| # Copyright 2025 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. | |
| from collections import defaultdict | |
| from dataclasses import dataclass | |
| from typing import TYPE_CHECKING, Any, Optional | |
| from ...extras import logging | |
| from ...extras.constants import IGNORE_INDEX | |
| from .processor_utils import DatasetProcessor, greedy_knapsack, infer_seqlen | |
| if TYPE_CHECKING: | |
| from ..mm_plugin import AudioInput, ImageInput, VideoInput | |
| logger = logging.get_logger(__name__) | |
| class SupervisedDatasetProcessor(DatasetProcessor): | |
| SIL_TOKENS: list[int] | None = None | |
| def _encode_data_example( | |
| self, | |
| task: str, | |
| prompt: list[dict[str, str]], | |
| response: list[dict[str, str]], | |
| system: Optional[str], | |
| tools: Optional[str], | |
| images: list["ImageInput"], | |
| videos: list["VideoInput"], | |
| audios: list["AudioInput"], | |
| ) -> tuple[list[int], list[int]]: | |
| cls = type(self) | |
| sil = type(self).SIL_TOKENS | |
| if sil is None: | |
| # "<|silence|>" 的 token 序列（不是 special token） | |
| sil = self.tokenizer.encode("<|silence|>", add_special_tokens=False) | |
| type(self).SIL_TOKENS = sil | |
| messages = self.template.mm_plugin.process_messages([[prompt,response]], images, videos, audios, self.processor) # Qwen2_5OmniProcessor #### 需要改！！！ | |
| input_ids, labels = self.template.mm_plugin.process_token_ids( | |
| [], [], images, videos, audios, self.tokenizer, self.processor | |
| ) | |
| encoded_pairs = self.template.encode_multiturn(self.tokenizer, messages, system, tools) #[{'content': '<|vision_bos|><|audio_bos|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|VIDEO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|AUDIO|><|audio_eos|><|vision_eos|>What is the video describing?', 'role': 'user'}, {'content': 'A girl who is drawing a picture of a guitar and feel nervous.', 'role': 'assistant'}] | |
| total_length = len(input_ids) + (1 if self.template.efficient_eos else 0) | |
| if self.data_args.mask_history: #false | |
| encoded_pairs = encoded_pairs[::-1] # high priority for last turns | |
| input_ids = [] | |
| labels = [] | |
| anchor_idx_list = [] | |
| gate_label_list = [] | |
| def startswith_silence(toks, sil=sil): | |
| return len(toks) >= len(sil) and toks[:len(sil)] == sil | |
| # # ################# 混合 ################ | |
| if cls.SIL_TOKENS is None: | |
| cls.SIL_TOKENS = self.tokenizer.encode("<|silence|>", add_special_tokens=False) | |
| SIL = cls.SIL_TOKENS | |
| if getattr(cls, "VISION_EOS_TOKENS", None) is None: | |
| cls.VISION_EOS_TOKENS = self.tokenizer.encode("<|vision_eos|>", add_special_tokens=False) | |
| VEOS = cls.VISION_EOS_TOKENS | |
| VEOS_L = len(VEOS) | |
| if getattr(cls, "ASSIST_TOKENS", None) is None: | |
| cls.ASSIST_TOKENS = self.tokenizer.encode( | |
| "<|im_end|>\n<|im_start|>assistant\n", add_special_tokens=False | |
| ) | |
| ASSIST = cls.ASSIST_TOKENS | |
| if getattr(cls, "USER_TOKENS", None) is None: | |
| cls.USER_TOKENS = self.tokenizer.encode("<|im_start|>user\n", add_special_tokens=False) | |
| USER = cls.USER_TOKENS | |
| def find_last_veos_position(source_ids: list[int], veos: list[int]) -> int: | |
| """只返回最后一次出现的“pattern 末尾”索引；找不到则返回 -1""" | |
| L = len(veos) | |
| if L == 0 or len(source_ids) < L: | |
| return -1 | |
| # 反向扫描，第一次命中就是最后一次出现 | |
| for i in range(len(source_ids) - L, -1, -1): | |
| if source_ids[i:i+L] == veos: | |
| return i + L - 1 | |
| return -1 | |
| def is_alert_example(prompt_msg, resp_msg) -> bool: | |
| if len(resp_msg) > 0 and isinstance(resp_msg[0].get("text", ""), str): | |
| return resp_msg[0]["text"].lstrip().startswith("alert") | |
| return False | |
| is_alert = is_alert_example(prompt, response) | |
| chunk_gate_labels: list[float] = [] | |
