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unitorch.cli.models.mistral¤

MistralProcessor¤

Tip

core/process/mistral is the section for configuration of MistralProcessor.

Bases: MistralProcessor

Processor for Mistral models.

Source code in src/unitorch/cli/models/mistral/processing.py 
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def __init__(
    self,
    tokenizer_file: str,
    tokenizer_config: Optional[str] = None,
    special_tokens_map: Optional[str] = None,
    max_seq_length: Optional[int] = 128,
    max_gen_seq_length: Optional[int] = 128,
):
    super().__init__(
        tokenizer_file=tokenizer_file,
        tokenizer_config=tokenizer_config,
        special_tokens_map=special_tokens_map,
        max_seq_length=max_seq_length,
        max_gen_seq_length=max_gen_seq_length,
    )

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/models/mistral/processing.py 
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@classmethod
@config_defaults_init("core/process/mistral")
def from_config(cls, config, **kwargs):
    config.set_default_section("core/process/mistral")
    pretrained_name = config.getoption(
        "pretrained_name", "mistral-7b-instruct-v0.1"
    )
    tokenizer_file = config.getoption("tokenizer_file", None)
    tokenizer_file = pop_value(
        tokenizer_file,
        nested_dict_value(pretrained_mistral_infos, pretrained_name, "tokenizer"),
    )
    tokenizer_file = cached_path(tokenizer_file)

    tokenizer_config = config.getoption("tokenizer_config", None)
    tokenizer_config = pop_value(
        tokenizer_config,
        nested_dict_value(
            pretrained_mistral_infos, pretrained_name, "tokenizer_config"
        ),
        check_none=False,
    )
    tokenizer_config = (
        cached_path(tokenizer_config) if tokenizer_config is not None else None
    )

    special_tokens_map = config.getoption("special_tokens_map", None)
    special_tokens_map = pop_value(
        special_tokens_map,
        nested_dict_value(
            pretrained_mistral_infos, pretrained_name, "special_tokens_map"
        ),
        check_none=False,
    )
    special_tokens_map = (
        cached_path(special_tokens_map) if special_tokens_map is not None else None
    )

    return {
        "tokenizer_file": tokenizer_file,
        "tokenizer_config": tokenizer_config,
        "special_tokens_map": special_tokens_map,
    }

_chat_template ¤

_chat_template(messages: List[Dict[str, Any]])
Source code in src/unitorch/cli/models/mistral/processing.py 
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@register_process("core/process/mistral/chat_template")
def _chat_template(
    self,
    messages: List[Dict[str, Any]],
):
    return super().chat_template(messages=messages)

_classification ¤

_classification(
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
)
Source code in src/unitorch/cli/models/mistral/processing.py 
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@register_process("core/process/mistral/classification")
def _classification(
    self,
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
):
    outputs = super().classification(
        text=text,
        text_pair=text_pair,
        max_seq_length=max_seq_length,
    )
    return TensorInputs(
        input_ids=outputs.input_ids,
        attention_mask=outputs.attention_mask,
    )

_generation_inputs ¤

_generation_inputs(
    text: str, max_seq_length: Optional[int] = None
)
Source code in src/unitorch/cli/models/mistral/processing.py 
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@register_process("core/process/mistral/generation/inputs")
def _generation_inputs(
    self,
    text: str,
    max_seq_length: Optional[int] = None,
):
    outputs = super().generation_inputs(
        text=text,
        max_seq_length=max_seq_length,
    )
    return TensorInputs(input_ids=outputs.input_ids)

_generation_labels ¤

_generation_labels(
    text: str, max_gen_seq_length: Optional[int] = None
)
Source code in src/unitorch/cli/models/mistral/processing.py 
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@register_process("core/process/mistral/generation/labels")
def _generation_labels(
    self,
    text: str,
    max_gen_seq_length: Optional[int] = None,
):
    outputs = super().generation_labels(
        text=text,
        max_gen_seq_length=max_gen_seq_length,
    )
    return GenerationTargets(
        refs=outputs.input_ids,
        masks=outputs.attention_mask,
    )

_generation ¤

_generation(
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
    max_gen_seq_length: Optional[int] = None,
)
Source code in src/unitorch/cli/models/mistral/processing.py 
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@register_process("core/process/mistral/generation")
def _generation(
    self,
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
    max_gen_seq_length: Optional[int] = None,
):
    outputs = super().generation(
        text=text,
        text_pair=text_pair,
        max_seq_length=max_seq_length,
        max_gen_seq_length=max_gen_seq_length,
    )
    return TensorInputs(
        input_ids=outputs.input_ids,
        attention_mask=outputs.attention_mask,
    ), GenerationTargets(
        refs=outputs.input_ids_label,
        masks=outputs.attention_mask_label,
    )

