easydel.modules.gpt2.__init__

easydel.modules.gpt2.__init__#

class easydel.modules.gpt2.__init__.GPT2Config(vocab_size=50257, n_positions=1024, n_embd=768, n_layer=12, n_head=12, n_inner=None, activation_function='gelu_new', resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-05, initializer_range=0.02, summary_type='cls_index', summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, scale_attn_weights=True, use_cache=True, bos_token_id=50256, eos_token_id=50256, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, tie_word_embeddings: bool = False, bits: Optional[int] = 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 50257) – Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the inputs_ids passed to the forward method.

  • n_positions (int, optional, defaults to 1024) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 2048 or 4096).

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

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

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

  • n_inner (int, optional) – Dimensionality of the inner feed-forward layers.

  • activation_function (str, optional, defaults to “gelu_new”) – 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.

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

  • embd_pdrop (float, optional, defaults to 0.1) – The dropout ratio for the embeddings.

  • attn_pdrop (float, optional, defaults to 0.1) – The dropout ratio for the attention probabilities.

  • layer_norm_epsilon (float, optional, defaults to 1e-5) – The epsilon to use in the layer normalization layers.

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

  • summary_type (str, optional, defaults to “cls_index”) – The summary type to use. Possible values are “cls_index” (equivalent to the output of the last token of the first sentence in a sequence) and “last” (equivalent to the output of the last token in the sequence).

  • summary_use_proj (bool, optional, defaults to True) – Whether to use a projection after the vector extraction.

  • summary_activation (str, optional) – The activation to use for the summary.

  • summary_proj_to_labels (bool, optional, defaults to True) – Whether to project the summary to the labels.

  • summary_first_dropout (float, optional, defaults to 0.1) – The dropout ratio to be used after the projection and activation.

  • scale_attn_weights (bool, optional, defaults to True) – Scale attention weights by dividing by sqrt(hidden_size).

  • 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.

  • bos_token_id (int, optional, defaults to 50256) – The id of the beginning-of-sequence token.

  • eos_token_id (int, optional, defaults to 50256) – The id of the end-of-sequence token.

  • scale_attn_by_inverse_layer_idx (bool, optional, defaults to False) – Whether to scale attention weights by 1 / layer_idx + 1.

  • reorder_and_upcast_attn (bool, optional, defaults to False) – Whether to reorder and upcast attention.

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

  • tie_word_embeddings (bool, optional, defaults to False) – Whether to tie the weights of the input embeddings and the output embeddings.

  • bits (int, optional) – The number of bits to quantize the model to.

add_jax_args(gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, bits: Optional[int] = None, **kwargs)[source]#
attribute_map: Dict[str, str] = {'hidden_size': 'n_embd', 'max_position_embeddings': 'n_positions', 'num_attention_heads': 'n_head', 'num_hidden_layers': 'n_layer'}#
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 = 'gpt2'#
class easydel.modules.gpt2.__init__.GPT2LMHeadModel(*args: Any, **kwargs: Any)[source]#

Bases: EasyDeLBaseModule

class easydel.modules.gpt2.__init__.GPT2Model(*args: Any, **kwargs: Any)[source]#

Bases: EasyDeLBaseModule

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