llama-factory & paraphrase-multilingual-MiniLM-L12-v2

cc886b8 verified over 2 years ago

6.12 kB

from typing import TYPE_CHECKING, Any, Dict, List, Optional

from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .mm_utils import get_paligemma_token_type_ids, get_pixel_values


if TYPE_CHECKING:
    from transformers import ProcessorMixin
    from transformers.tokenization_utils import PreTrainedTokenizer

    from ...hparams import DataArguments
    from ..template import Template


logger = get_logger(__name__)


def preprocess_supervised_dataset(
    examples: Dict[str, List[Any]],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
    # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
    # for multiturn examples, we only mask the prompt part in each prompt-response pair.
    model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
    if processor is not None:
        model_inputs["pixel_values"] = []
        if hasattr(processor, "image_seq_length"):  # paligemma models
            model_inputs["token_type_ids"] = []

    for i in range(len(examples["prompt"])):
        if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
            logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
            continue

        if processor is not None and not hasattr(processor, "image_seq_length"):  # llava-like models
            examples["prompt"][i][0]["content"] = template.image_token + examples["prompt"][i][0]["content"]

        messages = examples["prompt"][i] + examples["response"][i]
        input_ids, labels = [], []

        if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma models
            image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
            input_ids += [image_token_id] * getattr(processor, "image_seq_length")
            labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")

        for turn_idx, (source_ids, target_ids) in enumerate(
            template.encode_multiturn(
                tokenizer,
                messages,
                examples["system"][i],
                examples["tools"][i],
                data_args.cutoff_len,
                data_args.reserved_label_len,
            )
        ):
            if data_args.train_on_prompt:
                source_mask = source_ids
            elif turn_idx != 0 and template.efficient_eos:
                source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
            else:
                source_mask = [IGNORE_INDEX] * len(source_ids)

            input_ids += source_ids + target_ids
            labels += source_mask + target_ids

        if template.efficient_eos:
            input_ids += [tokenizer.eos_token_id]
            labels += [tokenizer.eos_token_id]

        model_inputs["input_ids"].append(input_ids)
        model_inputs["attention_mask"].append([1] * len(input_ids))
        model_inputs["labels"].append(labels)
        if processor is not None:
            model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor))
            if hasattr(processor, "image_seq_length"):  # paligemma models
                model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor))

    return model_inputs


def preprocess_packed_supervised_dataset(
    examples: Dict[str, List[Any]],
    template: "Template",
    tokenizer: "PreTrainedTokenizer",
    data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
    # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
    # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
    model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
    input_ids, labels = [], []
    for i in range(len(examples["prompt"])):
        if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
            logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
            continue

        messages = examples["prompt"][i] + examples["response"][i]
        for source_ids, target_ids in template.encode_multiturn(
            tokenizer, messages, examples["system"][i], examples["tools"][i]
        ):
            if data_args.train_on_prompt:
                source_mask = source_ids
            elif len(input_ids) != 0 and template.efficient_eos:
                source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
            else:
                source_mask = [IGNORE_INDEX] * len(source_ids)

            input_ids += source_ids + target_ids
            labels += source_mask + target_ids

    if template.efficient_eos:
        input_ids += [tokenizer.eos_token_id]
        labels += [tokenizer.eos_token_id]

    total_length = len(input_ids)
    block_size = data_args.cutoff_len
    # we drop the small remainder, and if the total_length < block_size, we exclude this batch
    total_length = (total_length // block_size) * block_size
    # split by chunks of cutoff_len
    for i in range(0, total_length, block_size):
        if not all(label == IGNORE_INDEX for label in labels[i : i + block_size]):
            model_inputs["input_ids"].append(input_ids[i : i + block_size])
            model_inputs["attention_mask"].append([1] * block_size)
            model_inputs["labels"].append(labels[i : i + block_size])

    return model_inputs


def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
    valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"]))
    print("input_ids:\n{}".format(example["input_ids"]))
    print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
    print("label_ids:\n{}".format(example["labels"]))
    print("labels:\n{}".format(tokenizer.decode(valid_labels, skip_special_tokens=False)))

	from typing import TYPE_CHECKING, Any, Dict, List, Optional

	from ...extras.constants import IGNORE_INDEX
	from ...extras.logging import get_logger
	from .mm_utils import get_paligemma_token_type_ids, get_pixel_values


