Merge branch 'hiyouga:main' into main
This commit is contained in:
BUAADreamer
GitHub
commit
047a06a1e5
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@ -359,7 +359,7 @@ To utilize Ascend NPU devices for (distributed) training and inference, you need
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Docker image:
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- 32GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
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- 64GB: Coming soon
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- 64GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
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Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
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@ -359,7 +359,7 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
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Docker 镜像：
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- 32GB：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
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- 64GB：敬请期待
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- 64GB：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
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请记得使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定您使用的设备。
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@ -61,7 +61,7 @@ class HuggingfaceEngine(BaseEngine):
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and image is not None
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and not hasattr(processor, "image_seq_length")
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and IMAGE_TOKEN not in messages[0]["content"]
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): # llava case
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): # llava-like models
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messages[0]["content"] = IMAGE_TOKEN + messages[0]["content"]
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paired_messages = messages + [{"role": "assistant", "content": ""}]
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@ -74,7 +74,7 @@ class HuggingfaceEngine(BaseEngine):
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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batch_feature = image_processor(image, return_tensors="pt")
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pixel_values = batch_feature.to(model.device)["pixel_values"] # shape (B, C, H, W)
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if hasattr(processor, "image_seq_length"): # paligemma case
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if hasattr(processor, "image_seq_length"): # paligemma models
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image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
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prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
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@ -98,7 +98,7 @@ class VllmEngine(BaseEngine):
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and image is not None
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and not hasattr(self.processor, "image_seq_length")
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and IMAGE_TOKEN not in messages[0]["content"]
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): # llava case
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): # llava-like models
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messages[0]["content"] = IMAGE_TOKEN * self.image_feature_size + messages[0]["content"]
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paired_messages = messages + [{"role": "assistant", "content": ""}]
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@ -1,5 +1,5 @@
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from dataclasses import dataclass
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from typing import Any, Dict, List, Sequence, Tuple
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from typing import Any, Dict, Sequence
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import torch
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from transformers import DataCollatorForSeq2Seq
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@ -11,21 +11,6 @@ class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
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Data collator for pairwise data.
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"""
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def _pad_labels(self, batch: torch.Tensor, positions: List[Tuple[int, int]]) -> torch.Tensor:
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r"""
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Masks out the input ids except for the responses.
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"""
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padded_labels = []
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for feature, (prompt_len, answer_len) in zip(batch, positions):
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if self.tokenizer.padding_side == "left":
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start, end = feature.size(0) - answer_len, feature.size(0)
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else:
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start, end = prompt_len, prompt_len + answer_len
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padded_tensor = self.label_pad_token_id * torch.ones_like(feature)
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padded_tensor[start:end] = feature[start:end]
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padded_labels.append(padded_tensor)
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return torch.stack(padded_labels, dim=0).contiguous() # in contiguous memory
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def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
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r"""
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Pads batched data to the longest sequence in the batch.
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@ -34,21 +19,22 @@ class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
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the last n examples represent rejected examples.
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"""
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concatenated_features = []
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label_positions = []
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for key in ("chosen_ids", "rejected_ids"):
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for key in ("chosen", "rejected"):
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for feature in features:
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prompt_len, answer_len = len(feature["prompt_ids"]), len(feature[key])
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concatenated_features.append(
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{
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"input_ids": feature["prompt_ids"] + feature[key],
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"attention_mask": [1] * (prompt_len + answer_len),
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}
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)
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label_positions.append((prompt_len, answer_len))
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target_feature = {
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"input_ids": feature["{}_input_ids".format(key)],
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"attention_mask": feature["{}_attention_mask".format(key)],
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"labels": feature["{}_labels".format(key)],
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}
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if "pixel_values" in feature:
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target_feature["pixel_values"] = feature["pixel_values"]
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batch = super().__call__(concatenated_features)
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batch["labels"] = self._pad_labels(batch["input_ids"], label_positions)
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return batch
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if "{}_token_type_ids".format(key) in feature:
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target_feature["token_type_ids"] = feature["{}_token_type_ids".format(key)]
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concatenated_features.append(target_feature)
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return super().__call__(concatenated_features)
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@dataclass
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@ -62,20 +48,25 @@ class KTODataCollatorWithPadding(DataCollatorForSeq2Seq):
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kl_features = []
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kto_tags = []
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for feature in features:
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target_features.append(
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{
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"input_ids": feature["input_ids"],
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"attention_mask": feature["attention_mask"],
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"labels": feature["labels"],
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}
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)
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kl_features.append(
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{
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"input_ids": feature["kl_input_ids"],
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"attention_mask": feature["kl_attention_mask"],
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"labels": feature["kl_labels"],
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}
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)
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target_feature = {
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"input_ids": feature["input_ids"],
