1644 lines
68 KiB
Python
1644 lines
68 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import importlib
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from abc import abstractmethod
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from collections.abc import Iterable
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from enum import Enum
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from typing import Callable, Literal, Optional, overload
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import torch
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import torch.nn.functional as F
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from torch.nn.parameter import UninitializedParameter
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import vllm.envs as envs
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from vllm.config import get_current_vllm_config
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from vllm.distributed import (get_dp_group, get_ep_group,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.distributed.eplb.eplb_state import EplbState
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.custom_op import CustomOp
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# yapf: disable
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from vllm.model_executor.layers.fused_moe.config import (
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FusedMoEConfig, FusedMoEParallelConfig)
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# yapf: enable
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from vllm.model_executor.layers.fused_moe.modular_kernel import (
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FusedMoEActivationFormat, FusedMoEModularKernel,
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FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize)
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# from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
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# is_rocm_aiter_moe_enabled)
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.layers.fused_moe.fused_moe import (
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fused_topk, grouped_topk, is_power_of_two)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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from vllm.platforms.interface import CpuArchEnum
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from vllm.utils import direct_register_custom_op, has_deep_ep, has_pplx
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from vllm import _custom_ops as ops
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from lightop import op
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if current_platform.is_cuda_alike():
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from .fused_batched_moe import BatchedTritonExperts
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from .fused_moe import TritonExperts, fused_experts
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if has_pplx():
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from .pplx_prepare_finalize import (PplxPrepareAndFinalize,
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pplx_hidden_dim_scale_bytes)
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if has_deep_ep():
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from .deepep_ht_prepare_finalize import DeepEPHTPrepareAndFinalize
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from .deepep_ll_prepare_finalize import (DEEPEP_QUANT_BLOCK_SHAPE,
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DeepEPLLPrepareAndFinalize)
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else:
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fused_experts = None # type: ignore
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FusedMoEPermuteExpertsUnpermute = None # type: ignore
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FusedMoEPrepareAndFinalize = None # type: ignore
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# if is_rocm_aiter_moe_enabled():
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# from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import ( # noqa: E501
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# rocm_aiter_grouped_topk as grouped_topk)
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if current_platform.is_cpu():
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pass
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else:
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from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk
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if current_platform.is_tpu():
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from .moe_pallas import fused_moe as fused_moe_pallas
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else:
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fused_moe_pallas = None # type: ignore
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logger = init_logger(__name__)
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class FusedMoeWeightScaleSupported(Enum):
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TENSOR = "tensor"
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CHANNEL = "channel"
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GROUP = "group"
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BLOCK = "block"
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class FusedMoEMethodBase(QuantizeMethodBase):
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moe: FusedMoEConfig
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@abstractmethod
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def create_weights(self, layer: torch.nn.Module, num_experts: int,
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hidden_size: int, intermediate_size_per_partition: int,
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params_dtype: torch.dtype, **extra_weight_attrs):
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raise NotImplementedError
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def init_prepare_finalize(self, moe: FusedMoEConfig,
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quant_config: Optional[QuantizationConfig]):
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all2all_manager = get_ep_group().device_communicator.all2all_manager
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assert all2all_manager is not None
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self.moe = moe
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prepare_finalize: Optional[FusedMoEPrepareAndFinalize] = None
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if moe.use_pplx_kernels:
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hidden_dim_bytes, hidden_scale_bytes = pplx_hidden_dim_scale_bytes(
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moe.max_num_tokens,
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moe.hidden_dim,
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moe.in_dtype,
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moe.quant_dtype,
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per_act_token_quant=moe.per_act_token_quant,
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block_shape=moe.block_shape,
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)
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all_to_all_args = dict(
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max_num_tokens=moe.max_num_tokens,
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num_experts=moe.num_experts,
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experts_per_token=moe.experts_per_token, # topk
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rank=all2all_manager.rank,
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world_size=all2all_manager.world_size,
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# dp_size actually means tp_size, bug in pplx kernels
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dp_size=all2all_manager.tp_group.world_size,
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hidden_dim=moe.hidden_dim,
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hidden_dim_bytes=hidden_dim_bytes,
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hidden_dim_scale_bytes=hidden_scale_bytes,
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)
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num_dispatchers = (all2all_manager.world_size //
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all2all_manager.tp_group.world_size)
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# Intranode pplx a2a takes a group name while internode does not.
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if not all2all_manager.internode:
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all_to_all_args[
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"group_name"] = all2all_manager.cpu_group.group_name
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handle = all2all_manager.get_handle(all_to_all_args)
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prepare_finalize = PplxPrepareAndFinalize(
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handle,
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max_num_tokens=moe.max_num_tokens,
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num_local_experts=moe.num_local_experts,
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num_dispatchers=num_dispatchers,
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)
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elif moe.use_deepep_ht_kernels:
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assert moe.dp_size == all2all_manager.dp_world_size
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all_to_all_args = dict()
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handle = all2all_manager.get_handle(all_to_all_args)
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prepare_finalize = DeepEPHTPrepareAndFinalize(
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handle,
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num_dispatchers=all2all_manager.world_size,
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dp_size=all2all_manager.dp_world_size,
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rank_expert_offset=all2all_manager.rank *
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moe.num_local_experts,
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)
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elif moe.use_deepep_ll_kernels:
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all_to_all_args = dict(
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max_num_tokens_per_dp_rank=moe.max_num_tokens,
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token_hidden_size=moe.hidden_dim,
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num_ep_ranks=all2all_manager.world_size,
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num_global_experts=moe.num_experts,
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num_local_experts=moe.num_experts //
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all2all_manager.world_size)
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handle = all2all_manager.get_handle(all_to_all_args)
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# Note : We may want to use FP8 dispatch even otherwise just to
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# reduce datamovement
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use_fp8_dispatch = (moe.quant_config is not None
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and moe.quant_config.quant_dtype
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== current_platform.fp8_dtype()
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and moe.quant_config.block_shape
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== DEEPEP_QUANT_BLOCK_SHAPE)
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# Note (varun): Whether to use FP8 dispatch or not needs some
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# profiling. Turning it off for now.
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prepare_finalize = DeepEPLLPrepareAndFinalize(
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handle,
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max_tokens_per_rank=moe.max_num_tokens,
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num_dispatchers=all2all_manager.world_size,
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use_fp8_dispatch=use_fp8_dispatch,
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)
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self.topk_indices_dtype = None
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if prepare_finalize is not None:
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logger.debug("%s", prepare_finalize.__class__.__name__)
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self.topk_indices_dtype = prepare_finalize.topk_indices_dtype()
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experts = self.select_gemm_impl(prepare_finalize, moe)
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self.fused_experts = FusedMoEModularKernel(
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prepare_finalize,
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experts,
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)
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def select_gemm_impl(
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self,
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prepare_finalize: FusedMoEPrepareAndFinalize,
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moe: FusedMoEConfig,
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) -> FusedMoEPermuteExpertsUnpermute:
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# based on the all2all implementation, select the appropriate
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# gemm implementation
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raise NotImplementedError(
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f"{self.__class__.__name__} must select appropriate gemm "
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"implementation based on the prepare_finalize")
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@abstractmethod
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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activation: str = "silu",
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enable_eplb: bool = False,
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expert_load_view: Optional[torch.Tensor] = None,
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logical_to_physical_map: Optional[torch.Tensor] = None,
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logical_replica_count: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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raise NotImplementedError
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@CustomOp.register("unquantized_fused_moe")
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class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
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"""MoE method without quantization."""
