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unitorch.models.llama¤

LlamaProcessor¤

Bases: HfTextClassificationProcessor, HfTextGenerationProcessor

Initialize the LlamaProcessor.

Parameters:

Name Type Description Default
vocab_file str

Path to the vocabulary file.

required
max_seq_length int

Maximum sequence length for text classification. Defaults to 128.

128
max_gen_seq_length int

Maximum sequence length for text generation. Defaults to 48.

48
Source code in src/unitorch/models/llama/processing.py
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def __init__(
    self,
    vocab_path: Optional[str] = None,
    tokenizer_file: Optional[str] = None,
    max_seq_length: Optional[int] = 128,
    max_gen_seq_length: Optional[int] = 48,
):
    """
    Initialize the LlamaProcessor.

    Args:
        vocab_file (str): Path to the vocabulary file.
        max_seq_length (int, optional): Maximum sequence length for text classification. Defaults to 128.
        max_gen_seq_length (int, optional): Maximum sequence length for text generation. Defaults to 48.
    """
    if vocab_path is not None:
        tokenizer = LlamaTokenizer(vocab_file=vocab_path)
    elif tokenizer_file is not None:
        tokenizer = LlamaTokenizerFast(
            tokenizer_file=tokenizer_file,
            bos_token="<|begin_of_text|>",
            eos_token="<|end_of_text|>",
        )
    else:
        raise ValueError("Either vocab_path or tokenizer_file must be provided")
    tokenizer.cls_token = tokenizer.bos_token
    tokenizer.sep_token = tokenizer.eos_token
    tokenizer.pad_token = tokenizer.unk_token
    tokenizer.cls_token_id = tokenizer.bos_token_id
    tokenizer.sep_token_id = tokenizer.eos_token_id
    tokenizer.pad_token_id = tokenizer.unk_token_id
    HfTextClassificationProcessor.__init__(
        self,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
    )
    HfTextGenerationProcessor.__init__(
        self,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        max_gen_seq_length=max_gen_seq_length,
    )

classification ¤

classification(
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
)

Process text for classification.

Parameters:

Name Type Description Default
text str

Input text.

required
text_pair str

Input text pair. Defaults to None.

None
max_seq_length int

Maximum sequence length. Defaults to None.

None

Returns:

Name Type Description
GenericOutputs

Processed input_ids and attention_mask tensors.

Source code in src/unitorch/models/llama/processing.py
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def classification(
    self,
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
):
    """
    Process text for classification.

    Args:
        text (str): Input text.
        text_pair (str, optional): Input text pair. Defaults to None.
        max_seq_length (int, optional): Maximum sequence length. Defaults to None.

    Returns:
        GenericOutputs: Processed input_ids and attention_mask tensors.
    """
    max_seq_length = pop_value(
        max_seq_length,
        self.max_seq_length,
    )

    tokens = self.tokenizer.tokenize(str(text))
    if text_pair is None:
        tokens = tokens[:max_seq_length]
        input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
    else:
        tokens_pair = self.tokenizer.tokenize(str(text_pair))
        truncate_sequence_pair(tokens, tokens_pair, max_seq_length)
        tokens = tokens + tokens_pair
        input_ids = self.tokenizer.convert_tokens_to_ids(tokens)

    padding = [0] * (max_seq_length - len(input_ids))
    attention_mask = [0] * len(padding) + [1] * len(input_ids)
    input_ids = len(padding) * [self.pad_token_id] + input_ids

    assert len(input_ids) == max_seq_length
    assert len(attention_mask) == max_seq_length
    return GenericOutputs(
        input_ids=torch.tensor(input_ids, dtype=torch.long),
        attention_mask=torch.tensor(attention_mask, dtype=torch.long),
    )

generation ¤

generation(
    text: str,
    text_pair: str,
    max_seq_length: Optional[int] = None,
    max_gen_seq_length: Optional[int] = None,
)

Process text for generation.

Parameters:

Name Type Description Default
text str

Input text.

required
text_pair str

Input text pair.

required
max_seq_length int

Maximum sequence length. Defaults to None.

None
max_gen_seq_length int

Maximum generation sequence length. Defaults to None.

None

Returns:

Name Type Description
GenericOutputs

Processed input_ids, attention_mask, input_ids_label, and attention_mask_label tensors.