| for _src_ids, tgt_ids in encoded_pairs: | |
| t = list(tgt_ids) | |
| should_speak = (len(t) > 0) and (not startswith_silence(t)) | |
| chunk_gate_labels.append(1.0 if should_speak else 0.0) | |
| if is_alert: | |
| new_pairs: list[tuple[list[int], list[int]]] = [] | |
| for i, (src_ids, tgt_ids) in enumerate(encoded_pairs): | |
| src = list(src_ids) | |
| tgt = list(tgt_ids) | |
| if chunk_gate_labels[i] == 1.0 and len(tgt) > 0: | |
| if ASSIST and len(src) >= len(ASSIST): | |
| if src[-len(ASSIST) :] == ASSIST: | |
| src = src[: -len(ASSIST)] | |
| tgt = [] | |
| if i + 1 < len(encoded_pairs): | |
| next_src, next_tgt = encoded_pairs[i + 1] | |
| next_src = list(next_src) | |
| if USER and len(next_src) >= len(USER): | |
| if next_src[: len(USER)] == USER: | |
| next_src = next_src[len(USER) :] | |
| encoded_pairs[i + 1] = (next_src, list(next_tgt)) | |
| new_pairs.append((src, tgt)) | |
| encoded_pairs = new_pairs | |
| for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs): | |
| if total_length >= self.data_args.cutoff_len: | |
| break | |
| # 截断长度 | |
| source_len, target_len = infer_seqlen( | |
| len(source_ids), len(target_ids), self.data_args.cutoff_len - total_length | |
| ) | |
| source_ids = list(source_ids[:source_len]) | |
| target_ids = list(target_ids[:target_len]) | |
| # ---------- source_label ---------- | |
| if self.data_args.train_on_prompt: | |
| source_label = source_ids | |
| elif self.template.efficient_eos: | |
| source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1) | |
| else: | |
| source_label = [IGNORE_INDEX] * source_len | |
| # ---------- target_label---------- | |
| if self.data_args.mask_history and turn_idx != 0: | |
| target_label = [IGNORE_INDEX] * target_len | |
| # else: | |
| # # gate-only：无论 alert 还是 narration，都不训 LM | |
| # target_label = [IGNORE_INDEX] * target_len | |
| else: | |
| if 151643 in target_ids: | |
| if task == "turn-taking": | |
| target_label = target_ids[:-3] + [IGNORE_INDEX] * 3 | |
| else: | |
| target_label = [IGNORE_INDEX] * target_len | |
| else: | |
| # Narration：若以 <|silence|> 开头，该轮不训练 LM | |
| ##target_label = [IGNORE_INDEX] * target_len if startswith_silence(target_ids) else target_ids | |
| if task == "turn-taking": | |
| target_label = [IGNORE_INDEX] * target_len if startswith_silence(target_ids) else target_ids | |
| else: | |
| target_label = [IGNORE_INDEX] * target_len | |
| # ---------- flatten ---------- | |
| base_len = len(input_ids) | |
| input_ids += source_ids + target_ids | |
| labels += source_label + target_label | |
| total_length += len(source_ids) + len(target_ids) | |
| # ---------- time head：anchor + gate ---------- | |
| veos_pos = find_last_veos_position(source_ids, VEOS) | |
| if veos_pos != -1: | |
| anchor_abs = base_len + veos_pos | |
| anchor_idx_list.append(anchor_abs) | |
| gate_label_list.append(chunk_gate_labels[turn_idx]) | |
| # efficient_eos 收尾 | |
| if self.template.efficient_eos: | |
| input_ids += [self.tokenizer.eos_token_id] | |
| labels += [self.tokenizer.eos_token_id] | |
| if task == "turn-taking": | |
| pos_anchor_idxs = [a for a, y in zip(anchor_idx_list, gate_label_list) if y == 1.0] | |
| pos_gate_label_list = [1.0] * len(pos_anchor_idxs) | |
| return input_ids, labels, pos_anchor_idxs, pos_gate_label_list, [[prompt, response]] | |
| return input_ids, labels, anchor_idx_list, gate_label_list, [[prompt, response]] | |
| ################# 训 narration 的################### | |
| # # "<|silence|>" 的 token 序列（非 special token，按文本编码） | |
| # if getattr(cls, "SIL_TOKENS", None) is None: | |
| # cls.SIL_TOKENS = self.tokenizer.encode("<|silence|>", add_special_tokens=False) | |
| # SIL = cls.SIL_TOKENS | |
| # # "<|vision_eos|>" 的 token 序列 | |
| # if getattr(cls, "VISION_EOS_TOKENS", None) is None: | |
