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unitorch.cli.models.bloom¤

BloomProcessor¤

Tip

core/process/bloom is the section for configuration of BloomProcessor.

Bases: BloomProcessor

Processor for Bloom language models.

Initialize the BloomProcessor.

Parameters:

Name Type Description Default
tokenizer_file str

The path to the tokenizer file.

 required
max_seq_length int

The maximum sequence length. Defaults to 128.

 128
max_gen_seq_length int

The maximum generation sequence length. Defaults to 128.

 128
Source code in src/unitorch/cli/models/bloom/processing.py 
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def __init__(
    self,
    tokenizer_file: str,
    max_seq_length: Optional[int] = 128,
    max_gen_seq_length: Optional[int] = 128,
):
    """
    Initialize the BloomProcessor.

    Args:
        tokenizer_file (str): The path to the tokenizer file.
        max_seq_length (int, optional): The maximum sequence length. Defaults to 128.
        max_gen_seq_length (int, optional): The maximum generation sequence length. Defaults to 128.
    """
    super().__init__(
        tokenizer_file=tokenizer_file,
        max_seq_length=max_seq_length,
        max_gen_seq_length=max_gen_seq_length,
    )

from_core_configure classmethod ¤

from_core_configure(config, **kwargs)

Create an instance of BloomProcessor from the core configuration.

Parameters:

Name Type Description Default
config Config

The core configuration object.

 required

Returns:

Name Type Description
BloomProcessor

An instance of BloomProcessor initialized with the provided configuration.
Source code in src/unitorch/cli/models/bloom/processing.py 
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@classmethod
@add_default_section_for_init("core/process/bloom")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BloomProcessor from the core configuration.

    Args:
        config (Config): The core configuration object.

    Returns:
        BloomProcessor: An instance of BloomProcessor initialized with the provided configuration.
    """
    config.set_default_section("core/process/bloom")
    pretrained_name = config.getoption("pretrained_name", "bloom-560m")
    tokenizer_file = config.getoption("tokenizer_file", None)
    tokenizer_file = pop_value(
        tokenizer_file,
        nested_dict_value(pretrained_bloom_infos, pretrained_name, "tokenizer"),
    )
    tokenizer_file = cached_path(tokenizer_file)

    return {
        "tokenizer_file": tokenizer_file,
    }

BloomForClassification¤

Tip

core/model/classification/bloom is the section for configuration of BloomForClassification.

Bases: BloomForClassification

Bloom model for classification.

Initialize the BloomForClassification model.

Parameters:

Name Type Description Default
config_path str

The path to the model configuration file.

 required
num_classes int

The number of classes for classification. Defaults to 1.

 1
gradient_checkpointing bool

Whether to use gradient checkpointing during training. Defaults to False.

 False
Source code in src/unitorch/cli/models/bloom/modeling.py 
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def __init__(
    self,
    config_path: str,
    num_classes: Optional[int] = 1,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Initialize the BloomForClassification model.

    Args:
        config_path (str): The path to the model configuration file.
        num_classes (int, optional): The number of classes for classification. Defaults to 1.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing during training. Defaults to False.
    """
    super().__init__(
        config_path=config_path,
        num_classes=num_classes,
        gradient_checkpointing=gradient_checkpointing,
    )

forward ¤

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

Perform forward pass of the BloomForClassification model.

Parameters:

Name Type Description Default
input_ids Tensor

The input token IDs.

 required
attention_mask Tensor

The attention mask. Defaults to None.

 None

Returns:

Name Type Description
ClassificationOutputs

The classification outputs.
Source code in src/unitorch/cli/models/bloom/modeling.py 
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@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    input_ids: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
):
    """
    Perform forward pass of the BloomForClassification model.

    Args:
        input_ids (torch.Tensor): The input token IDs.
        attention_mask (torch.Tensor, optional): The attention mask. Defaults to None.

    Returns:
        ClassificationOutputs: The classification outputs.
    """
    outputs = super().forward(
        input_ids=input_ids,
        attention_mask=attention_mask,
    )
    return ClassificationOutputs(outputs=outputs)

from_core_configure classmethod ¤

from_core_configure(config, **kwargs)

Create an instance of BloomForClassification from the core configuration.

