vllm.compilation.passes.fusion.act_quant_fusion ¶

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.

    Because patterns can only be registered once, the pass is a singleton.
    This will be addressed in a future version of PyTorch:
    https://github.com/pytorch/pytorch/pull/139321#issuecomment-2452354980
    """

    def __init__(self, config: VllmConfig) -> None:
        super().__init__(config, "activation_quant_fusion_pass")

        self.register(SiluMulFp8StaticQuantPattern())

        if silu_and_mul_nvfp4_quant_supported:
            self.register(SiluMulNvfp4QuantPattern())

        if current_platform.is_cuda():
            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,
                    )
                )

        self.dump_patterns(config, self.pm_pass)

class ActivationQuantPattern(VllmPatternReplacement):
    """
    Base class for Activation+Quant fusions.
    Should not be used directly.
    """

    def __init__(
        self,
        quant_key: QuantKey,
    ) -> None:
        self.quant_key = quant_key
        self.quant_dtype = quant_key.dtype

        assert self.quant_key in QUANT_OPS, (
            f"unsupported quantization scheme {self.quant_key}"
        )
        self.QUANT_OP = QUANT_OPS[self.quant_key]

        assert self.quant_key in FUSED_OPS, (
            f"unsupported fusion scheme {self.quant_key}"
        )
        self.FUSED_OP = FUSED_OPS[self.quant_key]

        self.silu_and_mul_matcher = MatcherSiluAndMul()

    def empty_quant(self, *args: Any, **kwargs: Any) -> torch.Tensor:
        kwargs = {"dtype": self.quant_dtype, "device": "cuda", **kwargs}
        return torch.empty(*args, **kwargs)

class SiluMulBlockQuantPattern(ActivationQuantPattern):
    """
    Fusion for SiluMul+BlockQuant (FP8 dynamic per-group) Pattern.
    Supports group_size 128 and 64 via QuantKey.
    Parameterized on is_scale_transposed for different scale layouts.
    """

    def __init__(
        self,
        quant_key: QuantKey,
        is_scale_transposed: bool = False,
        is_e8m0: bool = False,
        is_tma_aligned: bool = False,
    ) -> None:
        super().__init__(quant_key)
        self.quant_matcher = MatcherQuantFP8(
            quant_key,
            has_col_major_scales=is_scale_transposed,
            is_e8m0=is_e8m0,
            is_tma_aligned=is_tma_aligned,
        )
        self.group_size = quant_key.scale.group_shape[1]
        self.is_scale_transposed = is_scale_transposed
        self.is_e8m0 = is_e8m0
        self.is_tma_aligned = is_tma_aligned

    def get_inputs(self) -> list[torch.Tensor]:
        scale = self.quant_matcher.empty_f32(1, 1)
        return self.silu_and_mul_matcher.inputs() + [scale]

    @property
    def pattern(self):
        def _pattern(
            input: torch.Tensor,
            scale: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            silu_out = self.silu_and_mul_matcher(input)
            result = torch.empty(
                silu_out.shape,
                device=silu_out.device,
                dtype=self.quant_dtype,
            )
            assert scale is not None
            finfo = torch.finfo(self.quant_dtype)
            _, result, scale = auto_functionalized(
                self.quant_matcher.QUANT_OP,
                input=silu_out,
                output_q=result,
                output_s=scale,
                group_size=self.group_size,
                eps=1e-10,
                fp8_min=finfo.min,
                fp8_max=finfo.max,
                scale_ue8m0=self.is_e8m0,
                dummy_is_scale_transposed=self.is_scale_transposed,
                dummy_is_tma_aligned=self.is_tma_aligned,
            )
            return result, scale

        return _pattern

    @property
    def replacement(self):
        def _replacement(
            input: torch.Tensor,
            scale: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            d = input.shape[-1] // 2
            output_shape = input.shape[:-1] + (d,)
            result = torch.empty(
                output_shape, device=input.device, dtype=self.quant_dtype
            )
            if self.is_scale_transposed:
                scale = torch.empty(
                    (d // self.group_size, input.shape[0]),
                    device=input.device,
                    dtype=torch.float32,
                ).permute(-1, -2)
            else:
                scale = torch.empty(
                    (input.shape[0], d // self.group_size),
                    device=input.device,
                    dtype=torch.float32,
                )
            at = auto_functionalized(
                self.FUSED_OP,
                out=result,
                input=input,
                scales=scale,
                group_size=self.group_size,
                scale_ub=None,
                is_scale_transposed=self.is_scale_transposed,
            )
            return at[1], at[2]

        return _replacement

class SiluMulFp8StaticQuantPattern(ActivationQuantPattern):
    """
    Fusion for SiluMul+Fp8StaticQuant Pattern
    """

    def __init__(self) -> None:
        super().__init__(kFp8StaticTensorSym)
        self.quant_matcher = MatcherQuantFP8(kFp8StaticTensorSym)

    def get_inputs(self) -> list[torch.Tensor]:
        scale = self.quant_matcher.inputs()[1]
        return [
            *self.silu_and_mul_matcher.inputs(),  # input
            scale,
        ]

    @property
    def pattern(self):
        def _pattern(
            input: torch.Tensor,
            scale: torch.Tensor,
        ) -> torch.Tensor:
            result_silu_mul = self.silu_and_mul_matcher(input)
            result_quant = self.quant_matcher(result_silu_mul, scale)
            return result_quant[0]

        return _pattern

    @property
    def replacement(self):
        def _replacement(
            input: torch.Tensor,
            scale: torch.Tensor,
        ) -> torch.Tensor:
            d = input.shape[-1] // 2
            output_shape = input.shape[:-1] + (d,)
            result = torch.empty(
                output_shape, device=input.device, dtype=self.quant_dtype
            )
            at = auto_functionalized(
                self.FUSED_OP, result=result, input=input, scale=scale
            )
            return at[1]

        return _replacement

class SiluMulNvfp4QuantPattern(ActivationQuantPattern):
    """
    Fusion for SiluMul+Nvfp4Quant Pattern
    """

    def __init__(self) -> None:
        super().__init__(kNvfp4Dynamic)

    def get_inputs(self) -> list[torch.Tensor]:
        result = self.empty_quant(5, 32)
        output_scale = empty_i32(128, 4)
        input_ = empty_bf16(5, 64)
        scale = empty_fp32(1, 1)
        return [result, output_scale, input_, scale]

    @property
    def pattern(self):
        def _pattern(
            result: torch.Tensor,
            output_scale: torch.Tensor,
            input: torch.Tensor,
            scale: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            result_silu_mul = self.silu_and_mul_matcher(input)
            at = auto_functionalized(
                self.QUANT_OP,
                input=result_silu_mul,
                input_scale=scale,
                is_sf_swizzled_layout=True,
                output=result,
                output_scale=output_scale,
            )
            return at[1], at[2]

        return _pattern

    @property
    def replacement(self):
        def _replacement(
            result: torch.Tensor,
            output_scale: torch.Tensor,
            input: torch.Tensor,
            scale: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            at = auto_functionalized(
                self.FUSED_OP,
                result=result,
                result_block_scale=output_scale,
                input=input,
                input_global_scale=scale,
            )
            return at[1], at[2]

        return _replacement