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def __init__(self, moe: FusedMoEConfig):
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super().__init__()
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self.fused_experts = fused_experts # type: ignore
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self.topk_indices_dtype = None
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self.moe = moe
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self.rocm_aiter_moe_enabled = False # is_rocm_aiter_moe_enabled()
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if self.rocm_aiter_moe_enabled:
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from .rocm_aiter_fused_moe import rocm_aiter_fused_experts
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self.rocm_aiter_fused_experts = rocm_aiter_fused_experts
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else:
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self.rocm_aiter_fused_experts = None # type: ignore
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def select_gemm_impl(
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self,
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prepare_finalize: FusedMoEPrepareAndFinalize,
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moe: FusedMoEConfig,
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) -> FusedMoEPermuteExpertsUnpermute:
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assert self.fused_experts == fused_experts
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if (prepare_finalize.activation_format ==
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FusedMoEActivationFormat.BatchedExperts):
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logger.debug("BatchedTritonExperts %s", self.moe)
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return BatchedTritonExperts(
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max_num_tokens=self.moe.max_num_tokens,
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num_dispatchers=prepare_finalize.num_dispatchers(),
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)
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else:
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logger.debug("TritonExperts %s", self.moe)
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return TritonExperts()
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def create_weights(self, layer: torch.nn.Module, num_experts: int,
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hidden_size: int, intermediate_size_per_partition: int,
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params_dtype: torch.dtype, use_nn_moe: bool,
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**extra_weight_attrs):
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# Fused gate_up_proj (column parallel)
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if not use_nn_moe:
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w13_weight = torch.nn.Parameter(torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size,
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dtype=params_dtype),
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requires_grad=False)
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else:
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w13_weight = torch.nn.Parameter(torch.empty(
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num_experts,
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hidden_size,
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2 * intermediate_size_per_partition,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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# down_proj (row parallel)
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if not use_nn_moe:
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w2_weight = torch.nn.Parameter(torch.empty(
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num_experts,
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hidden_size,
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intermediate_size_per_partition,
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dtype=params_dtype),
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requires_grad=False)
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else:
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w2_weight = torch.nn.Parameter(torch.empty(
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num_experts,
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intermediate_size_per_partition,
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hidden_size,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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def _maybe_pad_weight(self, weight: torch.Tensor) -> torch.Tensor:
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# Pad the weight tensor. This is an optimization on ROCm platform, which
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# can benefit from tensors located far enough from one another in memory
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if (envs.VLLM_ROCM_MOE_PADDING and current_platform.is_rocm()
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and weight.stride(-1) == 1
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and (weight.stride(-2) * weight.element_size()) % 512 == 0):
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num_pad = 256 // weight.element_size()
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weight = F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad]
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torch.cuda.empty_cache()
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return weight
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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super().process_weights_after_loading(layer)
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# Padding the weight for better performance on ROCm
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layer.w13_weight.data = self._maybe_pad_weight(layer.w13_weight.data)
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layer.w2_weight.data = self._maybe_pad_weight(layer.w2_weight.data)
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# Lazy import to avoid importing triton.
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# from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
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# shuffle_weights)
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# if self.rocm_aiter_moe_enabled:
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# shuffled_w13, shuffled_w2 = shuffle_weights(
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# layer.w13_weight.data, layer.w2_weight.data)
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# layer.w13_weight.data = shuffled_w13
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# layer.w2_weight.data = shuffled_w2
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if current_platform.is_cpu():
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if current_platform.get_cpu_architecture() == CpuArchEnum.X86:
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from vllm.model_executor.layers.fused_moe import cpu_fused_moe
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dtype = layer.w13_weight.dtype
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if (envs.VLLM_CPU_SGL_KERNEL
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and torch._C._cpu._is_amx_tile_supported()
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and dtype == torch.bfloat16):
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packed_w13_weight = torch.ops._C.convert_weight_packed(
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layer.w13_weight)
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assert packed_w13_weight.size() == layer.w13_weight.size()
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layer.w13_weight.copy_(packed_w13_weight)
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del packed_w13_weight
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packed_w2_weight = torch.ops._C.convert_weight_packed(
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layer.w2_weight)
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assert packed_w2_weight.size() == layer.w2_weight.size()
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layer.w2_weight.copy_(packed_w2_weight)
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layer.cpu_fused_moe = cpu_fused_moe.SGLFusedMOE(layer)
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else:
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layer.cpu_fused_moe = cpu_fused_moe.IPEXFusedMOE(layer)
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else:
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raise NotImplementedError("CPU MOE only supports x86 arch.")
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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activation: str = "silu",
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enable_eplb: bool = False,
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expert_load_view: Optional[torch.Tensor] = None,
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logical_to_physical_map: Optional[torch.Tensor] = None,
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logical_replica_count: Optional[torch.Tensor] = None,
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use_nn_moe: Optional[bool] = False,
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routed_scaling_factor: Optional[float] = None,
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use_fused_gate: Optional[bool] = False,
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) -> torch.Tensor:
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if enable_eplb:
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raise NotImplementedError(
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"EPLB not supported for `UnquantizedFusedMoEMethod` yet.")
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return self.forward(
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x=x,
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layer=layer,
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router_logits=router_logits,
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top_k=top_k,
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renormalize=renormalize,
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use_grouped_topk=use_grouped_topk,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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global_num_experts=global_num_experts,
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expert_map=expert_map,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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activation=activation,
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apply_router_weight_on_input=apply_router_weight_on_input,
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use_nn_moe=use_nn_moe,
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routed_scaling_factor=routed_scaling_factor,
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use_fused_gate=use_fused_gate)
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def forward_cuda(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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use_grouped_topk: bool,
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top_k: int,
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router_logits: torch.Tensor,
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renormalize: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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activation: str = "silu",
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use_nn_moe: Optional[bool] = False,
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routed_scaling_factor: Optional[float] = None,
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use_fused_gate: Optional[bool] = False,
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) -> torch.Tensor:
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topk_weights, topk_ids = FusedMoE.select_experts(
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hidden_states=x,
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router_logits=router_logits,
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use_grouped_topk=use_grouped_topk,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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indices_type=self.topk_indices_dtype,
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routed_scaling_factor=routed_scaling_factor,
|
|
use_fused_gate=use_fused_gate)
|
|
|
|
if self.rocm_aiter_moe_enabled:
|
|
return self.rocm_aiter_fused_experts(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
expert_map=expert_map,
|
|
activation=activation,
|
|
apply_router_weight_on_input=apply_router_weight_on_input)
|
|
else:
|
|
return self.fused_experts(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=True,
|
|
activation=activation,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
use_nn_moe=use_nn_moe
|
|
)
|
|
|
|
def forward_cpu(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
use_grouped_topk: bool,
|
|
top_k: int,
|
|
router_logits: torch.Tensor,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
use_nn_moe: Optional[bool] = False,
|
|
routed_scaling_factor: Optional[float] = None,
|
|
use_fused_gate: Optional[bool] = False,
|
|
**kwargs,
|
|
):
|
|
return layer.cpu_fused_moe(
|
|
layer,
|
|
x,
|
|
use_grouped_topk,
|
|
top_k,
|
|
router_logits,
|
|
renormalize,
|
|
topk_group,
|
|
num_expert_group,
|
|
global_num_experts,
|
|
expert_map,
|
|
custom_routing_function,
|
|
scoring_func,
|
|
e_score_correction_bias,
|
|
apply_router_weight_on_input,
|
|
activation,
|
|
)
|
|
|
|
def forward_hpu(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
use_grouped_topk: bool,
|
|
top_k: int,
|
|
router_logits: torch.Tensor,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
use_nn_moe: Optional[bool] = False,
|
|
routed_scaling_factor: Optional[float] = None,
|
|
use_fused_gate: Optional[bool] = False,
|
|
) -> torch.Tensor:
|
|
assert not use_grouped_topk
|
|
assert num_expert_group is None
|
|
assert topk_group is None
|
|
assert custom_routing_function is None
|
|
assert layer is not None
|
|
assert apply_router_weight_on_input is False
|
|
if scoring_func != "softmax":
|
|
raise NotImplementedError(
|
|
"Only softmax scoring function is supported for HPU.")