Parameters:

Name Type Description Default
config Config

The core configuration object.

 required

Returns:

Name Type Description
BloomForClassification

An instance of BloomForClassification initialized with the provided configuration.
Source code in src/unitorch/cli/models/bloom/modeling.py 
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@classmethod
@add_default_section_for_init("core/model/classification/bloom")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BloomForClassification from the core configuration.

    Args:
        config (Config): The core configuration object.

    Returns:
        BloomForClassification: An instance of BloomForClassification initialized with the provided configuration.
    """
    config.set_default_section("core/model/classification/bloom")
    pretrained_name = config.getoption("pretrained_name", "bloom-560m")
    pretrained_lora_name = config.getoption(
        "pretrained_lora_name", "bloom-560m-lora"
    )
    config_path = config.getoption("config_path", None)
    num_classes = config.getoption("num_classes", 1)

    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_bloom_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)

    inst = cls(config_path, num_classes, gradient_checkpointing)
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_bloom_infos, pretrained_name, "weight"),
        check_none=False,
    )

    if weight_path is not None:
        inst.from_pretrained(weight_path)

    pretrained_lora_weight_path = config.getoption(
        "pretrained_lora_weight_path", None
    )
    lora_weight_path = pop_value(
        pretrained_lora_weight_path,
        nested_dict_value(pretrained_bloom_extensions_infos, pretrained_lora_name),
        check_none=False,
    )
    pretrained_lora_weight = config.getoption("pretrained_lora_weight", 1.0)
    pretrained_lora_alpha = config.getoption("pretrained_lora_alpha", 32.0)
    if lora_weight_path is not None:
        inst.load_lora_weights(
            lora_weight_path,
            lora_weights=pretrained_lora_weight,
            lora_alphas=pretrained_lora_alpha,
        )

    return inst

BloomForGeneration¤

Tip

core/model/generation/bloom is the section for configuration of BloomForGeneration.

Bases: BloomForGeneration

Bloom model for text generation.

Initialize the BloomForGeneration model.

Parameters:

Name Type Description Default
config_path str

The path to the model configuration file.

 required
gradient_checkpointing bool

Whether to use gradient checkpointing during training. Defaults to False.

 False
Source code in src/unitorch/cli/models/bloom/modeling.py 
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def __init__(
    self,
    config_path: str,
    gradient_checkpointing: Optional[bool] = False,
):
    """
    Initialize the BloomForGeneration model.

    Args:
        config_path (str): The path to the model configuration file.
        gradient_checkpointing (bool, optional): Whether to use gradient checkpointing during training. Defaults to False.
    """
    super().__init__(
        config_path=config_path,
        gradient_checkpointing=gradient_checkpointing,
    )

forward ¤

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

Perform forward pass of the BloomForGeneration model.

Parameters:

Name Type Description Default
input_ids Tensor

The input token IDs.

 required
attention_mask Tensor

The attention mask. Defaults to None.

 None

Returns:

Name Type Description
GenerationOutputs

The generation outputs.
Source code in src/unitorch/cli/models/bloom/modeling.py 
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@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    input_ids: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
):
    """
    Perform forward pass of the BloomForGeneration model.

    Args:
        input_ids (torch.Tensor): The input token IDs.
        attention_mask (torch.Tensor, optional): The attention mask. Defaults to None.

    Returns:
        GenerationOutputs: The generation outputs.
    """
    outputs = super().forward(
        input_ids=input_ids,
        attention_mask=attention_mask,
    )
    return GenerationOutputs(sequences=outputs)

from_core_configure classmethod ¤

from_core_configure(config, **kwargs)

Create an instance of BloomForGeneration from the core configuration.

Parameters:

Name Type Description Default
config Config

The core configuration object.

 required

Returns:

Name Type Description
BloomForGeneration

An instance of BloomForGeneration initialized with the provided configuration.
Source code in src/unitorch/cli/models/bloom/modeling.py 
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@classmethod
@add_default_section_for_init("core/model/generation/bloom")
def from_core_configure(cls, config, **kwargs):
    """
    Create an instance of BloomForGeneration from the core configuration.

    Args:
        config (Config): The core configuration object.