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"attention_mask": feature["attention_mask"],
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"labels": feature["labels"],
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}
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kl_feature = {
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"input_ids": feature["kl_input_ids"],
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"attention_mask": feature["kl_attention_mask"],
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"labels": feature["kl_labels"],
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}
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if "pixel_values" in feature:
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target_feature["pixel_values"] = feature["pixel_values"]
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if "token_type_ids" in feature:
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target_feature["token_type_ids"] = feature["token_type_ids"]
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kl_feature["token_type_ids"] = feature["kl_token_type_ids"]
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target_features.append(target_feature)
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kl_features.append(kl_feature)
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kto_tags.append(feature["kto_tags"])
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batch = super().__call__(target_features)
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@ -83,5 +74,8 @@ class KTODataCollatorWithPadding(DataCollatorForSeq2Seq):
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batch["kl_input_ids"] = kl_batch["input_ids"]
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batch["kl_attention_mask"] = kl_batch["attention_mask"]
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batch["kl_labels"] = kl_batch["labels"]
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if "token_type_ids" in batch:
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batch["kl_token_type_ids"] = kl_batch["token_type_ids"]
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batch["kto_tags"] = torch.tensor(kto_tags)
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return batch
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@ -1,5 +1,6 @@
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import inspect
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import os
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import sys
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from typing import TYPE_CHECKING, Literal, Optional, Union
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from datasets import load_dataset, load_from_disk
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@ -167,12 +168,15 @@ def get_dataset(
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logger.info("Tokenized dataset saved at {}.".format(data_args.tokenized_path))
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logger.info("Please restart the training with `--tokenized_path {}`.".format(data_args.tokenized_path))
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exit(0)
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sys.exit(0)
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if training_args.should_log:
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try:
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print_function(next(iter(dataset)))
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except StopIteration:
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raise RuntimeError("Cannot find valid samples, check `data/README.md` for the data format.")
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if stage == "pt":
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raise RuntimeError("Cannot find sufficient samples, consider increasing dataset size.")
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else:
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raise RuntimeError("Cannot find valid samples, check `data/README.md` for the data format.")
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return dataset
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@ -1,398 +1,25 @@
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from functools import partial
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from itertools import chain
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple
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from typing import TYPE_CHECKING, Callable, Literal, Optional, Tuple
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from ..extras.constants import IGNORE_INDEX, IMAGE_TOKEN
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from ..extras.logging import get_logger
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from ..extras.packages import is_pillow_available
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from .utils import Role
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if is_pillow_available():
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from PIL import Image
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from .processors.feedback import preprocess_feedback_dataset
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from .processors.pairwise import preprocess_pairwise_dataset, print_pairwise_dataset_example
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from .processors.pretrain import preprocess_pretrain_dataset
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from .processors.supervised import (
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preprocess_packed_supervised_dataset,
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preprocess_supervised_dataset,
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print_supervised_dataset_example,
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)
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from .processors.unsupervised import preprocess_unsupervised_dataset, print_unsupervised_dataset_example
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if TYPE_CHECKING:
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from numpy.typing import NDArray
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from PIL.Image import Image as ImageObject
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from transformers import ProcessorMixin, Seq2SeqTrainingArguments
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from transformers.image_processing_utils import BaseImageProcessor
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from transformers.tokenization_utils import PreTrainedTokenizer
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from ..hparams import DataArguments
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from .template import Template
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logger = get_logger(__name__)
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def _preprocess_visual_inputs(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> "NDArray":
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# process visual inputs (currently only supports a single image)
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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image = images[0] if len(images) != 0 else Image.new("RGB", (100, 100), (255, 255, 255))
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return image_processor(image, return_tensors="pt")["pixel_values"][0]
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def preprocess_pretrain_dataset(
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examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
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) -> Dict[str, List[List[int]]]:
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# build grouped texts with format `X1 X2 X3 ...` if packing is enabled
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text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
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if not data_args.packing:
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if data_args.template == "gemma":
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text_examples = [tokenizer.bos_token + example for example in text_examples]
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result = tokenizer(text_examples, add_special_tokens=False, max_length=data_args.cutoff_len)
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else:
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tokenized_examples = tokenizer(text_examples, add_special_tokens=False)
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concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
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total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
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block_size = data_args.cutoff_len
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total_length = (total_length // block_size) * block_size
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result = {
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k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
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for k, t in concatenated_examples.items()
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}
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if data_args.template == "gemma":
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for i in range(len(result["input_ids"])):
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result["input_ids"][i][0] = tokenizer.bos_token_id
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return result
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def preprocess_supervised_dataset(
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examples: Dict[str, List[Any]],
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template: "Template",
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"],
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data_args: "DataArguments",
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) -> Dict[str, List[List[int]]]:
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# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
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# for multiturn examples, we only mask the prompt part in each prompt-response pair.