|
|
if e_score_correction_bias is not None:
|
|
raise NotImplementedError(
|
|
"Expert score correction bias is not supported for HPU.")
|
|
return layer.hpu_fused_moe(x, layer.w13_weight, layer.w2_weight,
|
|
router_logits, top_k)
|
|
|
|
def forward_tpu(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
use_grouped_topk: bool,
|
|
top_k: int,
|
|
router_logits: torch.Tensor,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
) -> torch.Tensor:
|
|
assert not use_grouped_topk
|
|
assert num_expert_group is None
|
|
assert topk_group is None
|
|
assert custom_routing_function is None
|
|
assert apply_router_weight_on_input is False
|
|
if scoring_func != "softmax":
|
|
raise NotImplementedError(
|
|
"Only softmax scoring function is supported for TPU.")
|
|
if e_score_correction_bias is not None:
|
|
raise NotImplementedError(
|
|
"Expert score correction bias is not supported for TPU.")
|
|
assert activation == "silu", f"{activation} is not supported for TPU."
|
|
return fused_moe_pallas(hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk=top_k,
|
|
gating_output=router_logits,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
renormalize=renormalize)
|
|
|
|
if current_platform.is_tpu():
|
|
forward_native = forward_tpu
|
|
elif current_platform.is_cpu():
|
|
forward_native = forward_cpu
|
|
else:
|
|
forward_native = forward_cuda
|
|
|
|
|
|
def determine_expert_map(
|
|
ep_size: int, ep_rank: int,
|
|
global_num_experts: int) -> tuple[int, Optional[torch.Tensor]]:
|
|
"""
|
|
Calculates how many experts should be assigned to each rank for EP and
|
|
creates a mapping from global to local expert index. Experts are
|
|
distributed evenly across ranks. Any remaining are assigned to the
|
|
last rank.
|
|
|
|
Args:
|
|
ep_size (int): The size of the expert parallel group
|
|
global_num_experts (int): The total number of experts in the model.
|
|
|
|
Returns:
|
|
tuple[int, Optional[torch.Tensor]]: A tuple containing:
|
|
- local_num_experts (int): The number of experts assigned
|
|
to the current rank.
|
|
- expert_map (Optional[torch.Tensor]): A tensor of shape
|
|
(global_num_experts,) mapping from global to local index.
|
|
Contains -1 for experts not assigned to the current rank.
|
|
Returns None if ep_size is 1.
|
|
"""
|
|
assert ep_size > 0
|
|
if ep_size == 1:
|
|
return (global_num_experts, None)
|
|
|
|
local_num_experts = global_num_experts // ep_size
|
|
|
|
# Create a tensor of size num_experts filled with -1
|
|
expert_map = torch.full((global_num_experts, ), -1, dtype=torch.int32)
|
|
# Create a expert map for the local experts
|
|
if ep_rank < (ep_size - 1):
|
|
# Each non-last rank gets local_num_experts experts.
|
|
expert_map[ep_rank * local_num_experts:
|
|
(ep_rank + 1) * local_num_experts] = \
|
|
torch.arange(0, local_num_experts, dtype=torch.int32)
|
|
else:
|
|
# All remaining experts are assigned to the last rank.
|
|
local_num_experts = (global_num_experts - ep_rank * local_num_experts)
|
|
|
|
expert_map[-local_num_experts:] = \
|
|
torch.arange(0, local_num_experts, dtype=torch.int32)
|
|
return (local_num_experts, expert_map)
|
|
|
|
|
|
class FusedMoE(torch.nn.Module):
|
|
"""FusedMoE layer for MoE models.
|
|
|
|
This layer contains both MergedColumnParallel weights (gate_up_proj /
|
|
w13) and RowParallelLinear weights (down_proj/ w2).
|
|
|
|
Note: Mixtral uses w1, w2, and w3 for gate, up, and down_proj. We
|
|
copy that naming convention here and handle any remapping in the
|
|
load_weights function in each model implementation.
|
|
|
|
Args:
|
|
num_experts: Number of experts in the model
|
|
top_k: Number of experts selected for each token
|
|
hidden_size: Input hidden state size of the transformer
|
|
intermediate_size: Intermediate size of the experts
|
|
params_dtype: Data type for the parameters.
|
|
reduce_results: Whether to all all_reduce on the output of the layer
|
|
renomalize: Whether to renormalize the logits in the fused_moe kernel
|
|
quant_config: Quantization configure.
|
|
enable_eplb: Whether to enable expert parallelism load balancer.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
num_experts: int, # Global number of experts
|
|
top_k: int,
|
|
hidden_size: int,
|
|
intermediate_size: int,
|
|
params_dtype: Optional[torch.dtype] = None,
|
|
reduce_results: bool = False,
|
|
renormalize: bool = True,
|
|
use_grouped_topk: bool = False,
|
|
num_expert_group: Optional[int] = None,
|
|
topk_group: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
tp_size: Optional[int] = None,
|
|
ep_size: Optional[int] = None,
|
|
dp_size: Optional[int] = None,
|
|
prefix: str = "",
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
enable_eplb: bool = False,
|
|
num_redundant_experts: int = 0,
|
|
routed_scaling_factor: Optional[float] = None,
|
|
):
|
|
super().__init__()
|
|
if params_dtype is None:
|
|
params_dtype = torch.get_default_dtype()
|
|
self.params_dtype = params_dtype
|
|
|
|
tp_size_ = (tp_size if tp_size is not None else
|
|
get_tensor_model_parallel_world_size())
|
|
dp_size_ = (dp_size
|
|
if dp_size is not None else get_dp_group().world_size)
|
|
|
|
vllm_config = get_current_vllm_config()
|
|
self.moe_parallel_config: FusedMoEParallelConfig = (
|
|
FusedMoEParallelConfig.make(
|
|
tp_size_=tp_size_,
|
|
dp_size_=dp_size_,
|
|
vllm_parallel_config=vllm_config.parallel_config))
|
|
|
|
self.global_num_experts = num_experts + num_redundant_experts
|
|
|
|
# For smuggling this layer into the fused moe custom op
|
|
compilation_config = vllm_config.compilation_config
|
|
if prefix in compilation_config.static_forward_context:
|
|
raise ValueError("Duplicate layer name: {}".format(prefix))
|
|
compilation_config.static_forward_context[prefix] = self
|
|
self.layer_name = prefix
|
|
|
|
self.enable_eplb = enable_eplb
|
|
self.expert_load_view: Optional[torch.Tensor] = None
|
|
self.logical_to_physical_map: Optional[torch.Tensor] = None
|
|
self.logical_replica_count: Optional[torch.Tensor] = None
|
|
|
|
# Determine expert maps
|
|
if self.use_ep:
|
|
if self.enable_eplb:
|
|
assert self.global_num_experts % self.ep_size == 0, \
|
|
"EPLB currently only supports even distribution of " \
|
|
"experts across ranks."
|
|
else:
|
|
assert num_redundant_experts == 0, \
|
|
"Redundant experts are only supported with EPLB."