| # cls.VISION_EOS_TOKENS = self.tokenizer.encode("<|vision_eos|>", add_special_tokens=False) | |
| # VEOS = cls.VISION_EOS_TOKENS | |
| # VEOS_L = len(VEOS) | |
| # if self.data_args.mask_history: | |
| # encoded_pairs = encoded_pairs[::-1] | |
| # def find_last_veos_position(source_ids: list[int], veos: list[int]) -> int: | |
| # """只返回最后一次出现的“pattern 末尾”索引；找不到则返回 -1""" | |
| # L = len(veos) | |
| # if L == 0 or len(source_ids) < L: | |
| # return -1 | |
| # # 反向扫描，第一次命中就是最后一次出现 | |
| # for i in range(len(source_ids) - L, -1, -1): | |
| # if source_ids[i:i+L] == veos: | |
| # return i + L - 1 | |
| # return -1 | |
| # for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs): | |
| # if total_length >= self.data_args.cutoff_len: | |
| # break | |
| # # 截断本轮可用长度 | |
| # source_len, target_len = infer_seqlen( | |
| # len(source_ids), len(target_ids), self.data_args.cutoff_len - total_length | |
| # ) | |
| # source_ids = list(source_ids[:source_len]) | |
| # target_ids = list(target_ids[:target_len]) | |
| # # -------- SFT: 构造 labels -------- | |
| # if self.data_args.train_on_prompt: | |
| # source_label = source_ids | |
| # elif self.template.efficient_eos: | |
| # source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1) | |
| # else: | |
| # source_label = [IGNORE_INDEX] * source_len | |
| # if self.data_args.mask_history and turn_idx != 0: | |
| # # 仅训练最后一轮（若启用） | |
| # target_label = [IGNORE_INDEX] * target_len | |
| # else: | |
| # # 一些你的自定义规则保留 | |
| # if 151643 in target_ids: # 你之前的 “没生成完句子” 特判 | |
| # #target_label = target_ids[:-3] + [IGNORE_INDEX] * 3 | |
| # target_label = [IGNORE_INDEX] * target_len | |
| # # elif turn_idx == 0 and (messages[0]["content"] == "Narration History"): | |
| # # target_label = [IGNORE_INDEX] * target_len | |
| # else: | |
| # # Narration：若以 <|silence|> 开头，该轮不训练 LM | |
| # ##target_label = [IGNORE_INDEX] * target_len if startswith_silence(target_ids) else target_ids | |
| # target_label = [IGNORE_INDEX] * target_len | |
| # # 将本轮拼到扁平序列 | |
| # base_len = len(input_ids) | |
| # input_ids += source_ids + target_ids | |
| # labels += source_label + target_label | |
| # total_length += len(source_ids) + len(target_ids) | |
| # # -------- Time Head: 记录锚点与标签 -------- | |
| # # 该 turn 是否“应该说话”：target 非空 且 不以 <|silence|> 开头 | |
| # should_speak = (target_len > 0) and (not startswith_silence(target_ids)) | |
| # # 找出本轮 source 内最后一个 <|vision_eos|> 的位置（相对 source_ids） | |
| # veos_pos = find_last_veos_position(source_ids,VEOS) | |
| # # 将这些位置映射到扁平后的 input_ids 绝对下标 | |
| # anchor_abs = base_len + veos_pos # pos 落在 source 段 | |
| # anchor_idx_list.append(anchor_abs) | |
| # gate_label_list.append(1.0 if should_speak else 0.0) | |
| # # 可选：efficient_eos 末尾补 eos | |
| # if self.template.efficient_eos: | |
| # input_ids += [self.tokenizer.eos_token_id] | |
| # labels += [self.tokenizer.eos_token_id] | |
| # # 返回给上层（collator 负责把 anchor/label pad 成等长，并生成 gate_mask） | |
| # return input_ids, labels, anchor_idx_list, gate_label_list, [[prompt, response]] | |
| ################# 训 narration 的################### | |
| ################# 训 time head 的（proactive）################### | |
| # 1) 先记录“原始”每个 chunk 是否有 target（有 target = 需要开口） | |
| # chunk_gate_labels: list[float] = [] | |
| # for (src_ids, tgt_ids) in encoded_pairs: | |
| # chunk_gate_labels.append(1.0 if (len(tgt_ids) > 0 and not startswith_silence(tgt_ids)) else 0.0) | |
| # cls = type(self) | |
| # # 用 tokenizer 直接拿模板 token 序列，避免硬编码 ID | |
| # if getattr(cls, "ASSIST_TOKENS", None) is None: | |
| # # 对应 "<|im_end|>\n<|im_start|>assistant\n" | |
| # cls.ASSIST_TOKENS = self.tokenizer.encode( | |
| # "<|im_end|>\n<|im_start|>assistant\n", add_special_tokens=False | |
| # ) | |
| # if getattr(cls, "USER_TOKENS", None) is None: | |