Source code in src/unitorch/models/llama/processing.py
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def generation(
    self,
    text: str,
    text_pair: str,
    max_seq_length: Optional[int] = None,
    max_gen_seq_length: Optional[int] = None,
):
    """
    Process text for generation.

    Args:
        text (str): Input text.
        text_pair (str): Input text pair.
        max_seq_length (int, optional): Maximum sequence length. Defaults to None.
        max_gen_seq_length (int, optional): Maximum generation sequence length. Defaults to None.

    Returns:
        GenericOutputs: Processed input_ids, attention_mask, input_ids_label, and attention_mask_label tensors.
    """
    max_seq_length = pop_value(
        max_seq_length,
        self.max_seq_length,
    )
    max_gen_seq_length = pop_value(
        max_gen_seq_length,
        self.max_gen_seq_length,
    )

    tokens = [self.bos_token] + self.tokenizer.tokenize(str(text))[
        1 - max_seq_length :
    ]
    tokens_pair = self.tokenizer.tokenize(str(text_pair))[
        : max_gen_seq_length - 1
    ] + [self.eos_token]
    padding_a = [self.pad_token] * (max_seq_length - len(tokens))
    padding_b = [self.pad_token] * (max_gen_seq_length - len(tokens_pair))
    attention_mask = (
        [0] * len(padding_a)
        + [1] * (len(tokens) + len(tokens_pair))
        + [0] * len(padding_b)
    )
    _tokens = padding_a + tokens + tokens_pair + padding_b
    input_ids = self.tokenizer.convert_tokens_to_ids(_tokens)

    tokens_label = tokens_pair + [self.pad_token] * (
        max_gen_seq_length - len(tokens_pair) + 1
    )
    input_ids_label = self.tokenizer.convert_tokens_to_ids(tokens_label)
    input_ids_label = [0] * (max_seq_length - 1) + input_ids_label
    attention_mask_label = [1] * len(tokens_pair) + [0] * (
        max_gen_seq_length - len(tokens_pair) + 1
    )
    attention_mask_label = [0] * (max_seq_length - 1) + attention_mask_label

    return GenericOutputs(
        input_ids=torch.tensor(input_ids, dtype=torch.long),
        attention_mask=torch.tensor(attention_mask, dtype=torch.long),
        input_ids_label=torch.tensor(input_ids_label, dtype=torch.long),
        attention_mask_label=torch.tensor(attention_mask_label, dtype=torch.long),
    )

generation_inputs ¤

generation_inputs(
    text: str, max_seq_length: Optional[int] = None
)

Process text for generation inputs.

Parameters:

Name Type Description Default
text str

Input text.

required
max_seq_length int

Maximum sequence length. Defaults to None.

None

Returns:

Name Type Description
GenericOutputs

Processed input_ids tensor.

Source code in src/unitorch/models/llama/processing.py
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def generation_inputs(
    self,
    text: str,
    max_seq_length: Optional[int] = None,
):
    """
    Process text for generation inputs.

    Args:
        text (str): Input text.
        max_seq_length (int, optional): Maximum sequence length. Defaults to None.

    Returns:
        GenericOutputs: Processed input_ids tensor.
    """
    max_seq_length = pop_value(
        max_seq_length,
        self.max_seq_length,
    )
    tokens = [self.bos_token] + self.tokenizer.tokenize(str(text))[
        1 - max_seq_length :
    ]
    padding = [self.pad_token] * (max_seq_length - len(tokens))
    tokens = padding + tokens
    input_ids = self.tokenizer.convert_tokens_to_ids(tokens)

    assert len(input_ids) == max_seq_length
    return GenericOutputs(
        input_ids=torch.tensor(input_ids, dtype=torch.long),
    )

generation_labels ¤

generation_labels(
    text: str, max_gen_seq_length: Optional[int] = None
)

Process text for generation labels.

Parameters:

Name Type Description Default
text str

Input text.

required
max_gen_seq_length int

Maximum generation sequence length. Defaults to None.

None

Returns:

Name Type Description
GenericOutputs

Processed input_ids and attention_mask tensors.

Source code in src/unitorch/models/llama/processing.py
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def generation_labels(
    self,
    text: str,
    max_gen_seq_length: Optional[int] = None,
):
    """
    Process text for generation labels.

    Args:
        text (str): Input text.
        max_gen_seq_length (int, optional): Maximum generation sequence length. Defaults to None.

