2ae0bad96d
`VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE` is only used together with
`VLLM_ASCEND_ENABLE_PREFETCH_MLP` which is useless totally. This PR
remove it.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
716 lines
27 KiB
Python
716 lines
27 KiB
Python
# 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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This file extends the functionality of linear operations by encapsulating custom
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communication groups and forward functions into classes (linear ops).
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Current class inheritance structure:
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CustomLinearOp
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├── CustomColumnParallelOp
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│ ├── MLPColumnParallelOp
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│ ├── SequenceColumnParallelOp
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└── CustomRowParallelOp
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│ ├── MLPRowParallelOp
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│ ├── OProjRowParallelOp
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| ├── Flashcomm2OProjRowParallelOp
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│ ├── MatmulAllreduceRowParallelOp
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│ └── SequenceRowParallelOp
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└── CustomReplicatedOp
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How to extend a new linear op? Taking column parallel op as an example:
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1. Inherit from CustomColumnParallelOp and create a new class MyColumnParallelOp
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2. [Optional] The default communication group is the TP group. If a custom communication group is needed, override the comm_group method
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3. Override the apply method according to requirements, which will replace the original linear.forward
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4. Add selection logic for MyColumnParallelOp in the get_column_parallel_op method, typically based on prefix and configuration judgments
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Row parallel op follows a similar approach - inherit from RowColumnParallelOp and register the new class in get_row_parallel_op.
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"""
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import re
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from functools import lru_cache
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from typing import Optional, Union
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import torch
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import torch.distributed as dist
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import torch.nn.functional as F
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import torch_npu
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from torch import nn
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from torch.distributed import ProcessGroup
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from torch.nn.parameter import Parameter
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from vllm.distributed import (split_tensor_along_last_dim,
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tensor_model_parallel_all_reduce,
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tensor_model_parallel_reduce_scatter)
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from vllm.distributed.parallel_state import get_tp_group
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from vllm.forward_context import get_forward_context
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from vllm_ascend import envs as envs_ascend
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.distributed.parallel_state import (get_flashcomm2_odp_group,
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get_flashcomm2_otp_group,
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get_mlp_tp_group,
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get_otp_group)
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from vllm_ascend.utils import (enable_sp, flashcomm2_enable,
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get_flashcomm2_reorgnized_batch_ids,
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matmul_allreduce_enable, mlp_tp_enable,
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oproj_tp_enable, shared_expert_dp_enabled)
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class CustomLinearOp:
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def __init__(self, layer):
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self.layer = layer
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self.bias = None
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self.skip_bias_add = None
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self.return_bias = None
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self.quant_method = None
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# Custom communication group, while determining weight sharding
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@property
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def comm_group(self):
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return get_tp_group()
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@property
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def tp_rank(self):
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return self.comm_group.rank_in_group
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@property
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def tp_size(self):
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return self.comm_group.world_size
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# Update the attributes required by apply(), obtaining them from the layer.
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# Call this after the layer completes its initialization, specifically at the end of layer.init().
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def update_attrs(self):
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if hasattr(self.layer, "bias"):
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self.bias = self.layer.bias
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self.skip_bias_add = self.layer.skip_bias_add
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self.return_bias = self.layer.return_bias
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self.quant_method = self.layer.quant_method
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self.prefix = self.layer.prefix
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def apply_impl(self, input_):
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raise NotImplementedError
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# Replace layer.forward to customize the layer computation process.
