easydel.modules.opt.opt_configuration

easydel.modules.opt.opt_configuration#

class easydel.modules.opt.opt_configuration.OPTConfig(vocab_size: int = 50272, hidden_size: int = 768, num_hidden_layers: int = 12, ffn_dim: int = 3072, max_position_embeddings: int = 2048, do_layer_norm_before: bool = True, _remove_final_layer_norm: bool = False, word_embed_proj_dim: int = None, dropout: float = 0.1, attention_dropout: float = 0.0, num_attention_heads: int = 12, activation_function: str = 'relu', layerdrop: float = 0.0, init_std: float = 0.02, use_cache: bool = True, pad_token_id: int = 1, bos_token_id: int = 2, eos_token_id: int = 2, enable_bias: bool = True, layer_norm_elementwise_affine: bool = True, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, **kwargs)[source]#

Bases: EasyDeLBaseConfig

Configuration objects inherit from [EasyDeLBaseConfig] and can be used to control the model outputs. Read the documentation from [EasyDeLBaseConfig] for more information.

Parameters

  • vocab_size (int, optional, defaults to 50272) – Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the inputs_ids passed to the forward method.

  • hidden_size (int, optional, defaults to 768) – Dimensionality of the encoder layers and the pooler layer.

  • num_hidden_layers (int, optional, defaults to 12) – Number of hidden layers in the Transformer encoder.

  • ffn_dim (int, optional, defaults to 3072) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

  • 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).

  • do_layer_norm_before (bool, optional, defaults to True) – Whether to perform layer normalization before the attention block.

  • _remove_final_layer_norm (bool, optional, defaults to False) – Whether to remove the final layer norm.

  • word_embed_proj_dim (int, optional) – The dimension of the word embedding projection. If not provided, it will default to hidden_size.

  • dropout (float, optional, defaults to 0.1) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

  • num_attention_heads (int, optional, defaults to 12) – Number of attention heads for each attention layer in the Transformer encoder.

  • activation_function (str or function, optional, defaults to “relu”) – The non-linear activation function (function or string) to use in the encoder and pooler. If string, “gelu”, “relu”, “swish” and “gelu_new” are supported.

  • layerdrop (float, optional, defaults to 0.0) – The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details.

  • init_std (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • 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, defaults to 1) – The index of the padding token in the vocabulary.

  • bos_token_id (int, optional, defaults to 2) – The id of the beginning-of-sequence token.

  • eos_token_id (int, optional, defaults to 2) – The id of the end-of-sequence token.

  • enable_bias (bool, optional, defaults to True) – Whether to use bias in the linear layers.

  • layer_norm_elementwise_affine (bool, optional, defaults to True) – Whether to use elementwise affine in the layer normalization layers.

  • gradient_checkpointing (str, optional, defaults to “nothing_saveable”) – The gradient checkpointing configuration.

add_jax_args(vocab_size: int = 50272, hidden_size: int = 768, num_hidden_layers: int = 12, ffn_dim: int = 3072, max_position_embeddings: int = 2048, do_layer_norm_before: bool = True, _remove_final_layer_norm: bool = False, word_embed_proj_dim: int = None, dropout: float = 0.1, attention_dropout: float = 0.0, num_attention_heads: int = 12, activation_function: str = 'relu', layerdrop: float = 0.0, init_std: float = 0.02, use_cache: bool = True, pad_token_id: int = 1, bos_token_id: int = 2, eos_token_id: int = 2, enable_bias: bool = True, layer_norm_elementwise_affine: bool = True, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, **kwargs)[source]#
get_partition_rules(fully_sharded_data_parallel: bool = True)[source]#

Get the partition rules for the model.

Parameters

fully_sharded_data_parallel (bool, optional, defaults to True) – Whether to use fully sharded data parallelism.

Returns

The partition rules.

Return type

tp.Tuple[tp.Tuple[str, PartitionSpec]]

keys_to_ignore_at_inference = ['past_key_values']#
model_type: str = 'opt'#
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