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model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
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if processor is not None:
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model_inputs["pixel_values"] = []
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preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
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for i in range(len(examples["prompt"])):
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if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
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logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
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continue
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if processor is not None and not hasattr(processor, "image_seq_length"): # llava case
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examples["prompt"][i][0]["content"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
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messages = examples["prompt"][i] + examples["response"][i]
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input_ids, labels = [], []
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if processor is not None and hasattr(processor, "image_seq_length"): # paligemma case
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image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
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input_ids += [image_token_id] * getattr(processor, "image_seq_length")
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labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")
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for turn_idx, (source_ids, target_ids) in enumerate(
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template.encode_multiturn(
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tokenizer,
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messages,
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examples["system"][i],
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examples["tools"][i],
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data_args.cutoff_len,
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data_args.reserved_label_len,
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)
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):
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if data_args.train_on_prompt:
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source_mask = source_ids
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elif turn_idx != 0 and template.efficient_eos:
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source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
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else:
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source_mask = [IGNORE_INDEX] * len(source_ids)
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input_ids += source_ids + target_ids
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labels += source_mask + target_ids
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if template.efficient_eos:
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input_ids += [tokenizer.eos_token_id]
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labels += [tokenizer.eos_token_id]
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model_inputs["input_ids"].append(input_ids)
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model_inputs["attention_mask"].append([1] * len(input_ids))
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model_inputs["labels"].append(labels)
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if processor is not None:
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model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
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return model_inputs
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def preprocess_packed_supervised_dataset(
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examples: Dict[str, List[Any]],
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template: "Template",
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tokenizer: "PreTrainedTokenizer",
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data_args: "DataArguments",
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) -> Dict[str, List[List[int]]]:
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# build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
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# and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
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model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
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input_ids, labels = [], []
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for i in range(len(examples["prompt"])):
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if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
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logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
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continue
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messages = examples["prompt"][i] + examples["response"][i]
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for source_ids, target_ids in template.encode_multiturn(
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tokenizer, messages, examples["system"][i], examples["tools"][i]
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):
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if data_args.train_on_prompt:
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source_mask = source_ids
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elif len(input_ids) != 0 and template.efficient_eos:
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source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
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else:
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source_mask = [IGNORE_INDEX] * len(source_ids)
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input_ids += source_ids + target_ids
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labels += source_mask + target_ids
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if template.efficient_eos:
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input_ids += [tokenizer.eos_token_id]
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labels += [tokenizer.eos_token_id]
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total_length = len(input_ids)
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block_size = data_args.cutoff_len
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# we drop the small remainder, and if the total_length < block_size, we exclude this batch
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total_length = (total_length // block_size) * block_size
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# split by chunks of cutoff_len
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for i in range(0, total_length, block_size):
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if not all(label == IGNORE_INDEX for label in labels[i : i + block_size]):
