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[Chore] Clean up deepseek v2/v3 config copy (#28055)

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Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
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Isotr0py 2025-11-06 11:46:30 +08:00 committed by GitHub
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commit 43ecd0a900
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9 changed files with 15 additions and 222 deletions
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3
vllm/model_executor/models/deepseek.py
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@ -292,6 +292,7 @@ class DeepseekDecoderLayer(nn.Module):
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
moe_layer_freq = getattr(config, "moe_layer_freq", 1)
self.self_attn = DeepseekAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
@ -306,7 +307,7 @@ class DeepseekDecoderLayer(nn.Module):
if (
config.n_routed_experts is not None
and layer_idx >= config.first_k_dense_replace
and layer_idx % config.moe_layer_freq == 0
and layer_idx % moe_layer_freq == 0
):
self.mlp = DeepseekMoE(
config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"

3
vllm/model_executor/models/deepseek_v2.py
View File

@ -994,6 +994,7 @@ class DeepseekV2DecoderLayer(nn.Module):
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
moe_layer_freq = getattr(config, "moe_layer_freq", 1)
# DecoderLayers are created with `make_layers` which passes the prefix
# with the layer's index.
layer_idx = int(prefix.split(sep=".")[-1])
@ -1024,7 +1025,7 @@ class DeepseekV2DecoderLayer(nn.Module):
if (
config.n_routed_experts is not None
and layer_idx >= config.first_k_dense_replace
and layer_idx % config.moe_layer_freq == 0
and layer_idx % moe_layer_freq == 0
):
self.mlp = DeepseekV2MoE(
config=config,

3
vllm/model_executor/models/kimi_vl.py
View File

@ -50,7 +50,7 @@ from typing import Annotated, Any, Literal
import torch
from torch import nn
from transformers import BatchFeature
from transformers import BatchFeature, DeepseekV2Config
from transformers.activations import GELUActivation
from vllm.config import VllmConfig
@ -91,7 +91,6 @@ from vllm.multimodal.processing import (
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import KimiVLConfig, MoonViTConfig
from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekV2Config
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .utils import PPMissingLayer, is_pp_missing_parameter, maybe_prefix

10
vllm/transformers_utils/config.py
View File

@ -24,7 +24,7 @@ from huggingface_hub.utils import (
RepositoryNotFoundError,
RevisionNotFoundError,
)
from transformers import GenerationConfig, PretrainedConfig
from transformers import DeepseekV3Config, GenerationConfig, PretrainedConfig
from transformers.models.auto.image_processing_auto import get_image_processor_config
from transformers.models.auto.modeling_auto import (
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
@ -68,16 +68,18 @@ def _get_hf_token() -> str | None:
class LazyConfigDict(dict):
def __getitem__(self, key):
if isinstance(value := super().__getitem__(key), type):
return value
import vllm.transformers_utils.configs as configs
return getattr(configs, super().__getitem__(key))
return getattr(configs, value)
_CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
chatglm="ChatGLMConfig",
deepseek_vl_v2="DeepseekVLV2Config",
deepseek_v3="DeepseekV3Config",
deepseek_v32="DeepseekV3Config",
deepseek_v32=DeepseekV3Config,
flex_olmo="FlexOlmoConfig",
kimi_linear="KimiLinearConfig",
kimi_vl="KimiVLConfig",

2
vllm/transformers_utils/configs/__init__.py
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@ -8,7 +8,6 @@ Model configs may be defined in this directory for the following reasons:
"""
from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
from vllm.transformers_utils.configs.deepseek_v3 import DeepseekV3Config
from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config
from vllm.transformers_utils.configs.dotsocr import DotsOCRConfig
from vllm.transformers_utils.configs.eagle import EAGLEConfig
@ -43,7 +42,6 @@ from vllm.transformers_utils.configs.ultravox import UltravoxConfig
__all__ = [
"ChatGLMConfig",
"DeepseekVLV2Config",
"DeepseekV3Config",
"DotsOCRConfig",
"EAGLEConfig",
"FlexOlmoConfig",

