mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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479 lines
16 KiB
Python
479 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from importlib.util import find_spec
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from typing import Optional
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import torch
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import torch._inductor.pattern_matcher as pm
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import torch.fx as fx
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from torch._higher_order_ops.auto_functionalize import auto_functionalized
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from torch._inductor.pattern_matcher import PatternMatcherPass
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from torch.distributed._symmetric_memory import enable_symm_mem_for_group
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from vllm.config import VllmConfig
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from vllm.distributed import get_tp_group, tensor_model_parallel_all_reduce
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from vllm.distributed.parallel_state import (
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get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
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from vllm.logger import init_logger
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from vllm.utils import direct_register_custom_op
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from .vllm_inductor_pass import VllmInductorPass
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if find_spec("flashinfer"):
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try:
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import flashinfer.comm as flashinfer_comm
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flashinfer_comm = (flashinfer_comm if hasattr(
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flashinfer_comm, "trtllm_allreduce_fusion") else None)
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except ImportError:
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flashinfer_comm = None
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else:
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flashinfer_comm = None
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from vllm.platforms import current_platform
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logger = init_logger(__name__)
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ALLREDUCE_OP = torch.ops.vllm.all_reduce.default
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RMS_OP = torch.ops._C.rms_norm.default
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RMS_ADD_OP = torch.ops._C.fused_add_rms_norm.default
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class BasePattern:
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def __init__(self, dtype: torch.dtype, device: str):
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self.dtype = dtype
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self.device = device
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self.tp = get_tp_group()
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self.tp_size = get_tensor_model_parallel_world_size()
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class GEMMReduceScatterPattern(BasePattern):
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def get_inputs(self):
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mul = torch.empty([16, 4], device=self.device, dtype=self.dtype)
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mm_weight = torch.empty([4, 4], device=self.device, dtype=self.dtype)
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return [mul, mm_weight]
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def register(self, pm_pass: PatternMatcherPass):
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def pattern(mul: torch.Tensor, mm_weight: torch.Tensor):
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mm = torch.ops.aten.mm.default(mul, mm_weight)
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reduce_scatter = torch.ops.vllm.reduce_scatter.default(
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mm,
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dim=0,
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world_size=self.tp_size,
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group_name=self.tp.unique_name,
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)
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return reduce_scatter
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def replacement(mul: torch.Tensor, mm_weight: torch.Tensor):
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gemm_rs = torch.ops.symm_mem.fused_matmul_reduce_scatter(
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mul,
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mm_weight,
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"avg",
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scatter_dim=0,
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group_name=self.tp.device_group.group_name,
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)
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return gemm_rs
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pm.register_replacement(pattern, replacement, self.get_inputs(),
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pm.fwd_only, pm_pass)
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class AllGatherGEMMPattern(BasePattern):
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def get_inputs(self):
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x = torch.empty([4, 4], device=self.device, dtype=self.dtype)
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weight = torch.empty([4, 4], device=self.device, dtype=self.dtype)
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return [x, weight]
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def register(self, pm_pass: PatternMatcherPass):
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def pattern(
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x: torch.Tensor,
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weight: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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all_gather = torch.ops.vllm.all_gather.default(
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x,
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dim=0,
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world_size=self.tp_size,
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group_name=self.tp.unique_name,
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)
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return torch.ops.aten.mm.default(all_gather, weight)
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def replacement(
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x: torch.Tensor,
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weight: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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ag_output, mm_outputs = torch.ops.symm_mem.fused_all_gather_matmul(
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x,
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[weight],
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gather_dim=0,
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group_name=self.tp.device_group.group_name,
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)
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return mm_outputs
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pm.register_replacement(pattern, replacement, self.get_inputs(),
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pm.fwd_only, pm_pass)
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class AsyncTPPass(VllmInductorPass):
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def __init__(self, config: VllmConfig):
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super().__init__(config)
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# Enable symmetric memory for the TP process group
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enable_symm_mem_for_group(get_tp_group().device_group.group_name)
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self.patterns: PatternMatcherPass = PatternMatcherPass(
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pass_name="async_tp_pass")
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GEMMReduceScatterPattern(self.model_dtype,
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self.device).register(self.patterns)
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AllGatherGEMMPattern(self.model_dtype,
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self.device).register(self.patterns)
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def is_applicable_for_shape(self, shape: Optional[int]) -> bool:
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# only do replace for specific shapes
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tp_size = get_tensor_model_parallel_world_size()