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model_inputs["input_ids"].append(input_ids[i : i + block_size])
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model_inputs["attention_mask"].append([1] * block_size)
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model_inputs["labels"].append(labels[i : i + block_size])
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return model_inputs
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def preprocess_unsupervised_dataset(
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examples: Dict[str, List[Any]],
|
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template: "Template",
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"],
|
||||
data_args: "DataArguments",
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) -> Dict[str, List[List[int]]]:
|
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# build inputs with format `<bos> X` and labels with format `Y <eos>`
|
||||
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
|
||||
if processor is not None:
|
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model_inputs["pixel_values"] = []
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preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
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|
||||
for i in range(len(examples["prompt"])):
|
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if len(examples["prompt"][i]) % 2 != 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 case
|
||||
examples["prompt"][i][0]["content"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
|
||||
|
||||
if len(examples["response"][i]) == 1:
|
||||
messages = examples["prompt"][i] + examples["response"][i]
|
||||
else:
|
||||
messages = examples["prompt"][i] + [{"role": Role.ASSISTANT.value, "content": ""}]
|
||||
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||||
input_ids, labels = template.encode_oneturn(
|
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tokenizer,
|
||||
messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
|
||||
if template.efficient_eos:
|
||||
labels += [tokenizer.eos_token_id]
|
||||
|
||||
if processor is not None and hasattr(processor, "image_seq_length"): # paligemma case
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
|
||||
input_ids = [image_token_id] * getattr(processor, "image_seq_length") + input_ids
|
||||
|
||||
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(preprocess_visual_inputs(examples["images"][i]))
|
||||
|
||||
return model_inputs
|
||||
|
||||
|
||||
def preprocess_pairwise_dataset(
|
||||
examples: Dict[str, List[Any]],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
data_args: "DataArguments",
|
||||
) -> Dict[str, List[List[int]]]:
|
||||
# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
|
||||
model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
|
||||
if processor is not None:
|
||||
model_inputs["pixel_values"] = []
|
||||
preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
|
||||
|
||||
for i in range(len(examples["prompt"])):
|
||||
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
|
||||
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 case
|
||||
examples["prompt"][i][0]["content"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
|
||||
|
||||
chosen_messages = examples["prompt"][i] + [examples["response"][i][0]]
|
||||
rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
|
||||
prompt_ids, chosen_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
chosen_messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
_, rejected_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
rejected_messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
|
||||
if template.efficient_eos:
|
||||
chosen_ids += [tokenizer.eos_token_id]
|
||||
rejected_ids += [tokenizer.eos_token_id]
|
||||
|
||||
if processor is not None and hasattr(processor, "image_seq_length"): # paligemma case
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
|
||||
prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
|
||||
|
||||
model_inputs["prompt_ids"].append(prompt_ids)
|
||||
model_inputs["chosen_ids"].append(chosen_ids)
|
||||
model_inputs["rejected_ids"].append(rejected_ids)
|
||||
if processor is not None:
|
||||
model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
|
||||
|
||||
return model_inputs
|
||||
|
||||
|
||||
def preprocess_kto_dataset(
|
||||
examples: Dict[str, List[Any]],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
data_args: "DataArguments",
|
||||
) -> Dict[str, List[List[int]]]:
|
||||
# create unrelated input-output pairs for estimating the KL term by flipping the matched pairs
|
||||
kl_response = examples["response"][::-1]
|
||||
model_inputs = {
|
||||
"input_ids": [],
|
||||
"attention_mask": [],
|
||||
"labels": [],
|
||||
"kl_input_ids": [],
|
||||
"kl_attention_mask": [],
|
||||
"kl_labels": [],
|
||||
"kto_tags": [],
|
||||
}
|
||||
if processor is not None:
|
||||
model_inputs["pixel_values"] = []
|
||||
preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
|
||||
|
||||
for i in range(len(examples["prompt"])):
|
||||
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
|
||||
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 case
|
||||
examples["prompt"][i][0]["content"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
|
||||
|
||||
if examples["response"][i][0]["content"]: # desired example
|
||||
kto_tag = True
|
||||
messages = examples["prompt"][i] + [examples["response"][i][0]]
|
||||
else: # undesired example
|
||||
kto_tag = False
|
||||
messages = examples["prompt"][i] + [examples["response"][i][1]]
|
||||
|
||||
if kl_response[i][0]["content"]:
|
||||
kl_messages = examples["prompt"][i] + [kl_response[i][0]]
|
||||
else:
|
||||
kl_messages = examples["prompt"][i] + [kl_response[i][1]]
|
||||
|
||||
prompt_ids, response_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
_, kl_response_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
kl_messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
|
||||
if template.efficient_eos:
|
||||
response_ids += [tokenizer.eos_token_id]
|
||||
kl_response_ids += [tokenizer.eos_token_id]
|
||||
|
||||
if processor is not None and hasattr(processor, "image_seq_length"): # paligemma case
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
|
||||
prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
|
||||
|
||||
input_ids = prompt_ids + response_ids
|
||||
labels = [IGNORE_INDEX] * len(prompt_ids) + response_ids
|
||||
kl_input_ids = prompt_ids + kl_response_ids
|
||||
kl_labels = [IGNORE_INDEX] * len(prompt_ids) + kl_response_ids
|
||||
model_inputs["input_ids"].append(input_ids)
|
||||
model_inputs["attention_mask"].append([1] * len(input_ids))
|
||||
model_inputs["labels"].append(labels)
|
||||
model_inputs["kl_input_ids"].append(kl_input_ids)
|
||||
model_inputs["kl_attention_mask"].append([1] * len(kl_input_ids))
|
||||
model_inputs["kl_labels"].append(kl_labels)
|
||||
model_inputs["kto_tags"].append(kto_tag)
|
||||
if processor is not None:
|
||||
model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
|
||||
|
||||
desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
|
||||
undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
|
||||
if desirable_num == 0 or undesirable_num == 0:
|
||||
logger.warning("Your dataset only has one preference type.")
