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""" LongLLaMA 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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LONGLLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"syzymon/long_llama_3b": "https://huggingface.co/syzymon/long_llama_3b/resolve/main/config.json", |
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} |
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class LongLlamaConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`LongLlamaModel`]. It is used to instantiate an LongLLaMA |
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
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defaults will yield a similar configuration to that of the LongLLaMA-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 32000): |
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Vocabulary size of the LongLLaMA model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`LongLlamaModel`] |
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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 11008): |
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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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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 2048): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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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-12): |
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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 to tie weight embeddings |
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mem_layers (`List[int]`, defaults to `[]`): |
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Layers with memory |
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mem_positionals (`bool`, *optional*, defaults to `True`): |
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Whether to use positional embeddings in memory layers |
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mem_dtype (`str`, *optional*, defaults to `"bfloat16"`): |
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Type for keys and values stored in memory |
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mem_attention_grouping (`Tuple[int, int]`, *optional*, defaults to `None`): |
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One can trade speed for memory by performing attention |
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in memory layers sequentially. |
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When equal to `(4, 2048)` the memory layers will process at most 4 heads and 2048 queries from each head at once. |
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That is at most 4*2048 queries at once. |
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torch_attention (`bool`, *optional*, defaults to `False`): |
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Whether to use torch scaled_dot_product_attention |
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gradient_checkpoint_every_ith (`int`, *optional*, defaults to `1`): |
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When gradient checkpointing is enabled checkpoint every ith layer |
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Example: |
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```python |
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>>> from transformers import LongLlamaModel, LongLlamaConfig |
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>>> # Initializing a LongLLaMA longllama-7b style configuration |
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>>> configuration = LongLlamaConfig() |
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>>> # Initializing a model from the longllama-7b style configuration |
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>>> model = LongLlamaModel(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 = "longllama" |
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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=32000, |
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hidden_size=4096, |
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intermediate_size=11008, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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hidden_act="silu", |
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max_position_embeddings=2048, |
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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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pad_token_id=0, |
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bos_token_id=1, |
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eos_token_id=2, |
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tie_word_embeddings=False, |
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last_context_length=1024, |
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mem_layers=[], |
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mem_positionals=True, |
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mem_dtype="bfloat16", |
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mem_attention_grouping=None, |
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torch_attention=False, |
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gradient_checkpoint_every_ith=1, |
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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.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.last_context_length = last_context_length |
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self.mem_layers = mem_layers |
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self.mem_positionals = mem_positionals |
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self.mem_dtype = mem_dtype |
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self.mem_attention_grouping = mem_attention_grouping |
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self.torch_attention = torch_attention |
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self.gradient_checkpoint_every_ith = gradient_checkpoint_every_ith |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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