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def apply(self, input_):
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output, output_bias = self.apply_impl(input_)
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if not self.return_bias:
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return output
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return output, output_bias
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class CustomColumnParallelOp(CustomLinearOp):
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def __init__(self, layer):
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super().__init__(layer)
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self.gather_output = None
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def update_attrs(self):
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super().update_attrs()
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self.gather_output = self.layer.gather_output
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class CustomRowParallelOp(CustomLinearOp):
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def __init__(self, layer):
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super().__init__(layer)
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self.reduce_results = None
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self.input_is_parallel = None
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self.input_size_per_partition = None
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def update_attrs(self):
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super().update_attrs()
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self.input_is_parallel = self.layer.input_is_parallel
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self.reduce_results = self.layer.reduce_results
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self.input_size_per_partition = self.layer.input_size_per_partition
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def apply(self, input_):
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output, output_bias = self.apply_impl(input_)
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if envs_ascend.VLLM_ASCEND_ENABLE_PREFETCH_MLP:
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torch.ops.vllm.maybe_prefetch_mlp_gate_up_proj(output, self.prefix)
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if not self.return_bias:
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return output
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return output, output_bias
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class CustomReplicatedOp(CustomLinearOp):
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def apply_impl(self, input_):
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bias = self.bias if not self.skip_bias_add else None
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assert self.quant_method is not None
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output = self.quant_method.apply(self.layer, input_, bias)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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class MLPColumnParallelOp(CustomColumnParallelOp):
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def __init__(self, layer):
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super().__init__(layer)
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@property
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def comm_group(self):
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return get_mlp_tp_group()
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def apply_impl(
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self,
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input_: torch.Tensor,
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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bias = self.bias if not self.skip_bias_add else None
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# Matrix multiply.
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assert self.quant_method is not None
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input_parallel = self.comm_group.all_gather(input_, 0)
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output = self.quant_method.apply(self.layer, input_parallel, bias)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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class MLPRowParallelOp(CustomRowParallelOp):
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def __init__(self, layer):
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super().__init__(layer)
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@property
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def comm_group(self):
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return get_mlp_tp_group()
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def apply_impl(
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self, input_: torch.Tensor
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[self.tp_rank].contiguous()
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assert self.quant_method is not None
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bias_ = None if (self.tp_rank > 0
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or self.skip_bias_add) else self.layer.bias
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output_parallel = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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output = self.comm_group.reduce_scatter(output_parallel, 0)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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class OProjRowParallelOp(CustomRowParallelOp):
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def __init__(self, layer):
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super().__init__(layer)
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@property
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def comm_group(self):
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return get_otp_group()
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def apply_impl(
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self,
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input_: torch.Tensor,
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[self.tp_rank].contiguous()
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# Prepare tensors for all-to-all communication
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local_batch_size = input_parallel.size(0)
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chunk_size = self.input_size_per_partition
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total_batch_size = local_batch_size * self.tp_size
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# Reshape tensor for efficient cross-device transfer:
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# [batch, dim] -> [tp_size, batch, chunk] -> flattened
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send_buf = (input_parallel.reshape(-1,
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self.tp_size, chunk_size).transpose(
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0, 1).contiguous().view(-1))
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# Create receive buffer
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recv_buf = torch.empty(total_batch_size * chunk_size,
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dtype=input_parallel.dtype,
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device=input_parallel.device)
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# Perform all-to-all communication
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dist.all_to_all_single(recv_buf,
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send_buf,
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group=self.comm_group.device_group)
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input_parallel = recv_buf.view(total_batch_size, chunk_size)
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# Only fuse bias add for rank 0 to avoid duplicate bias addition in TP>1
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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assert self.quant_method is not None
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output_parallel = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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# otp-specific: Combine partial results across devices
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output = self.comm_group.reduce_scatter(output_parallel, dim=0)
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output = output.view(input_.shape[0], self.layer.output_size)
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# Handle bias return based on configuration
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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def update_attrs(self):
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super().update_attrs()
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self.input_is_parallel = self.layer.input_is_parallel
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self.input_size_per_partition = self.layer.input_size_per_partition
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class Flashcomm2OProjRowParallelOp(CustomRowParallelOp):
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def __init__(self, layer):
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super().__init__(layer)
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self.odp_group = get_flashcomm2_odp_group()
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self.odp_size = self.odp_group.world_size
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self.reorgnized_batch_ids = get_flashcomm2_reorgnized_batch_ids(
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get_tp_group().world_size)
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self.group_indices = torch.tensor(self.reorgnized_batch_ids).npu()
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self.layer._quant_comm_config = {}
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@property
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def comm_group(self):
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return get_flashcomm2_otp_group()
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@property
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def tp_rank(self):
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if get_ascend_config().flashcomm2_oproj_tensor_parallel_size == 1:
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return 0
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return self.comm_group.rank_in_group
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@property
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def tp_size(self):
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if get_ascend_config().flashcomm2_oproj_tensor_parallel_size == 1:
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return 1
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return self.comm_group.world_size
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def apply_impl(
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self,
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input_: torch.Tensor,
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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"""Linear layer for Flashcomm2.