    Returns:
        BloomForGeneration: An instance of BloomForGeneration initialized with the provided configuration.
    """
    config.set_default_section("core/model/generation/bloom")
    pretrained_name = config.getoption("pretrained_name", "bloom-560m")
    pretrained_lora_name = config.getoption(
        "pretrained_lora_name", "bloom-560m-lora"
    )
    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_bloom_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)

    inst = cls(config_path, gradient_checkpointing)
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_bloom_infos, pretrained_name, "weight"),
        check_none=False,
    )

    if weight_path is not None:
        inst.from_pretrained(weight_path)

    pretrained_lora_weight_path = config.getoption(
        "pretrained_lora_weight_path", None
    )
    lora_weight_path = pop_value(
        pretrained_lora_weight_path,
        nested_dict_value(pretrained_bloom_extensions_infos, pretrained_lora_name),
        check_none=False,
    )
    pretrained_lora_weight = config.getoption("pretrained_lora_weight", 1.0)
    pretrained_lora_alpha = config.getoption("pretrained_lora_alpha", 32.0)
    if lora_weight_path is not None:
        inst.load_lora_weights(
            lora_weight_path,
            lora_weights=pretrained_lora_weight,
            lora_alphas=pretrained_lora_alpha,
        )

    return inst

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 sequences using the Bloom model.

Parameters:

Name Type Description Default
input_ids Tensor

Input token IDs.

 required
num_beams int

Number of beams for beam search. Defaults to 5.

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decoder_start_token_id int

Decoder start token ID. Defaults to 0.

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decoder_end_token_id int or List[int]

The ID(s) of the decoder end token(s). Defaults to 1.

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num_return_sequences int

Number of generated sequences to return. Defaults to 1.

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min_gen_seq_length int

Minimum generation sequence length. Defaults to 0.

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max_gen_seq_length int

Maximum generation sequence length. Defaults to 48.

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repetition_penalty float

Repetition penalty. Defaults to 1.0.

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no_repeat_ngram_size int

Size of n-grams to prevent repetition. Defaults to 0.

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early_stopping bool

Whether to perform early stopping. Defaults to True.

 True
length_penalty float

Length penalty. Defaults to 1.0.

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num_beam_groups int

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

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diversity_penalty float

Diversity penalty for diverse beam search. Defaults to 0.0.

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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 sampling parameter. Defaults to 50.

 50
top_p float

Top-p sampling parameter. Defaults to 1.0.

 1.0

Returns:

Name Type Description
GenerationOutputs

The generation outputs.
Source code in src/unitorch/cli/models/bloom/modeling.py 
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@add_default_section_for_function("core/model/generation/bloom")
@torch.no_grad()
@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
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 sequences using the Bloom model.

    Args:
        input_ids (torch.Tensor): Input token IDs.
        num_beams (int, optional): Number of beams for beam search. Defaults to 5.
        decoder_start_token_id (int, optional): Decoder start token ID. Defaults to 0.
        decoder_end_token_id (int or List[int], optional): The ID(s) of the decoder end token(s). Defaults to 1.
        num_return_sequences (int, optional): Number of generated sequences to return. Defaults to 1.
        min_gen_seq_length (int, optional): Minimum generation sequence length. Defaults to 0.
        max_gen_seq_length (int, optional): Maximum generation sequence length. Defaults to 48.
        repetition_penalty (float, optional): Repetition penalty. Defaults to 1.0.
        no_repeat_ngram_size (int, optional): Size of n-grams to prevent repetition. Defaults to 0.
        early_stopping (bool, optional): Whether to perform early stopping. Defaults to True.
        length_penalty (float, optional): Length penalty. Defaults to 1.0.
        num_beam_groups (int, optional): Number of beam groups for diverse beam search. Defaults to 1.
        diversity_penalty (float, optional): Diversity penalty for 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 sampling parameter. Defaults to 50.
        top_p (float, optional): Top-p sampling parameter. Defaults to 1.0.

    Returns:
        GenerationOutputs: The generation outputs.
    """
    outputs = super().generate(
        input_ids,
        num_beams=num_beams,
        decoder_start_token_id=decoder_start_token_id,
        decoder_end_token_id=decoder_end_token_id,
        num_return_sequences=num_return_sequences,
        min_gen_seq_length=min_gen_seq_length,
        max_gen_seq_length=max_gen_seq_length,
        repetition_penalty=repetition_penalty,
        no_repeat_ngram_size=no_repeat_ngram_size,
        early_stopping=early_stopping,
        length_penalty=length_penalty,
        num_beam_groups=num_beam_groups,
        diversity_penalty=diversity_penalty,
        do_sample=do_sample,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
    )

    return GenerationOutputs(
        sequences=outputs.sequences,
        sequences_scores=outputs.sequences_scores,
    )