| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| """ LongLLaMA model configuration""" |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
| LONGLLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
| "syzymon/long_llama_3b": "https://huggingface.co/syzymon/long_llama_3b/resolve/main/config.json", |
| } |
|
|
|
|
| class LongLlamaConfig(PretrainedConfig): |
| r""" |
| This is the configuration class to store the configuration of a [`LongLlamaModel`]. It is used to instantiate an LongLLaMA |
| model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
| defaults will yield a similar configuration to that of the LongLLaMA-7B. |
| |
| 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 32000): |
| Vocabulary size of the LongLLaMA model. Defines the number of different tokens that can be represented by the |
| `inputs_ids` passed when calling [`LongLlamaModel`] |
| hidden_size (`int`, *optional*, defaults to 4096): |
| Dimension of the hidden representations. |
| intermediate_size (`int`, *optional*, defaults to 11008): |
| 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. |
| 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 2048): |
| The maximum sequence length that this model might ever be used with. Typically set this to something large |
| just in case (e.g., 512 or 1024 or 2048). |
| 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-12): |
| 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 to tie weight embeddings |
| mem_layers (`List[int]`, defaults to `[]`): |
| Layers with memory |
| mem_positionals (`bool`, *optional*, defaults to `True`): |
| Whether to use positional embeddings in memory layers |
| mem_dtype (`str`, *optional*, defaults to `"bfloat16"`): |
| Type for keys and values stored in memory |
| mem_attention_grouping (`Tuple[int, int]`, *optional*, defaults to `None`): |
| One can trade speed for memory by performing attention |
| in memory layers sequentially. |
| When equal to `(4, 2048)` the memory layers will process at most 4 heads and 2048 queries from each head at once. |
| That is at most 4*2048 queries at once. |
| torch_attention (`bool`, *optional*, defaults to `False`): |
| Whether to use torch scaled_dot_product_attention |
| gradient_checkpoint_every_ith (`int`, *optional*, defaults to `1`): |
| When gradient checkpointing is enabled checkpoint every ith layer |
| |
| Example: |
| |
| ```python |
| >>> from transformers import LongLlamaModel, LongLlamaConfig |
| |
| >>> # Initializing a LongLLaMA longllama-7b style configuration |
| >>> configuration = LongLlamaConfig() |
| |
| >>> # Initializing a model from the longllama-7b style configuration |
| >>> model = LongLlamaModel(configuration) |
| |
| >>> # Accessing the model configuration |
| >>> configuration = model.config |
| ```""" |
| model_type = "longllama" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=32000, |
| hidden_size=4096, |
| intermediate_size=11008, |
| num_hidden_layers=32, |
| num_attention_heads=32, |
| hidden_act="silu", |
| max_position_embeddings=2048, |
| initializer_range=0.02, |
| rms_norm_eps=1e-6, |
| use_cache=True, |
| pad_token_id=0, |
| bos_token_id=1, |
| eos_token_id=2, |
| tie_word_embeddings=False, |
| rope_theta=10000.0, |
| rope_scaling=None, |
| last_context_length=1024, |
| mem_layers=[], |
| mem_positionals=True, |
| mem_dtype="bfloat16", |
| mem_attention_grouping=None, |
| torch_attention=False, |
| gradient_checkpoint_every_ith=1, |
| **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.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.last_context_length = last_context_length |
| self.mem_layers = mem_layers |
| self.mem_positionals = mem_positionals |
| self.mem_dtype = mem_dtype |
| self.mem_attention_grouping = mem_attention_grouping |
| self.torch_attention = torch_attention |
| self.gradient_checkpoint_every_ith = gradient_checkpoint_every_ith |
|
|
| self._rope_scaling_validation() |
| super().__init__( |
| pad_token_id=pad_token_id, |
| bos_token_id=bos_token_id, |
| eos_token_id=eos_token_id, |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs, |
| ) |
|
|
| def _rope_scaling_validation(self): |
| """ |
| Validate the `rope_scaling` configuration. |
| """ |
| if self.rope_scaling is not None: |
| raise ValueError("LongLLaMA does not use rope_scaling") |
|
|
