collective_fusion.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from importlib.util import find_spec

import torch
import torch._inductor.pattern_matcher as pm
import torch.fx as fx
from torch._higher_order_ops.auto_functionalize import auto_functionalized
from torch._inductor.pattern_matcher import PatternMatcherPass
from torch.distributed._symmetric_memory import enable_symm_mem_for_group

from vllm.config import VllmConfig
from vllm.distributed import get_tp_group, tensor_model_parallel_all_reduce
from vllm.distributed.parallel_state import (
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
)
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    kFp8StaticTensorSym,
)
from vllm.platforms import current_platform
from vllm.utils.torch_utils import direct_register_custom_op

from .inductor_pass import enable_fake_mode
from .matcher_utils import MatcherFusedAddRMSNorm, MatcherQuantFP8, MatcherRMSNorm
from .vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass

FP8_DTYPE = current_platform.fp8_dtype()

if find_spec("flashinfer"):
    try:
        import flashinfer.comm as flashinfer_comm

        flashinfer_comm = (
            flashinfer_comm
            if hasattr(flashinfer_comm, "trtllm_allreduce_fusion")
            else None
        )
    except ImportError:
        flashinfer_comm = None
else:
    flashinfer_comm = None

logger = init_logger(__name__)

if hasattr(torch.ops._C, "scaled_fp4_quant"):
    STATIC_FP4_QUANT_OP = torch.ops._C.scaled_fp4_quant.default


class BasePattern:
    def __init__(self, dtype: torch.dtype, device: str):
        self.dtype = dtype
        self.device = device
        self.tp = get_tp_group()
        self.tp_size = get_tensor_model_parallel_world_size()


class GEMMReduceScatterPattern(BasePattern):
    def get_inputs(self):
        mul = torch.empty([16, 4], device=self.device, dtype=self.dtype)
        mm_weight = torch.empty([4, 4], device=self.device, dtype=self.dtype)
        return [mul, mm_weight]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(mul: torch.Tensor, mm_weight: torch.Tensor):
            mm = torch.ops.aten.mm.default(mul, mm_weight)
            reduce_scatter = torch.ops.vllm.reduce_scatter.default(
                mm,
                dim=0,
                world_size=self.tp_size,
                group_name=self.tp.unique_name,
            )
            return reduce_scatter

        def replacement(mul: torch.Tensor, mm_weight: torch.Tensor):
            gemm_rs = torch.ops.symm_mem.fused_matmul_reduce_scatter(
                mul,
                mm_weight,
                "avg",
                scatter_dim=0,
                group_name=self.tp.device_group.group_name,
            )

            return gemm_rs

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )


class AllGatherGEMMPattern(BasePattern):
    def get_inputs(self):
        x = torch.empty([4, 4], device=self.device, dtype=self.dtype)
        weight = torch.empty([4, 4], device=self.device, dtype=self.dtype)

        return [x, weight]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(
            x: torch.Tensor,
            weight: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            all_gather = torch.ops.vllm.all_gather.default(
                x,
                dim=0,
                world_size=self.tp_size,
                group_name=self.tp.unique_name,
            )

            return torch.ops.aten.mm.default(all_gather, weight)

        def replacement(
            x: torch.Tensor, weight: torch.Tensor
        ) -> tuple[torch.Tensor, torch.Tensor]:
            ag_output, mm_outputs = torch.ops.symm_mem.fused_all_gather_matmul(
                x,
                [weight],
                gather_dim=0,
                group_name=self.tp.device_group.group_name,
            )
            return mm_outputs

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )


class ScaledMMReduceScatterPattern(BasePattern):
    def get_inputs(self):
        input = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
        mm_weight = (
            torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
            .contiguous()
            .transpose(0, 1)
        )
        scale_a = torch.empty([16, 1], device=self.device, dtype=torch.float32)
        scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32)
        return [input, mm_weight, scale_a, scale_b]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(
            input: torch.Tensor,
            mat2: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
        ) -> torch.Tensor:
            scaled_mm = torch.ops.aten._scaled_mm.default(
                input,
                mat2=mat2,
                scale_a=scale_a,
                scale_b=scale_b,
                bias=None,
                scale_result=None,
                out_dtype=self.dtype,
            )
            reduce_scatter = torch.ops.vllm.reduce_scatter.default(
                scaled_mm,
                dim=0,
                world_size=self.tp_size,
                group_name=self.tp.unique_name,
            )
            return reduce_scatter

