# Copyright 2025 the HuggingFace 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. from ...configuration_utils import PreTrainedConfig from ..auto import CONFIG_MAPPING, AutoConfig class GlmAsrEncoderConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`GlmAsrEncoder`]. It is used to instantiate a glmasr audio encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the glmasr architecture. e.g. [zai-org/GLM-ASR-Nano-2512](https://huggingface.co/zai-org/GLM-ASR-Nano-2512) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1280): Dimensionality of the hidden representations. intermediate_size (`int`, *optional*, defaults to 5120): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 20): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. max_position_embeddings (`int`, *optional*, defaults to 1500): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_mel_bins (`int`, *optional*, defaults to 128): Number of mel features used per input features. Should correspond to the value used in the `GlmAsrProcessor` class. ```python >>> from transformers import GlmAsrEncoderConfig, GlmAsrEncoder >>> # Initializing a GlmAsrEncoderConfig >>> configuration = GlmAsrEncoderConfig() >>> # Initializing a GlmAsrEncoder (with random weights) >>> model = GlmAsrEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glmasr_encoder" def __init__( self, hidden_size=1280, intermediate_size=5120, num_hidden_layers=32, num_attention_heads=20, num_key_value_heads=None, hidden_act="gelu", max_position_embeddings=1500, initializer_range=0.02, rope_parameters=None, attention_dropout=0.0, num_mel_bins=128, **kwargs, ): self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads 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.head_dim = hidden_size // num_attention_heads self.max_position_embeddings = max_position_embeddings self.rope_parameters = rope_parameters self.attention_dropout = attention_dropout self.num_mel_bins = num_mel_bins kwargs.setdefault("partial_rotary_factor", 0.5) super().__init__(**kwargs) class GlmAsrConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`GlmAsrForConditionalGeneration`]. It is used to instantiate an glmasr model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the glmasr-Mini-3B. e.g. [zai-org/GLM-ASR-Nano-2512](https://huggingface.co/zai-org/GLM-ASR-Nano-2512) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: audio_config (`Union[AutoConfig, dict]`, *optional*): The config object or dictionary of the audio encoder. text_config (`Union[AutoConfig, dict]`, *optional*): The config object or dictionary of the text model. audio_token_id (`int`, *optional*, defaults to 59260): The audio token index to encode the audio prompt. projector_hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function (function or string) in the multi-modal projector. ```python >>> from transformers import GlmAsrForConditionalGeneration, GlmAsrConfig >>> # Initializing a glmasr configuration >>> configuration = GlmAsrConfig() >>> # Initializing a GLM-ASR-Nano-2512 model with random weights >>> model = GlmAsrForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glmasr" sub_configs = {"text_config": AutoConfig, "audio_config": AutoConfig} _default_text_config_kwargs = { "vocab_size": 59264, "hidden_size": 2048, "intermediate_size": 6144, "num_hidden_layers": 28, "num_attention_heads": 16, "num_key_value_heads": 4, "max_position_embeddings": 8192, "rms_norm_eps": 1e-05, "use_cache": True, "eos_token_id": [59246, 59253, 59255], "rope_parameters": {"rope_theta": 10000.0, "rope_type": "default"}, } def __init__( self, audio_config=None, text_config=None, audio_token_id=59260, projector_hidden_act="gelu", **kwargs, ): if isinstance(audio_config, dict): audio_config["model_type"] = audio_config.get("model_type", "glmasr_encoder") audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config) elif audio_config is None: audio_config = CONFIG_MAPPING["glmasr_encoder"]() self.audio_config = audio_config if isinstance(text_config, dict): text_config["model_type"] = text_config.get("model_type", "llama") text_config = CONFIG_MAPPING[text_config["model_type"]]( **{**self._default_text_config_kwargs, **text_config} ) elif text_config is None: text_config = CONFIG_MAPPING["llama"](**self._default_text_config_kwargs) self.text_config = text_config self.vocab_size = text_config.vocab_size self.hidden_size = text_config.hidden_size self.audio_token_id = audio_token_id self.projector_hidden_act = projector_hidden_act super().__init__(**kwargs) __all__ = ["GlmAsrEncoderConfig", "GlmAsrConfig"]