|
|
self.local_num_experts, self.expert_map = determine_expert_map(
|
|
ep_size=self.ep_size,
|
|
ep_rank=self.ep_rank,
|
|
global_num_experts=self.global_num_experts)
|
|
else:
|
|
self.local_num_experts, self.expert_map = (self.global_num_experts,
|
|
None)
|
|
|
|
self.top_k = top_k
|
|
|
|
assert intermediate_size % self.tp_size == 0
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size_per_partition = intermediate_size // self.tp_size
|
|
self.reduce_results = reduce_results
|
|
self.renormalize = renormalize
|
|
self.use_grouped_topk = use_grouped_topk
|
|
if self.use_grouped_topk:
|
|
assert num_expert_group is not None and topk_group is not None
|
|
self.num_expert_group = num_expert_group
|
|
self.topk_group = topk_group
|
|
self.custom_routing_function = custom_routing_function
|
|
self.scoring_func = scoring_func
|
|
self.e_score_correction_bias = e_score_correction_bias
|
|
self.apply_router_weight_on_input = apply_router_weight_on_input
|
|
self.activation = activation
|
|
self.routed_scaling_factor = routed_scaling_factor
|
|
|
|
if self.scoring_func != "softmax" and not self.use_grouped_topk:
|
|
raise ValueError("Only softmax scoring function is supported for "
|
|
"non-grouped topk.")
|
|
if current_platform.is_hpu():
|
|
from vllm_hpu_extension.ops import DynamicFusedMOE
|
|
self.hpu_fused_moe = DynamicFusedMOE(self.global_num_experts)
|
|
|
|
if vllm_config.model_config is not None:
|
|
model_dtype = vllm_config.model_config.dtype
|
|
else:
|
|
# TODO (bnell): This is a hack to get test_mixtral_moe to work
|
|
# since model_config is not set in the pytest test.
|
|
model_dtype = params_dtype
|
|
|
|
moe = FusedMoEConfig.make(
|
|
num_experts=self.global_num_experts,
|
|
experts_per_token=top_k,
|
|
hidden_dim=hidden_size,
|
|
num_local_experts=self.local_num_experts,
|
|
moe_parallel_config=self.moe_parallel_config,
|
|
in_dtype=model_dtype,
|
|
max_num_tokens=envs.VLLM_MOE_DP_CHUNK_SIZE,
|
|
quant_config=quant_config,
|
|
)
|
|
self.moe_config = moe
|
|
self.quant_config = quant_config
|
|
|
|
# Note: get_quant_method will look at the layer's local_num_experts
|
|
# for heuristic purposes, so it must be initialized first.
|
|
quant_method: Optional[QuantizeMethodBase] = None
|
|
quant_method = (UnquantizedFusedMoEMethod(moe) if quant_config is None
|
|
else quant_config.get_quant_method(self, prefix))
|
|
|
|
assert quant_method is not None
|
|
assert isinstance(quant_method, FusedMoEMethodBase)
|
|
self.quant_method = quant_method
|
|
|
|
if self.enable_eplb:
|
|
from vllm.model_executor.layers.quantization.fp8 import (
|
|
Fp8MoEMethod)
|
|
if not isinstance(quant_method, Fp8MoEMethod):
|
|
# TODO: Add support for additional quantization methods.
|
|
# The implementation for other quantization methods does not
|
|
# contain essential differences, but the current quant API
|
|
# design causes duplicated work when extending to new
|
|
# quantization methods, so I'm leaving it for now.
|
|
# If you plan to add support for more quantization methods,
|
|
# please refer to the implementation in `Fp8MoEMethod`.
|
|
raise NotImplementedError("EPLB is only supported for FP8 "
|
|
"quantization for now.")
|
|
|
|
if quant_config is None:
|
|
# Not considering quant for now, temporarily
|
|
self.use_nn_moe = int(os.environ.get('MOE_NN', 1)) == 1
|
|
else:
|
|
self.use_nn_moe = False
|
|
|
|
moe_quant_params = {
|
|
"num_experts": self.local_num_experts,
|
|
"hidden_size": hidden_size,
|
|
"intermediate_size_per_partition":
|
|
self.intermediate_size_per_partition,
|
|
"params_dtype": params_dtype,
|
|
"weight_loader": self.weight_loader,
|
|
"use_nn_moe": self.use_nn_moe,
|
|
}
|
|
# need full intermediate size pre-sharding for WNA16 act order
|
|
if (self.quant_method.__class__.__name__
|
|
in ("GPTQMarlinMoEMethod",
|
|
"CompressedTensorsWNA16MarlinMoEMethod",
|
|
"CompressedTensorsWNA16MoEMethod")):
|
|
moe_quant_params["intermediate_size_full"] = intermediate_size
|
|
|
|
if (self.quant_method.__class__.__name__ in ("BlockInt8MoEMethod",
|
|
"SlimQuantW4A8Int8MoEMethod",
|
|
"SlimQuantW4A8Int8MarlinMoEMethod")):
|
|
moe_quant_params["intermediate_size"] = self.intermediate_size_per_partition
|
|
|
|
|
|
self.quant_method.create_weights(layer=self, **moe_quant_params)
|
|
|
|
from vllm.two_batch_overlap.two_batch_overlap import tbo_all_reduce
|
|
self.tbo_all_reduce = tbo_all_reduce
|
|
|
|
# moe_fused_gate kernel ensure that num_experts/num_expert_group does not exceed MAX_VPT=32 now. And when kernel can handle MAX_VPT > 32, we can remove this assertion.
|
|
self.use_fused_gate = envs.VLLM_ENABLE_MOE_FUSED_GATE \
|
|
and self.e_score_correction_bias is not None \
|
|
and self.global_num_experts // num_expert_group <= 32 \
|
|
and is_power_of_two(e_score_correction_bias.shape[0])
|
|
|
|
# Chunked all2all staging tensor
|
|
self.batched_hidden_states: Optional[torch.Tensor] = None
|
|
self.batched_router_logits: Optional[torch.Tensor] = None
|
|
if (self.moe_parallel_config.use_pplx_kernels
|
|
or self.moe_parallel_config.use_deepep_ll_kernels):
|
|
self.batched_hidden_states = torch.zeros(
|
|
(moe.max_num_tokens, self.hidden_size),
|
|
dtype=moe.in_dtype,
|
|
device=torch.cuda.current_device())
|
|
|
|
# Note here we use `num_experts` which is logical expert count
|
|
self.batched_router_logits = torch.zeros(
|
|
(moe.max_num_tokens, num_experts),
|
|
dtype=moe.in_dtype,
|
|
device=torch.cuda.current_device())
|
|
|
|
@property
|
|
def tp_size(self):
|
|
return self.moe_parallel_config.tp_size
|
|
|
|
@property
|
|
def dp_size(self):
|
|
return self.moe_parallel_config.dp_size
|
|
|
|
@property
|
|
def ep_size(self):
|
|
return self.moe_parallel_config.ep_size
|
|
|
|
@property
|
|
def tp_rank(self):
|
|
return self.moe_parallel_config.tp_rank
|
|
|
|
@property
|
|
def dp_rank(self):
|
|
return self.moe_parallel_config.dp_rank
|
|
|
|
@property
|
|
def ep_rank(self):
|
|
return self.moe_parallel_config.ep_rank
|
|
|
|
@property
|
|
def use_ep(self):
|
|
return self.moe_parallel_config.use_ep
|
|
|
|
@property
|
|
def use_pplx_kernels(self):
|
|
return self.moe_parallel_config.use_pplx_kernels
|
|
|
|
@property
|
|
def use_deepep_ht_kernels(self):
|
|
return self.moe_parallel_config.use_deepep_ht_kernels
|
|
|
|
@property
|
|
def use_deepep_ll_kernels(self):
|
|
return self.moe_parallel_config.use_deepep_ll_kernels
|
|
|
|
def _load_per_tensor_weight_scale(self, shard_id: str,
|
|
param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor,
|
|
expert_id: int):
|
|
param_data = param.data
|
|
# for per tensor weight quantization
|
|
if shard_id in ("w1", "w3"):