        def replacement(
            input: torch.Tensor,
            mat2: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
        ) -> torch.Tensor:
            # Calculate output shape: input @ mat2 with scatter_dim reduced
            output_shape = [*input.shape[:-1], mat2.shape[1]]
            scatter_dim = 0
            gemm_rs = torch.ops.vllm.patched_fused_scaled_matmul_reduce_scatter(
                input,
                mat2,
                scale_a,
                scale_b,
                "avg",
                scatter_dim,  # orig_scatter_dim
                scatter_dim,  # scatter_dim_after_maybe_reshape
                self.tp.device_group.group_name,
                output_shape,
                None,  # bias
                None,  # result_scale
                self.dtype,  # out_dtype
                False,  # use_fast_accum
            )

            return gemm_rs

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )


class AllGatherScaledMMPattern(BasePattern):
    def get_inputs(self):
        x = torch.empty([8, 16], device=self.device, dtype=FP8_DTYPE)
        weight = (
            torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
            .contiguous()
            .transpose(0, 1)
        )

        s1 = x.shape[0] * self.tp_size

        scale_a = torch.empty([s1, 1], device=self.device, dtype=torch.float32)
        scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32)

        return [x, weight, scale_a, scale_b]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(
            x: torch.Tensor,
            weight: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
        ) -> torch.Tensor:
            all_gather = torch.ops.vllm.all_gather.default(
                x, dim=0, world_size=self.tp_size, group_name=self.tp.unique_name
            )

            return torch.ops.aten._scaled_mm.default(
                all_gather,
                mat2=weight,
                scale_a=scale_a,
                scale_b=scale_b,
                bias=None,
                scale_result=None,
                out_dtype=self.dtype,
            )

        def replacement(
            x: torch.Tensor,
            weight: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
        ) -> torch.Tensor:
            ag_output, mm_outputs = torch.ops.symm_mem.fused_all_gather_scaled_matmul(  # noqa
                x,
                [weight],
                scale_a,
                [scale_b],
                gather_dim=0,
                biases=[None],
                result_scales=[None],
                out_dtypes=[self.dtype],
                use_fast_accum=[False],
                group_name=self.tp.device_group.group_name,
            )
            return mm_outputs

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )


class CutlassScaledMMReduceScatterPattern(BasePattern):
    def get_inputs(self):
        input = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
        mm_weight = (
            torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
            .contiguous()
            .transpose(0, 1)
        )
        scale_a = torch.empty([16, 1], device=self.device, dtype=torch.float32)
        scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32)

        cutlass_mm_output = torch.empty([16, 16], device=self.device, dtype=self.dtype)
        return [input, mm_weight, scale_a, scale_b, cutlass_mm_output]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(
            input: torch.Tensor,
            weight: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
            cutlass_mm_output: torch.Tensor,
        ) -> torch.Tensor:
            cutlass_scaled_mm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.cutlass_scaled_mm.default,
                out=cutlass_mm_output,
                a=input,
                b=weight,
                a_scales=scale_a,
                b_scales=scale_b,
                bias=None,
            )

            reduce_scatter = torch.ops.vllm.reduce_scatter.default(
                cutlass_scaled_mm[1],
                dim=0,
                world_size=self.tp_size,
                group_name=self.tp.unique_name,
            )
            return reduce_scatter

        def replacement(
            input: torch.Tensor,
            mat2: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
            cutlass_mm_output: torch.Tensor,
        ) -> torch.Tensor:
            # Calculate output shape: input @ mat2 with scatter_dim reduced
            output_shape = [*input.shape[:-1], mat2.shape[1]]
            scatter_dim = 0
            gemm_rs = torch.ops.vllm.patched_fused_scaled_matmul_reduce_scatter(
                input,
                mat2,
                scale_a,
                scale_b,
                "avg",
                scatter_dim,  # orig_scatter_dim
                scatter_dim,  # scatter_dim_after_maybe_reshape
                self.tp.device_group.group_name,
                output_shape,
                None,  # bias
                None,  # result_scale
                self.dtype,  # out_dtype
                False,  # use_fast_accum
            )

            return gemm_rs

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )


class AllGatherCutlassScaledMMPattern(BasePattern):
    def get_inputs(self):
        x = torch.empty([8, 16], device=self.device, dtype=FP8_DTYPE)
        weight = (
            torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE)
            .contiguous()
            .transpose(0, 1)
        )

        s1 = x.shape[0] * self.tp_size

        scale_a = torch.empty([s1, 1], device=self.device, dtype=torch.float32)
        scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32)

        s2 = weight.shape[1]
        output = torch.empty([s1, s2], device=self.device, dtype=self.dtype)

        return [x, weight, scale_a, scale_b, output]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(
            x: torch.Tensor,
            weight: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
            output: torch.Tensor,
        ) -> torch.Tensor:
            all_gather = torch.ops.vllm.all_gather.default(
                x, dim=0, world_size=self.tp_size, group_name=self.tp.unique_name
            )

