content: # Copyright 2024 The Qwen Team and The HuggingFace Inc. team.
  # SPDX-License-Identifier: Apache-2.0
  
  """Qwen2 model configuration"""
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.modeling_rope_utils import rope_config_validation
  from transformers.utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  
  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.
          rope_scaling (`Dict`, *optional*):
              Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
              and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
              accordingly.
              Expected contents:
                  `rope_type` (`str`):
                      The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                      'llama3'], with 'default' being the original RoPE implementation.
                  `factor` (`float`, *optional*):
                      Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                      most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                      original maximum pre-trained length.
                  `original_max_position_embeddings` (`int`, *optional*):
                      Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
                      pretraining.
                  `attention_factor` (`float`, *optional*):
                      Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                      computation. If unspecified, it defaults to value recommended by the implementation, using the
                      `factor` field to infer the suggested value.
                  `beta_fast` (`float`, *optional*):
                      Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                      ramp function. If unspecified, it defaults to 32.
                  `beta_slow` (`float`, *optional*):
                      Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                      ramp function. If unspecified, it defaults to 1.
                  `short_factor` (`List[float]`, *optional*):
                      Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                      `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                      size divided by the number of attention heads divided by 2
                  `long_factor` (`List[float]`, *optional*):
                      Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                      `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                      size divided by the number of attention heads divided by 2
                  `low_freq_factor` (`float`, *optional*):
                      Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                  `high_freq_factor` (`float`, *optional*):
                      Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
          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,
          rope_scaling=None,
          use_sliding_window=False,
          sliding_window=4096,
          max_window_layers=28,
          attention_dropout=0.0,
          is_causal=True,
          _attn_implementation="flash_attention_2",
          **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 if use_sliding_window else None
          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.rope_scaling = rope_scaling
          self.attention_dropout = attention_dropout
          self.is_causal = is_causal
          self._attn_implementation = _attn_implementation
  
          # Validate the correctness of rotary position embeddings parameters
          # BC: if there is a 'type' field, move it to 'rope_type'.
          if self.rope_scaling is not None and "type" in self.rope_scaling:
              self.rope_scaling["rope_type"] = self.rope_scaling["type"]
          rope_config_validation(self)
  
          super().__init__(
              tie_word_embeddings=tie_word_embeddings,
              **kwargs,
          )
  