    Returns:
        GenericOutputs: Processed input_ids and attention_mask tensors.
    """
    max_gen_seq_length = pop_value(
        max_gen_seq_length,
        self.max_gen_seq_length,
    )
    tokens = self.tokenizer.tokenize(str(text))[: max_gen_seq_length - 1] + [
        self.eos_token
    ]
    padding = [self.pad_token] * (max_gen_seq_length - len(tokens))
    input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
    attention_mask = [1] * len(input_ids)

    padding = [0] * (max_gen_seq_length - len(input_ids))
    input_ids += [self.pad_token_id] * len(padding)
    attention_mask += padding

    assert len(input_ids) == max_gen_seq_length
    assert len(attention_mask) == max_gen_seq_length
    return GenericOutputs(
        input_ids=torch.tensor(input_ids, dtype=torch.long),
        attention_mask=torch.tensor(attention_mask, dtype=torch.long),
    )

instruction_generation_inputs ¤

instruction_generation_inputs(
    instruction: str,
    input: str,
    max_seq_length: Optional[int] = None,
) -> GenericOutputs

Preprocesses text as generation inputs.

Parameters:

Name Type Description Default
text str

The input text for generation.

required
max_seq_length Optional[int]

The maximum sequence length. Defaults to None.

None

Returns:

Name Type Description
GenericOutputs GenericOutputs

The processed input IDs tensor.

Source code in src/unitorch/models/llama/processing.py
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def instruction_generation_inputs(
    self,
    instruction: str,
    input: str,
    max_seq_length: Optional[int] = None,
) -> GenericOutputs:
    """
    Preprocesses text as generation inputs.

    Args:
        text (str): The input text for generation.
        max_seq_length (Optional[int]): The maximum sequence length. Defaults to None.

    Returns:
        GenericOutputs: The processed input IDs tensor.
    """
    max_seq_length = pop_value(
        max_seq_length,
        self.max_seq_length,
    )
    tokens = self._instrution_tokenize(instruction, input, max_seq_length)
    padding = [self.pad_token] * (max_seq_length - len(tokens))
    tokens = padding + tokens
    input_ids = self.tokenizer.convert_tokens_to_ids(tokens)

    assert len(input_ids) == max_seq_length
    assert len(input_ids) == max_seq_length
    return GenericOutputs(
        input_ids=torch.tensor(input_ids, dtype=torch.long),
    )

LlamaForClassification¤

Bases: GenericModel, QuantizationMixin, PeftWeightLoaderMixin

Llama model for classification tasks.

Parameters:

Name Type Description Default
config_path str

Path to the model configuration file.

required
num_classes int

Number of classes for classification. Defaults to 1.

1
hidden_dropout_prob float

Dropout probability for hidden layers. Defaults to 0.1.

0.1
gradient_checkpointing bool

Whether to use gradient checkpointing. Defaults to False.

False
Source code in src/unitorch/models/llama/modeling.py
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def __init__(
    self,
    config_path: str,
    quant_config_path: Optional[str] = None,
    num_classes: Optional[int] = 1,
    hidden_dropout_prob: Optional[float] = 0.1,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Llama model for classification tasks.

    Args:
        config_path (str): Path to the model configuration file.
        num_classes (int, optional): Number of classes for classification. Defaults to 1.
        hidden_dropout_prob (float, optional): Dropout probability for hidden layers. Defaults to 0.1.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
    """
    super().__init__()
    self.config = LlamaConfig.from_json_file(config_path)
    self.config.gradient_checkpointing = gradient_checkpointing
    self.model = LlamaModel(self.config)
    self.dropout = nn.Dropout(hidden_dropout_prob)
    self.classifier = nn.Linear(self.config.hidden_size, num_classes)
    self.init_weights()

    if quant_config_path is not None:
        self.quant_config = QuantizationConfig.from_json_file(quant_config_path)
        self.quantize(self.quant_config, ignore_modules=["lm_head"])

forward ¤

forward(
    input_ids: Tensor,
    attention_mask: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
)

Forward pass of the classification model.

Parameters:

Name Type Description Default
input_ids Tensor

Input tensor of shape (batch_size, sequence_length).

required
attention_mask Tensor

Attention mask tensor of shape (batch_size, sequence_length). Defaults to None.

None
position_ids Tensor

Position IDs tensor of shape (batch_size, sequence_length). Defaults to None.

None

Returns:

Type Description

torch Output logits.Tensor: tensor of shape (batch_size, num_classes).

Source code in src/unitorch/models/llama/modeling.py
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def forward(
    self,
    input_ids: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None,
):
    """
    Forward pass of the classification model.

    Args:
        input_ids (torch.Tensor): Input tensor of shape (batch_size, sequence_length).
        attention_mask (torch.Tensor, optional): Attention mask tensor of shape (batch_size, sequence_length). Defaults to None.
        position_ids (torch.Tensor, optional): Position IDs tensor of shape (batch_size, sequence_length). Defaults to None.

    Returns:
        torch Output logits.Tensor: tensor of shape (batch_size, num_classes).
    """
    outputs = self.model(
        input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
    )[0]
    pooled_output = outputs[:, -1]
    pooled_output = self.dropout(pooled_output)
    logits = self.classifier(pooled_output)
    return logits

LlamaForGeneration¤

Bases: GenericModel, QuantizationMixin, PeftWeightLoaderMixin

Llama model for text generation tasks.

Parameters:

Name Type Description Default
config_path str

Path to the model configuration file.

required
gradient_checkpointing bool

Whether to use gradient checkpointing. Defaults to False.

False
Source code in src/unitorch/models/llama/modeling.py
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def __init__(
    self,
    config_path: str,
    quant_config_path: Optional[str] = None,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Llama model for text generation tasks.

    Args:
        config_path (str): Path to the model configuration file.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
    """
    super().__init__()
    self.config = LlamaConfig.from_json_file(config_path)
    self.config.gradient_checkpointing = gradient_checkpointing
    self.base_model = LlamaForCausalLM(self.config)
    self.init_weights()

    if quant_config_path is not None:
        self.quant_config = QuantizationConfig.from_json_file(quant_config_path)
        self.quantize(self.quant_config, ignore_modules=["lm_head"])

forward ¤

forward(
    input_ids: Tensor,
    attention_mask: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
)

Forward pass of the generation model.

Parameters:

Name Type Description Default
input_ids Tensor

Input tensor of shape (batch_size, sequence_length). Defaults to None.

required
attention_mask Tensor

Attention mask tensor of shape (batch_size, sequence_length). Defaults to None.

None
position_ids Tensor

Position IDs tensor of shape (batch_size, sequence_length). Defaults to None.

None

Returns:

Type Description

torch Output logits.Tensor: tensor of shape (batch_size, sequence_length, vocab_size).

Source code in src/unitorch/models/llama/modeling.py
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def forward(
    self,
    input_ids: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None,
):
    """
    Forward pass of the generation model.

    Args:
        input_ids (torch.Tensor, optional): Input tensor of shape (batch_size, sequence_length). Defaults to None.
        attention_mask (torch.Tensor, optional): Attention mask tensor of shape (batch_size, sequence_length). Defaults to None.
        position_ids (torch.Tensor, optional): Position IDs tensor of shape (batch_size, sequence_length). Defaults to None.

    Returns:
        torch Output logits.Tensor: tensor of shape (batch_size, sequence_length, vocab_size).
    """
    outputs = self.base_model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        return_dict=True,
    )
    logits = outputs.logits
    return logits

generate ¤

generate(
    input_ids: Tensor,
    num_beams: Optional[int] = 5,
    decoder_start_token_id: Optional[int] = 1,
    decoder_end_token_id: Optional[
        Union[int, List[int]]
    ] = 2,
    num_return_sequences: Optional[int] = 1,
    min_gen_seq_length: Optional[int] = 0,
    max_gen_seq_length: Optional[int] = 48,
    repetition_penalty: Optional[float] = 1.0,
    no_repeat_ngram_size: Optional[int] = 0,
    early_stopping: Optional[bool] = True,
    length_penalty: Optional[float] = 1.0,
    num_beam_groups: Optional[int] = 1,
    diversity_penalty: Optional[float] = 0.0,
    do_sample: Optional[bool] = False,
    temperature: Optional[float] = 1.0,
    top_k: Optional[int] = 50,
    top_p: Optional[float] = 1.0,
)

Generate text using the generation model.

Parameters:

Name Type Description Default
input_ids Tensor

Input tensor of shape (batch_size, sequence_length).

required
num_beams int

Number of beams for beam search. Defaults to 5.

5
decoder_start_token_id int

The ID of the decoder start token. Defaults to 2.

1
decoder_end_token_id int or List[int]

The ID(s) of the decoder end token(s). Defaults to 2.

