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""" Qwen2 model configuration""" |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class Qwen2idaeConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a |
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Qwen2 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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Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta). |
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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 Qwen2 model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`Qwen2Model`] |
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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 checkout [this |
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. |
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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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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 that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full 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 Qwen2Model, Qwen2Config |
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>>> # Initializing a Qwen2 style configuration |
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>>> configuration = Qwen2Config() |
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>>> # Initializing a model from the Qwen2-7B style configuration |
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>>> model = Qwen2Model(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 = "qwen2idae" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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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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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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use_sliding_window=False, |
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sliding_window=4096, |
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max_window_layers=28, |
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attention_dropout=0.0, |
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moe_dtype="bfloat16", |
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moe_scaling=0.25, |
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num_experts=8, |
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topk=1, |
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output_router_logits=True, |
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adapter_dim=64, |
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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 |
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self.max_window_layers = max_window_layers |
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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.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.attention_dropout = attention_dropout |
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self.moe_dtype = moe_dtype |
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self.moe_scaling = moe_scaling |
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self.num_experts = num_experts |
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self.topk = topk |
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self.output_router_logits = output_router_logits |
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self.adapter_dim = adapter_dim |
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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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) |
