d07d8a4535
### What this PR does / why we need it?
Add LongCat-Flash support.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed
- vLLM version: v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: chuyuelin <923822139@qq.com>
Co-authored-by: chuyuelin <chuyuelin1@huawei.com>
482 lines
21 KiB
Python
482 lines
21 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Any, Callable, Optional
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import torch
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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, get_tp_group,
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tensor_model_parallel_all_reduce)
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from vllm.forward_context import get_forward_context
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from vllm.logger import logger
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from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, UnquantizedFusedMoEMethod, get_compressed_expert_map)
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from vllm.model_executor.layers.fused_moe.shared_fused_moe import \
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SharedFusedMoE
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.ascend_forward_context import MoECommType
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from vllm_ascend.distributed.parallel_state import get_mc2_group
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from vllm_ascend.eplb.core.eplb_utils import init_eplb_config
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from vllm_ascend.eplb.utils import moe_load_async_stream
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from vllm_ascend.flash_common3_context import (get_flash_common3_context,
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set_flash_common3_context)
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from vllm_ascend.ops.fused_moe.experts_selector import (select_experts,
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zero_experts_compute)
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from vllm_ascend.ops.fused_moe.moe_comm_method import (AllGatherCommImpl,
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FusedExpertsResult,
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setup_moe_comm_method)
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from vllm_ascend.ops.fused_moe.prepare_finalize import QuantType
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from vllm_ascend.quantization.w4a8_dynamic import \
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AscendW4A8DynamicFusedMoEMethod
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from vllm_ascend.quantization.w8a8_dynamic import \
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AscendW8A8DynamicFusedMoEMethod
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from vllm_ascend.utils import (AscendDeviceType, enable_sp,
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get_ascend_device_type, maybe_trans_nz,
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npu_stream_switch, shared_expert_dp_enabled,
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shared_experts_calculation_stream)
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class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
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def __init__(self, moe: FusedMoEConfig = None):
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super().__init__(moe=moe)
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self.dynamic_eplb = get_ascend_config().dynamic_eplb
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def process_weights_after_loading(self, layer):
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super(UnquantizedFusedMoEMethod,
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self).process_weights_after_loading(layer)
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w13_data = self._maybe_pad_weight(layer.w13_weight.data).transpose(
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1, 2).contiguous()
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layer.w13_weight = torch.nn.Parameter(w13_data, requires_grad=False)
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w2_data = self._maybe_pad_weight(layer.w2_weight.data).transpose(
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1, 2).contiguous()
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layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
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if get_ascend_device_type() != AscendDeviceType._310P:
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layer.w13_weight.data = maybe_trans_nz(layer.w13_weight.data)
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layer.w2_weight.data = maybe_trans_nz(layer.w2_weight.data)
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def apply(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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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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routed_scaling_factor: float = 1.0,
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e_score_correction_bias: Optional[torch.Tensor] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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enable_force_load_balance: bool = False,
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shared_experts: Optional[Any] = None,
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**kwargs) -> torch.Tensor:
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zero_expert_num = getattr(layer, "zero_expert_num", 0)
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zero_expert_type = getattr(layer, "zero_expert_type", None)
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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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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routed_scaling_factor=routed_scaling_factor,
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e_score_correction_bias=e_score_correction_bias,
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global_num_experts=global_num_experts)
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if zero_expert_num > 0 and zero_expert_type is not None:
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topk_ids, topk_weights, zero_expert_result = zero_experts_compute(
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expert_indices=topk_ids,
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expert_scales=topk_weights,
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num_experts=global_num_experts,
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zero_expert_type=zero_expert_type,
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hidden_states=x,
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)
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topk_weights = topk_weights.to(x.dtype)
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# this is a naive implementation for experts load balance so as
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# to avoid accumulating too much tokens on a single rank.
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# currently it is only activated when doing profile runs.
