delete: transfer from llamafactory to openrlhf.

TODO

feat: add openrlhf.diff file for pending modifications

- Added openrlhf.diff file to track pending modifications to the openrlhf project.

supervised.py

-# Copyright 2024 the LlamaFactory team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Replacement for src/llamafactory/data/processors/supervised.py
-# Modifications:
-# - Do not mask the first turn in the chat history when `mask_history`.
-# - Preserve the original order of messages (do not reverse the list).
-
-from collections import defaultdict
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
-
-from ...extras.constants import IGNORE_INDEX
-from ...extras.logging import get_logger
-from .processor_utils import greedy_knapsack, infer_seqlen
-
-
-if TYPE_CHECKING:
-    from transformers import PreTrainedTokenizer, ProcessorMixin
-
-    from ...hparams import DataArguments
-    from ..mm_plugin import ImageInput, VideoInput
-    from ..template import Template
-
-
-logger = get_logger(__name__)
-
-
-def _encode_supervised_example(
-    prompt: Sequence[Dict[str, str]],
-    response: Sequence[Dict[str, str]],
-    system: Optional[str],
-    tools: Optional[str],
-    images: Sequence["ImageInput"],
-    videos: Sequence["VideoInput"],
-    template: "Template",
-    tokenizer: "PreTrainedTokenizer",
-    processor: Optional["ProcessorMixin"],
-    cutoff_len: int,
-    train_on_prompt: bool,
-    mask_history: bool,
-) -> Tuple[List[int], List[int]]:
-    messages = template.mm_plugin.process_messages(prompt + response, images, videos, processor)
-    input_ids, labels = template.mm_plugin.process_token_ids([], [], images, videos, tokenizer, processor)
-    encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools)
-    total_length = len(input_ids) + (1 if template.efficient_eos else 0)
-
-    for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
-        if total_length >= cutoff_len:
-            break
-
-        source_len, target_len = infer_seqlen(len(source_ids), len(target_ids), cutoff_len - total_length)
-        source_ids = source_ids[:source_len]
-        target_ids = target_ids[:target_len]
-        total_length += source_len + target_len
-
-        if train_on_prompt:
-            source_label = source_ids
-        elif template.efficient_eos:
-            source_label = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
-        else:
-            source_label = [IGNORE_INDEX] * source_len
-
-        if mask_history and turn_idx == 0:  # don't train on the first turn
-            target_label = [IGNORE_INDEX] * target_len
-        else:
-            target_label = target_ids
-
-        input_ids += source_ids + target_ids
-        labels += source_label + target_label
-
-    if template.efficient_eos:
-        input_ids += [tokenizer.eos_token_id]
-        labels += [tokenizer.eos_token_id]
-
-    return input_ids, labels
-
-
-def preprocess_supervised_dataset(
-    examples: Dict[str, List[Any]],
-    template: "Template",
-    tokenizer: "PreTrainedTokenizer",
-    processor: Optional["ProcessorMixin"],
-    data_args: "DataArguments",
-) -> Dict[str, List[Any]]:
-    # 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 = defaultdict(list)
-    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
-
-        input_ids, labels = _encode_supervised_example(
-            prompt=examples["_prompt"][i],
-            response=examples["_response"][i],
-            system=examples["_system"][i],
-            tools=examples["_tools"][i],
-            images=examples["_images"][i] or [],
-            videos=examples["_videos"][i] or [],
-            template=template,
-            tokenizer=tokenizer,
-            processor=processor,
-            cutoff_len=data_args.cutoff_len,
-            train_on_prompt=data_args.train_on_prompt,
-            mask_history=data_args.mask_history,
-        )
-        model_inputs["input_ids"].append(input_ids)
-        model_inputs["attention_mask"].append([1] * len(input_ids))
-        model_inputs["labels"].append(labels)
-        model_inputs["images"].append(examples["_images"][i])
-        model_inputs["videos"].append(examples["_videos"][i])
-
-    return model_inputs
-
-
-def preprocess_packed_supervised_dataset(
-    examples: Dict[str, List[Any]],
-    template: "Template",
-    tokenizer: "PreTrainedTokenizer",
-    processor: Optional["ProcessorMixin"],
-    data_args: "DataArguments",
-) -> Dict[str, List[Any]]:
-    # TODO: use `position_ids` to achieve packing
-    # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
-    # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
-    valid_num = 0
-    batch_input_ids, batch_labels, batch_images, batch_videos = [], [], [], []
-    lengths = []
-    length2indexes = defaultdict(list)
-    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
-
-        input_ids, labels = _encode_supervised_example(
-            prompt=examples["_prompt"][i],
-            response=examples["_response"][i],
-            system=examples["_system"][i],
-            tools=examples["_tools"][i],
-            images=examples["_images"][i] or [],
-            videos=examples["_videos"][i] or [],
-            template=template,
-            tokenizer=tokenizer,
-            processor=processor,
-            cutoff_len=data_args.cutoff_len - 1,  # reserved for the padding token
-            train_on_prompt=data_args.train_on_prompt,
-            mask_history=data_args.mask_history,
-        )
-        length = len(input_ids)
-        if length > data_args.cutoff_len:
-            logger.warning("Dropped lengthy example with length {} > {}.".format(length, data_args.cutoff_len))
-        else:
-            lengths.append(length)
-            length2indexes[length].append(valid_num)
-            batch_input_ids.append(input_ids)
-            batch_labels.append(labels)
-            batch_images.append(examples["_images"][i] or [])
-            batch_videos.append(examples["_videos"][i] or [])
-            valid_num += 1
-
-    model_inputs = defaultdict(list)
-    knapsacks = greedy_knapsack(lengths, data_args.cutoff_len - 1)  # reserved for the padding token
-    for knapsack in knapsacks:
-        packed_input_ids, packed_attention_masks, packed_labels = [], [], []
-        packed_images, packed_videos = [], []
-        for i, length in enumerate(knapsack):
-            index = length2indexes[length].pop()
-            packed_input_ids += batch_input_ids[index]
-            packed_labels += batch_labels[index]
-            packed_images += batch_images[index]
-            packed_videos += batch_videos[index]
-            if data_args.neat_packing:
-                packed_attention_masks += [i + 1] * len(batch_input_ids[index])  # start from 1
-            else:
-                packed_attention_masks += [1] * len(batch_input_ids[index])
-
-        if len(packed_input_ids) < data_args.cutoff_len:
-            pad_length = data_args.cutoff_len - len(packed_input_ids)
-            packed_input_ids += [tokenizer.pad_token_id] * pad_length
-            packed_labels += [IGNORE_INDEX] * pad_length
-            if data_args.neat_packing:
-                packed_attention_masks += [0] * pad_length
-            else:
-                packed_attention_masks += [1] * pad_length  # more efficient flash_attn
-
-        if len(packed_input_ids) != data_args.cutoff_len:
-            raise ValueError("The length of packed example should be identical to the cutoff length.")
-
-        model_inputs["input_ids"].append(packed_input_ids)
-        model_inputs["attention_mask"].append(packed_attention_masks)
-        model_inputs["labels"].append(packed_labels)
-        model_inputs["images"].append(packed_images or None)
-        model_inputs["videos"].append(packed_videos or None)
-
-    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)))
-