """
 
-    @enable_fake_mode
     def __init__(self, config: VllmConfig) -> None:
-        super().__init__(config)
-
-        self.patterns: PatternMatcherPass = PatternMatcherPass(
-            pass_name="rocm_aiter_silu_mul_fp8_group_quant_fusion_pass"
-        )
-
-        AiterSiluMulFp8GroupQuantPattern().register(self.patterns)
-
-        self.dump_patterns(config, self.patterns)
+        super().__init__(config, "rocm_aiter_silu_mul_fp8_group_quant_fusion_pass")
 
-    @VllmInductorPass.time_and_log
-    def __call__(self, graph: torch.fx.Graph) -> None:
-        self.matched_count = self.patterns.apply(graph)
-        logger.debug("Replaced %s patterns", self.matched_count)
+        self.register(AiterSiluMulFp8GroupQuantPattern())
 
-    def uuid(self) -> str:
-        fusion_patterns = [
-            ActivationQuantPattern,
-            AiterSiluMulFp8GroupQuantPattern,
-        ]
-        return VllmInductorPass.hash_source(self, *fusion_patterns)
+        self.dump_patterns(config, self.pm_pass)
 
 
 class AddAiterRMSNormPadPattern: