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https://github.com/hiyouga/LlamaFactory.git
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[breaking change] refactor data pipeline (#6901)
* refactor data * rename file Former-commit-id: 7a1a4ce6451cb782573d0bd9dd27a5e443e3a18b
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hoshi-hiyouga
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src/llamafactory/data/processor/supervised.py
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src/llamafactory/data/processor/supervised.py
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# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import defaultdict
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
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from ...extras import logging
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from ...extras.constants import IGNORE_INDEX
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from .processor_utils import DatasetProcessor, greedy_knapsack, infer_seqlen
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if TYPE_CHECKING:
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from ..mm_plugin import AudioInput, ImageInput, VideoInput
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logger = logging.get_logger(__name__)
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@dataclass
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class SupervisedDatasetProcessor(DatasetProcessor):
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def _encode_data_example(
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self,
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prompt: Sequence[Dict[str, str]],
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response: Sequence[Dict[str, str]],
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system: Optional[str],
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tools: Optional[str],
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images: Sequence["ImageInput"],
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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) -> Tuple[List[int], List[int]]:
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messages = self.template.mm_plugin.process_messages(prompt + response, images, videos, audios, self.processor)
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input_ids, labels = self.template.mm_plugin.process_token_ids(
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[], [], images, videos, audios, self.tokenizer, self.processor
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)
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encoded_pairs = self.template.encode_multiturn(self.tokenizer, messages, system, tools)
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total_length = len(input_ids) + (1 if self.template.efficient_eos else 0)
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if self.data_args.mask_history:
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encoded_pairs = encoded_pairs[::-1] # high priority for last turns
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for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
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if total_length >= self.data_args.cutoff_len:
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break
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source_len, target_len = infer_seqlen(
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len(source_ids), len(target_ids), self.data_args.cutoff_len - total_length
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)
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source_ids = source_ids[:source_len]
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target_ids = target_ids[:target_len]
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total_length += source_len + target_len
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if self.data_args.train_on_prompt:
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source_label = source_ids
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elif self.template.efficient_eos:
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source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
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else:
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source_label = [IGNORE_INDEX] * source_len
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if self.data_args.mask_history and turn_idx != 0: # train on the last turn only
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target_label = [IGNORE_INDEX] * target_len
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else:
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target_label = target_ids
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if self.data_args.mask_history: # reversed sequences
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input_ids = source_ids + target_ids + input_ids
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labels = source_label + target_label + labels
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else:
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input_ids += source_ids + target_ids
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labels += source_label + target_label
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if self.template.efficient_eos:
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input_ids += [self.tokenizer.eos_token_id]
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labels += [self.tokenizer.eos_token_id]
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return input_ids, labels
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def preprocess_dataset(self, examples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
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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 = defaultdict(list)
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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_rank0(
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"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
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)
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continue
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input_ids, labels = self._encode_data_example(
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prompt=examples["_prompt"][i],
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response=examples["_response"][i],
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system=examples["_system"][i],
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tools=examples["_tools"][i],
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images=examples["_images"][i] or [],
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videos=examples["_videos"][i] or [],
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audios=examples["_audios"][i] or [],
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)
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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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model_inputs["images"].append(examples["_images"][i])
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model_inputs["videos"].append(examples["_videos"][i])
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model_inputs["audios"].append(examples["_audios"][i])
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return model_inputs
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def print_data_example(self, example: Dict[str, List[int]]) -> None:
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valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"]))
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print("input_ids:\n{}".format(example["input_ids"]))
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print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
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print("label_ids:\n{}".format(example["labels"]))
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print(f"labels:\n{self.tokenizer.decode(valid_labels, skip_special_tokens=False)}")
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@dataclass
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class PackedSupervisedDatasetProcessor(SupervisedDatasetProcessor):
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def preprocess_dataset(self, examples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
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# TODO: use `position_ids` to achieve packing
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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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valid_num = 0
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batch_input_ids, batch_labels, batch_images, batch_videos, batch_audios = [], [], [], [], []
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lengths = []
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length2indexes = defaultdict(list)
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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_rank0(
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"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
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)
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continue
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input_ids, labels = self._encode_data_example(
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prompt=examples["_prompt"][i],
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response=examples["_response"][i],
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system=examples["_system"][i],
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tools=examples["_tools"][i],
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images=examples["_images"][i] or [],
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videos=examples["_videos"][i] or [],
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audios=examples["_audios"][i] or [],
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)
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length = len(input_ids)
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if length > self.data_args.cutoff_len:
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logger.warning_rank0(f"Dropped lengthy example with length {length} > {self.data_args.cutoff_len}.")
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else:
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lengths.append(length)
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length2indexes[length].append(valid_num)
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batch_input_ids.append(input_ids)
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batch_labels.append(labels)
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batch_images.append(examples["_images"][i] or [])
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batch_videos.append(examples["_videos"][i] or [])
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batch_audios.append(examples["_audios"][i] or [])
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valid_num += 1
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model_inputs = defaultdict(list)
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knapsacks = greedy_knapsack(lengths, self.data_args.cutoff_len)
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for knapsack in knapsacks:
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packed_input_ids, packed_attention_masks, packed_labels = [], [], []
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packed_images, packed_videos, packed_audios = [], [], []
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for i, length in enumerate(knapsack):
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index = length2indexes[length].pop()
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packed_input_ids += batch_input_ids[index]
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packed_labels += batch_labels[index]
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packed_images += batch_images[index]
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packed_videos += batch_videos[index]
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packed_audios += batch_audios[index]
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if self.data_args.neat_packing:
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packed_attention_masks += [i + 1] * len(batch_input_ids[index]) # start from 1
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else:
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packed_attention_masks += [1] * len(batch_input_ids[index])
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if len(packed_input_ids) < self.data_args.cutoff_len + 1: # avoid flash_attn drops attn mask
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pad_length = self.data_args.cutoff_len - len(packed_input_ids) + 1
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packed_input_ids += [self.tokenizer.pad_token_id] * pad_length
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packed_labels += [IGNORE_INDEX] * pad_length
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if self.data_args.neat_packing:
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packed_attention_masks += [0] * pad_length
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else:
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packed_attention_masks += [1] * pad_length # more efficient flash_attn
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if len(packed_input_ids) != self.data_args.cutoff_len + 1:
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raise ValueError("The length of packed example should be identical to the cutoff length.")
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model_inputs["input_ids"].append(packed_input_ids)
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model_inputs["attention_mask"].append(packed_attention_masks)
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model_inputs["labels"].append(packed_labels)
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model_inputs["images"].append(packed_images or None)
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model_inputs["videos"].append(packed_videos or None)
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model_inputs["audios"].append(packed_audios or None)
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return model_inputs
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