100
vllm/transformers_utils/configs/deepseek_v3.py
View File

@ -1,100 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class DeepseekV3Config(PretrainedConfig):
model_type = "deepseek_v3"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=129280,
hidden_size=7168,
intermediate_size=18432,
moe_intermediate_size=2048,
num_hidden_layers=61,
num_nextn_predict_layers=1,
num_attention_heads=128,
num_key_value_heads=128,
n_shared_experts=1,
n_routed_experts=256,
ep_size=1,
routed_scaling_factor=2.5,
kv_lora_rank=512,
q_lora_rank=1536,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
topk_method="noaux_tc",
n_group=8,
topk_group=4,
num_experts_per_tok=8,
moe_layer_freq=1,
first_k_dense_replace=3,
norm_topk_prob=True,
scoring_func="sigmoid",
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=0,
eos_token_id=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_nextn_predict_layers = num_nextn_predict_layers
self.num_attention_heads = num_attention_heads
self.n_shared_experts = n_shared_experts
self.n_routed_experts = n_routed_experts
self.ep_size = ep_size
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.moe_layer_freq = moe_layer_freq
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.scoring_func = scoring_func
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)

102
vllm/transformers_utils/configs/deepseek_vl2.py
View File

@ -3,7 +3,7 @@
# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py#L115-L268
from transformers.configuration_utils import PretrainedConfig
from transformers import DeepseekV2Config, PretrainedConfig
class VisionEncoderConfig(PretrainedConfig):
@ -87,106 +87,6 @@ class MlpProjectorConfig(PretrainedConfig):
super().__init__(**kwargs)
class DeepseekV2Config(PretrainedConfig):
model_type = "deepseek_v2"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=102400,
hidden_size=4096,
intermediate_size=11008,
moe_intermediate_size=1407,
num_hidden_layers=30,
num_attention_heads=32,
num_key_value_heads=32,
n_shared_experts=None,
n_routed_experts=None,
ep_size=1,
routed_scaling_factor=1.0,
kv_lora_rank=512,
q_lora_rank=1536,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
topk_method="gready",
n_group=None,
topk_group=None,
num_experts_per_tok=None,
moe_layer_freq=1,
first_k_dense_replace=0,
norm_topk_prob=False,
scoring_func="softmax",
aux_loss_alpha=0.001,
seq_aux=True,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=100000,
eos_token_id=100001,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
use_mla=True,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_shared_experts = n_shared_experts
self.n_routed_experts = n_routed_experts
self.ep_size = ep_size
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.moe_layer_freq = moe_layer_freq
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.scoring_func = scoring_func
self.aux_loss_alpha = aux_loss_alpha
self.seq_aux = seq_aux
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = float(rms_norm_eps)
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.use_mla = use_mla
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class DeepseekVLV2Config(PretrainedConfig):
model_type = "deepseek_vl_v2"
vision_config: VisionEncoderConfig

12
vllm/transformers_utils/configs/eagle.py
View File

@ -3,9 +3,7 @@
import os
from transformers import AutoConfig, PretrainedConfig
from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekV2Config
from transformers import AutoConfig, DeepseekV2Config, PretrainedConfig
class EAGLEConfig(PretrainedConfig):
@ -20,13 +18,7 @@ class EAGLEConfig(PretrainedConfig):
):
model_config: PretrainedConfig | DeepseekV2Config | None
if isinstance(model, dict):
archs = model.get("architectures", [])
target_archs = ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]
if any(target_arch in archs for target_arch in target_archs):
# AutoConfig does not support DeepSeek MoE models yet
model_config = DeepseekV2Config(**model)
else:
model_config = AutoConfig.for_model(**model)
model_config = AutoConfig.for_model(**model)
else:
model_config = model

2
vllm/transformers_utils/configs/kimi_vl.py
View File

@ -2,9 +2,9 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/configuration_kimi_vl.py
from transformers import DeepseekV2Config
from transformers.configuration_utils import PretrainedConfig
from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekV2Config
from vllm.transformers_utils.configs.moonvit import MoonViTConfig