206 lines
7.5 KiB
Python
206 lines
7.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2025 The Qwen Team and The HuggingFace Inc. team.
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# All rights reserved.
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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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"""Qwen3.5-MoE model configuration"""
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from transformers.configuration_utils import PretrainedConfig, layer_type_validation
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class Qwen3_5MoeTextConfig(PretrainedConfig):
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model_type = "qwen3_5_moe_text"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.experts.gate_up_proj": "packed_colwise",
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"layers.*.mlp.experts.down_proj": "rowwise",
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"layers.*.mlp.shared_expert.gate_proj": "colwise",
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"layers.*.mlp.shared_expert.up_proj": "colwise",
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"layers.*.mlp.shared_expert.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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base_config_key = "text_config"
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def __init__(
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self,
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vocab_size=248320,
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hidden_size=2048,
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num_hidden_layers=40,
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num_attention_heads=16,
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num_key_value_heads=2,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_parameters=None,
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attention_bias=False,
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attention_dropout=0.0,
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head_dim=256,
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linear_conv_kernel_dim=4,
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linear_key_head_dim=128,
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linear_value_head_dim=128,
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linear_num_key_heads=16,
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linear_num_value_heads=32,
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moe_intermediate_size=512,
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shared_expert_intermediate_size=512,
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num_experts_per_tok=8,
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num_experts=256,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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layer_types=None,
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pad_token_id=None,
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bos_token_id=None,
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eos_token_id=None,
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**kwargs,
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):
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kwargs["ignore_keys_at_rope_validation"] = {
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"mrope_section",
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"mrope_interleaved",
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}
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.head_dim = head_dim
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self.rope_parameters = rope_parameters
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kwargs.setdefault("partial_rotary_factor", 0.25)
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self.layer_types = layer_types
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if self.layer_types is None:
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interval_pattern = kwargs.get("full_attention_interval", 4)
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self.layer_types = [
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"linear_attention"
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if bool((i + 1) % interval_pattern)
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else "full_attention"
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for i in range(self.num_hidden_layers)
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]
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layer_type_validation(self.layer_types, self.num_hidden_layers)
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# linear attention part
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self.linear_conv_kernel_dim = linear_conv_kernel_dim
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self.linear_key_head_dim = linear_key_head_dim
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self.linear_value_head_dim = linear_value_head_dim
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self.linear_num_key_heads = linear_num_key_heads
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self.linear_num_value_heads = linear_num_value_heads
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self.moe_intermediate_size = moe_intermediate_size
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self.shared_expert_intermediate_size = shared_expert_intermediate_size
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self.num_experts_per_tok = num_experts_per_tok
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self.num_experts = num_experts
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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super().__init__(**kwargs)
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# Set these AFTER super().__init__() because transformers v4's
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# PretrainedConfig.__init__ has these as explicit params with different
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# defaults (e.g. tie_word_embeddings=True) that would overwrite our values.
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.tie_word_embeddings = tie_word_embeddings
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class Qwen3_5MoeVisionConfig(PretrainedConfig):
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model_type = "qwen3_5_moe"
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base_config_key = "vision_config"
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def __init__(
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self,
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depth=27,
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hidden_size=1152,
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hidden_act="gelu_pytorch_tanh",
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intermediate_size=4304,
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num_heads=16,
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in_channels=3,
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patch_size=16,
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spatial_merge_size=2,
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temporal_patch_size=2,
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out_hidden_size=3584,
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num_position_embeddings=2304,
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initializer_range=0.02,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.depth = depth
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self.hidden_size = hidden_size
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.num_heads = num_heads
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self.in_channels = in_channels
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self.patch_size = patch_size
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self.spatial_merge_size = spatial_merge_size
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self.temporal_patch_size = temporal_patch_size
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self.out_hidden_size = out_hidden_size
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self.num_position_embeddings = num_position_embeddings
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self.initializer_range = initializer_range
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class Qwen3_5MoeConfig(PretrainedConfig):
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model_type = "qwen3_5_moe"
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sub_configs = {
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"vision_config": Qwen3_5MoeVisionConfig,
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"text_config": Qwen3_5MoeTextConfig,
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}
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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text_config=None,
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vision_config=None,
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image_token_id=248056,
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video_token_id=248057,
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vision_start_token_id=248053,
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vision_end_token_id=248054,
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tie_word_embeddings=False,
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**kwargs,
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):
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if isinstance(vision_config, dict):
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self.vision_config = self.sub_configs["vision_config"](**vision_config)
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elif vision_config is None:
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self.vision_config = self.sub_configs["vision_config"]()
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if isinstance(text_config, dict):
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self.text_config = self.sub_configs["text_config"](**text_config)
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elif text_config is None:
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self.text_config = self.sub_configs["text_config"]()
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self.image_token_id = image_token_id
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self.video_token_id = video_token_id
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self.vision_start_token_id = vision_start_token_id
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self.vision_end_token_id = vision_end_token_id
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super().__init__(**kwargs)
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# Set after super().__init__() to avoid v4 PretrainedConfig overwrite
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self.tie_word_embeddings = tie_word_embeddings
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__all__ = ["Qwen3_5MoeConfig", "Qwen3_5MoeTextConfig"]
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