|
|
# We have to keep the weight scales of w1 and w3 because
|
|
# we need to re-quantize w1/w3 weights after weight loading.
|
|
idx = 0 if shard_id == "w1" else 1
|
|
param_data[expert_id][idx] = loaded_weight
|
|
# If we are in the row parallel case (down_proj)
|
|
elif shard_id == "w2":
|
|
param_data[expert_id] = loaded_weight
|
|
|
|
def _load_model_weight_or_group_weight_scale(self,
|
|
shard_dim: int,
|
|
expert_data: torch.Tensor,
|
|
shard_id: str,
|
|
loaded_weight: torch.Tensor,
|
|
tp_rank: int,
|
|
load_full_w2: bool = False):
|
|
"""
|
|
Load grouped weight scales for group quantization or model weights
|
|
:param shard_dim: dimension to shard
|
|
:param expert_data: parameter for a particular expert
|
|
:param shard_id: either w1, w2, or w3
|
|
:param loaded_weight: checkpoint weight to load into the param
|
|
:param tp_rank: tensor parallel rank
|
|
:param load_full_w2: whether or not the w2 loaded should be sharded.
|
|
"""
|
|
if shard_id == "w2":
|
|
# In the case where we have actorder/g_idx, we do not partition the
|
|
# w2 scales, as indicated by `load_full` argument, for all tp cases
|
|
self._load_w2(shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank,
|
|
load_full=load_full_w2)
|
|
elif shard_id in ("w1", "w3"):
|
|
self._load_w13(shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank)
|
|
|
|
def _load_per_channel_weight_scale(self, expert_data: torch.Tensor,
|
|
shard_dim: int, shard_id: str,
|
|
loaded_weight: torch.Tensor,
|
|
tp_rank: int):
|
|
# for per channel weight quantization
|
|
if shard_id == "w2":
|
|
expert_data.copy_(loaded_weight)
|
|
elif shard_id in ("w1", "w3"):
|
|
self._load_w13(shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank)
|
|
|
|
def _load_w13(self, expert_data: torch.Tensor, shard_dim: int,
|
|
shard_id: str, loaded_weight: torch.Tensor, tp_rank: int):
|
|
|
|
# Index the loaded weight for tp sharding.
|
|
# gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
|
|
shard_size = expert_data.shape[shard_dim] // 2
|
|
loaded_weight = loaded_weight.narrow(shard_dim if not self.use_nn_moe else ~shard_dim,
|
|
shard_size * tp_rank,
|
|
shard_size)
|
|
# Narrow parameter and load.
|
|
# w1, gate_proj: Load into first logical weight of w13.
|
|
if shard_id == "w1":
|
|
expert_data = expert_data.narrow(shard_dim, 0, shard_size)
|
|
# w3, up_proj: Load into second logical weight of w13.
|
|
else:
|
|
assert shard_id == "w3"
|
|
expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
|
|
if not self.use_nn_moe:
|
|
expert_data.copy_(loaded_weight)
|
|
else:
|
|
expert_data.copy_(loaded_weight.T)
|
|
|
|
def _load_w2(self,
|
|
expert_data: torch.Tensor,
|
|
shard_dim: int,
|
|
loaded_weight: torch.Tensor,
|
|
tp_rank: int,
|
|
load_full: bool = False):
|
|
|
|
# Index the loaded weight for tp sharding.
|
|
# down_proj: "RowParallel" so tp sharding on input_dim
|
|
# Narrow parameter and load.
|
|
shard_size = expert_data.shape[shard_dim]
|
|
if not load_full:
|
|
loaded_weight = loaded_weight.narrow(shard_dim if not self.use_nn_moe else ~shard_dim,
|
|
shard_size * tp_rank,
|
|
shard_size)
|
|
# w2, down_proj: Load into only logical weight of w2.
|
|
if not self.use_nn_moe:
|
|
expert_data.copy_(loaded_weight)
|
|
else:
|
|
expert_data.copy_(loaded_weight.T)
|
|
|
|
def _load_single_value(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor, expert_id: int):
|
|
param_data = param.data
|
|
|
|
# Input scales can be loaded directly and should be equal.
|
|
if not self.use_nn_moe:
|
|
param_data[expert_id] = loaded_weight
|
|
else:
|
|
param_data[expert_id] = loaded_weight.T
|
|
|
|
def _load_g_idx(self, shard_id: str, expert_data: torch.Tensor,
|
|
shard_dim: int, loaded_weight: torch.Tensor, tp_rank: int):
|
|
|
|
if shard_id == "w2":
|
|
self._load_w2(shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=tp_rank)
|
|
else:
|
|
assert shard_id in ("w1", "w3")
|
|
if not self.use_nn_moe:
|
|
expert_data.copy_(loaded_weight)
|
|
else:
|
|
expert_data.copy_(loaded_weight.T)
|
|
|
|
def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
|
|
if self.expert_map is None:
|
|
return expert_id
|
|
return self.expert_map[expert_id].item()
|
|
|
|
@overload
|
|
def weight_loader(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor, weight_name: str,
|
|
shard_id: str, expert_id: int,
|
|
return_success: Literal[False]) -> None:
|
|
...
|
|
|
|
@overload
|
|
def weight_loader(self, param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor, weight_name: str,
|
|
shard_id: str, expert_id: int,
|
|
return_success: Literal[True]) -> bool:
|
|
...
|
|
|
|
def weight_loader(self,
|
|
param: torch.nn.Parameter,
|
|
loaded_weight: torch.Tensor,
|
|
weight_name: str,
|
|
shard_id: str,
|
|
expert_id: int,
|
|
return_success: bool = False) -> Optional[bool]:
|
|
expert_id = self._map_global_expert_id_to_local_expert_id(expert_id)
|
|
if expert_id == -1:
|
|
# Failed to load this param since it's not local to this rank
|
|
return False if return_success else None
|
|
# Hereafter, `expert_id` is local physical id
|
|
|
|
quant_method_name = self.quant_method.__class__.__name__
|
|
# compressed-tensors checkpoints with packed weights are stored flipped
|
|
# TODO (mgoin): check self.quant_method.quant_config.quant_format
|
|
# against known CompressionFormat enum values that have this quality
|
|
if self.quant_method.__class__.__name__ in (
|
|
"CompressedTensorsWNA16MarlinMoEMethod",
|
|
"CompressedTensorsWNA16MoEMethod"):
|
|
loaded_weight = loaded_weight.t().contiguous()
|
|
|
|
if shard_id not in ("w1", "w2", "w3"):
|
|
raise ValueError(f"shard_id must be ['w1','w2','w3'] but "
|
|
f"got {shard_id}.")