            cutlass_scaled_mm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.cutlass_scaled_mm.default,
                out=output,
                a=all_gather,
                b=weight,
                a_scales=scale_a,
                b_scales=scale_b,
                bias=None,
            )
            return cutlass_scaled_mm[1]

        def replacement(
            x: torch.Tensor,
            weight: torch.Tensor,
            scale_a: torch.Tensor,
            scale_b: torch.Tensor,
            output: torch.Tensor,
        ) -> torch.Tensor:
            ag_output, mm_outputs = torch.ops.symm_mem.fused_all_gather_scaled_matmul(  # noqa
                x,
                [weight],
                scale_a,
                [scale_b],
                gather_dim=0,
                biases=[None],
                result_scales=[None],
                out_dtypes=[self.dtype],
                use_fast_accum=[False],
                group_name=self.tp.device_group.group_name,
            )
            return mm_outputs

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )


class AsyncTPPass(VllmPatternMatcherPass):
    @enable_fake_mode
    def __init__(self, config: VllmConfig):
        super().__init__(config)

        # Enable symmetric memory for the TP process group
        enable_symm_mem_for_group(get_tp_group().device_group.group_name)
        self.patterns: PatternMatcherPass = PatternMatcherPass(
            pass_name="async_tp_pass"
        )
        GEMMReduceScatterPattern(self.model_dtype, self.device).register(self.patterns)

        AllGatherGEMMPattern(self.model_dtype, self.device).register(self.patterns)

        # These fusions are enabled only for bfloat16 models because
        # `scaled_mm` or `cutlass_scaled_mm` with per-token (row-wise) scaling
        # only supports bfloat16 as the output dtype.
        if self.model_dtype == torch.bfloat16:
            ScaledMMReduceScatterPattern(self.model_dtype, self.device).register(
                self.patterns
            )
            AllGatherScaledMMPattern(self.model_dtype, self.device).register(
                self.patterns
            )

            CutlassScaledMMReduceScatterPattern(self.model_dtype, self.device).register(
                self.patterns
            )
            AllGatherCutlassScaledMMPattern(self.model_dtype, self.device).register(
                self.patterns
            )

        self.dump_patterns(config, self.patterns)

    def is_applicable(self, shape: int | None) -> bool:
        # This pass is applied on top of the sequence parallelism pass.
        # It inherits the same applicability condition as `SequenceParallelismPass`.
        # See `SequenceParallelismPass.is_applicable` for more details.
        if (
            not self.compilation_config.splitting_ops
            or self.compilation_config.use_inductor_graph_partition
        ):
            return True
        tp_size = get_tensor_model_parallel_world_size()
        return shape is not None and shape % tp_size == 0

    @VllmInductorPass.time_and_log
    def __call__(self, graph: fx.Graph):
        self.matched_count = self.patterns.apply(graph)
        logger.debug("Replaced %s patterns", self.matched_count)


# Max size of the input tensor per world size per device capability
# to use flashinfer fused allreduce
FI_ALLREDUCE_FUSION_MAX_SIZE_MB: dict[int, dict[int, float]] = {
    90: {
        2: 64,  # 64MB
        4: 2,  # 2MB
        8: 0.5,  # 0.5MB
    },
    100: {
        2: 64,  # 64MB
        4: 32,  # 32MB
        8: 1,  # 1MB
    },
}

# Max size of the input tensor per world size per device capability
# to use flashinfer one shot fused allreduce
# OneShot max size is at most 64MB / world size (FlashInfer restriction)
_FI_ALLREDUCE_ONE_SHOT_MAX_SIZES_MB: dict[int, dict[int, float]] = {
    90: {
        2: 32,  # 32MB
        4: 2,  # 2MB
        8: 0.5,  # 0.5MB
    },
    100: {
        2: 32,  # 32MB
        4: 4,  # 4MB
        8: 1,  # 1MB
    },
}


if flashinfer_comm is not None:
    _FI_WORKSPACE_TENSOR = None
    MiB = 1024 * 1024

    def call_trtllm_fused_allreduce_norm(
        allreduce_in: torch.Tensor,
        residual: torch.Tensor,
        rms_gamma: torch.Tensor,
        rms_eps: float,
        world_rank: int,
        world_size: int,
        launch_with_pdl: bool,
        trigger_completion_at_end: bool,
        fp32_acc: bool,
        max_token_num: int,
        pattern_code: int,
        norm_out: torch.Tensor | None = None,
        quant_out: torch.Tensor | None = None,
        scale_out: torch.Tensor | None = None,
        scale_factor: torch.Tensor | None = None,
    ) -> None:
        num_tokens, hidden_size = allreduce_in.shape
        element_size = allreduce_in.element_size()
        current_tensor_size = num_tokens * hidden_size * element_size

