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unitorch.models.bart¤

BartProcessor¤

Bases: HfTextGenerationProcessor

Text generation processor for BART models.

Source code in src/unitorch/models/bart/processing.py 
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def __init__(
    self,
    vocab_path: str,
    merge_path: str,
    special_input_ids: Optional[Dict] = None,
    max_seq_length: int = 128,
    max_gen_seq_length: int = 48,
) -> None:
    super().__init__(
        tokenizer=get_bart_tokenizer(vocab_path, merge_path, special_input_ids),
        max_seq_length=max_seq_length,
        max_gen_seq_length=max_gen_seq_length,
    )

BartForGeneration¤

Bases: GenericModel

BART model for sequence-to-sequence text generation.

Source code in src/unitorch/models/bart/modeling.py 
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def __init__(
    self,
    config_path: str,
    gradient_checkpointing: bool = False,
) -> None:
    super().__init__()
    self.config = BartConfig.from_json_file(config_path)
    self.config.gradient_checkpointing = gradient_checkpointing
    self.config.forced_bos_token_id = None
    self.model = BartForConditionalGeneration(self.config)
    self.init_weights()

prefix_keys_in_state_dict class-attribute instance-attribute ¤

prefix_keys_in_state_dict = {
    "^(?!model\\.model\\.|model\\.).*": "model.model."
}

config instance-attribute ¤

config = from_json_file(config_path)

model instance-attribute ¤

model = BartForConditionalGeneration(config)

forward ¤

forward(
    input_ids: Tensor,
    attention_mask: Tensor,
    decoder_input_ids: Tensor,
    decoder_attention_mask: Tensor,
) -> Tensor
Source code in src/unitorch/models/bart/modeling.py 
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def forward(
    self,
    input_ids: torch.Tensor,
    attention_mask: torch.Tensor,
    decoder_input_ids: torch.Tensor,
    decoder_attention_mask: torch.Tensor,
) -> torch.Tensor:
    return self.model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        decoder_input_ids=decoder_input_ids,
        decoder_attention_mask=decoder_attention_mask,
        return_dict=True,
    ).logits

generate ¤

generate(
    input_ids: Tensor,
    num_beams: int = 5,
    decoder_start_token_id: int = 2,
    decoder_end_token_id: Union[int, List[int]] = 2,
    num_return_sequences: int = 1,
    min_gen_seq_length: int = 0,
    max_gen_seq_length: int = 48,
    repetition_penalty: float = 1.0,
    no_repeat_ngram_size: int = 0,
    early_stopping: bool = True,
    length_penalty: float = 1.0,
    num_beam_groups: int = 1,
    diversity_penalty: float = 0.0,
    do_sample: bool = False,
    temperature: float = 1.0,
    top_k: int = 50,
    top_p: float = 1.0,
) -> GenericOutputs
Source code in src/unitorch/models/bart/modeling.py 
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@torch.no_grad()
def generate(
    self,
    input_ids: torch.Tensor,
    num_beams: int = 5,
    decoder_start_token_id: int = 2,
    decoder_end_token_id: Union[int, List[int]] = 2,
    num_return_sequences: int = 1,
    min_gen_seq_length: int = 0,
    max_gen_seq_length: int = 48,
    repetition_penalty: float = 1.0,
    no_repeat_ngram_size: int = 0,
    early_stopping: bool = True,
    length_penalty: float = 1.0,
    num_beam_groups: int = 1,
    diversity_penalty: float = 0.0,
    do_sample: bool = False,
    temperature: float = 1.0,
    top_k: int = 50,
    top_p: float = 1.0,
) -> GenericOutputs:
    outputs = self.model.generate(
        input_ids,
        max_length=max_gen_seq_length,
        min_length=min_gen_seq_length,
        num_beams=num_beams,
        do_sample=do_sample,
        no_repeat_ngram_size=no_repeat_ngram_size,
        early_stopping=early_stopping,
        length_penalty=length_penalty,
        repetition_penalty=repetition_penalty,
        num_return_sequences=num_return_sequences,
        bos_token_id=decoder_start_token_id,
        eos_token_id=decoder_end_token_id,
        num_beam_groups=num_beam_groups,
        diversity_penalty=diversity_penalty,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        return_dict_in_generate=True,
        output_scores=True,
    )

    sequences = outputs.sequences.reshape(
        -1, num_return_sequences, outputs.sequences.size(-1)
    )
    padded = torch.full(
        (sequences.size(0), num_return_sequences, max_gen_seq_length),
        fill_value=decoder_start_token_id,
        device=sequences.device,
        dtype=sequences.dtype,
    )
    padded[:, :, : sequences.size(-1)].copy_(sequences)

    if num_return_sequences == 1:
        padded = padded.reshape(-1, max_gen_seq_length)

    return GenericOutputs(
        sequences=padded, sequences_scores=outputs.sequences_scores
    )