437 lines
20 KiB
Python
437 lines
20 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# ruff: noqa
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# mypy: ignore-errors
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# coding=utf-8
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# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. 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 transformers.configuration_utils import PretrainedConfig
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from transformers.modeling_rope_utils import rope_config_validation
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class Qwen3ASRAudioEncoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen3ASRAudioEncoder`]. It is used to instantiate a
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Qwen3-ASR 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 Qwen2-Audio
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architecture.
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e.g. [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B)
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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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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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`Qwen3ASRProcessor` class.
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encoder_layers (`int`, *optional*, defaults to 32):
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Number of encoder layers.
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encoder_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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encoder_ffn_dim (`int`, *optional*, defaults to 5120):
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Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
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d_model (`int`, *optional*, defaults to 1280):
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Dimensionality of the layers.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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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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activation_function (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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scale_embedding (`bool`, *optional*, defaults to `False`):
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Scale embeddings by diving by sqrt(d_model).
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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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max_source_positions (`int`, *optional*, defaults to 1500):
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The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
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n_window (`int`, *optional*, defaults to 100):
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The chunk for conv and flash attn in AudioEncoder.
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output_dim (`int`, *optional*, defaults to 3584):
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The output dimension of AudioEncoder.
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Example:
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```python
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>>> from transformers import Qwen3ASRAudioEncoderConfig, Qwen3ASRAudioEncoder
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>>> # Initializing a Qwen3ASRAudioEncoderConfig
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>>> configuration = Qwen3ASRAudioEncoderConfig()
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>>> # Initializing a Qwen3ASRAudioEncoder (with random weights)
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>>> model = Qwen3ASRAudioEncoder(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 = "qwen3_asr_audio_encoder"
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def __init__(
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self,
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num_mel_bins=128,
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encoder_layers=32,
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encoder_attention_heads=20,
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encoder_ffn_dim=5120,
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d_model=1280,
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dropout=0,
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attention_dropout=0,
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activation_function="gelu",
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activation_dropout=0,
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scale_embedding=False,
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initializer_range=0.02,
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max_source_positions=1500,
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n_window=100,
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output_dim=3584,
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n_window_infer=400,
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conv_chunksize=500,
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downsample_hidden_size=480,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.num_mel_bins = num_mel_bins
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self.d_model = d_model
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self.encoder_layers = encoder_layers
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self.encoder_attention_heads = encoder_attention_heads
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self.encoder_ffn_dim = encoder_ffn_dim
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_function = activation_function
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self.activation_dropout = activation_dropout
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self.num_hidden_layers = encoder_layers
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self.initializer_range = initializer_range
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self.scale_embedding = (
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scale_embedding # scale factor will be sqrt(d_model) if True
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)
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self.max_source_positions = max_source_positions
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self.n_window = n_window
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self.output_dim = output_dim
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self.n_window_infer = n_window_infer
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self.conv_chunksize = conv_chunksize
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self.downsample_hidden_size = downsample_hidden_size
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class Qwen3ASRTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen3ASRTextModel`]. It is used to instantiate a
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Qwen3-ASR 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
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Qwen3-ASR-1.7B [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B)
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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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vocab_size (`int`, *optional*, defaults to 151936):
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Vocabulary size of the Qwen3ASR model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Qwen3ASRModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 22016):
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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 32):
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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*, defaults to 32):
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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 `32`.
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head_dim (`int`, *optional*, defaults to 128):
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The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 128000):
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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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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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rope_theta (`float`, *optional*, defaults to 5000000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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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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```python
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>>> from transformers import Qwen3ASRTextModel, Qwen3ASRTextConfig
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>>> # Initializing a Qwen3ASR style configuration
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>>> configuration = Qwen3ASRTextConfig()
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>>> # Initializing a model from the Qwen3-VL-7B style configuration
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>>> model = Qwen3ASRTextModel(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 = "qwen3_asr_text"
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base_config_key = "text_config"
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def __init__(
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self,
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vocab_size=151936,
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hidden_size=4096,
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intermediate_size=22016,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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head_dim=128,
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hidden_act="silu",
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max_position_embeddings=128000,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=5000000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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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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# for backward compatibility
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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.head_dim = head_dim
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, move it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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class Qwen3ASRThinkerConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen3ASRThinker`]. It is used to instantiate a
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Qwen3-ASR-Thinker model 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 thinker component of the Qwen3-Omni
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architecture.