|
|
|
|
WEIGHT_SCALE_SUPPORTED = [
|
|
e.value for e in FusedMoeWeightScaleSupported
|
|
]
|
|
# Fetch the dim to shard the parameter/loaded weight
|
|
# based on the shard id. This will be whatever
|
|
# dimension intermediate_size_per_partition is used.
|
|
SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
|
|
|
|
expert_data = param.data[expert_id]
|
|
|
|
is_gguf_weight = getattr(param, "is_gguf_weight", False)
|
|
is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
|
|
if is_gguf_weight_type:
|
|
param.weight_type = loaded_weight.item()
|
|
param.data.copy_(loaded_weight)
|
|
return True if return_success else None
|
|
|
|
# is_transposed: if the dim to shard the weight
|
|
# should be flipped. Required by GPTQ, compressed-tensors
|
|
# should be whatever dimension intermediate_size_per_partition is
|
|
is_transposed = getattr(param, "is_transposed", False) or self.use_nn_moe
|
|
shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
|
|
if is_transposed:
|
|
shard_dim = int(not shard_dim)
|
|
|
|
full_load = len(loaded_weight.shape) == 3
|
|
if full_load:
|
|
shard_dim += 1
|
|
|
|
# Materialize GGUF UninitializedParameter
|
|
if is_gguf_weight and isinstance(param, UninitializedParameter):
|
|
final_shape = list(loaded_weight.shape)
|
|
if shard_id in ["w1", "w3"]:
|
|
final_shape[1] *= 2
|
|
final_shape[shard_dim] = final_shape[shard_dim] // self.tp_size
|
|
param.materialize(final_shape, dtype=loaded_weight.dtype)
|
|
|
|
expert_data = param.data if full_load else param.data[expert_id]
|
|
|
|
# Case input scale: input_scale loading is only supported for fp8
|
|
if "input_scale" in weight_name:
|
|
# this is needed for compressed-tensors only
|
|
loaded_weight = loaded_weight.to(param.data.device)
|
|
|
|
if ("compressed" in quant_method_name.lower()
|
|
and param.data[expert_id] != 1
|
|
and (param.data[expert_id] - loaded_weight).abs() > 1e-5):
|
|
raise ValueError(
|
|
"input_scales of w1 and w3 of a layer "
|
|
f"must be equal. But got {param.data[expert_id]} "
|
|
f"vs. {loaded_weight}")
|
|
|
|
self._load_single_value(param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id)
|
|
return True if return_success else None
|
|
|
|
# Case g_idx
|
|
if "g_idx" in weight_name:
|
|
self._load_g_idx(shard_dim=0,
|
|
shard_id=shard_id,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank)
|
|
return True if return_success else None
|
|
|
|
# TODO @dsikka: ModelOpt should follow the proper MoE loading pattern
|
|
if "ModelOpt" in quant_method_name:
|
|
if ('weight_scale_2' in weight_name
|
|
or 'input_scale' in weight_name):
|
|
self._load_per_tensor_weight_scale(shard_id=shard_id,
|
|
param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id)
|
|
elif "weight" in weight_name:
|
|
self._load_model_weight_or_group_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank)
|
|
return True if return_success else None
|
|
|
|
# Case weight scales, zero_points and offset, weight/input global scales
|
|
if ("scale" in weight_name or "zero" in weight_name
|
|
or "offset" in weight_name):
|
|
# load the weight scales and zp based on the quantization scheme
|
|
# supported weight scales/zp can be found in
|
|
# FusedMoeWeightScaleSupported
|
|
# TODO @dsikka: once hardened, refactor to use vLLM Parameters
|
|
# specific to each case
|
|
quant_method = getattr(param, "quant_method", None)
|
|
if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
|
|
self._load_per_channel_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank)
|
|
elif quant_method in [
|
|
FusedMoeWeightScaleSupported.GROUP.value,
|
|
FusedMoeWeightScaleSupported.BLOCK.value,
|
|
]:
|
|
self._load_model_weight_or_group_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank,
|
|
load_full_w2=getattr(param, "load_full_w2", False))
|
|
elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
|
|
self._load_per_tensor_weight_scale(shard_id=shard_id,
|
|
param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id)
|
|
else:
|
|
raise ValueError(
|
|
f"quant method must be one of {WEIGHT_SCALE_SUPPORTED}")
|
|
return True if return_success else None
|
|
|
|
# Case weight_shape
|
|
if "weight_shape" in weight_name:
|
|
# only required by compressed-tensors
|
|
self._load_single_value(param=param,
|
|
loaded_weight=loaded_weight,
|
|
expert_id=expert_id)
|
|
return True if return_success else None
|
|
|
|
# Case model weights
|
|
if "weight" in weight_name:
|
|
self._load_model_weight_or_group_weight_scale(
|
|
shard_id=shard_id,
|
|
shard_dim=shard_dim,
|
|
loaded_weight=loaded_weight,
|
|
expert_data=expert_data,
|
|
tp_rank=self.tp_rank)
|
|
return True if return_success else None
|
|
|
|
return False if return_success else None
|
|
|
|
def get_expert_weights(self) -> Iterable[torch.Tensor]:
|
|
weights = list(self.named_parameters())
|
|
assert all(weight.is_contiguous() for _, weight in weights)
|
|
|
|
# Filter out the non-expert weights.
|
|
# `e_score_correction_bias` is a bias for each logical expert,
|
|
# with shape (num_logical_experts,), not an expert weight.
|
|
NON_EXPERT_WEIGHTS = {
|
|
"e_score_correction_bias",
|
|
}
|
|
|
|
return [
|
|
weight.view(self.local_num_experts, -1) for name, weight in weights
|
|
if name not in NON_EXPERT_WEIGHTS
|
|
]
|
|
|
|
def set_eplb_state(
|
|
self,
|
|
moe_layer_idx: int,
|
|
expert_load_view: torch.Tensor,
|
|
logical_to_physical_map: torch.Tensor,
|
|
logical_replica_count: torch.Tensor,
|
|
) -> None:
|
|
"""
|
|
Register the EPLB state in this layer.
|
|
|
|
This is used later in forward pass, where we get the expert mapping
|
|
and record the load metrics in `expert_load_view`.
|
|
"""
|
|
self.expert_load_view = expert_load_view[moe_layer_idx]
|
|
self.logical_to_physical_map = logical_to_physical_map[moe_layer_idx]
|
|
self.logical_replica_count = logical_replica_count[moe_layer_idx]
|
|
|
|
@staticmethod
|
|
def select_experts(
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
use_grouped_topk: bool,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
indices_type: Optional[torch.dtype] = None,
|
|
enable_eplb: bool = False,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
expert_load_view: Optional[torch.Tensor] = None,
|
|
logical_to_physical_map: Optional[torch.Tensor] = None,
|
|
logical_replica_count: Optional[torch.Tensor] = None,
|
|
routed_scaling_factor: Optional[float] = None,
|
|
use_fused_gate: Optional[bool] = False
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Route the input hidden states to the top-k experts based on the
|
|
router logits.
|
|
|
|
Returns:
|
|
(topk_weights, topk_ids) (tuple[torch.Tensor, torch.Tensor]):
|
|
The weights and *global physical* expert ids of the top-k experts.
|
|
|
|
**Compatibility**: When EPLB is not enabled, the returned ids are
|
|
equivalent to global logical ids, so should be compatible with
|
|
plain MoE implementations without redundant experts.
