# coding=utf-8 # Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. 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. """Qwen2 model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) # We can also consider to pass the encoder config dict to the Qwen2Config config as well. encoder_config_dict = { "_name_or_path": "alexchen4ai/Qwen2-0.5B", "add_cross_attention": False, "architectures": ["Qwen2ForCausalLM"], "attention_dropout": 0.0, "bad_words_ids": None, "begin_suppress_tokens": None, "bos_token_id": 151643, "chunk_size_feed_forward": 0, "cross_attention_hidden_size": None, "decoder_start_token_id": None, "diversity_penalty": 0.0, "do_sample": False, "early_stopping": False, "encoder_config": None, "encoder_no_repeat_ngram_size": 0, "eos_token_id": 151643, "exponential_decay_length_penalty": None, "finetuning_task": None, "forced_bos_token_id": None, "forced_eos_token_id": None, "hidden_act": "silu", "hidden_size": 896, "id2label": {"0": "LABEL_0", "1": "LABEL_1"}, "initializer_range": 0.02, "intermediate_size": 4864, "is_decoder": False, "is_encoder_decoder": False, "label2id": {"LABEL_0": 0, "LABEL_1": 1}, "length_penalty": 1.0, "max_length": 20, "max_position_embeddings": 131072, "max_window_layers": 24, "min_length": 0, "model_type": "qwen2", "no_repeat_ngram_size": 0, "num_attention_heads": 14, "num_beam_groups": 1, "num_beams": 1, "num_hidden_layers": 24, "num_key_value_heads": 2, "num_return_sequences": 1, "output_attentions": False, "output_hidden_states": False, "output_scores": False, "pad_token_id": None, "prefix": None, "problem_type": None, "pruned_heads": {}, "remove_invalid_values": False, "repetition_penalty": 1.0, "return_dict": True, "return_dict_in_generate": False, "rms_norm_eps": 1e-06, "rope_theta": 1000000.0, "sep_token_id": None, "sliding_window": 131072, "suppress_tokens": None, "task_specific_params": None, "temperature": 1.0, "tf_legacy_loss": False, "tie_encoder_decoder": False, "tie_word_embeddings": True, "tokenizer_class": None, "top_k": 50, "top_p": 1.0, "torch_dtype": "bfloat16", "torchscript": False, "typical_p": 1.0, "use_bfloat16": False, "use_cache": True, "use_sliding_window": False, "vocab_size": 151936, "attn_implementation": None, } class Qwen2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 151936): Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Qwen2Model`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22016): 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 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 32): 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): 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. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*, defaults to 4096): Sliding window attention (SWA) window size. If not specified, will default to `4096`. max_window_layers (`int`, *optional*, defaults to 28): The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import Qwen2Model, Qwen2Config >>> # Initializing a Qwen2 style configuration >>> configuration = Qwen2Config() >>> # Initializing a model from the Qwen2-7B style configuration >>> model = Qwen2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen2" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=151936, hidden_size=4096, intermediate_size=22016, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=32, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=28, attention_dropout=0.0, encoder_config=None, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.max_window_layers = max_window_layers # for backward compatibility 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.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.encoder_config = encoder_config super().__init__( tie_word_embeddings=tie_word_embeddings, **kwargs, )