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if enable_force_load_balance:
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random_matrix = torch.rand(topk_ids.size(0),
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global_num_experts,
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device=topk_ids.device)
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topk_ids = torch.argsort(
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random_matrix, dim=1)[:, :topk_ids.size(1)].to(topk_ids.dtype)
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moe_comm_method = get_forward_context().moe_comm_method
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final_hidden_states = moe_comm_method.fused_experts(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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global_num_experts=global_num_experts,
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expert_map=expert_map,
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shared_experts=shared_experts,
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apply_router_weight_on_input=apply_router_weight_on_input,
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dynamic_eplb=self.dynamic_eplb,
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mc2_mask=kwargs.get("mc2_mask", None))
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if zero_expert_num > 0 and zero_expert_type is not None:
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final_hidden_states += zero_expert_result
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return final_hidden_states
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class AscendFusedMoE(FusedMoE):
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moe_counter = -1
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gate_stream: Optional[torch.npu.Stream] = None
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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num_experts = kwargs["num_experts"]
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intermediate_size = kwargs["intermediate_size"]
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AscendFusedMoE.moe_counter += 1
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self.moe_instance_id = AscendFusedMoE.moe_counter
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self._expert_map = None
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self.log2phy = None
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if self.quant_config is None:
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self.quant_method = AscendUnquantizedFusedMoEMethod(
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self.moe_config)
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else:
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self.quant_method = self.quant_config.get_quant_method(
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self, self.layer_name)
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assert self.quant_method is not None
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self.moe_config.tp_group = get_tp_group()
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self.moe_config.dp_group = get_dp_group()
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self.moe_config.ep_group = get_ep_group()
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self.moe_config.mc2_group = get_mc2_group()
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self.moe_config.supports_eplb = self.quant_method.supports_eplb
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ascend_config = get_ascend_config()
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# flashcommon3 gate stream
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self.multistream_overlap_gate = ascend_config.multistream_overlap_gate
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if self.multistream_overlap_gate and AscendFusedMoE.gate_stream is None:
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AscendFusedMoE.gate_stream = torch.npu.Stream()
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if self.custom_routing_function is None and self.e_score_correction_bias is not None:
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vllm_config = get_current_vllm_config()
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self.e_score_correction_bias.data = self.e_score_correction_bias.data.to(
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dtype=vllm_config.model_config.dtype)
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# init moe
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self._expert_map, self.log2phy, self.global_redundant_expert_num = init_eplb_config(
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ascend_config, self.moe_instance_id, self.moe_config)
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self.global_num_experts = num_experts + self.global_redundant_expert_num
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self.dynamic_eplb = (ascend_config.dynamic_eplb
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or ascend_config.expert_map_record_path) and (
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self.log2phy is not None)
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self.local_num_experts = (torch.sum(
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self._expert_map != -1).item() if self._expert_map is not None else
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self.global_num_experts)
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if self._expert_map is not None:
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logger.info_once(
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"[EP Rank %s/%s] Expert parallelism is enabled. Local/global"
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" number of experts: %s/%s. Experts local to global index map:"
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" %s.", self.ep_rank, self.ep_size, self.local_num_experts,
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self.global_num_experts,
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get_compressed_expert_map(self._expert_map))
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if self.dynamic_eplb:
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self.moe_load = torch.zeros(self.local_num_experts,
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dtype=torch.int64).npu()
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self.moe_config.num_experts = self.global_num_experts
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self.moe_config.num_local_experts = self.local_num_experts
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self.moe_config.original_num_experts = num_experts
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moe_quant_params = {
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"num_experts": self.local_num_experts,
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"hidden_size": self.hidden_size,
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"intermediate_size_per_partition":
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self.intermediate_size_per_partition,
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"params_dtype": self.params_dtype,
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"weight_loader": self.weight_loader,
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}
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# need full intermediate size pre-sharding for WNA16 act order
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if (self.quant_method.__class__.__name__
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in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
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moe_quant_params["intermediate_size_full"] = intermediate_size
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self.quant_method.create_weights(layer=self, **moe_quant_params)
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self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
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setup_moe_comm_method(self.moe_config)
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self.quant_type = self._get_quant_type()
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def _get_quant_type(self) -> QuantType:
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quant_method = self.quant_method
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if not hasattr(quant_method,
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"quant_method") or quant_method.quant_method is None:
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return QuantType.NONE
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method = quant_method.quant_method
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if isinstance(method, AscendW8A8DynamicFusedMoEMethod):
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return QuantType.W8A8
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elif isinstance(method, AscendW4A8DynamicFusedMoEMethod):
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return QuantType.W4A8
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else:
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return QuantType.NONE
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def update_expert_map(self, new_expert_map):
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self._expert_map = new_expert_map
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def get_log2phy_map(self):
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return self.log2phy
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def clear_moe_load(self):
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if self.moe_load is not None:
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self.moe_load.zero_()
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def maybe_all_reduce_tensor_model_parallel(
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self, final_hidden_states: torch.Tensor):
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"""NOTE(Yizhou): This is to override the parent class method. In `mc2commimpl`,
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and `alltoallcommimpl`, we do not need to all-reduce the final outputs since
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the outputs are already aggregated across tensor parallel ranks in the
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`finalize` function. In `allgathercommimpl`, we still need to all-reduce the
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outputs since each rank only has partial outputs.