|
|
"""
|
|
from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
|
|
|
|
# DeepSeekv2 uses grouped_top_k
|
|
if use_grouped_topk:
|
|
assert topk_group is not None
|
|
assert num_expert_group is not None
|
|
if use_fused_gate:
|
|
if envs.VLLM_USE_LIGHT_OP:
|
|
topk_weights, topk_ids = op.moe_fused_gate(
|
|
router_logits,
|
|
e_score_correction_bias,
|
|
num_expert_group,
|
|
topk_group,
|
|
top_k,
|
|
0,
|
|
routed_scaling_factor,
|
|
)
|
|
else:
|
|
topk_weights, topk_ids = ops.moe_fused_gate(
|
|
router_logits,
|
|
e_score_correction_bias,
|
|
num_expert_group,
|
|
topk_group,
|
|
top_k,
|
|
routed_scaling_factor=routed_scaling_factor,
|
|
n_share_experts_fusion=0,
|
|
)
|
|
else:
|
|
topk_weights, topk_ids = grouped_topk(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias)
|
|
if indices_type is not None:
|
|
topk_ids = topk_ids.to(dtype=indices_type)
|
|
elif custom_routing_function is None:
|
|
topk_weights, topk_ids, token_expert_indices = fused_topk(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
indices_type=indices_type,
|
|
)
|
|
else:
|
|
topk_weights, topk_ids = custom_routing_function(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize)
|
|
if indices_type is not None:
|
|
topk_ids = topk_ids.to(dtype=indices_type)
|
|
|
|
if enable_eplb:
|
|
assert expert_load_view is not None
|
|
assert logical_to_physical_map is not None
|
|
assert logical_replica_count is not None
|
|
|
|
# 1. Convert the logical expert ids to physical expert ids
|
|
# Directly select a random replica for each logical expert
|
|
|
|
# TODO: maybe optimize this by using specified kernels,
|
|
# or compute pseudo-random indices by modulo
|
|
|
|
# In case `indices_type` is not `torch.long` or `torch.int`,
|
|
# e.g. `torch.uint32` as required by dispatch/combine kernels
|
|
topk_ids_long = topk_ids.long()
|
|
replica_indices = (
|
|
torch.rand_like(topk_ids, dtype=torch.float) *
|
|
logical_replica_count[topk_ids_long]).long().unsqueeze(-1)
|
|
physical_ids = logical_to_physical_map[topk_ids_long].gather(
|
|
-1, replica_indices).squeeze(-1)
|
|
|
|
topk_ids = physical_ids
|
|
|
|
# 2. Record expert load metrics.
|
|
|
|
# TODO(bowen): When using `FusedMoEModularKernel`, this
|
|
# can be done in a more unified way, since
|
|
# `FusedMoEPrepareAndFinalize` will return the expert
|
|
# token count, in some cases directly from the kernel.
|
|
# However, now there are many code paths not using
|
|
# the modular kernel, e.g. calling `fused_experts`,
|
|
# so we decide to keep the logic here.
|
|
#
|
|
# If later refactor moved all the MoE kernel calls
|
|
# to the modular kernel, we can move this logic there
|
|
# to achieve better efficiency.
|
|
|
|
# `expert_load_view`: (num_logical_experts,)
|
|
|
|
# Mask out non-local experts
|
|
if expert_map is not None:
|
|
topk_ids_local = expert_map[topk_ids]
|
|
topk_ids_flatten = topk_ids_local.flatten()
|
|
else:
|
|
topk_ids_flatten = topk_ids.flatten()
|
|
|
|
# Should be equivalent to:
|
|
# ```
|
|
# topk_ids_masked = topk_ids_local[topk_ids_local >= 0]
|
|
# expert_load_view += topk_ids_masked.bincount(
|
|
# minlength=expert_load_view.shape[0])
|
|
# ```
|
|
# We use `scatter_add_` since `bincount` cannot be compiled
|
|
|
|
# Performance optimization:
|
|
# `masked_fill` is significantly faster than `masked_select`
|
|
invalid_mask = topk_ids_flatten < 0
|
|
# Replace invalid expert ids with 0 (just a dummy position)
|
|
# to avoid out-of-bounds errors in scatter_add_
|
|
index = topk_ids_flatten.masked_fill_(invalid_mask, 0)
|
|
# `src` is the valid mask, which is 1 for valid and 0 for invalid
|
|
src = ~invalid_mask
|
|
|
|
expert_load_view.scatter_add_(dim=0,
|
|
index=index.long(),
|
|
src=src.to(expert_load_view))
|
|
|
|
topk_ids = topk_ids.to(dtype=indices_type)
|
|
|
|
assert topk_ids.dtype == indices_type or indices_type is None
|
|
|
|
return topk_weights, topk_ids
|
|
|
|
def must_reduce_shared_expert_outputs(self) -> bool:
|
|
"""
|
|
The shared_experts are typically computed using the RowParallelLinear
|
|
layer. The result of this function is typically used as
|
|
the reduce_results argument to the module.
|
|
When just tensor-parallel is used, it is not required to reduce
|
|
the shared_experts results immediately. Instead we reduce at the
|
|
once at the end of the MoE op. (Refer to DeepSeekV2MoE module)
|
|
With EP and all2all kernels - this is no longer viable as all
|
|
GPU ranks in DP, produce the complete set of hidden_states.
|
|
Therefore it is required that we reduce the shared_experts output
|
|
early.
|
|
"""
|
|
return (self.use_pplx_kernels or self.use_deepep_ht_kernels
|
|
or self.use_deepep_ll_kernels)
|
|
|
|
def maybe_all_reduce_tensor_model_parallel(
|
|
self, final_hidden_states: torch.Tensor):
|
|
"""
|
|
The pplx combine kernel reduces across GPU ranks by default.
|
|
"""
|
|
if (self.use_pplx_kernels or self.use_deepep_ht_kernels
|
|
or self.use_deepep_ll_kernels):
|
|
return final_hidden_states
|
|
else:
|
|
return tensor_model_parallel_all_reduce(final_hidden_states)
|
|
|
|
def forward(self, hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor):
|
|
# TODO: Once the OOM issue for the TPU backend is resolved, we will
|
|
# switch to using the moe_forward custom op.
|
|
if current_platform.is_tpu():
|
|
return self.forward_impl(hidden_states, router_logits)
|
|
else:
|
|
return torch.ops.vllm.moe_forward(hidden_states, router_logits,
|
|
self.layer_name)
|
|
|
|
def forward_impl_chunked(self, full_hidden_states: torch.Tensor,
|
|
full_router_logits: torch.Tensor):
|
|
assert self.batched_hidden_states is not None
|
|
assert self.batched_router_logits is not None
|
|
assert self.batched_hidden_states.dtype == full_hidden_states.dtype
|
|
assert self.batched_router_logits.dtype == full_router_logits.dtype
|
|
# Check size compatibility.
|
|
assert (
|
|
self.batched_hidden_states.size(-1) == full_hidden_states.size(-1))
|
|
assert (
|
|
self.batched_router_logits.size(-1) == full_router_logits.size(-1))
|
|
|
|
full_final_hidden_states = torch.empty_like(full_hidden_states)
|
|
|
|
def process_chunk(chunk_start, chunk_end, skip_result_store=False):
|
|
chunk_size = chunk_end - chunk_start
|
|
hidden_states = full_hidden_states[chunk_start:chunk_end, :]
|
|
router_logits = full_router_logits[chunk_start:chunk_end, :]
|
|
|
|
assert (self.batched_hidden_states.size(0) # type: ignore
|
|
>= chunk_size)
|
|
assert (self.batched_router_logits.size(0) # type: ignore
|
|
>= chunk_size)
|
|
staged_hidden_states = self.batched_hidden_states[:
|
|
chunk_size, :] # type: ignore
|
|
staged_router_logits = self.batched_router_logits[:
|
|
chunk_size, :] # type: ignore
|
|
staged_hidden_states.copy_(hidden_states, non_blocking=True)
|
|
staged_router_logits.copy_(router_logits, non_blocking=True)