2
num_return_sequences int

Number of generated sequences to return. Defaults to 1.

1
min_gen_seq_length int

Minimum length of generated sequences. Defaults to 0.

0
max_gen_seq_length int

Maximum length of generated sequences. Defaults to 48.

48
repetition_penalty float

Penalty for repeated tokens. Defaults to 1.0.

1.0
no_repeat_ngram_size int

Size of n-grams to avoid repeating. Defaults to 0.

0
early_stopping bool

Whether to stop generation early. Defaults to True.

True
length_penalty float

Penalty for longer sequences. Defaults to 1.0.

1.0
num_beam_groups int

Number of beam groups for diverse beam search. Defaults to 1.

1
diversity_penalty float

Penalty for diverse sequences in diverse beam search. Defaults to 0.0.

0.0
do_sample bool

Whether to use sampling for generation. Defaults to False.

False
temperature float

Sampling temperature. Defaults to 1.0.

1.0
top_k int

Top-k value for sampling. Defaults to 50.

50
top_p float

Top-p value for sampling. Defaults to 1.0.

1.0

Returns:

Name Type Description
GenericOutputs

Generated sequences and their scores.

Source code in src/unitorch/models/llama/modeling.py
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@torch.no_grad()
def generate(
    self,
    input_ids: torch.Tensor,
    num_beams: Optional[int] = 5,
    decoder_start_token_id: Optional[int] = 1,
    decoder_end_token_id: Optional[Union[int, List[int]]] = 2,
    num_return_sequences: Optional[int] = 1,
    min_gen_seq_length: Optional[int] = 0,
    max_gen_seq_length: Optional[int] = 48,
    repetition_penalty: Optional[float] = 1.0,
    no_repeat_ngram_size: Optional[int] = 0,
    early_stopping: Optional[bool] = True,
    length_penalty: Optional[float] = 1.0,
    num_beam_groups: Optional[int] = 1,
    diversity_penalty: Optional[float] = 0.0,
    do_sample: Optional[bool] = False,
    temperature: Optional[float] = 1.0,
    top_k: Optional[int] = 50,
    top_p: Optional[float] = 1.0,
):
    """
    Generate text using the generation model.

    Args:
        input_ids: Input tensor of shape (batch_size, sequence_length).
        num_beams (int, optional): Number of beams for beam search. Defaults to 5.
        decoder_start_token_id (int, optional): The ID of the decoder start token. Defaults to 2.
        decoder_end_token_id (int or List[int], optional): The ID(s) of the decoder end token(s). Defaults to 2.
        num_return_sequences (int, optional): Number of generated sequences to return. Defaults to 1.
        min_gen_seq_length (int, optional): Minimum length of generated sequences. Defaults to 0.
        max_gen_seq_length (int, optional): Maximum length of generated sequences. Defaults to 48.
        repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
        no_repeat_ngram_size (int, optional): Size of n-grams to avoid repeating. Defaults to 0.
        early_stopping (bool, optional): Whether to stop generation early. Defaults to True.
        length_penalty (float, optional): Penalty for longer sequences. Defaults to 1.0.
        num_beam_groups (int, optional): Number of beam groups for diverse beam search. Defaults to 1.
        diversity_penalty (float, optional): Penalty for diverse sequences in diverse beam search. Defaults to 0.0.
        do_sample (bool, optional): Whether to use sampling for generation. Defaults to False.
        temperature (float, optional): Sampling temperature. Defaults to 1.0.
        top_k (int, optional): Top-k value for sampling. Defaults to 50.
        top_p (float, optional): Top-p value for sampling. Defaults to 1.0.

    Returns:
        GenericOutputs: Generated sequences and their scores.
    """
    input_seq_length = input_ids.size(1)
    outputs = self.base_model.generate(
        input_ids,
        max_length=max_gen_seq_length + input_seq_length,
        min_length=min_gen_seq_length + input_seq_length,
        num_beams=num_beams,
        do_sample=do_sample,
        decoder_start_token_id=decoder_start_token_id,
        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)
    )
    outputs.sequences = torch.zeros(
        sequences.size(0), num_return_sequences, max_gen_seq_length
    ).to(device=sequences.device)
    outputs.sequences[:, :, : sequences.size(-1) - input_seq_length].copy_(
        sequences[:, :, input_seq_length : sequences.size(-1)]
    )

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

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