        if num_tokens <= max_token_num:
            device_capability = current_platform.get_device_capability().to_int()
            # Get one shot input size limit for the current world size
            # for the current device capability
            max_one_shot_size_mb = _FI_ALLREDUCE_ONE_SHOT_MAX_SIZES_MB.get(
                device_capability, {}
            ).get(world_size, None)
            # Use one shot if no max size for one shot is specified
            use_oneshot = (
                max_one_shot_size_mb is None
                or current_tensor_size <= max_one_shot_size_mb * MiB
            )

            assert _FI_WORKSPACE_TENSOR is not None, (
                "Flashinfer must be enabled when using flashinfer"
            )
            if norm_out is None:
                norm_out = allreduce_in
                residual_out = residual
            else:
                # return residual_out as allreduce_out with zeroed residual_in
                # as flashinfer does not support rms_norm
                # and allreduce_out together
                residual_out = allreduce_in
            # For the sizes that are smaller than the max size,
            # we only use flashinfer one shot allreduce
            flashinfer_comm.trtllm_allreduce_fusion(
                allreduce_in=allreduce_in,
                token_num=allreduce_in.shape[0],
                residual_in=residual,
                residual_out=residual_out,
                norm_out=norm_out,
                rms_gamma=rms_gamma,
                rms_eps=rms_eps,
                world_rank=world_rank,
                world_size=world_size,
                hidden_dim=allreduce_in.shape[-1],
                workspace_ptrs=_FI_WORKSPACE_TENSOR,
                launch_with_pdl=launch_with_pdl,
                use_oneshot=use_oneshot,
                trigger_completion_at_end=trigger_completion_at_end,
                fp32_acc=fp32_acc,
                pattern_code=pattern_code,
                allreduce_out=None,
                quant_out=quant_out,
                scale_out=scale_out,
                # in vllm we only support swizzled layout
                layout_code=flashinfer_comm.QuantizationSFLayout.SWIZZLED_128x4,
                scale_factor=scale_factor,
            )
        else:
            allreduce_out = tensor_model_parallel_all_reduce(allreduce_in)
            if scale_factor is not None and scale_out is None:
                # Do fused rms norm static fp8 quant fused op
                if norm_out is None:
                    torch.ops._C.fused_add_rms_norm_static_fp8_quant(
                        quant_out,
                        allreduce_out,
                        residual,
                        rms_gamma,
                        scale_factor,
                        rms_eps,
                    )
                else:
                    torch.ops._C.rms_norm_static_fp8_quant(
                        quant_out, allreduce_out, rms_gamma, scale_factor, rms_eps
                    )
            else:
                if norm_out is None:
                    torch.ops._C.fused_add_rms_norm(
                        allreduce_out, residual, rms_gamma, rms_eps
                    )
                    norm_out = allreduce_out
                else:
                    torch.ops._C.rms_norm(norm_out, allreduce_out, rms_gamma, rms_eps)
                if scale_factor is not None and scale_out is not None:
                    torch.ops._C.scaled_fp4_quant(
                        quant_out, norm_out, scale_out, scale_factor
                    )
            if scale_factor is None or norm_out is not None:
                # we need to return allreduce output
                # in cases of non quant fused AR + RMS norm
                # and fused AR + RMS norm + quant without fused add
                allreduce_in.copy_(allreduce_out)

    def call_trtllm_fused_allreduce_norm_fake(
        allreduce_in: torch.Tensor,
        residual: torch.Tensor,
        rms_gamma: torch.Tensor,
        rms_eps: float,
        world_rank: int,
        world_size: int,
        launch_with_pdl: bool,
        trigger_completion_at_end: bool,
        fp32_acc: bool,
        max_token_num: int,
        pattern_code: int,
        norm_out: torch.Tensor | None = None,
        quant_out: torch.Tensor | None = None,
        scale_out: torch.Tensor | None = None,
        scale_factor: torch.Tensor | None = None,
    ) -> None:
        pass

    direct_register_custom_op(
        op_name="flashinfer_trtllm_fused_allreduce_norm",
        op_func=call_trtllm_fused_allreduce_norm,
        mutates_args=[
            "allreduce_in",
            "residual",
            "norm_out",
            "quant_out",
            "scale_out",
        ],
        fake_impl=call_trtllm_fused_allreduce_norm_fake,
    )
    flashinfer_trtllm_fused_allreduce_norm = (
        torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default
    )


class FlashInferFusedAllReduceParams:
    """Parameters for FlashInfer fused allreduce operations."""