|
||||
|
||||
return model_inputs
|
||||
|
||||
|
||||
def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
|
||||
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(list(filter(lambda x: x != IGNORE_INDEX, example["labels"])), skip_special_tokens=False)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
|
||||
print("prompt_ids:\n{}".format(example["prompt_ids"]))
|
||||
print("prompt:\n{}".format(tokenizer.decode(example["prompt_ids"], skip_special_tokens=False)))
|
||||
print("chosen_ids:\n{}".format(example["chosen_ids"]))
|
||||
print("chosen:\n{}".format(tokenizer.decode(example["chosen_ids"], skip_special_tokens=False)))
|
||||
print("rejected_ids:\n{}".format(example["rejected_ids"]))
|
||||
print("rejected:\n{}".format(tokenizer.decode(example["rejected_ids"], skip_special_tokens=False)))
|
||||
|
||||
|
||||
def print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
|
||||
print("input_ids:\n{}".format(example["input_ids"]))
|
||||
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
|
||||
|
||||
|
||||
def get_preprocess_and_print_func(
|
||||
data_args: "DataArguments",
|
||||
training_args: "Seq2SeqTrainingArguments",
|
||||
|
|
@ -437,7 +64,7 @@ def get_preprocess_and_print_func(
|
|||
print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
|
||||
elif stage == "kto":
|
||||
preprocess_func = partial(
|
||||
preprocess_kto_dataset,
|
||||
preprocess_feedback_dataset,
|
||||
template=template,
|
||||
tokenizer=tokenizer,
|
||||
processor=processor,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,110 @@
|
|||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from ...extras.constants import IGNORE_INDEX, IMAGE_TOKEN
|
||||
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_feedback_dataset(
|
||||
examples: Dict[str, List[Any]],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
data_args: "DataArguments",
|
||||
) -> Dict[str, List[List[int]]]:
|
||||
# create unrelated input-output pairs for estimating the KL term by flipping the matched pairs
|
||||
kl_response = examples["response"][::-1]
|
||||
model_inputs = {
|
||||
"input_ids": [],
|
||||
"attention_mask": [],
|
||||
"labels": [],
|
||||
"kl_input_ids": [],
|
||||
"kl_attention_mask": [],
|
||||
"kl_labels": [],
|
||||
"kto_tags": [],
|
||||
}
|
||||
if processor is not None:
|
||||
model_inputs["pixel_values"] = []
|
||||
if hasattr(processor, "image_seq_length"): # paligemma models
|
||||
model_inputs["token_type_ids"] = []
|
||||
model_inputs["kl_token_type_ids"] = []
|
||||
|
||||
for i in range(len(examples["prompt"])):
|
||||
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
|
||||
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"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
|
||||
|
||||
if examples["response"][i][0]["content"]: # desired example
|
||||
kto_tag = True
|
||||
messages = examples["prompt"][i] + [examples["response"][i][0]]
|
||||
else: # undesired example
|
||||
kto_tag = False
|
||||
messages = examples["prompt"][i] + [examples["response"][i][1]]
|
||||
|
||||
if kl_response[i][0]["content"]:
|
||||
kl_messages = examples["prompt"][i] + [kl_response[i][0]]
|
||||
else:
|
||||
kl_messages = examples["prompt"][i] + [kl_response[i][1]]
|
||||
|
||||
prompt_ids, response_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
_, kl_response_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
kl_messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
|
||||
if template.efficient_eos:
|
||||
response_ids += [tokenizer.eos_token_id]
|
||||
kl_response_ids += [tokenizer.eos_token_id]
|
||||
|
||||
if processor is not None and hasattr(processor, "image_seq_length"): # paligemma models
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
|
||||
prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
|
||||
|
||||
input_ids = prompt_ids + response_ids
|
||||
labels = [IGNORE_INDEX] * len(prompt_ids) + response_ids
|
||||
kl_input_ids = prompt_ids + kl_response_ids
|
||||
kl_labels = [IGNORE_INDEX] * len(prompt_ids) + kl_response_ids
|
||||
model_inputs["input_ids"].append(input_ids)
|
||||
model_inputs["attention_mask"].append([1] * len(input_ids))
|
||||
model_inputs["labels"].append(labels)
|
||||
model_inputs["kl_input_ids"].append(kl_input_ids)
|
||||
model_inputs["kl_attention_mask"].append([1] * len(kl_input_ids))
|
||||
model_inputs["kl_labels"].append(kl_labels)
|
||||
model_inputs["kto_tags"].append(kto_tag)
|
||||
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))
|
||||
model_inputs["kl_token_type_ids"].append(get_paligemma_token_type_ids(len(kl_input_ids), processor))
|
||||
|
||||
desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
|
||||
undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
|
||||
if desirable_num == 0 or undesirable_num == 0:
|
||||
logger.warning("Your dataset only has one preference type.")
