# coding=utf-8 # Copyright 2024 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. 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. """Qwen2Audio model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from transformers import CONFIG_MAPPING import os from typing import Union logger = logging.get_logger(__name__) class Qwen2SeamlessEncoderConfig(PretrainedConfig): model_type = "qwen2_seamless_encoder" def __init__( self, speech_encoder_layers=24, speech_encoder_attention_heads=16, speech_encoder_intermediate_size=4096, speech_encoder_hidden_act="swish", speech_encoder_dropout=0.0, add_adapter=True, speech_encoder_layerdrop=0.1, feature_projection_input_dim=160, adaptor_kernel_size=8, adaptor_stride=8, adaptor_dropout=0.1, num_adapter_layers=1, position_embeddings_type="relative_key", conv_depthwise_kernel_size=31, left_max_position_embeddings=64, right_max_position_embeddings=8, speech_encoder_chunk_size=20000, speech_encoder_left_chunk_num=128, **kwargs, ): super().__init__(**kwargs) self.speech_encoder_layers = speech_encoder_layers self.speech_encoder_hidden_act = speech_encoder_hidden_act self.speech_encoder_dropout = speech_encoder_dropout self.speech_encoder_attention_heads = speech_encoder_attention_heads self.speech_encoder_layerdrop = speech_encoder_layerdrop self.speech_encoder_intermediate_size = speech_encoder_intermediate_size self.feature_projection_input_dim = feature_projection_input_dim self.adaptor_kernel_size = adaptor_kernel_size self.adaptor_stride = adaptor_stride self.adaptor_dropout = adaptor_dropout self.num_adapter_layers = num_adapter_layers self.position_embeddings_type = position_embeddings_type self.conv_depthwise_kernel_size = conv_depthwise_kernel_size self.add_adapter = add_adapter self.left_max_position_embeddings = left_max_position_embeddings self.right_max_position_embeddings = right_max_position_embeddings self.speech_encoder_chunk_size = speech_encoder_chunk_size self.speech_encoder_left_chunk_num = speech_encoder_left_chunk_num self.audio_path = "/mnt/diskhd/Backup/DownloadModel/seamless-m4t-v2-large/" class Qwen2VLVisionConfig(PretrainedConfig): model_type = "qwen2_vl" def __init__( self, depth=32, embed_dim=1280, hidden_size=3584, hidden_act="quick_gelu", mlp_ratio=4, num_heads=16, in_channels=3, patch_size=14, spatial_merge_size=2, temporal_patch_size=2, **kwargs, ): super().__init__(**kwargs) self.depth = depth self.embed_dim = embed_dim self.hidden_size = hidden_size self.hidden_act = hidden_act self.mlp_ratio = mlp_ratio self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size @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 1:#config_dict.get("model_type") == "qwen2_vl": config_dict = config_dict["vision_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 Qwen2MMConfig(PretrainedConfig): model_type = "qwen2_mm" is_composition = False def __init__( self, vocab_size=152064, hidden_size=8192, intermediate_size=29568, num_hidden_layers=80, num_attention_heads=64, num_key_value_heads=8, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=80, attention_dropout=0.0, audio_config=None, vision_config=None, rope_scaling=None, **kwargs, ): if isinstance(vision_config, dict): self.vision_config = Qwen2VLVisionConfig(**vision_config) elif vision_config is None: self.vision_config = Qwen2VLVisionConfig() if isinstance(audio_config, dict): self.audio_config = Qwen2SeamlessEncoderConfig(**audio_config) elif audio_config is None: self.audio_config = Qwen2SeamlessEncoderConfig() self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.max_window_layers = max_window_layers # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.llm_path = "/mnt/diskhd/Backup/DownloadModel/Qwen2.5-3B-Instruct/" self.auto_map = { "AutoConfig": "configuration_qwen2_seamless.Qwen2MMConfig", "AutoModel": "modeling_qwen2_seamless.Qwen2SeamlessForConditionalGeneration" } self.rope_scaling = rope_scaling super().__init__(**kwargs)