    def __init__(
        self,
        rank: int,
        world_size: int,
        use_fp32_lamport: bool = False,
        max_token_num: int = 1024,
    ):
        self.rank = rank
        self.world_size = world_size
        self.use_fp32_lamport = use_fp32_lamport
        self.trigger_completion_at_end = True
        self.launch_with_pdl = True
        self.fp32_acc = True
        self.max_token_num = max_token_num

    def get_trtllm_fused_allreduce_kwargs(self):
        return {
            "world_rank": self.rank,
            "world_size": self.world_size,
            "launch_with_pdl": self.launch_with_pdl,
            "trigger_completion_at_end": self.trigger_completion_at_end,
            "fp32_acc": self.fp32_acc,
            "max_token_num": self.max_token_num,
        }


class AllReduceRMSNormPattern(BasePattern):
    """
    This pattern replaces the allreduce + rms norm (without residual)
    with fused flashinfer implementation.
    Applies to allreduce + rmsnorm before attn in the first Transformer block.
    """

    def __init__(
        self,
        epsilon: float,
        dtype: torch.dtype,
        device: str,
        allreduce_params: FlashInferFusedAllReduceParams,
    ):
        super().__init__(dtype, device)
        self.epsilon = epsilon
        self.allreduce_params = allreduce_params
        self.rmsnorm_matcher = MatcherRMSNorm(epsilon)

    def get_inputs(self):
        input, weight = self.rmsnorm_matcher.inputs()

        # input goes through allreduce first, always 16-bit
        return [input.to(self.dtype), weight]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(input: torch.Tensor, weight: torch.Tensor):
            allreduce_output = tensor_model_parallel_all_reduce(input)
            rms = self.rmsnorm_matcher(allreduce_output, weight)

            return rms, allreduce_output

        def replacement(input: torch.Tensor, weight: torch.Tensor):
            residual = torch.zeros_like(input)
            rms_result = torch.empty_like(input)
            allreduce = auto_functionalized(
                flashinfer_trtllm_fused_allreduce_norm,
                allreduce_in=input,
                residual=residual,
                norm_out=rms_result,
                quant_out=None,
                scale_out=None,
                rms_gamma=weight,
                rms_eps=self.epsilon,
                pattern_code=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNorm,
                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
            )
            # rms_result, allreduce_in
            return allreduce[3], allreduce[1]

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )


class AllReduceFusedAddRMSNormPattern(BasePattern):
    """
    This pattern replaces the allreduce + rms norm (with residual)
    with fused flashinfer implementation.
    Applies to o_proj + rmsnorm after attn and mlp + rmsnorm before attn.
    """

    def __init__(
        self,
        epsilon: float,
        dtype: torch.dtype,
        device: str,
        allreduce_params: FlashInferFusedAllReduceParams,
    ):
        super().__init__(dtype, device)
        self.epsilon = epsilon
        self.allreduce_params = allreduce_params
        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)

    def get_inputs(self):
        input, residual, weight = self.rmsnorm_matcher.inputs()

        # input goes through allreduce first, always 16-bit
        return [residual, input.to(self.dtype), weight]

    def register(self, pm_pass: PatternMatcherPass):
        def pattern(residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor):
            allreduce_output = tensor_model_parallel_all_reduce(input)
            rms, residual = self.rmsnorm_matcher(allreduce_output, weight, residual)
            return rms, residual

        def replacement(
            residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor
        ):
            allreduce = auto_functionalized(
                flashinfer_trtllm_fused_allreduce_norm,
                allreduce_in=input,
                residual=residual,
                norm_out=None,
                quant_out=None,
                scale_out=None,
                rms_gamma=weight,
                rms_eps=self.epsilon,
                pattern_code=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNorm,
                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
            )
            # allreduce_in, residual
            return allreduce[1], allreduce[2]

        pm.register_replacement(
            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
        )

        # Same pattern, but only return the output and not residual
        # (helpful for end of graph where residual is not used again)
        first_return_only = lambda fn: lambda a, b, c: fn(a, b, c)[0]

        pm.register_replacement(
            first_return_only(pattern),
            first_return_only(replacement),
            self.get_inputs(),
            pm.fwd_only,
            pm_pass,
        )


class AllReduceFusedRMSNormStaticQuantFP8Pattern(BasePattern):
    """
    This pattern replaces the allreduce + rms norm (without residual)
    + static fp8 quant with fused flashinfer implementation.
    Applies to allreduce + rmsnorm + quant before attn
    in the first Transformer block.
    """

    def __init__(
        self,
        epsilon: float,
        dtype: torch.dtype,
        device: str,
        allreduce_params: FlashInferFusedAllReduceParams,
    ):
        super().__init__(dtype, device)
        self.epsilon = epsilon
        self.allreduce_params = allreduce_params
        self.quant_dtype = torch.float8_e4m3fn
        self.rmsnorm_matcher = MatcherRMSNorm(epsilon)
        self.quant_matcher = MatcherQuantFP8(kFp8StaticTensorSym)

    def register(self, pm_pass: PatternMatcherPass):
        def get_inputs():
            input, weight = self.rmsnorm_matcher.inputs()
            _, scale = self.quant_matcher.inputs()