|
||||
|
||||
return model_inputs
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
from typing import TYPE_CHECKING, List, Sequence
|
||||
|
||||
from ...extras.packages import is_pillow_available
|
||||
|
||||
|
||||
if is_pillow_available():
|
||||
from PIL import Image
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from numpy.typing import NDArray
|
||||
from PIL.Image import Image as ImageObject
|
||||
from transformers import ProcessorMixin
|
||||
from transformers.image_processing_utils import BaseImageProcessor
|
||||
|
||||
|
||||
def get_pixel_values(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> "NDArray":
|
||||
# process visual inputs (currently only supports a single image)
|
||||
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
|
||||
image = images[0] if len(images) != 0 else Image.new("RGB", (100, 100), (255, 255, 255))
|
||||
return image_processor(image, return_tensors="pt")["pixel_values"][0] # shape (C, H, W)
|
||||
|
||||
|
||||
def get_paligemma_token_type_ids(input_len: int, processor: "ProcessorMixin") -> List[int]:
|
||||
# get paligemma token type ids for computing loss
|
||||
image_seq_length = getattr(processor, "image_seq_length")
|
||||
return [0] * image_seq_length + [1] * (input_len - image_seq_length)
|
||||
|
|
@ -0,0 +1,109 @@
|
|||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from ...extras.constants import IGNORE_INDEX, IMAGE_TOKEN
|
||||
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_pairwise_dataset(
|
||||
examples: Dict[str, List[Any]],
|
||||
template: "Template",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
processor: Optional["ProcessorMixin"],
|
||||
data_args: "DataArguments",
|
||||
) -> Dict[str, List[List[int]]]:
|
||||
# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
|
||||
model_inputs = {
|
||||
"chosen_input_ids": [],
|
||||
"chosen_attention_mask": [],
|
||||
"chosen_labels": [],
|
||||
"rejected_input_ids": [],
|
||||
"rejected_attention_mask": [],
|
||||
"rejected_labels": [],
|
||||
}
|
||||
if processor is not None:
|
||||
model_inputs["pixel_values"] = []
|
||||
if hasattr(processor, "image_seq_length"): # paligemma models
|
||||
model_inputs["chosen_token_type_ids"] = []
|
||||
model_inputs["rejected_token_type_ids"] = []
|
||||
|
||||
for i in range(len(examples["prompt"])):
|
||||
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
|
||||
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"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
|
||||
|
||||
chosen_messages = examples["prompt"][i] + [examples["response"][i][0]]
|
||||
rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
|
||||
prompt_ids, chosen_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
chosen_messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
_, rejected_ids = template.encode_oneturn(
|
||||
tokenizer,
|
||||
rejected_messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
|
||||
if template.efficient_eos:
|
||||
chosen_ids += [tokenizer.eos_token_id]
|
||||
rejected_ids += [tokenizer.eos_token_id]
|
||||
|
||||
if processor is not None and hasattr(processor, "image_seq_length"): # paligemma models
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
|
||||
prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
|
||||
|
||||
chosen_input_ids = prompt_ids + chosen_ids
|
||||
chosen_labels = [IGNORE_INDEX] * len(prompt_ids) + chosen_ids
|
||||
rejected_input_ids = prompt_ids + rejected_ids
|
||||
rejected_labels = [IGNORE_INDEX] * len(prompt_ids) + rejected_ids
|
||||
model_inputs["chosen_input_ids"].append(chosen_input_ids)
|
||||
model_inputs["chosen_attention_mask"].append([1] * len(chosen_input_ids))
|
||||
model_inputs["chosen_labels"].append(chosen_labels)
|
||||
model_inputs["rejected_input_ids"].append(rejected_input_ids)
|
||||
model_inputs["rejected_attention_mask"].append([1] * len(rejected_input_ids))
|
||||
model_inputs["rejected_labels"].append(rejected_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["chosen_token_type_ids"].append(
|
||||
get_paligemma_token_type_ids(len(chosen_input_ids), processor)
|
||||
)
|
||||
model_inputs["rejected_token_type_ids"].append(
|
||||
get_paligemma_token_type_ids(len(rejected_input_ids), processor)
|
||||
)
|
||||
|
||||
return model_inputs
|
||||
|
||||
|
||||
def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
|
||||
valid_chosen_labels = list(filter(lambda x: x != IGNORE_INDEX, example["chosen_labels"]))
|
||||
valid_rejected_labels = list(filter(lambda x: x != IGNORE_INDEX, example["rejected_labels"]))
|
||||
print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
|
||||
print("chosen_inputs:\n{}".format(tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False)))
|
||||
print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
|
||||
print("chosen_labels:\n{}".format(tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)))
|
||||
print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