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Input.ahspe = [batchsize*seqlength, headnum*headdim/TP]
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Output.shape = [(batchsize*seqlength+padsize)/TP, hiddensize]
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"""
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# Handle input parallelism - split or use as-is
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if self.input_is_parallel:
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input_parallel = input_
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else:
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tp_rank = self.tp_rank
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[tp_rank].contiguous()
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# padding for all-to-all
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forward_context = get_forward_context()
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num_padding_tokens = forward_context.pad_size
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if num_padding_tokens > 0:
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input_parallel = nn.functional.pad(input_parallel,
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(0, 0, 0, num_padding_tokens))
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def otp_maybe_quant_comm(x):
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# Reorganize the tensor so that the batch id and rank id correspond to each other.
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chunk_num = len(self.reorgnized_batch_ids) * len(
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self.reorgnized_batch_ids[0])
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batch_size = x.size(0)
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assert batch_size % chunk_num == 0, f"Batch_size({batch_size}) must be divisible by chunk_num({chunk_num})"
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batch_size_per_chunk = batch_size // chunk_num
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# Indices of reorganized tensor
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chunked = x.view(chunk_num, batch_size_per_chunk, x.shape[1])
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reorganized_chunks = chunked[self.group_indices]
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send_buf = reorganized_chunks.flatten(1, 2)
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# all-to-all operation parameters
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all2all_tp_size = self.odp_size
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local_intermediate_size = x.size(1)
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chunk_size = x.size(0) // all2all_tp_size
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total_intermediate_size = local_intermediate_size * all2all_tp_size
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# Create receive buffer
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recv_buf = torch.empty(total_intermediate_size * chunk_size,
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dtype=x.dtype,
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device=x.device)
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# Perform all-to-all communication
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dist.all_to_all_single(recv_buf,
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send_buf,
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group=self.odp_group.device_group)
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return recv_buf.view(all2all_tp_size, chunk_size,
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-1).transpose(0, 1).reshape(chunk_size, -1)
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if not hasattr(self, "_quant_comm_config"):
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self.layer._quant_comm_config = {}
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self.layer._quant_comm_config[
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"communication_fn"] = otp_maybe_quant_comm
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actual_quant_method = getattr(self.quant_method, 'quant_method',
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self.quant_method)
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from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
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if not isinstance(actual_quant_method, AscendW8A8LinearMethod):
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# Check if w8a8 quantization is enabled. If not, communicate immediately.
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input_parallel = otp_maybe_quant_comm(input_parallel)
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# Matrix multiply.
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assert self.quant_method is not None
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# Only fuse bias add into GEMM for rank 0 (this ensures that
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# bias will not get added more than once in TP>1 case)
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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output_parallel = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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# output_parallel shape: [bs/(TP/flashcomm2_otp_size), hiddenstate]
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if self.tp_size > 1:
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# flashcomm2 with reduce-scatter
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output = self.comm_group.reduce_scatter(output_parallel, dim=0)
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else:
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output = output_parallel
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if not forward_context.sp_enabled:
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# flashcomm1 not enabled
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output = get_tp_group().all_gather(output, 0)
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if num_padding_tokens > 0:
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output = output[:-num_padding_tokens]
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# Handle bias return based on configuration
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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def update_attrs(self):
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super().update_attrs()
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self.input_is_parallel = self.layer.input_is_parallel
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self.input_size_per_partition = self.layer.input_size_per_partition
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class MatmulAllreduceRowParallelOp(CustomRowParallelOp):
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_HCOMM_INFO = None
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def __init__(self, layer):
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super().__init__(layer)
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self.hcomm_info = self.get_hcomm_info(self.comm_group.device_group)
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def apply_impl(
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self, input_: torch.Tensor
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[self.tp_rank].contiguous()
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"""Calculate the output tensor of forward by considering
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fusing communication and computation."""
