189 lines
6.5 KiB
Python
189 lines
6.5 KiB
Python
# Adapted from transformers 5.2.0 for compatibility with transformers 4.55.3 + torch 2.1.0
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# Stubs layer_type_validation and RopeParameters which do not exist in 4.55.3
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from typing import Optional, List
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from ...configuration_utils import PretrainedConfig as PreTrainedConfig
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# --- Local stubs for APIs not present in transformers 4.55.3 ---
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# Always use these definitions; do NOT import from the older transformers
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# as same-named functions there have incompatible signatures.
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def layer_type_validation(layer_types, num_hidden_layers=None, attention=True):
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allowed = {"full_attention", "linear_attention"}
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if not all(lt in allowed for lt in layer_types):
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raise ValueError(f"layer_types entries must be in {allowed}, got {layer_types}")
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if num_hidden_layers is not None and num_hidden_layers != len(layer_types):
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raise ValueError(
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f"num_hidden_layers ({num_hidden_layers}) != len(layer_types) ({len(layer_types)})"
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)
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try:
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from typing import TypedDict
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class RopeParameters(TypedDict, total=False):
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rope_theta: float
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rope_type: str
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partial_rotary_factor: float
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factor: float
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except Exception:
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RopeParameters = dict
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# --- End stubs ---
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class Qwen3_5TextConfig(PreTrainedConfig):
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r"""
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Configuration for the text backbone of Qwen3.5 / Qwen3.6-27B models.
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model_type is "qwen3_5_text" (used internally by the nested config).
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"""
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model_type = "qwen3_5_text"
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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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vocab_size=248320,
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hidden_size=4096,
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intermediate_size=12288,
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num_hidden_layers=32,
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num_attention_heads=16,
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num_key_value_heads=4,
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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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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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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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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.intermediate_size = intermediate_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" if bool((i + 1) % interval_pattern) 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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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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super().__init__(**kwargs)
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class Qwen3_5VisionConfig(PreTrainedConfig):
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model_type = "qwen3_5_vision"
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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_5Config(PreTrainedConfig):
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r"""
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Top-level configuration for Qwen3.5 / Qwen3.6-27B.
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model_type = "qwen3_5" matches the model card / config.json.
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Wraps Qwen3_5TextConfig (and optionally Qwen3_5VisionConfig for multimodal use).
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For vLLM text-only inference only text_config is consumed.
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"""
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model_type = "qwen3_5"
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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(text_config, dict):
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self.text_config = Qwen3_5TextConfig(**text_config)
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elif text_config is None:
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self.text_config = Qwen3_5TextConfig()
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else:
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self.text_config = text_config
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if isinstance(vision_config, dict):
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self.vision_config = Qwen3_5VisionConfig(**vision_config)
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elif vision_config is None:
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self.vision_config = Qwen3_5VisionConfig()
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else:
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self.vision_config = vision_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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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(**kwargs)
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__all__ = ["Qwen3_5Config", "Qwen3_5TextConfig", "Qwen3_5VisionConfig"]
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