|
||||
print("rejected_inputs:\n{}".format(tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)))
|
||||
print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
|
||||
print("rejected_labels:\n{}".format(tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)))
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
from itertools import chain
|
||||
from typing import TYPE_CHECKING, Any, Dict, List
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from transformers.tokenization_utils import PreTrainedTokenizer
|
||||
|
||||
from ...hparams import DataArguments
|
||||
|
||||
|
||||
def preprocess_pretrain_dataset(
|
||||
examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
|
||||
) -> Dict[str, List[List[int]]]:
|
||||
# build grouped texts with format `X1 X2 X3 ...` if packing is enabled
|
||||
text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
|
||||
|
||||
if not data_args.packing:
|
||||
if data_args.template == "gemma":
|
||||
text_examples = [tokenizer.bos_token + example for example in text_examples]
|
||||
|
||||
result = tokenizer(text_examples, add_special_tokens=False, max_length=data_args.cutoff_len)
|
||||
else:
|
||||
tokenized_examples = tokenizer(text_examples, add_special_tokens=False)
|
||||
concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
|
||||
total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
|
||||
block_size = data_args.cutoff_len
|
||||
total_length = (total_length // block_size) * block_size
|
||||
result = {
|
||||
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
||||
for k, t in concatenated_examples.items()
|
||||
}
|
||||
if data_args.template == "gemma":
|
||||
for i in range(len(result["input_ids"])):
|
||||
result["input_ids"][i][0] = tokenizer.bos_token_id
|
||||
|
||||
return result
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from ...extras.constants import IGNORE_INDEX, IMAGE_TOKEN
|
||||
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"] = 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(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)))
|
||||
|
|
@ -0,0 +1,76 @@
|
|||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from ...extras.constants import IMAGE_TOKEN
|
||||
from ...extras.logging import get_logger
|
||||
from ..utils import Role
|
||||
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_unsupervised_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` and labels with format `Y <eos>`
|
||||
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:
|
||||
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"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
|
||||
|
||||
if len(examples["response"][i]) == 1:
|
||||
messages = examples["prompt"][i] + examples["response"][i]
|
||||
else:
|
||||
messages = examples["prompt"][i] + [{"role": Role.ASSISTANT.value, "content": ""}]
|
||||
|
||||
input_ids, labels = template.encode_oneturn(
|
||||
tokenizer,
|
||||
messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
|
||||
if template.efficient_eos:
|
||||
labels += [tokenizer.eos_token_id]
|
||||
|
||||
if processor is not None and hasattr(processor, "image_seq_length"): # paligemma models
|
||||
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
|
||||
input_ids = [image_token_id] * getattr(processor, "image_seq_length") + input_ids
|
||||
|
||||
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 print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
|
||||
print("input_ids:\n{}".format(example["input_ids"]))
|
||||
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
|
||||
|
|
@ -149,7 +149,7 @@ class ModelArguments:
|
|||
default=1,
|
||||
metadata={"help": "The file shard size (in GB) of the exported model."},
|
||||
)
|
||||
export_device: str = field(
|
||||
export_device: Literal["cpu", "cuda"] = field(
|
||||
default="cpu",
|
||||
metadata={"help": "The device used in model export, use cuda to avoid addmm errors."},
|
||||
)
|
||||
|
|
|
|||
|
|
@ -328,8 +328,8 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
|
|||
_verify_model_args(model_args, finetuning_args)
|
||||
_check_extra_dependencies(model_args, finetuning_args)
|
||||
|
||||
if model_args.export_dir is not None:
|
||||
model_args.device_map = {"": torch.device(model_args.export_device)}
|
||||
if model_args.export_dir is not None and model_args.export_device == "cpu":
|
||||
model_args.device_map = {"": torch.device("cpu")}
|
||||
else:
|
||||
model_args.device_map = "auto"
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from types import MethodType
|
|||
from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from transformers import BatchEncoding, Trainer
|
||||
from transformers import Trainer
|
||||
from trl import DPOTrainer
|
||||
from trl.trainer.utils import disable_dropout_in_model
|
||||
|
||||
|
|
@ -108,14 +108,8 @@ class CustomDPOTrainer(DPOTrainer):
|
|||
|
||||
Otherwise the average log probabilities.