	if TYPE_CHECKING:
	from transformers import ProcessorMixin
	from transformers.tokenization_utils import PreTrainedTokenizer

	from ...hparams import DataArguments
	from ..template import Template


	logger = get_logger(__name__)


	def preprocess_supervised_dataset(
	examples: Dict[str, List[Any]],
	template: "Template",
	tokenizer: "PreTrainedTokenizer",
	processor: Optional["ProcessorMixin"],
	data_args: "DataArguments",
	) -> Dict[str, List[List[int]]]:
	# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
	# for multiturn examples, we only mask the prompt part in each prompt-response pair.
	model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
	if processor is not None:
	model_inputs["pixel_values"] = []
	if hasattr(processor, "image_seq_length"): # paligemma models
	model_inputs["token_type_ids"] = []

	for i in range(len(examples["prompt"])):
	if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
	logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
	continue

	if processor is not None and not hasattr(processor, "image_seq_length"): # llava-like models
	examples["prompt"][i][0]["content"] = template.image_token + examples["prompt"][i][0]["content"]

	messages = examples["prompt"][i] + examples["response"][i]
	input_ids, labels = [], []

	if processor is not None and hasattr(processor, "image_seq_length"): # paligemma models
	image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
	input_ids += [image_token_id] * getattr(processor, "image_seq_length")
	labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")

	for turn_idx, (source_ids, target_ids) in enumerate(
	template.encode_multiturn(
	tokenizer,
	messages,
	examples["system"][i],
	examples["tools"][i],
	data_args.cutoff_len,
	data_args.reserved_label_len,
	)
	):
	if data_args.train_on_prompt:
	source_mask = source_ids
	elif turn_idx != 0 and template.efficient_eos:
	source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
	else:
	source_mask = [IGNORE_INDEX] * len(source_ids)

	input_ids += source_ids + target_ids
	labels += source_mask + target_ids

	if template.efficient_eos:
	input_ids += [tokenizer.eos_token_id]
	labels += [tokenizer.eos_token_id]

	model_inputs["input_ids"].append(input_ids)
	model_inputs["attention_mask"].append([1] * len(input_ids))
	model_inputs["labels"].append(labels)
	if processor is not None:
	model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor))
	if hasattr(processor, "image_seq_length"): # paligemma models
	model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor))

	return model_inputs


	def preprocess_packed_supervised_dataset(
	examples: Dict[str, List[Any]],
	template: "Template",
	tokenizer: "PreTrainedTokenizer",
	data_args: "DataArguments",
	) -> Dict[str, List[List[int]]]:
	# build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
	# and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
	model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
	input_ids, labels = [], []
	for i in range(len(examples["prompt"])):
	if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
	logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
	continue

	messages = examples["prompt"][i] + examples["response"][i]
	for source_ids, target_ids in template.encode_multiturn(
	tokenizer, messages, examples["system"][i], examples["tools"][i]
	):
	if data_args.train_on_prompt:
	source_mask = source_ids
	elif len(input_ids) != 0 and template.efficient_eos:
	source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
	else:
	source_mask = [IGNORE_INDEX] * len(source_ids)

	input_ids += source_ids + target_ids
	labels += source_mask + target_ids

	if template.efficient_eos:
	input_ids += [tokenizer.eos_token_id]
	labels += [tokenizer.eos_token_id]

	total_length = len(input_ids)
	block_size = data_args.cutoff_len
	# we drop the small remainder, and if the total_length < block_size, we exclude this batch
	total_length = (total_length // block_size) * block_size
	# split by chunks of cutoff_len
	for i in range(0, total_length, block_size):
	if not all(label == IGNORE_INDEX for label in labels[i : i + block_size]):
	model_inputs["input_ids"].append(input_ids[i : i + block_size])
	model_inputs["attention_mask"].append([1] * block_size)
	model_inputs["labels"].append(labels[i : i + block_size])

	return model_inputs


	def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
	valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"]))
	print("input_ids:\n{}".format(example["input_ids"]))
	print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
	print("label_ids:\n{}".format(example["labels"]))
	print("labels:\n{}".format(tokenizer.decode(valid_labels, skip_special_tokens=False)))