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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if self.reduce_results and self.tp_size > 1:
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output = torch_npu.npu_mm_all_reduce_base(input_parallel,
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self.weight_t,
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self.hcomm_info,
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bias=bias_)
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else:
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assert self.quant_method is not None
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output = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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@classmethod
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def get_hcomm_info(cls, group: ProcessGroup) -> str:
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"""Get the HCCL communication information for the given group."""
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if cls._HCOMM_INFO is not None:
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return cls._HCOMM_INFO
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rank = torch.distributed.get_rank(group)
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if torch.__version__ > "2.0":
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global_rank = torch.distributed.get_global_rank(group, rank)
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cls._HCOMM_INFO = group._get_backend(
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torch.device("npu")).get_hccl_comm_name(global_rank)
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else:
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cls._HCOMM_INFO = group.get_hccl_comm_name(rank)
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return cls._HCOMM_INFO
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def update_attrs(self):
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super().update_attrs()
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self.weight_t = self.layer.weight.t()
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class SequenceColumnParallelOp(CustomColumnParallelOp):
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def apply_impl(
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self, input_: torch.Tensor
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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"""Linear layer with column parallelism.
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Implemented multiple optimization projects for dense models, such as FlashComm and
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communication-computation fusion.
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"""
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bias = self.bias if not self.skip_bias_add else None
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# Matrix multiply.
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assert self.quant_method is not None
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input_ = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(input_, True)
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output_parallel = self.quant_method.apply(self.layer, input_, bias)
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if self.gather_output:
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# All-gather across the partitions.
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output = self.comm_group.all_gather(output_parallel)
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else:
