wehub-resource-sync
110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from transformers import PretrainedConfig, SiglipVisionConfig
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from transformers.modeling_rope_utils import rope_config_validation
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class CheersTextConfig(PretrainedConfig):
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"""Qwen2-based text config with Cheers-specific defaults."""
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model_type = "umm"
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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=152064,
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hidden_size=3584,
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intermediate_size=18944,
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num_hidden_layers=28,
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num_attention_heads=28,
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num_key_value_heads=4,
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hidden_act="silu",
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max_position_embeddings=131072,
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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_theta=1000000.0,
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rope_scaling=None,
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use_sliding_window=False,
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sliding_window=131072,
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max_window_layers=28,
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layer_types=None,
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attention_dropout=0.0,
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**kwargs,
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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.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.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window if self.use_sliding_window else None
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self.max_window_layers = max_window_layers
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if num_key_value_heads is None:
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num_key_value_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.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_dropout = attention_dropout
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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self.layer_types = layer_types
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention"
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if self.sliding_window is not None and i >= self.max_window_layers
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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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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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class CheersConfig(PretrainedConfig):
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"""Configuration class for Cheers (UMM) model."""
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model_type = "umm"
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def __init__(
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self,
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text_config: dict | CheersTextConfig | None = None,
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vision_representation_config: dict | SiglipVisionConfig | None = None,
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vae_encoder_config: dict | None = None,
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vae_decoder_config: dict | None = None,
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**kwargs,
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):
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super().__init__(**kwargs)
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if isinstance(text_config, dict):
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self.text_config = CheersTextConfig(**text_config)
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else:
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self.text_config = text_config or CheersTextConfig()
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if isinstance(vision_representation_config, dict):
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self.vision_representation_config = SiglipVisionConfig(
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**vision_representation_config
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)
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else:
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self.vision_representation_config = (
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vision_representation_config or SiglipVisionConfig()
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)
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self.vae_encoder_config = vae_encoder_config or {"resolution": 512}
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self.vae_decoder_config = vae_decoder_config or {"resolution": 512}
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@property
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def hidden_size(self) -> int:
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"""Return the hidden size of the language model."""
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return self.text_config.hidden_size
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