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configuration_qwen3.py
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configuration_qwen3.py
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# coding=utf-8
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# Copyright 2024 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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"""Qwen3 model configuration"""
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from transformers.configuration_utils import PretrainedConfig, layer_type_validation
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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 Qwen3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen3Model`]. It is used to instantiate a
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Qwen3 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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Qwen3-8B [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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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 Qwen3 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Qwen3Model`]
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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 attention head dimension.
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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 32768):
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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 10000.0):
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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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use_sliding_window (`bool`, *optional*, defaults to `False`):
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Whether to use sliding window attention.
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sliding_window (`int`, *optional*, defaults to 4096):
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Sliding window attention (SWA) window size. If not specified, will default to `4096`.
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max_window_layers (`int`, *optional*, defaults to 28):
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The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
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additional layer afterwards will use SWA (Sliding Window Attention).
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layer_types (`list`, *optional*):
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Attention pattern for each layer.
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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 Qwen3Model, Qwen3Config
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>>> # Initializing a Qwen3 style configuration
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>>> configuration = Qwen3Config()
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>>> # Initializing a model from the Qwen3-8B style configuration
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>>> model = Qwen3Model(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"
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keys_to_ignore_at_inference = ["past_key_values"]
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# Default tensor parallel plan for base model `Qwen3`
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary.
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Excludes the EdgeRazor runtime object from serialization.
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"""
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output = super().to_dict()
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# Remove non-serializable EdgeRazor objects
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if "edgerazor_config" in output:
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del output["edgerazor_config"]
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return output
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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=32768,
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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=10000.0,
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rope_scaling=None,
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attention_bias=False,
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use_sliding_window=False,
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sliding_window=4096,
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max_window_layers=28,
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layer_types=None,
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attention_dropout=0.0,
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quant_mode="w4a8kv8",
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is_w_quantized=None,
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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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self.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window if self.use_sliding_window else None
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self.max_window_layers = max_window_layers
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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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self.layer_types = layer_types
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention"
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if self.sliding_window is not None and i >= self.max_window_layers
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else "full_attention"
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for i in range(self.num_hidden_layers)
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]
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layer_type_validation(self.layer_types, self.num_hidden_layers)
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# EdgeRazor config loading
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self.quant_mode = quant_mode
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try:
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from edgerazor import EdgeRazorConfig
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from edgerazor import quant_config_map
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if quant_mode not in quant_config_map:
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raise ValueError(
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f"Invalid quant_mode: '{quant_mode}'. "
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f"Available modes: {list(quant_config_map.keys())}"
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)
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config_dict = quant_config_map[quant_mode]
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self.edgerazor_config = EdgeRazorConfig.from_dict(config_dict=config_dict)
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if is_w_quantized is not None:
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self.edgerazor_config.qat_config.function.is_w_quantized = is_w_quantized
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except ImportError:
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logger.warning(
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"EdgeRazor is not installed. Please install it to use EdgeRazor features."
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)
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self.edgerazor_config = None
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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__all__ = ["Qwen3Config"]
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