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"""
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return torch.ops.vllm.maybe_all_reduce_tensor_model_parallel(
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final_hidden_states)
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def forward_impl(self, hidden_states: torch.Tensor,
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router_logits: torch.Tensor):
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assert self.quant_method is not None
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# For w8a8 dynamic we can do npu_dynamic_quant and gate in parallel.
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quantized_x_for_share, dynamic_scale_for_share = None, None
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forward_context = get_forward_context()
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# Load balancing for token distribution among experts in dummy_run
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# TODO: The community only considers load balancing when DP > 1.
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# This approach may overlook some extreme scenarios.
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enable_force_load_balance = forward_context.in_profile_run
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forward_context = get_forward_context()
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if self.multistream_overlap_gate:
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assert AscendFusedMoE.gate_stream is not None
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fc3_context = get_flash_common3_context()
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assert fc3_context is not None
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AscendFusedMoE.gate_stream.wait_stream(torch.npu.current_stream())
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with npu_stream_switch(AscendFusedMoE.gate_stream,
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enabled=self.multistream_overlap_gate):
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# share_expert
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assert fc3_context.shared_experts is not None
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shared_out = fc3_context.shared_experts(hidden_states)
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# NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel`
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moe_comm_type = forward_context.moe_comm_type
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if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2, MoECommType.FUSED_MC2} \
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and not shared_expert_dp_enabled():
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shared_out = tensor_model_parallel_all_reduce(shared_out)
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set_flash_common3_context(shared_out=shared_out)
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topk_weights, topk_ids = select_experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=self.top_k,
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use_grouped_topk=self.use_grouped_topk,
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renormalize=self.renormalize,
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topk_group=self.topk_group,
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num_expert_group=self.num_expert_group,
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custom_routing_function=self.custom_routing_function,
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scoring_func=self.scoring_func,
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routed_scaling_factor=self.routed_scaling_factor,
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e_score_correction_bias=self.e_score_correction_bias,
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global_num_experts=self.global_num_experts)
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if isinstance(forward_context.moe_comm_method,
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AllGatherCommImpl):
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topk_weights = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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topk_weights, True, True)
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topk_ids = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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topk_ids, True, True)
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set_flash_common3_context(topk_weights=topk_weights,
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topk_ids=topk_ids)
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hidden_states, router_logits, mc2_mask, context_metadata = forward_context.moe_comm_method.prepare(
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hidden_states=hidden_states,
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router_logits=router_logits,
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replace_allreduce=forward_context.sp_enabled,
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enable_shared_expert_dp=self.enable_shared_expert_dp,
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quant_type=self.quant_type)
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# Make sure the default stream waits for the gate stream to finish.
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if self.multistream_overlap_gate:
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torch.npu.current_stream().wait_stream(AscendFusedMoE.gate_stream)
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if isinstance(hidden_states, tuple):
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hidden_states, pertoken_scale = hidden_states
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else:
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pertoken_scale = None
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# Matrix multiply.
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fused_experts_results: FusedExpertsResult = self.quant_method.apply(
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layer=self,
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x=hidden_states,
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router_logits=router_logits,
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pertoken_scale=pertoken_scale,
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top_k=self.top_k,
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renormalize=self.renormalize,
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use_grouped_topk=self.use_grouped_topk,
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global_num_experts=self.global_num_experts,
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expert_map=self._expert_map,
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topk_group=self.topk_group,
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num_expert_group=self.num_expert_group,
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custom_routing_function=self.custom_routing_function,
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scoring_func=self.scoring_func,
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routed_scaling_factor=self.routed_scaling_factor,
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e_score_correction_bias=self.e_score_correction_bias,
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activation=self.activation,
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apply_router_weight_on_input=self.apply_router_weight_on_input,
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quantized_x_for_share=quantized_x_for_share,
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dynamic_scale_for_share=dynamic_scale_for_share,
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shared_experts=None,
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enable_force_load_balance=enable_force_load_balance,
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log2phy=self.log2phy,
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global_redundant_expert_num=self.global_redundant_expert_num,
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mc2_mask=mc2_mask)
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if self.dynamic_eplb:
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expert_tokens = fused_experts_results.expert_tokens
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group_list_type = fused_experts_results.group_list_type
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assert expert_tokens is not None and group_list_type is not None, \
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"expert_tokens and group_list_type should not be None when dynamic_eplb is enabled."
