| """ MiniMaxText01 model configuration""" |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class MiniMaxText01Config(PretrainedConfig): |
| r""" |
| This is the configuration class to store the configuration of a [`MiniMaxText01Model`]. It is used to instantiate an |
| MiniMaxText01 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 MiniMaxText01. |
| |
| 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 MiniMaxText01 model. Defines the number of different tokens that can be represented by the |
| `inputs_ids` passed when calling [`MiniMaxText01Model`] |
| hidden_size (`int`, *optional*, defaults to 4096): |
| Dimension of the hidden representations. |
| intermediate_size (`int`, *optional*, defaults to 14336): |
| 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 8): |
| 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 `8`. |
| 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 `4096*32`): |
| The maximum sequence length that this model might ever be used with. MiniMaxText01's sliding window attention |
| allows sequence of up to 4096*32 tokens. |
| 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-05): |
| 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`. |
| pad_token_id (`int`, *optional*): |
| The id of the padding token. |
| bos_token_id (`int`, *optional*, defaults to 1): |
| The id of the "beginning-of-sequence" token. |
| eos_token_id (`int`, *optional*, defaults to 2): |
| The id of the "end-of-sequence" token. |
| 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 1000000.0): |
| The base period of the RoPE embeddings. |
| sliding_window (`int`, *optional*): |
| Sliding window attention window size. If not specified, will default to `4096`. |
| attention_dropout (`float`, *optional*, defaults to 0.0): |
| The dropout ratio for the attention probabilities. |
| num_experts_per_tok (`int`, *optional*, defaults to 2): |
| The number of experts to route per-token, can be also interpreted as the `top-k` routing |
| parameter |
| num_local_experts (`int`, *optional*, defaults to 8): |
| Number of experts per Sparse MLP layer. |
| output_router_logits (`bool`, *optional*, defaults to `False`): |
| Whether or not the router logits should be returned by the model. Enabeling this will also |
| allow the model to output the auxiliary loss. See [here]() for more details |
| router_aux_loss_coef (`float`, *optional*, defaults to 0.001): |
| The aux loss factor for the total loss. |
| router_jitter_noise (`float`, *optional*, defaults to 0.0): |
| Amount of noise to add to the router. |
| |
| ```python |
| >>> from transformers import MiniMaxText01Model, MiniMaxText01Config |
| |
| >>> # Initializing a MiniMaxText01 style configuration |
| >>> configuration = MiniMaxText01Config() |
| |
| >>> # Initializing a model from the MiniMaxText01 style configuration |
| >>> model = MiniMaxText01Model(configuration) |
| |
| >>> # Accessing the model configuration |
| >>> configuration = model.config |
| ```""" |
|
|
| model_type = "MiniMaxText01" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=32000, |
| hidden_size=4096, |
| intermediate_size=14336, |
| num_hidden_layers=32, |
| num_attention_heads=32, |
| num_key_value_heads=8, |
| hidden_act="silu", |
| max_position_embeddings=4096 * 32, |
| initializer_range=0.02, |
| rms_norm_eps=1e-5, |
| use_cache=True, |
| pad_token_id=None, |
| bos_token_id=None, |
| eos_token_id=None, |
| tie_word_embeddings=False, |
| rope_theta=1e6, |
| sliding_window=None, |
| attention_dropout=0.0, |
| num_experts_per_tok=2, |
| num_local_experts=8, |
| output_router_logits=False, |
| router_aux_loss_coef=0.001, |
| router_jitter_noise=0.0, |
| **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.sliding_window = sliding_window |
|
|
| |
| 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.num_experts_per_tok = num_experts_per_tok |
| self.num_local_experts = num_local_experts |
| self.output_router_logits = output_router_logits |
| self.router_aux_loss_coef = router_aux_loss_coef |
| self.router_jitter_noise = router_jitter_noise |
| 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, |
| ) |
|
|
