forked from EngineX-MetaX/enginex-c_series-vllm
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""MiniMaxVL01 model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.models.auto import CONFIG_MAPPING
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from .minimax_text_01 import MiniMaxText01Config
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class MiniMaxVL01Config(PretrainedConfig):
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model_type = "minimax_vl_01"
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def __init__(
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self,
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vision_config=None,
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text_config=None,
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ignore_index=-100,
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image_token_index=32000,
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projector_hidden_act="gelu",
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vision_feature_select_strategy="default",
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vision_feature_layer=-2,
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image_grid_pinpoints=None,
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tie_word_embeddings=False,
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image_seq_length=576,
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**kwargs,
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):
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self.ignore_index = ignore_index
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self.image_token_index = image_token_index
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self.projector_hidden_act = projector_hidden_act
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self.image_seq_length = image_seq_length
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if vision_feature_select_strategy not in ["default", "full"]:
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raise ValueError("vision_feature_select_strategy should " +
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"be one of 'default', 'full'." +
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f"Got: {vision_feature_select_strategy}")
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self.vision_feature_select_strategy = vision_feature_select_strategy
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self.vision_feature_layer = vision_feature_layer
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image_grid_pinpoints = (
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image_grid_pinpoints if image_grid_pinpoints is not None else
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[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]])
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self.image_grid_pinpoints = image_grid_pinpoints
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if isinstance(vision_config, dict):
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if "model_type" not in vision_config:
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vision_config["model_type"] = "clip_vision_model"
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vision_config = CONFIG_MAPPING[vision_config["model_type"]](
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**vision_config)
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elif vision_config is None:
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vision_config = CONFIG_MAPPING["clip_vision_model"](
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intermediate_size=4096,
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hidden_size=1024,
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patch_size=14,
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image_size=336,
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num_hidden_layers=24,
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num_attention_heads=16,
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vocab_size=32000,
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projection_dim=768,
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)
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self.vision_config = vision_config
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if text_config is not None:
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text_config = MiniMaxText01Config(**text_config)
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else:
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text_config = MiniMaxText01Config()
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self.text_config = text_config
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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