Delete configuration_minicpm.py

configuration_minicpm.py

@@ -1,210 +0,0 @@
-# coding=utf-8
-# Copyright 2025 The OpenBMB Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-from typing import Union
-
-from transformers import PretrainedConfig
-from transformers import Qwen2Config
-from transformers import WhisperConfig
-from transformers.utils import logging
-
-from .modeling_navit_siglip import SiglipVisionConfig
-
-logger = logging.get_logger(__name__)
-
-
-class MiniCPMVSliceConfig(PretrainedConfig):
-    model_type = "minicpmv"
-
-    def __init__(
-        self,
-        patch_size=14,
-        max_slice_nums=9,
-        scale_resolution=448,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.patch_size = patch_size
-        self.max_slice_nums = max_slice_nums
-        self.scale_resolution = scale_resolution
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        if config_dict.get("model_type") == "minicpmv":
-            config_dict = config_dict["slice_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class ConditionalChatTTSConfig(PretrainedConfig):
-    model_type = "conditional_chattts"
-
-    def __init__(
-        self,
-        llm_dim: int = 2560,
-        hidden_size: int = 768,
-        intermediate_size: int = 3072,
-        num_attention_heads: int = 12,
-        num_hidden_layers: int = 20,
-        max_position_embeddings: int = 4096,
-        num_audio_tokens: int = 626,
-        num_text_tokens: int = 21178,
-        num_mel_bins: int = 100,
-        num_vq: int = 4,
-        use_speaker_embedding: bool = True,
-        use_llm_hidden_state: bool = False,
-        spk_emb_token_id: int = 21143,
-        num_spk_embs: int = 1,
-        audio_bos_token_id: int = 21132,
-        text_eos_token_id: int = 21133,
-        use_text: bool = True,
-        streaming: bool = True,
-        streaming_text_chunk_size: int = 10,
-        streaming_text_reserved_len: int = 300,
-        streaming_audio_chunk_size: int = 50,
-        attn_implementation: str = "sdpa",
-        use_mlp: bool = True,
-        aug_loss_weight: bool = True,
-        do_sample: bool = True,
-        top_p: float = 0.7,
-        top_k: int = 20,
-        repetition_penalty: float = 1.0,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.llm_dim = llm_dim
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_attention_heads = num_attention_heads
-        self.num_hidden_layers = num_hidden_layers
-        self.max_position_embeddings = max_position_embeddings
-        self.num_audio_tokens = num_audio_tokens
-        self.num_text_tokens = num_text_tokens
-        self.num_mel_bins = num_mel_bins
-        self.num_vq = num_vq
-        self.use_speaker_embedding = use_speaker_embedding
-        self.use_llm_hidden_state = use_llm_hidden_state
-        self.spk_emb_token_id = spk_emb_token_id
-        self.num_spk_embs = num_spk_embs
-        self.audio_bos_token_id = audio_bos_token_id
-        self.text_eos_token_id = text_eos_token_id
-        self.use_text = use_text
-        self.streaming = streaming
-        self.streaming_text_chunk_size = streaming_text_chunk_size
-        self.streaming_text_reserved_len = streaming_text_reserved_len
-        self.streaming_audio_chunk_size = streaming_audio_chunk_size
-        self.attn_implementation = attn_implementation
-        self.use_mlp = use_mlp
-        self.aug_loss_weight = aug_loss_weight
-        self.do_sample = do_sample
-        self.top_p = top_p
-        self.top_k = top_k
-        self.repetition_penalty = repetition_penalty
-
-
-class MiniCPMOConfig(Qwen2Config):
-    model_type = "minicpmo"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    default_vision_config = {
-        "hidden_size": 1152,
-        "image_size": 980,
-        "intermediate_size": 4304,
-        "model_type": "siglip",
-        "num_attention_heads": 16,
-        "num_hidden_layers": 27,
-        "patch_size": 14,
-    }
-
-    def __init__(
-        self,
-        use_cache=True,
-        query_num=64,
-        image_size=448,
-        drop_vision_last_layer=True,
-        batch_vision_input=True,
-        slice_config=None,
-        vision_config=None,
-        audio_config=None,
-        tts_config=None,
-        use_image_id=True,
-        vision_batch_size=16,
-        audio_pool_step=2,
-        audio_chunk_length=1.0,
-        stream_input=False,
-        init_vision=True,
-        init_audio=True,
-        init_tts=True,
-        **kwargs,
-    ):
-        self.use_cache = use_cache
-        self.query_num = query_num
-        self.image_size = image_size
-        self.drop_vision_last_layer = drop_vision_last_layer
-        self.batch_vision_input = batch_vision_input
-        self.use_image_id = use_image_id
-        self.vision_batch_size = vision_batch_size
-        self.audio_pool_step = audio_pool_step
-        self.audio_chunk_length = audio_chunk_length
-        self.stream_input = stream_input
-        self.init_vision = init_vision
-        self.init_audio = init_audio
-        self.init_tts = init_tts
-
-        if slice_config is None:
-            self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1)
-        else:
-            self.slice_config = MiniCPMVSliceConfig(**slice_config)
-        self.slice_mode = True
-
-        # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
-        if vision_config is None:
-            self.vision_config = SiglipVisionConfig(**self.default_vision_config)
-            logger.info("vision_config is None, using default vision config")
-        elif isinstance(vision_config, dict):
-            self.vision_config = SiglipVisionConfig(**vision_config)
-        elif isinstance(vision_config, SiglipVisionConfig):
-            self.vision_config = vision_config
-
-        # same as openai/whisper-medium add use_cache
-        if audio_config is None:
-            self.audio_config = WhisperConfig()
-        elif isinstance(audio_config, dict):
-            self.audio_config = WhisperConfig(**audio_config)
-        elif isinstance(audio_config, WhisperConfig):
-            self.audio_config = audio_config
-
-        if tts_config is None:
-            self.tts_config = ConditionalChatTTSConfig()
-        elif isinstance(tts_config, dict):
-            self.tts_config = ConditionalChatTTSConfig(**tts_config)
-        elif isinstance(tts_config, ConditionalChatTTSConfig):
-            self.tts_config = tts_config
-
-        self.patch_size = self.vision_config.patch_size
-
-        super().__init__(**kwargs)