|
|
|
|
# Matrix multiply.
|
|
final_hidden_states = self.quant_method.apply(
|
|
layer=self,
|
|
x=staged_hidden_states,
|
|
router_logits=staged_router_logits,
|
|
top_k=self.top_k,
|
|
renormalize=self.renormalize,
|
|
use_grouped_topk=self.use_grouped_topk,
|
|
global_num_experts=self.global_num_experts,
|
|
expert_map=self.expert_map,
|
|
topk_group=self.topk_group,
|
|
num_expert_group=self.num_expert_group,
|
|
custom_routing_function=self.custom_routing_function,
|
|
scoring_func=self.scoring_func,
|
|
e_score_correction_bias=self.e_score_correction_bias,
|
|
activation=self.activation,
|
|
enable_eplb=self.enable_eplb,
|
|
expert_load_view=self.expert_load_view,
|
|
logical_to_physical_map=self.logical_to_physical_map,
|
|
logical_replica_count=self.logical_replica_count,
|
|
)
|
|
|
|
if not skip_result_store:
|
|
full_final_hidden_states[chunk_start:chunk_end, :].copy_(
|
|
final_hidden_states, non_blocking=True)
|
|
|
|
ctx = get_forward_context()
|
|
max_tokens_across_dp = ctx.dp_metadata.max_tokens_across_dp_cpu
|
|
moe_dp_chunk_size_per_rank = self.moe_config.max_num_tokens
|
|
|
|
num_tokens = full_hidden_states.size(0)
|
|
for chunk_start_ in range(0, max_tokens_across_dp,
|
|
moe_dp_chunk_size_per_rank):
|
|
chunk_start = chunk_start_
|
|
chunk_end = min(chunk_start + moe_dp_chunk_size_per_rank,
|
|
max_tokens_across_dp)
|
|
# clamp start and end
|
|
chunk_start = min(chunk_start, num_tokens - 1)
|
|
chunk_end = min(chunk_end, num_tokens)
|
|
|
|
process_chunk(chunk_start,
|
|
chunk_end,
|
|
skip_result_store=chunk_start_ >= num_tokens)
|
|
|
|
return full_final_hidden_states
|
|
|
|
def forward_impl(self, hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor):
|
|
assert self.quant_method is not None
|
|
if (self.moe_parallel_config.use_pplx_kernels
|
|
or self.moe_parallel_config.use_deepep_ll_kernels):
|
|
return self.forward_impl_chunked(hidden_states, router_logits)
|
|
|
|
do_naive_dispatch_combine: bool = (
|
|
self.dp_size > 1
|
|
and not self.moe_parallel_config.use_deepep_ht_kernels)
|
|
if do_naive_dispatch_combine:
|
|
hidden_states, router_logits = get_ep_group().dispatch(
|
|
hidden_states, router_logits)
|
|
|
|
# Matrix multiply.
|
|
final_hidden_states = self.quant_method.apply(
|
|
layer=self,
|
|
x=hidden_states,
|
|
router_logits=router_logits,
|
|
top_k=self.top_k,
|
|
renormalize=self.renormalize,
|
|
use_grouped_topk=self.use_grouped_topk,
|
|
global_num_experts=self.global_num_experts,
|
|
expert_map=self.expert_map,
|
|
topk_group=self.topk_group,
|
|
num_expert_group=self.num_expert_group,
|
|
custom_routing_function=self.custom_routing_function,
|
|
scoring_func=self.scoring_func,
|
|
e_score_correction_bias=self.e_score_correction_bias,
|
|
activation=self.activation,
|
|
apply_router_weight_on_input=self.apply_router_weight_on_input,
|
|
enable_eplb=self.enable_eplb,
|
|
expert_load_view=self.expert_load_view,
|
|
logical_to_physical_map=self.logical_to_physical_map,
|
|
logical_replica_count=self.logical_replica_count,
|
|
use_nn_moe=self.use_nn_moe,
|
|
routed_scaling_factor=self.routed_scaling_factor,
|
|
use_fused_gate=self.use_fused_gate
|
|
)
|
|
|
|
if do_naive_dispatch_combine:
|
|
final_hidden_states = get_ep_group().combine(final_hidden_states)
|
|
|
|
if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
|
|
# Default set to False. (May have to add shared expert outputs.
|
|
if envs.VLLM_ENABLE_TBO:
|
|
final_hidden_states = self.tbo_all_reduce(final_hidden_states)
|
|
else:
|
|
final_hidden_states = self.maybe_all_reduce_tensor_model_parallel(
|
|
final_hidden_states)
|
|
|
|
return final_hidden_states
|
|
|
|
@classmethod
|
|
def make_expert_params_mapping(
|
|
cls,
|
|
ckpt_gate_proj_name: str,
|
|
ckpt_down_proj_name: str,
|
|
ckpt_up_proj_name: str,
|
|
num_experts: int,
|
|
num_redundant_experts: int = 0) -> list[tuple[str, str, int, str]]:
|
|
|
|
num_physical_experts = num_experts + num_redundant_experts
|
|
|
|
# In the returned mapping:
|
|
# - `expert_id` is the physical expert id
|
|
# - `weight_name` contains the weight name of the logical expert
|
|
# So that we should map the expert id to logical in `weight_name`
|
|
physical_to_logical_map = \
|
|
EplbState.build_initial_global_physical_to_logical_map(
|
|
num_experts, num_redundant_experts)
|
|
|
|
return [
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
("experts.w13_" if weight_name
|
|
in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
|
|
f"experts.{physical_to_logical_map[expert_id]}.{weight_name}.",
|
|
expert_id, shard_id) for expert_id in range(num_physical_experts)
|
|
for shard_id, weight_name in [
|
|
("w1", ckpt_gate_proj_name),
|
|
("w2", ckpt_down_proj_name),
|
|
("w3", ckpt_up_proj_name),
|
|
]
|
|
]
|
|
|
|
|
|
def extra_repr(self) -> str:
|
|
|
|
s = (
|
|
f"global_num_experts={self.global_num_experts}, "
|
|
f"local_num_experts={self.local_num_experts}, "
|
|
f"top_k={self.top_k}, "
|
|
f"intermediate_size_per_partition={self.intermediate_size_per_partition}, " # noqa: E501
|
|
f"tp_size={self.tp_size},\n"
|
|
f"ep_size={self.ep_size}, "
|
|
f"reduce_results={self.reduce_results}, "
|
|
f"renormalize={self.renormalize}, "
|
|
f"use_grouped_topk={self.use_grouped_topk}")
|
|
|
|
if self.use_grouped_topk:
|
|
s += f", num_expert_group={self.num_expert_group}, topk_group={self.topk_group}" # noqa: E501
|
|
|
|
s += f", scoring_func='{self.scoring_func}', activation='{self.activation}'" # noqa: E501
|
|
|
|
return s
|
|
|
|
|
|
def moe_forward(hidden_states: torch.Tensor, router_logits: torch.Tensor,
|
|
layer_name: str) -> torch.Tensor:
|
|
forward_context: ForwardContext = get_forward_context()
|
|
self = forward_context.no_compile_layers[layer_name]
|
|
assert self.quant_method is not None
|
|
|
|
return self.forward_impl(hidden_states, router_logits)
|
|
|
|
|
|
def moe_forward_fake(hidden_states: torch.Tensor, router_logits: torch.Tensor,
|
|
layer_name: str) -> torch.Tensor:
|
|
return torch.empty_like(hidden_states)
|
|
|
|
|
|
direct_register_custom_op(
|
|
op_name="moe_forward",
|
|
op_func=moe_forward,
|
|
mutates_args=["hidden_states"],
|
|
fake_impl=moe_forward_fake,
|
|
dispatch_key=current_platform.dispatch_key,
|
|
tags=(torch.Tag.needs_fixed_stride_order, ),
|
|
)
|