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return shape is not None and shape % tp_size == 0
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def __call__(self, graph: fx.Graph):
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self.begin()
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self.dump_graph(graph, "before_async_tp_pass")
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count = self.patterns.apply(graph)
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logger.debug("Replaced %s patterns", count)
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self.dump_graph(graph, "after_async_tp_pass")
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self.end_and_log()
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if flashinfer_comm is not None:
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_FI_WORKSPACE_TENSOR = None
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MiB = 1024 * 1024
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# Max size of the input tensor per world size
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# to use flashinfer fused allreduce
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_FI_MAX_SIZES = {
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2: MiB, # 1MB
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4: MiB, # 1MB
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6: MiB // 2, # 512KB
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8: MiB // 2, # 512KB
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}
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def call_trtllm_fused_allreduce_norm(
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allreduce_in: torch.Tensor,
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residual: torch.Tensor,
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rms_gamma: torch.Tensor,
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rms_eps: float,
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world_rank: int,
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world_size: int,
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launch_with_pdl: bool,
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trigger_completion_at_end: bool,
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fp32_acc: bool,
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max_token_num: int,
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norm_out: Optional[torch.Tensor] = None,
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) -> None:
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use_flashinfer = allreduce_in.shape[0] * allreduce_in.shape[
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1] * allreduce_in.element_size() <= min(
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_FI_MAX_SIZES[world_size],
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max_token_num * allreduce_in.shape[0] *
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allreduce_in.element_size(),
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)
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if use_flashinfer:
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assert (_FI_WORKSPACE_TENSOR is not None
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), "Flashinfer must be enabled when using flashinfer"
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if norm_out is None:
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norm_out = allreduce_in
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residual_out = residual
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else:
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# return residual_out as allreduce_out with zeroed residual_in
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# as flashinfer does not support rms_norm
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# and allreduce_out together
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residual_out = allreduce_in
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# For the sizes that are smaller than the max size,
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# we only use flashinfer one shot allreduce
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flashinfer_comm.trtllm_allreduce_fusion(
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allreduce_in=allreduce_in,
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token_num=allreduce_in.shape[0],
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residual_in=residual,
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residual_out=residual_out,
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norm_out=norm_out,
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rms_gamma=rms_gamma,
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rms_eps=rms_eps,
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world_rank=world_rank,
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world_size=world_size,
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hidden_dim=allreduce_in.shape[-1],
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workspace_ptrs=_FI_WORKSPACE_TENSOR,
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launch_with_pdl=launch_with_pdl,
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use_oneshot=True,
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trigger_completion_at_end=trigger_completion_at_end,
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fp32_acc=fp32_acc,
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pattern_code=flashinfer_comm.AllReduceFusionPattern.
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kARResidualRMSNorm,
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allreduce_out=None,
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quant_out=None,
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scale_out=None,
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layout_code=None,
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scale_factor=None,
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)
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else:
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allreduce_out = tensor_model_parallel_all_reduce(allreduce_in)
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if norm_out is None:
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torch.ops._C.fused_add_rms_norm(allreduce_out, residual,
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rms_gamma, rms_eps)
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else:
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torch.ops._C.rms_norm(norm_out, allreduce_out, rms_gamma,
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rms_eps)
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allreduce_in.copy_(allreduce_out)
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def call_trtllm_fused_allreduce_norm_fake(
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allreduce_in: torch.Tensor,
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residual: torch.Tensor,
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rms_gamma: torch.Tensor,
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rms_eps: float,
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world_rank: int,
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world_size: int,
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launch_with_pdl: bool,
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trigger_completion_at_end: bool,
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fp32_acc: bool,
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max_token_num: int,
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norm_out: Optional[torch.Tensor] = None,
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) -> None:
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pass
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direct_register_custom_op(
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op_name="flashinfer_trtllm_fused_allreduce_norm",
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op_func=call_trtllm_fused_allreduce_norm,
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mutates_args=[
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"allreduce_in",
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"residual",
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"norm_out",
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],
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fake_impl=call_trtllm_fused_allreduce_norm_fake,
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dispatch_key=current_platform.dispatch_key,
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)
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flashinfer_trtllm_fused_allreduce_norm = (
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torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default)
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class FlashInferFusedAllReduceParams:
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"""Parameters for FlashInfer fused allreduce operations."""