            # input goes through allreduce first, always 16-bit
            return [input.to(self.dtype), weight, scale]

        def pattern(
            input: torch.Tensor,
            weight: torch.Tensor,
            scale: torch.Tensor,
        ):
            all_reduce = tensor_model_parallel_all_reduce(input)
            rms = self.rmsnorm_matcher(all_reduce, weight)
            quant, _ = self.quant_matcher(rms, scale)
            return quant, all_reduce

        def replacement(input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor):
            residual = torch.zeros_like(input)
            result_rms = torch.empty_like(input)
            result_quant = torch.empty_like(input, dtype=self.quant_dtype)
            allreduce = auto_functionalized(
                flashinfer_trtllm_fused_allreduce_norm,
                allreduce_in=input,
                residual=residual,
                norm_out=result_rms,
                quant_out=result_quant,
                scale_out=None,
                rms_gamma=weight,
                rms_eps=self.epsilon,
                # We don't use norm_out afterwards
                pattern_code=(
                    flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP8Quant
                ),
                scale_factor=scale,
                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
            )

            # quant_out, allreduce_output
            return allreduce[4], allreduce[1]

        pm.register_replacement(
            pattern, replacement, get_inputs(), pm.fwd_only, pm_pass
        )


class AllReduceFusedAddRMSNormStaticQuantFP8Pattern(BasePattern):
    """
    This pattern replaces the allreduce + rms norm (with residual)
    + static fp8 quant with fused flashinfer implementation.
    Applies to o_proj + rmsnorm after attn + quant and
    mlp + rmsnorm + quant before attn.
    """

    def __init__(
        self,
        epsilon: float,
        dtype: torch.dtype,
        device: str,
        allreduce_params: FlashInferFusedAllReduceParams,
    ):
        super().__init__(dtype, device)
        self.epsilon = epsilon
        self.allreduce_params = allreduce_params
        self.quant_dtype = torch.float8_e4m3fn

        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
        self.quant_matcher = MatcherQuantFP8(kFp8StaticTensorSym)

    def register(self, pm_pass: PatternMatcherPass):
        def get_inputs():
            input, residual, weight = self.rmsnorm_matcher.inputs()
            _, scale = self.quant_matcher.inputs()

            # input goes through allreduce first, always 16-bit
            return [residual, input.to(self.dtype), weight, scale]

        def pattern(
            residual: torch.Tensor,
            input: torch.Tensor,
            weight: torch.Tensor,
            scale: torch.Tensor,
        ):
            allreduce_output = tensor_model_parallel_all_reduce(input)
            rms, res = self.rmsnorm_matcher(allreduce_output, weight, residual)
            quant, _ = self.quant_matcher(rms, scale)

            return quant, res

        def replacement(
            residual: torch.Tensor,
            input: torch.Tensor,
            weight: torch.Tensor,
            scale: torch.Tensor,
        ):
            result_quant = torch.empty_like(input, dtype=self.quant_dtype)
            allreduce = auto_functionalized(
                flashinfer_trtllm_fused_allreduce_norm,
                allreduce_in=input,
                residual=residual,
                norm_out=None,
                quant_out=result_quant,
                scale_out=None,
                rms_gamma=weight,
                rms_eps=self.epsilon,
                # We don't use norm_out afterwards
                pattern_code=(
                    flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP8Quant
                ),
                scale_factor=scale,
                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
            )
            # quant_out, rms_norm_residual
            return allreduce[4], allreduce[2]

        pm.register_replacement(
            pattern, replacement, get_inputs(), pm.fwd_only, pm_pass
        )


class AllReduceFusedRMSNormStaticQuantNVFP4Pattern(BasePattern):
    """
    This pattern replaces the allreduce + rms norm (without residual)
    + static nvfp4 quant with fused flashinfer implementation.
    Applies to allreduce + rmsnorm + quant before attn
    in the first Transformer block.
    """

    def __init__(
        self,
        epsilon: float,
        dtype: torch.dtype,
        device: str,
        allreduce_params: FlashInferFusedAllReduceParams,
    ):
        super().__init__(dtype, device)
        self.epsilon = epsilon
        self.allreduce_params = allreduce_params
        self.rmsnorm_matcher = MatcherRMSNorm(epsilon)