|
||||
"""
|
||||
batch_copied = BatchEncoding({k: v.detach().clone() for k, v in batch.items()}) # avoid error
|
||||
|
||||
all_logits: "torch.Tensor" = model(
|
||||
input_ids=batch_copied["input_ids"],
|
||||
attention_mask=batch_copied["attention_mask"],
|
||||
return_dict=True,
|
||||
use_cache=False,
|
||||
).logits.to(torch.float32)
|
||||
batch_copied = {k: v.detach().clone() for k, v in batch.items()} # avoid error
|
||||
all_logits: "torch.Tensor" = model(**batch_copied, return_dict=True, use_cache=False).logits.to(torch.float32)
|
||||
|
||||
all_logps = self.get_batch_logps(
|
||||
logits=all_logits,
|
||||
|
|
|
|||
|
|
@ -104,19 +104,23 @@ class CustomKTOTrainer(KTOTrainer):
|
|||
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
|
||||
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
|
||||
with torch.no_grad():
|
||||
kl_logits = model(
|
||||
input_ids=batch["kl_input_ids"],
|
||||
attention_mask=batch["kl_attention_mask"],
|
||||
return_dict=True,
|
||||
use_cache=False,
|
||||
).logits.to(torch.float32)
|
||||
kl_model_inputs = {"input_ids": batch["kl_input_ids"], "attention_mask": batch["kl_attention_mask"]}
|
||||
if "pixel_values" in batch:
|
||||
kl_model_inputs["pixel_values"] = batch["pixel_values"]
|
||||
|
||||
target_logits = model(
|
||||
input_ids=batch["input_ids"],
|
||||
attention_mask=batch["attention_mask"],
|
||||
return_dict=True,
|
||||
use_cache=False,
|
||||
).logits.to(torch.float32)
|
||||
if "kl_token_type_ids" in batch:
|
||||
kl_model_inputs["token_type_ids"] = batch["kl_token_type_ids"]
|
||||
|
||||
kl_logits = model(**kl_model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
|
||||
|
||||
model_inputs = {"input_ids": batch["input_ids"], "attention_mask": batch["attention_mask"]}
|
||||
if "pixel_values" in batch:
|
||||
model_inputs["pixel_values"] = batch["pixel_values"]
|
||||
|
||||
if "token_type_ids" in batch:
|
||||
model_inputs["token_type_ids"] = batch["token_type_ids"]
|
||||
|
||||
target_logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
|
||||
|
||||
target_logps = self.get_batch_logps(
|
||||
logits=target_logits,
|
||||
|
|
|
|||
|
|
@ -85,9 +85,7 @@ class CustomORPOTrainer(DPOTrainer):
|
|||
r"""
|
||||
Computes the average log probabilities of the labels under the given logits.
|
||||
"""
|
||||
all_logits: "torch.Tensor" = model(
|
||||
input_ids=batch["input_ids"], attention_mask=batch["attention_mask"], return_dict=True, use_cache=False
|
||||
).logits.to(torch.float32)
|
||||
all_logits: "torch.Tensor" = model(**batch, return_dict=True, use_cache=False).logits.to(torch.float32)
|
||||
|
||||
all_logps = self.get_batch_logps(
|
||||
logits=all_logits,
|
||||
|
|
|
|||
Loading…
Reference in New Issue