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[gpt-oss] Add gpt-oss bf16 support

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wangjing
2025-08-13 21:25:57 +08:00
parent 5d2e7edf78
commit 17ea2ec6aa
1232 changed files with 777 additions and 36 deletions
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vllm/transformers_utils/configs/deepseek_vl2.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# 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
class VisionEncoderConfig(PretrainedConfig):
model_type: str = "vision"
model_name: str = "vit_so400m_patch14_siglip_384.webli"
image_size: int = 384
patch_size: int = 16
width: int = 1024
layers: int = 24
heads: int = 16
mlp_ratio: int = 4
global_pool: str = "map"
ignore_head: bool = True
class_token: bool = False
num_classes: int = 0
use_checkpoint: bool = False
weight_init: str = "skip"
deterministic: bool = False
num_recomputing_layers: int = 0
def __init__(self,
model_name: str = "vit_so400m_patch14_siglip_384.webli",
image_size: int = 384,
patch_size: int = 16,
width: int = 1024,
layers: int = 24,
heads: int = 16,
mlp_ratio: int = 4,
global_pool: str = "map",
ignore_head: bool = True,
class_token: bool = False,
num_classes: int = 0,
use_checkpoint: bool = False,
**kwargs):
self.model_name = model_name
self.image_size = image_size
self.patch_size = patch_size
self.width = width
self.layers = layers
self.heads = heads
self.mlp_ratio = mlp_ratio
self.global_pool = global_pool
self.ignore_head = ignore_head
self.class_token = class_token
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
super().__init__(**kwargs)
class MlpProjectorConfig(PretrainedConfig):
model_type = "mlp_projector"
projector_type: str = "downsample_mlp_gelu"
input_dim: int = 1152
n_embed: int = 2048
depth: int = 2
mlp_ratio: int = 1
downsample_ratio: int = 2
token_pooling: bool = False
def __init__(self,
projector_type: str = "downsample_mlp_gelu",
input_dim: int = 1152,
n_embed: int = 2048,
depth: int = 2,
mlp_ratio: int = 1,
downsample_ratio: int = 2,
**kwargs):
self.projector_type = projector_type
self.input_dim = input_dim
self.n_embed = n_embed
self.depth = depth
self.mlp_ratio = mlp_ratio
self.downsample_ratio = downsample_ratio
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
projector_config: MlpProjectorConfig
tile_tag: str = "2D"
global_view_pos: str = "head"
candidate_resolutions: tuple[tuple[int, int]] = ((384, 384), )
def __init__(self,
tile_tag: str = "tile_tag",
global_view_pos: str = "head",
candidate_resolutions: tuple[tuple[int,
int]] = ((384, 384), ),
**kwargs):
super().__init__(**kwargs)
vision_config = kwargs.get("vision_config", {})
self.vision_config = VisionEncoderConfig(**vision_config)
projector_config = kwargs.get("projector_config", {})
self.projector_config = MlpProjectorConfig(**projector_config)
language_config = kwargs.get("language_config", {})
self.text_config = DeepseekV2Config(**language_config)
self.tile_tag = tile_tag
self.global_view_pos = global_view_pos
self.candidate_resolutions = candidate_resolutions
self.vocab_size = self.text_config.vocab_size