    def register(self, pm_pass: PatternMatcherPass):
        def get_inputs():
            input = torch.empty([1, 16, 16], device=self.device, dtype=self.dtype)
            quant_result = torch.empty((16, 8), device=self.device, dtype=torch.uint8)
            input_global_scale = torch.empty(
                [1, 1], device=self.device, dtype=torch.float32
            )
            weight = torch.empty([16], device=self.device, dtype=self.dtype)
            output_scale = torch.empty([128, 4], device=self.device, dtype=torch.int32)

            return [input, quant_result, weight, input_global_scale, output_scale]

        def pattern(
            input: torch.Tensor,
            quant_result: torch.Tensor,
            weight: torch.Tensor,
            input_global_scale: torch.Tensor,
            output_scale: torch.Tensor,
        ):
            all_reduce = tensor_model_parallel_all_reduce(input)
            rms = self.rmsnorm_matcher(all_reduce, weight)
            quant_out_tuple = auto_functionalized(
                STATIC_FP4_QUANT_OP,
                output=quant_result,
                input=rms,
                output_scale=output_scale,
                input_scale=input_global_scale,
            )

            # quant_out, allreduce_output, output_scale
            return quant_out_tuple[1], all_reduce, quant_out_tuple[2]

        def replacement(
            input: torch.Tensor,
            quant_result: torch.Tensor,
            weight: torch.Tensor,
            input_global_scale: torch.Tensor,
            output_scale: torch.Tensor,
        ):
            residual = torch.zeros_like(input)
            result_rms = torch.empty_like(input)
            allreduce = auto_functionalized(
                flashinfer_trtllm_fused_allreduce_norm,
                allreduce_in=input,
                residual=residual,
                norm_out=result_rms,
                quant_out=quant_result,
                scale_out=output_scale,
                rms_gamma=weight,
                rms_eps=self.epsilon,
                # We don't use norm_out afterwards
                pattern_code=(
                    flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP4Quant
                ),
                scale_factor=input_global_scale,
                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
            )

            # quant_out, allreduce_output, output_scale
            return allreduce[4], allreduce[1], allreduce[5]

        pm.register_replacement(
            pattern, replacement, get_inputs(), pm.fwd_only, pm_pass
        )


class AllReduceFusedAddRMSNormStaticQuantNVFP4Pattern(BasePattern):
    """
    This pattern replaces the allreduce + rms norm (with residual)
    + static nvfp4 quant with fused flashinfer implementation.
    Applies to o_proj + rmsnorm after attn + quant and
    mlp + rmsnorm + quant before attn.
    """

    def __init__(
        self,
        epsilon: float,
        dtype: torch.dtype,
        device: str,
        allreduce_params: FlashInferFusedAllReduceParams,
    ):
        super().__init__(dtype, device)
        self.epsilon = epsilon
        self.allreduce_params = allreduce_params
        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)

    def register(self, pm_pass: PatternMatcherPass):
        def get_inputs():
            input = torch.empty([16, 16], device=self.device, dtype=self.dtype)

            residual = torch.empty([16, 16], device=self.device, dtype=self.dtype)
            weight = torch.empty([16, 16], device=self.device, dtype=self.dtype)
            quant_result = torch.empty((16, 8), device=self.device, dtype=torch.uint8)
            input_global_scale = torch.empty(
                [1, 1], device=self.device, dtype=torch.float32
            )
            output_scale = torch.empty([128, 4], device=self.device, dtype=torch.int32)

            return [
                quant_result,
                residual,
                input,
                output_scale,
                weight,
                input_global_scale,
            ]

        def pattern(
            quant_result: torch.Tensor,
            residual: torch.Tensor,
            input: torch.Tensor,
            output_scale: torch.Tensor,
            weight: torch.Tensor,
            input_global_scale: torch.Tensor,
        ):
            allreduce_output = tensor_model_parallel_all_reduce(input)
            rms, residual = self.rmsnorm_matcher(allreduce_output, weight, residual)
            quant_out_tuple = auto_functionalized(
                STATIC_FP4_QUANT_OP,
                output=quant_result,
                input=rms,
                output_scale=output_scale,
                input_scale=input_global_scale,
            )

            # quant_out, allreduce_output, output_scale
            return quant_out_tuple[1], residual, quant_out_tuple[2]

        def replacement(
            quant_result: torch.Tensor,
            residual: torch.Tensor,
            input: torch.Tensor,
            output_scale: torch.Tensor,
            weight: torch.Tensor,
            input_global_scale: torch.Tensor,
        ):
            allreduce = auto_functionalized(
                flashinfer_trtllm_fused_allreduce_norm,
                allreduce_in=input,
                residual=residual,
                norm_out=None,
                quant_out=quant_result,
                scale_out=output_scale,
                rms_gamma=weight,
                rms_eps=self.epsilon,
                # We don't use norm_out afterwards
                pattern_code=(
                    flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP4Quant
                ),
                scale_factor=input_global_scale,
                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
            )
            # quant_out, rms_norm_residual, output_scale
            return allreduce[4], allreduce[2], allreduce[5]

