# coding=utf-8
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#
# 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)