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197 lines
8.6 KiB
197 lines
8.6 KiB
# Copyright 2025 the HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PreTrainedConfig
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from ..auto import CONFIG_MAPPING, AutoConfig
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class GlmAsrEncoderConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`GlmAsrEncoder`]. It is used to instantiate a
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glmasr audio encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the audio encoder of the glmasr
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architecture.
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e.g. [zai-org/GLM-ASR-Nano-2512](https://huggingface.co/zai-org/GLM-ASR-Nano-2512)
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 1280):
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Dimensionality of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 5120):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 20):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler.
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max_position_embeddings (`int`, *optional*, defaults to 1500):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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num_mel_bins (`int`, *optional*, defaults to 128):
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Number of mel features used per input features. Should correspond to the value used in the
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`GlmAsrProcessor` class.
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```python
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>>> from transformers import GlmAsrEncoderConfig, GlmAsrEncoder
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>>> # Initializing a GlmAsrEncoderConfig
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>>> configuration = GlmAsrEncoderConfig()
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>>> # Initializing a GlmAsrEncoder (with random weights)
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>>> model = GlmAsrEncoder(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "glmasr_encoder"
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def __init__(
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self,
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hidden_size=1280,
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intermediate_size=5120,
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num_hidden_layers=32,
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num_attention_heads=20,
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num_key_value_heads=None,
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hidden_act="gelu",
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max_position_embeddings=1500,
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initializer_range=0.02,
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rope_parameters=None,
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attention_dropout=0.0,
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num_mel_bins=128,
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**kwargs,
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):
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.head_dim = hidden_size // num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.rope_parameters = rope_parameters
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self.attention_dropout = attention_dropout
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self.num_mel_bins = num_mel_bins
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kwargs.setdefault("partial_rotary_factor", 0.5)
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super().__init__(**kwargs)
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class GlmAsrConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`GlmAsrForConditionalGeneration`]. It is used to instantiate an
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glmasr model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the glmasr-Mini-3B.
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e.g. [zai-org/GLM-ASR-Nano-2512](https://huggingface.co/zai-org/GLM-ASR-Nano-2512)
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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audio_config (`Union[AutoConfig, dict]`, *optional*):
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The config object or dictionary of the audio encoder.
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text_config (`Union[AutoConfig, dict]`, *optional*):
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The config object or dictionary of the text model.
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audio_token_id (`int`, *optional*, defaults to 59260):
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The audio token index to encode the audio prompt.
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projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The activation function (function or string) in the multi-modal projector.
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```python
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>>> from transformers import GlmAsrForConditionalGeneration, GlmAsrConfig
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>>> # Initializing a glmasr configuration
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>>> configuration = GlmAsrConfig()
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>>> # Initializing a GLM-ASR-Nano-2512 model with random weights
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>>> model = GlmAsrForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "glmasr"
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sub_configs = {"text_config": AutoConfig, "audio_config": AutoConfig}
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_default_text_config_kwargs = {
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"vocab_size": 59264,
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"hidden_size": 2048,
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"intermediate_size": 6144,
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"num_hidden_layers": 28,
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"num_attention_heads": 16,
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"num_key_value_heads": 4,
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"max_position_embeddings": 8192,
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"rms_norm_eps": 1e-05,
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"use_cache": True,
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"eos_token_id": [59246, 59253, 59255],
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"rope_parameters": {"rope_theta": 10000.0, "rope_type": "default"},
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}
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def __init__(
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self,
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audio_config=None,
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text_config=None,
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audio_token_id=59260,
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projector_hidden_act="gelu",
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**kwargs,
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):
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if isinstance(audio_config, dict):
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audio_config["model_type"] = audio_config.get("model_type", "glmasr_encoder")
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audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config)
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elif audio_config is None:
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audio_config = CONFIG_MAPPING["glmasr_encoder"]()
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self.audio_config = audio_config
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if isinstance(text_config, dict):
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text_config["model_type"] = text_config.get("model_type", "llama")
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text_config = CONFIG_MAPPING[text_config["model_type"]](
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**{**self._default_text_config_kwargs, **text_config}
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)
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elif text_config is None:
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text_config = CONFIG_MAPPING["llama"](**self._default_text_config_kwargs)
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self.text_config = text_config
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self.vocab_size = text_config.vocab_size
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self.hidden_size = text_config.hidden_size
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self.audio_token_id = audio_token_id
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self.projector_hidden_act = projector_hidden_act
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super().__init__(**kwargs)
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__all__ = ["GlmAsrEncoderConfig", "GlmAsrConfig"]
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