        pm.register_replacement(
            pattern, replacement, get_inputs(), pm.fwd_only, pm_pass
        )


class AllReduceFusionPass(VllmPatternMatcherPass):
    def __init__(self, config: VllmConfig):
        super().__init__(config)
        self.disabled = True
        self.tp_size = get_tensor_model_parallel_world_size()
        if self.tp_size <= 1:
            return
        self.patterns: PatternMatcherPass = PatternMatcherPass(
            pass_name="all_reduce_fusion_pass"
        )
        if config.model_config is None:
            return
        self.hidden_dim = config.model_config.get_hidden_size()
        self.group = get_tp_group().device_group
        rank = get_tensor_model_parallel_rank()
        use_fp32_lamport = self.model_dtype == torch.float32
        if flashinfer_comm is None:
            logger.warning(
                "Flashinfer is not installed or comm module not found, "
                "skipping allreduce fusion pass"
            )
            return
        max_size = config.compilation_config.pass_config.flashinfer_max_size(
            self.tp_size
        )
        if max_size is None:
            # Flashinfer doesn't support current world size
            logger.warning(
                "Flashinfer allreduce fusion is not supported for world size %s",
                self.tp_size,
            )
            return
        element_size = 4 if use_fp32_lamport else 2
        self.max_token_num = max_size // (self.hidden_dim * element_size)
        # take the min to save workspace size and we'll never use more
        # than max_num_batched_tokens anyways
        self.max_token_num = min(
            self.max_token_num, config.scheduler_config.max_num_batched_tokens
        )
        logger.debug_once(
            f"Flashinfer max size: {max_size // (1024 * 1024)} MB,"
            "Maximal number of tokens used by "
            f"Flashinfer Allreduce Fusion: {self.max_token_num}",
            scope="global",
        )

        self.ipc_handles, workspace_tensor = (
            flashinfer_comm.trtllm_create_ipc_workspace_for_all_reduce_fusion(
                tp_rank=rank,
                tp_size=self.tp_size,
                max_token_num=self.max_token_num,
                hidden_dim=self.hidden_dim,
                group=self.group,
                use_fp32_lamport=use_fp32_lamport,
            )
        )

        global _FI_WORKSPACE_TENSOR
        _FI_WORKSPACE_TENSOR = workspace_tensor
        self.allreduce_params = FlashInferFusedAllReduceParams(
            rank=rank,
            world_size=self.tp_size,
            use_fp32_lamport=use_fp32_lamport,
            max_token_num=self.max_token_num,
        )

        self.register_patterns()
        self.dump_patterns(config, self.patterns)

    @enable_fake_mode
    def register_patterns(self):
        for epsilon in [1e-5, 1e-6]:
            AllReduceFusedRMSNormStaticQuantFP8Pattern(
                epsilon,
                self.model_dtype,
                self.device,
                self.allreduce_params,
            ).register(self.patterns)
            AllReduceFusedAddRMSNormStaticQuantFP8Pattern(
                epsilon,
                self.model_dtype,
                self.device,
                self.allreduce_params,
            ).register(self.patterns)
            if current_platform.has_device_capability(100):
                AllReduceFusedRMSNormStaticQuantNVFP4Pattern(
                    epsilon,
                    self.model_dtype,
                    self.device,
                    self.allreduce_params,
                ).register(self.patterns)
                AllReduceFusedAddRMSNormStaticQuantNVFP4Pattern(
                    epsilon,
                    self.model_dtype,
                    self.device,
                    self.allreduce_params,
                ).register(self.patterns)
            AllReduceRMSNormPattern(
                epsilon,
                self.model_dtype,
                self.device,
                self.allreduce_params,
            ).register(self.patterns)
            AllReduceFusedAddRMSNormPattern(
                epsilon,
                self.model_dtype,
                self.device,
                self.allreduce_params,
            ).register(self.patterns)

            # WARNING: This is a hack to clear the pattern matcher cache
            # and allow multiple values of epsilon.
            torch._inductor.pattern_matcher._seen_patterns.clear()

        self.disabled = False

    @VllmInductorPass.time_and_log
    def __call__(self, graph: fx.Graph):
        if self.disabled:
            logger.debug("AllReduceFusionPass disabled")
            return

        self.matched_count = self.patterns.apply(graph)
        logger.debug("Replaced %s patterns", self.matched_count)

    def __del__(self):
        if getattr(self, "disabled", True):
            return
        if flashinfer_comm is not None:
            flashinfer_comm.trtllm_destroy_ipc_workspace_for_all_reduce(
                self.ipc_handles, self.group
            )