_messages_generation ¤

_messages_generation(
    messages: List[Dict[str, Any]],
    max_seq_length: Optional[int] = None,
)
Source code in src/unitorch/cli/models/mistral/processing.py 
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@register_process("core/process/mistral/messages/generation")
def _messages_generation(
    self,
    messages: List[Dict[str, Any]],
    max_seq_length: Optional[int] = None,
):
    outputs = super().messages_generation(
        messages=messages,
        max_seq_length=max_seq_length,
    )
    return TensorInputs(
        input_ids=outputs.input_ids,
        attention_mask=outputs.attention_mask,
    ), GenerationTargets(
        refs=outputs.input_ids_label,
        masks=outputs.attention_mask_label,
    )

_detokenize ¤

_detokenize(outputs: GenerationOutputs)
Source code in src/unitorch/cli/models/mistral/processing.py 
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@register_process("core/postprocess/mistral/detokenize")
def _detokenize(
    self,
    outputs: GenerationOutputs,
):
    results = outputs.to_pandas()
    assert results.shape[0] == 0 or results.shape[0] == outputs.sequences.shape[0]

    decoded = super().detokenize(sequences=outputs.sequences)
    cleanup_string = lambda text: re.sub(r"\n", " ", text)
    if isinstance(decoded[0], list):
        decoded = [list(map(cleanup_string, sequence)) for sequence in decoded]
    elif isinstance(decoded[0], str):
        decoded = list(map(cleanup_string, decoded))
    else:
        raise ValueError(
            f"Unsupported type for mistral detokenize: {type(decoded[0])}"
        )
    results["decoded"] = decoded
    return WriterOutputs(results)

MistralForClassification¤

Tip

core/model/classification/mistral is the section for configuration of MistralForClassification.

Bases: MistralForClassification

Mistral model for classification tasks.

Source code in src/unitorch/cli/models/mistral/modeling.py 
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def __init__(
    self,
    config_path: str,
    num_classes: Optional[int] = 1,
    gradient_checkpointing: Optional[bool] = False,
):
    super().__init__(
        config_path=config_path,
        num_classes=num_classes,
        gradient_checkpointing=gradient_checkpointing,
    )

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/models/mistral/modeling.py 
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@classmethod
@config_defaults_init("core/model/classification/mistral")
def from_config(cls, config, **kwargs):
    config.set_default_section("core/model/classification/mistral")
    pretrained_name = config.getoption(
        "pretrained_name", "mistral-7b-instruct-v0.1"
    )
    pretrained_lora_name = config.getoption(
        "pretrained_lora_name", "mistral-7b-instruct-v0.1-lora"
    )
    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_mistral_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)

    gradient_checkpointing = config.getoption("gradient_checkpointing", False)
    num_classes = config.getoption("num_classes", 1)

    inst = cls(
        config_path,
        num_classes=num_classes,
        gradient_checkpointing=gradient_checkpointing,
    )

    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_mistral_infos, pretrained_name, "weight"),
        check_none=False,
    )

    if weight_path is not None:
        inst.from_pretrained(
            weight_path=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_mistral_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,
            save_base_state=False,
        )

    return inst

forward ¤

forward(
    input_ids: Tensor,
    attention_mask: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
)
Source code in src/unitorch/cli/models/mistral/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,
    position_ids: Optional[torch.Tensor] = None,
):
    outputs = super().forward(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
    )
    return ClassificationOutputs(outputs=outputs)

MistralForGeneration¤

Tip

core/model/generation/mistral is the section for configuration of MistralForGeneration.

Bases: MistralForGeneration

Mistral model for generation tasks.

Source code in src/unitorch/cli/models/mistral/modeling.py 
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def __init__(
    self,
    config_path: str,
    gradient_checkpointing: Optional[bool] = False,
):
    super().__init__(
        config_path=config_path,
        gradient_checkpointing=gradient_checkpointing,
    )

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/models/mistral/modeling.py 
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@classmethod
@config_defaults_init("core/model/generation/mistral")
def from_config(cls, config, **kwargs):
    config.set_default_section("core/model/generation/mistral")
    pretrained_name = config.getoption(
        "pretrained_name", "mistral-7b-instruct-v0.1"
    )
    pretrained_lora_name = config.getoption(
        "pretrained_lora_name", "mistral-7b-instruct-v0.1-lora"
    )
    config_path = config.getoption("config_path", None)
    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_mistral_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)

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

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

    if weight_path is not None:
        inst.from_pretrained(
            weight_path=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_mistral_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,
            save_base_state=False,
        )

    return inst

forward ¤

forward(
    input_ids: Tensor,
    attention_mask: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
)
Source code in src/unitorch/cli/models/mistral/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,
    position_ids: Optional[torch.Tensor] = None,
):
    outputs = super().forward(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
    )
    return GenerationOutputs(sequences=outputs)

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,
)
Source code in src/unitorch/cli/models/mistral/modeling.py 
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@config_defaults_method("core/model/generation/mistral")
@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,
):
    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,
    )