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output = output_parallel
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
|
|
|
|
|
|
class SequenceRowParallelOp(CustomRowParallelOp):
|
|
|
|
def __init__(self, layer):
|
|
super().__init__(layer)
|
|
self.unique_prefix = None
|
|
|
|
def apply_impl(
|
|
self, input_: torch.Tensor
|
|
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
|
|
"""Linear layer with column parallelism.
|
|
|
|
Implemented multiple optimization projects for dense models, such as FlashComm and
|
|
communication-computation fusion.
|
|
"""
|
|
|
|
if self.input_is_parallel:
|
|
input_parallel = input_
|
|
else:
|
|
splitted_input = split_tensor_along_last_dim(
|
|
input_, num_partitions=self.tp_size)
|
|
input_parallel = splitted_input[self.tp_rank].contiguous()
|
|
|
|
assert self.quant_method is not None
|
|
bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
|
|
|
|
if self.tp_size == 1 or not self.reduce_results:
|
|
output = self.quant_method.apply(self.layer,
|
|
input_parallel,
|
|
bias=bias_)
|
|
else:
|
|
output = torch.ops.vllm.matmul_and_reduce(input_parallel,
|
|
self.unique_prefix)
|
|
|
|
output_bias = self.bias if self.skip_bias_add else None
|
|
return output, output_bias
|
|
|
|
def matmul_and_reduce(self, input_parallel: torch.Tensor,
|
|
bias_: Optional[Parameter]) -> torch.Tensor:
|
|
assert self.quant_method is not None
|
|
try:
|
|
forward_context = get_forward_context()
|
|
sp_enabled = forward_context.sp_enabled
|
|
mmrs_fusion = forward_context.mmrs_fusion
|
|
except AssertionError:
|
|
sp_enabled = False
|
|
mmrs_fusion = False
|
|
|
|
x = input_parallel
|
|
|
|
if not sp_enabled:
|
|
output_parallel = self.layer.quant_method.apply(self.layer,
|
|
x,
|
|
bias=bias_)
|
|
return tensor_model_parallel_all_reduce(output_parallel)
|
|
|
|
pad_size = forward_context.pad_size
|
|
if pad_size > 0:
|
|
x = F.pad(x, (0, 0, 0, pad_size))
|
|
|
|
world_size = self.layer.tp_size
|
|
comm_mode = "aiv"
|
|
hcom_name = get_tp_group().device_group._get_backend(
|
|
torch.device('npu')).get_hccl_comm_name(self.layer.tp_rank)
|
|
|
|
from vllm.model_executor.layers.linear import UnquantizedLinearMethod
|
|
|
|
from vllm_ascend.quantization.quant_config import AscendLinearMethod
|
|
from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
|
|
|
|
# For unquant
|
|
if mmrs_fusion and isinstance(self.layer.quant_method,
|
|
UnquantizedLinearMethod):
|
|
output = torch_npu.npu_mm_reduce_scatter_base(
|
|
x,
|
|
self.layer.weight.t(),
|
|
hcom_name,
|
|
world_size,
|
|
reduce_op="sum",
|
|
bias=None,
|
|
comm_turn=0,
|
|
comm_mode=comm_mode)
|
|
if bias_ is not None:
|
|
output.add_(bias_)
|
|
# For w8a8 quant
|
|
elif mmrs_fusion and (
|
|
isinstance(self.layer.quant_method, AscendLinearMethod)
|
|
and isinstance(self.layer.quant_method.quant_method,
|
|
AscendW8A8LinearMethod)):
|
|
if x.dtype != torch.int8:
|
|
x_quant = torch.ops.vllm.quantize(
|
|
x, self.layer.aclnn_input_scale,
|
|
self.layer.aclnn_input_scale_reciprocal,
|
|
self.layer.aclnn_input_offset)
|
|
else:
|
|
x_quant = x
|
|
quant_bias = self.layer.quant_bias
|
|
deq_scale = self.layer.deq_scale
|
|
output_dtype = torch.bfloat16
|
|
output = torch_npu.npu_mm_reduce_scatter_base(
|
|
x_quant,
|
|
self.layer.weight,
|
|
hcom_name,
|
|
world_size,
|
|
reduce_op="sum",
|
|
bias=None,
|
|
comm_turn=0,
|
|
x2_scale=deq_scale,
|
|
output_dtype=output_dtype,
|
|
comm_mode=comm_mode)
|
|
output = torch.add(
|
|
output,
|
|
torch.mul(quant_bias, deq_scale).to(self.layer.params_dtype))
|
|
else:
|
|
output_parallel = self.layer.quant_method.apply(self.layer,
|
|
x,
|
|
bias=bias_)
|
|
output = tensor_model_parallel_reduce_scatter(output_parallel, 0)
|
|
|
|
return output
|
|
|
|
def update_attrs(self):
