ls_mlkit.model.decoder_tf package

Submodules

• ls_mlkit.model.decoder_tf.causal_transformer module
• ls_mlkit.model.decoder_tf.generate module
• ls_mlkit.model.decoder_tf.tokenizer module

Module contents

class ls_mlkit.model.decoder_tf.CausalLanguageModel(vocab_size, embed_dim, num_head, dropout=0, num_block=3, max_pos_len=5000, batch_first=True)

Bases: Module

forward(x: Tensor, att_mask: Tensor = None, key_padding_mask: Tensor = None, need_weights: bool = True, average_attn_weights: bool = True, use_cache: bool = False, past_key_values: Tensor = None, is_causal: bool = True, need_hidden_states: bool = False)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

generate_square_subsequent_mask(sz: int, device=None, dtype=None)

Generate a square causal mask for the sequence.

The masked positions are filled with ‘True’. Unmasked positions are filled with False

class ls_mlkit.model.decoder_tf.CausalLanguageModelConfig(vocab_size=32000, embed_dim=1024, num_head=2, dropout=0, num_block=3, max_pos_len=5000, batch_first=True, **kwargs)

Bases: object

class ls_mlkit.model.decoder_tf.CausalLanguageModelConfigForAuto(vocab_size=30000, embed_dim=1024, num_head=2, dropout=0, num_block=3, max_pos_len=5000, batch_first=True, **kwargs)

Bases: PretrainedConfig

model_type: str = 'D-TF-no-PE'

class ls_mlkit.model.decoder_tf.CausalLanguageModelForAuto(config: CausalLanguageModelConfigForAuto)

Bases: PreTrainedModel, GenerationMixin

base_model_prefix = 'zls_causal_tf'

config_class

alias of CausalLanguageModelConfigForAuto

forward(input_ids: LongTensor = None, attention_mask: Tensor | None = None, output_attentions: bool | None = True, average_attn_weights: bool = True, position_ids: LongTensor | None = None, past_key_values=None, inputs_embeds: FloatTensor | None = None, labels: LongTensor | None = None, use_cache: bool | None = False, output_hidden_states: bool | None = None, return_dict: bool | None = None, cache_position: LongTensor | None = None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_input_embeddings()

Returns the model’s input embeddings.

Returns:

A torch module mapping vocabulary to hidden states.

Return type:

nn.Module

get_output_embeddings()

Returns the model’s output embeddings.

Returns:

A torch module mapping hidden states to vocabulary.

Return type:

nn.Module

prepare_inputs_for_generation(input_ids, past_key_values=None, attention_mask=None, **kwargs)

Prepare the model inputs for generation. In includes operations like computing the 4D attention mask or slicing inputs given the existing cache.

See the forward pass in the model documentation for expected arguments (different models might have different requirements for e.g. past_key_values). This function should work as is for most LLMs.

ls_mlkit.model.decoder_tf.get_causal_model(vocab_size=5000, embed_dim=1024, num_head=8, dropout=0, num_block=16, max_pos_len=5000, batch_first=True, **kwargs)