#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2026 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 Qwen3Config 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 MiniCPMTTSConfig(PretrainedConfig): model_type = "minicpmtts" def __init__( self, llm_dim: int = 2560, llm_intermediate_size: int = 768, llm_down_scale: bool = False, llm_dim_model_base: int = 256, projector_type: str = "mlp", hidden_act: str = "silu", aug_loss_weight: bool = False, aug_layer_loss_weight: bool = False, filter_tts_loss: bool = False, tts_filter_loss_fix: bool = False, long_weight: float = 0.1, short_weight: float = 0.1, hidden_size: int = 768, intermediate_size: int = 3072, num_attention_heads: int = 12, num_hidden_layers: int = 20, num_key_value_heads: int = 12, max_position_embeddings: int = 4096, num_audio_tokens: int = 4097, num_text_tokens: int = 21178, num_mel_bins: int = 100, num_vq: int = 1, use_llm_hidden_state: bool = False, audio_bos_token_id: int = 21132, text_eos_token_id: int = 21133, use_text: bool = True, streaming: bool = False, streaming_text_chunk_min: int = 3, streaming_text_chunk_max: int = 7, streaming_text_reserved_len: int = 300, streaming_audio_chunk_size: int = 50, attn_implementation: str = "sdpa", condition_type: str = "llm_hidden", backbone_model: str = "llama", audio_tokenizer_type: str = "wavtokenizer", audio_tokenizer_sample_rate: int = 24000, streaming_sliding_window: bool = False, streaming_sliding_window_max_text_len: int = 500, streaming_sliding_window_average_speed: int = 5, streaming_sliding_window_fast_speed: int = 7, streaming_sliding_window_slow_speed: int = 3, streaming_sliding_window_audio_frame_rate: int = 50, streaming_sliding_window_audio_init_text_length: int = 10, streaming_sliding_window_audio_window_size: int = 300, normalize_projected_hidden: bool = False, interleaved: bool = False, attention_type: str = "sliding_recompute", recomputed_chunks: int = 1, window_size: int = 2, **kwargs, ): super().__init__(**kwargs) self.llm_dim = llm_dim self.llm_hidden_size = llm_dim self.llm_intermediate_size = llm_intermediate_size self.llm_down_scale = llm_down_scale self.llm_dim_model_base = llm_dim_model_base self.projector_type = projector_type self.aug_loss_weight = aug_loss_weight self.aug_layer_loss_weight = aug_layer_loss_weight self.tts_filter_loss_fix = tts_filter_loss_fix self.filter_tts_loss = filter_tts_loss self.long_weight = long_weight self.short_weight = short_weight self.hidden_act = hidden_act 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.num_key_value_heads = num_key_value_heads 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_llm_hidden_state = use_llm_hidden_state 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_min = streaming_text_chunk_min self.streaming_text_chunk_max = streaming_text_chunk_max self.streaming_text_reserved_len = streaming_text_reserved_len self.streaming_audio_chunk_size = streaming_audio_chunk_size self.attn_implementation = attn_implementation self.condition_type = condition_type self.backbone_model = backbone_model self.audio_tokenizer_type = audio_tokenizer_type self.audio_tokenizer_sample_rate = audio_tokenizer_sample_rate self.streaming_sliding_window = streaming_sliding_window self.streaming_sliding_window_max_text_len = streaming_sliding_window_max_text_len self.streaming_sliding_window_average_speed = streaming_sliding_window_average_speed self.streaming_sliding_window_fast_speed = streaming_sliding_window_fast_speed self.streaming_sliding_window_slow_speed = streaming_sliding_window_slow_speed self.streaming_sliding_window_audio_frame_rate = streaming_sliding_window_audio_frame_rate self.streaming_sliding_window_audio_init_text_length = streaming_sliding_window_audio_init_text_length self.streaming_sliding_window_audio_window_size = streaming_sliding_window_audio_window_size self.normalize_projected_hidden = normalize_projected_hidden self.interleaved = interleaved self.attention_type = attention_type self.recomputed_chunks = recomputed_chunks self.window_size = window_size class MiniCPMOConfig(Qwen3Config): 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=5, audio_chunk_length=1.0, stream_input=False, listen_speak_type="asr", 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.listen_speak_type = listen_speak_type 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 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 = MiniCPMTTSConfig() elif isinstance(tts_config, dict): self.tts_config = MiniCPMTTSConfig(**tts_config) elif isinstance(tts_config, MiniCPMTTSConfig): self.tts_config = tts_config self.patch_size = self.vision_config.patch_size super().__init__(**kwargs)