|
|
super().update_attrs()
|
|
self.input_is_parallel = self.layer.input_is_parallel
|
|
self.reduce_results = self.layer.reduce_results
|
|
self.unique_prefix = self.layer.unique_prefix
|
|
|
|
|
|
def _get_column_parallel_op(
|
|
prefix, layer
|
|
) -> Optional[Union[MLPColumnParallelOp, SequenceColumnParallelOp]]:
|
|
if "gate_up_proj" in prefix and mlp_tp_enable(
|
|
) and not is_moe_layer(prefix):
|
|
return MLPColumnParallelOp(layer)
|
|
if enable_sp():
|
|
if "shared_expert" in prefix:
|
|
return None
|
|
sp_column_prefix = [
|
|
"gate_up_proj", # first MLP of most LLMs
|
|
"in_proj", # gated deltanet of Qwen3 Next
|
|
"qkv_proj", # qkv linear of most LLMs
|
|
"conv1d", # gated deltanet of Qwen3 Next
|
|
"query_key_value", # qkv linear of Bailing
|
|
]
|
|
for a_prefix in sp_column_prefix:
|
|
if a_prefix in prefix:
|
|
return SequenceColumnParallelOp(layer)
|
|
|
|
return None
|
|
|
|
|
|
def _get_row_parallel_op(
|
|
prefix, layer
|
|
) -> Optional[Union[MLPRowParallelOp, OProjRowParallelOp,
|
|
Flashcomm2OProjRowParallelOp, MatmulAllreduceRowParallelOp,
|
|
SequenceRowParallelOp]]:
|
|
if "down_proj" in prefix and mlp_tp_enable() and not is_moe_layer(prefix):
|
|
return MLPRowParallelOp(layer)
|
|
if "o_proj" in prefix and oproj_tp_enable():
|
|
return OProjRowParallelOp(layer)
|
|
if matmul_allreduce_enable():
|
|
return MatmulAllreduceRowParallelOp(layer)
|
|
if flashcomm2_enable():
|
|
if "o_proj" in prefix or "out_proj" in prefix:
|
|
return Flashcomm2OProjRowParallelOp(layer)
|
|
if enable_sp():
|
|
if "shared_expert" in prefix:
|
|
return None
|
|
sp_row_prefixes = [
|
|
"o_proj", # attn output linear of most LLMs
|
|
"out_proj", # attn output linear of Qwen3 Next
|
|
"down_proj", # second MLP of most LLMs
|
|
"attention.dense", # attn output linear of Bailing
|
|
]
|
|
for a_prefix in sp_row_prefixes:
|
|
if a_prefix in prefix:
|
|
return SequenceRowParallelOp(layer)
|
|
|
|
return None
|
|
|
|
|
|
def get_parallel_op(disable_tp, prefix, layer, direct):
|
|
if disable_tp or ("shared_experts" in prefix
|
|
and shared_expert_dp_enabled()):
|
|
return None, 0, 1
|
|
custom_op: Optional[Union[MLPColumnParallelOp, SequenceColumnParallelOp,
|
|
MLPRowParallelOp, OProjRowParallelOp,
|
|
Flashcomm2OProjRowParallelOp,
|
|
MatmulAllreduceRowParallelOp,
|
|
SequenceRowParallelOp]] = None
|
|
if direct == "row":
|
|
custom_op = _get_row_parallel_op(prefix, layer)
|
|
|
|
if direct == "column":
|
|
custom_op = _get_column_parallel_op(prefix, layer)
|
|
|
|
if custom_op is not None:
|
|
return custom_op, custom_op.tp_rank, custom_op.tp_size
|
|
|
|
return None, get_tp_group().rank_in_group, get_tp_group().world_size
|
|
|
|
|
|
def get_replicated_op(disable_tp, prefix,
|
|
layer) -> Optional[Union[CustomReplicatedOp]]:
|
|
if disable_tp:
|
|
return None
|
|
|
|
return CustomReplicatedOp(layer)
|
|
|
|
|
|
def is_moe_layer(prefix: str) -> bool:
|
|
|
|
@lru_cache(maxsize=1)
|
|
def get_moe_params():
|
|
from vllm.config import get_current_vllm_config
|
|
vllm_config = get_current_vllm_config()
|
|
config = vllm_config.model_config.hf_config
|
|
n_routed_experts = getattr(config, 'n_routed_experts', 0)
|
|
first_k_dense_replace = getattr(config, 'first_k_dense_replace',
|
|
float('inf'))
|
|
moe_layer_freq = getattr(config, 'moe_layer_freq', 1)
|
|
return n_routed_experts, first_k_dense_replace, moe_layer_freq
|
|
|
|
match = re.search(r'layers\.(\d+)\.', prefix)
|
|
if match is None:
|
|
return False
|
|
layer_idx = int(match.group(1))
|
|
|
|
n_routed_experts, first_k_dense_replace, moe_layer_freq = get_moe_params()
|
|
|
|
return (n_routed_experts is not None and layer_idx >= first_k_dense_replace
|
|
and layer_idx % moe_layer_freq == 0)
|