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e.g. [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B)
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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 (`dict`, *optional*):
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The config dictionary of the audio backbone.
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text_config (`dict`, *optional*):
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The config dictionary of the text backbone.
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audio_token_id (`int`, *optional*, defaults to 151646):
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The audio token id to encode the audio prompt.
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audio_start_token_id (`int`, *optional*, defaults to 151647):
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The audio start token id to encode the audio prompt.
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user_token_id (`int`, *optional*, defaults to 872):
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The user token id to encode the user token.
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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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Example:
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```python
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>>> from transformers import Qwen3ASRThinkerModel, Qwen3ASRThinkerConfig
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>>> # Initializing a default Qwen3ASRThinkerConfig
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>>> configuration = Qwen3ASRThinkerConfig()
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>>> # Initializing a model (with random weights) from the default configuration
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>>> model = Qwen3ASRThinkerModel(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 = "qwen3_asr_thinker"
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attribute_map = {}
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sub_configs = {
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"audio_config": Qwen3ASRAudioEncoderConfig,
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"text_config": Qwen3ASRTextConfig,
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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=151646,
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audio_start_token_id=151647,
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user_token_id=872,
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initializer_range=0.02,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.user_token_id = user_token_id
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self.audio_start_token_id = audio_start_token_id
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self.initializer_range = initializer_range
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if isinstance(audio_config, dict):
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audio_config = Qwen3ASRAudioEncoderConfig(**audio_config)
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elif audio_config is None:
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audio_config = Qwen3ASRAudioEncoderConfig()
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self.audio_config = audio_config
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if isinstance(text_config, dict):
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text_config = Qwen3ASRTextConfig(**text_config)
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elif text_config is None:
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text_config = Qwen3ASRTextConfig()
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self.text_config = text_config
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self.audio_token_id = audio_token_id
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class Qwen3ASRConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`Qwen3ASRForConditionalGeneration`]. It is used to instantiate a Qwen3ASR
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model according to the specified sub-models configurations, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the
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[Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B) architecture.
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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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thinker_config (`dict`, *optional*): Configuration of the underlying thinker sub-model.
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support_languages (`List[str]`, *optional*): The languages supported by the model.
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Example:
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```python
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>>> from transformers import (
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... Qwen3ASRThinkerConfig,
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... Qwen3ASRForConditionalGeneration,
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... Qwen3ASRConfig,
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... )
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>>> # Initializing a Qwen3ASR style configuration
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>>> configuration = Qwen3ASRConfig()
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>>> # Initializing a model from the configuration
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>>> model = Qwen3ASRForConditionalGeneration(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 = "qwen3_asr"
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sub_configs = {
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"thinker_config": Qwen3ASRThinkerConfig,
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}
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def __init__(
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self,
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thinker_config=None,
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support_languages=None,
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**kwargs,
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):
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super().__init__(**kwargs)
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if thinker_config is None:
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thinker_config = {}
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logger.info(
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"thinker_config is None. Initializing thinker model with default values"
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)
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self.thinker_config = Qwen3ASRThinkerConfig(**thinker_config)
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self.support_languages = support_languages
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def get_text_config(self, decoder=False) -> "PretrainedConfig":
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"""
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Returns the config that is meant to be used with text IO. On most models, it is the original config instance
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itself. On specific composite models, it is under a set of valid names.
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Args:
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decoder (`Optional[bool]`, *optional*, defaults to `False`):
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If set to `True`, then only search for decoder config names.
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"""
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# Overridden for deeply nested config like Qwen2.5-Omni. We don't have any omni model
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# except for Qwen yet. This has to be generalized if more deeply nested configs are
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# added. NOTE: currently method used only by vLLM
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return self.thinker_config.get_text_config()
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__all__ = ["Qwen3ASRConfig", "Qwen3ASRThinkerConfig", "Qwen3ASRAudioEncoderConfig"]
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