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moe_load_stream = moe_load_async_stream()
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cur_stream = torch.npu.current_stream()
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moe_load_stream.wait_stream(cur_stream)
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with npu_stream_switch(moe_load_stream):
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self.moe_load += expert_tokens if group_list_type == 1 else \
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torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
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cur_stream.wait_stream(moe_load_stream)
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routed_out = forward_context.moe_comm_method.finalize(
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hidden_states=fused_experts_results.routed_out,
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reduce_results=self.reduce_results,
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context_metadata=context_metadata)
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return routed_out
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class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
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def __init__(
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self,
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shared_experts: torch.nn.Module,
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gate: Optional[torch.nn.Module] = None,
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use_overlapped: bool = True,
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**kwargs,
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):
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AscendFusedMoE.__init__(self, **kwargs)
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self._shared_experts = shared_experts
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self.use_overlapped = use_overlapped
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self.shared_expert_stream = None
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ascend_config = get_ascend_config()
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self.multistream_overlap_shared_expert = ascend_config.multistream_overlap_shared_expert
|
|
self.multistream_overlap_gate = ascend_config.multistream_overlap_gate
|
|
if enable_sp():
|
|
logger.info_once(
|
|
"Sequence parallelism is enabled, shared experts are replicated for best performance."
|
|
)
|
|
|
|
self._gate = gate
|
|
|
|
@property
|
|
def gate(self) -> Optional[torch.nn.Module]:
|
|
return self._gate if self.use_overlapped else None
|
|
|
|
@property
|
|
def is_internal_router(self) -> bool:
|
|
return False
|
|
|
|
@property
|
|
def use_dp_chunking(self) -> bool:
|
|
"""This func routes to the chunked forward path using the FlashInfer Cutlass kernel
|
|
only when data parallelism (DP) is enabled. Thus just returning False in vllm-ascend
|
|
"""
|
|
return False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
shared_out, fused_out = AscendFusedMoE.forward(
|
|
self,
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
)
|
|
return shared_out, fused_out
|
|
|
|
def forward_impl(self, hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor):
|
|
shared_out = None
|
|
if not self.multistream_overlap_gate:
|
|
# Make sure the shared experts stream begins after hidden_states are ready.
|
|
if self.multistream_overlap_shared_expert:
|
|
shared_experts_calculation_stream(
|
|
).wait_stream( # type: ignore
|
|
torch.npu.current_stream())
|
|
with npu_stream_switch(
|
|
shared_experts_calculation_stream(),
|
|
enabled=self.multistream_overlap_shared_expert):
|
|
# Use a separate stream to run shared experts.
|
|
shared_out = self._shared_experts(hidden_states)
|
|
else:
|
|
set_flash_common3_context(shared_experts=self._shared_experts)
|
|
|
|
routed_out = AscendFusedMoE.forward_impl(
|
|
self,
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
)
|
|
|
|
if not self.multistream_overlap_gate:
|
|
# Make sure the default stream waits for the shared experts stream to finish.
|
|
if self.multistream_overlap_shared_expert:
|
|
torch.npu.current_stream().wait_stream(
|
|
shared_experts_calculation_stream())
|
|
|
|
# NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel`
|
|
forward_context = get_forward_context()
|
|
moe_comm_type = forward_context.moe_comm_type
|
|
if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2, MoECommType.FUSED_MC2} \
|
|
and not shared_expert_dp_enabled():
|
|
shared_out = tensor_model_parallel_all_reduce(shared_out)
|
|
else:
|
|
fc3_context = get_flash_common3_context()
|
|
assert fc3_context is not None
|
|
shared_out = fc3_context.shared_out
|
|
|
|
return shared_out, routed_out
|