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def __init__(
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self,
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rank: int,
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world_size: int,
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use_fp32_lamport: bool = False,
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max_token_num: int = 1024,
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):
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self.rank = rank
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self.world_size = world_size
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self.use_fp32_lamport = use_fp32_lamport
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self.trigger_completion_at_end = True
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self.launch_with_pdl = True
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self.fp32_acc = True
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self.use_oneshot = False
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self.max_token_num = max_token_num
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def get_trtllm_fused_allreduce_kwargs(self):
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return {
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"world_rank": self.rank,
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"world_size": self.world_size,
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"launch_with_pdl": self.launch_with_pdl,
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"trigger_completion_at_end": self.trigger_completion_at_end,
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"fp32_acc": self.fp32_acc,
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"max_token_num": self.max_token_num,
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}
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class AllReduceRMSNORMPattern(BasePattern):
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def __init__(
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self,
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epsilon: float,
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dtype: torch.dtype,
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device: str,
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allreduce_params: FlashInferFusedAllReduceParams,
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):
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super().__init__(dtype, device)
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self.epsilon = epsilon
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self.allreduce_params = allreduce_params
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def get_inputs(self):
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input = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype)
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rms_result = torch.empty([1, 8, 4],
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device=self.device,
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dtype=self.dtype)
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weight = torch.empty([4], device=self.device, dtype=self.dtype)
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return [input, rms_result, weight]
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def register(self, pm_pass: PatternMatcherPass):
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def pattern(input: torch.Tensor, rms_result: torch.Tensor,
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weight: torch.Tensor):
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all_reduce_output = tensor_model_parallel_all_reduce(input)
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rms = auto_functionalized(
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RMS_OP,
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result=rms_result,
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input=all_reduce_output,
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weight=weight,
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epsilon=self.epsilon,
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)
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return rms[1], all_reduce_output
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def replacement(input: torch.Tensor, rms_result: torch.Tensor,
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weight: torch.Tensor):
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residual = torch.zeros_like(input)
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allreduce = auto_functionalized(
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torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default,
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allreduce_in=input,
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residual=residual,
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norm_out=rms_result,
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rms_gamma=weight,
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rms_eps=self.epsilon,
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**self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
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)
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return allreduce[3], allreduce[1]
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pm.register_replacement(pattern, replacement, self.get_inputs(),
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pm.fwd_only, pm_pass)
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class AllReduceFusedAddRMSNormPattern(BasePattern):
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def __init__(
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self,
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epsilon: float,
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dtype: torch.dtype,
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device: str,
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allreduce_params: FlashInferFusedAllReduceParams,
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):
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super().__init__(dtype, device)
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self.epsilon = epsilon
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self.allreduce_params = allreduce_params
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def get_inputs(self):
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input = torch.empty([4, 4], device=self.device, dtype=self.dtype)
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residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
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weight = torch.empty([4, 4], device=self.device, dtype=self.dtype)
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return [
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residual,
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input,
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weight,
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]
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def register(self, pm_pass: PatternMatcherPass):
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def pattern(residual: torch.Tensor, input: torch.Tensor,
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weight: torch.Tensor):
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all_reduce_output = tensor_model_parallel_all_reduce(input)
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rms = auto_functionalized(
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RMS_ADD_OP,
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input=all_reduce_output,
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residual=residual,
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weight=weight,
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epsilon=self.epsilon,
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)
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return rms[1], rms[2]
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def replacement(residual: torch.Tensor, input: torch.Tensor,
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weight: torch.Tensor):
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allreduce = auto_functionalized(
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torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default,
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allreduce_in=input,
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residual=residual,
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rms_gamma=weight,
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rms_eps=self.epsilon,
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norm_out=None,
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**self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
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)
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return allreduce[1], allreduce[2]
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pm.register_replacement(pattern, replacement, self.get_inputs(),
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pm.fwd_only, pm_pass)
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class AllReduceFusionPass(VllmInductorPass):
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def __init__(self, config: VllmConfig, max_token_num: int):
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super().__init__(config)
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self.disabled = True
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self.tp_size = get_tensor_model_parallel_world_size()
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if self.tp_size <= 1:
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return
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self.patterns: PatternMatcherPass = PatternMatcherPass(
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pass_name="all_reduce_fusion_pass")
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if config.model_config is None:
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return
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self.hidden_dim = config.model_config.get_hidden_size()
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self.group = get_tp_group().device_group
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rank = get_tensor_model_parallel_rank()
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use_fp32_lamport = self.model_dtype == torch.float32
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if flashinfer_comm is None:
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logger.warning(
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"Flashinfer is not installed or comm module not found, "
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"skipping allreduce fusion pass")
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return
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# Check if the world size is supported
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if self.tp_size not in _FI_MAX_SIZES:
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logger.warning(
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"Flashinfer allreduce fusion is not "
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"supported for world size %s",
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self.tp_size,
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)
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return
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self.ipc_handles, workspace_tensor = (
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flashinfer_comm.trtllm_create_ipc_workspace_for_all_reduce_fusion(
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tp_rank=rank,
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tp_size=self.tp_size,
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max_token_num=max_token_num,
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hidden_dim=self.hidden_dim,
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group=self.group,
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use_fp32_lamport=use_fp32_lamport,
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))
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global _FI_WORKSPACE_TENSOR
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_FI_WORKSPACE_TENSOR = workspace_tensor
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self.allreduce_params = FlashInferFusedAllReduceParams(
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rank=rank,
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world_size=self.tp_size,
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use_fp32_lamport=use_fp32_lamport,
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max_token_num=max_token_num,
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)
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for epsilon in [1e-5, 1e-6]:
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AllReduceRMSNORMPattern(
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epsilon,
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self.model_dtype,
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self.device,
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self.allreduce_params,
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).register(self.patterns)
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AllReduceFusedAddRMSNormPattern(
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epsilon,
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self.model_dtype,
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self.device,
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self.allreduce_params,
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).register(self.patterns)
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self.disabled = False
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def __call__(self, graph: fx.Graph):
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if self.disabled:
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return
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self.begin()
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self.dump_graph(graph, "before_all_reduce_fusion_pass")
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count = self.patterns.apply(graph)
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logger.debug("Replaced %s patterns", count)
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self.dump_graph(graph, "after_all_reduce_fusion_pass")
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self.end_and_log()
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def __del__(self):
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if self.disabled:
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return
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if flashinfer_comm is not None:
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flashinfer_comm.trtllm_destroy_ipc_workspace(
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self.ipc_handles, self.group)
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