| # # 对应 "<|im_start|>user\n" | |
| # cls.USER_TOKENS = self.tokenizer.encode( | |
| # "<|im_start|>user\n", add_special_tokens=False | |
| # ) | |
| # if getattr(cls, "VISION_EOS_TOKENS", None) is None: | |
| # # 对应 "<|vision_eos|>"，用来确定 anchor 的位置 | |
| # cls.VISION_EOS_TOKENS = self.tokenizer.encode( | |
| # "<|vision_eos|>", add_special_tokens=False | |
| # ) | |
| # assist_tokens = cls.ASSIST_TOKENS | |
| # user_tokens = cls.USER_TOKENS | |
| # vision_tokens = cls.VISION_EOS_TOKENS | |
| # # 2) 对 encoded_pairs 做一次 pass： | |
| # # - 对于原来有 target 的 chunk： | |
| # # * 从 source 末尾切掉 "<|im_end|>\n<|im_start|>assistant\n" | |
| # # * 把 target_ids 清空（不训练 LM） | |
| # # * 把下一条 source 开头的 "<|im_start|>user\n" 切掉 | |
| # new_pairs: list[tuple[list[int], list[int]]] = [] | |
| # for i, (src_ids, tgt_ids) in enumerate(encoded_pairs): | |
| # src_ids = list(src_ids) | |
| # tgt_ids = list(tgt_ids) | |
| # #if chunk_gate_labels[i] == 1.0 and tgt_ids: # 原本在这个 chunk 有 assistant 文本 → label=1 | |
| # # 切掉 source 末尾的 "<|im_end|>\n<|im_start|>assistant\n" | |
| # # if assist_tokens and len(src_ids) >= len(assist_tokens): | |
| # # if src_ids[-len(assist_tokens):] == assist_tokens: | |
| # # src_ids = src_ids[:-len(assist_tokens)] | |
| # # 只训练 time head，不训练 LM head → target 清空 | |
| # # tgt_ids = [] | |
| # # 下一条 pair[i+1]，如果以 "<|im_start|>user\n" 开头，就切掉这个前缀 | |
| # # if i + 1 < len(encoded_pairs): | |
| # # next_src, next_tgt = encoded_pairs[i + 1] | |
| # # next_src = list(next_src) | |
| # # if user_tokens and len(next_src) >= len(user_tokens): | |
| # # if next_src[:len(user_tokens)] == user_tokens: | |
| # # next_src = next_src[len(user_tokens):] | |
| # # encoded_pairs[i + 1] = (next_src, list(next_tgt)) | |
| # new_pairs.append((src_ids, tgt_ids)) | |
| # encoded_pairs = new_pairs | |
| # # =============================== | |
| # # flatten + label 构造 | |
| # # =============================== | |
| # for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs): | |
| # if total_length >= self.data_args.cutoff_len: | |
| # break | |
| # source_len, target_len = infer_seqlen( | |
| # len(source_ids), len(target_ids), self.data_args.cutoff_len - total_length | |
| # ) | |
| # source_ids = source_ids[:source_len] | |
| # target_ids = target_ids[:target_len] | |
| # if self.data_args.train_on_prompt: # prompt部分也要预测，也就是预训练的那种模式 | |
| # source_label = source_ids | |
| # elif self.template.efficient_eos: | |
| # source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1) | |
| # else: | |
| # source_label = [IGNORE_INDEX] * source_len | |
| # if self.data_args.mask_history and turn_idx != 0: # train on the last turn only | |
| # target_label = [IGNORE_INDEX] * target_len | |
| # else: | |
| # if 151643 in target_ids: ##### 没生成完句子。 | |
| # target_label = target_ids[:-3] + [IGNORE_INDEX] * 3 | |
| # elif turn_idx == 0 and (messages[0]["content"] == "Narration History"): | |
| # target_label = [IGNORE_INDEX] * target_len | |
| # else: | |
| # # 这里即便 startswith_silence 逻辑还在，对 proactive_gate 来说， | |
| # # 上面我们已经把所有有文本的 target 清空了，所以不会再训练 LM。 | |
| # target_label = target_ids #[IGNORE_INDEX] * target_len if startswith_silence(target_ids) else target_ids | |
| # total_length += len(source_ids) + len(target_ids) | |
| # if self.data_args.mask_history: # false # reversed sequences | |
| # base_len = len(input_ids) | |
| # input_ids = source_ids + target_ids + input_ids | |
| # labels = source_label + target_label + labels | |
| # else: | |
| # base_len = len(input_ids) | |
| # input_ids += source_ids + target_ids | |
| # labels += source_label + target_label | |
| # # # =============================== | |
| # # # Time head anchor & label | |
| # # # =============================== | |
| # # # 在当前 chunk 的 source 里找到最后一个 "<|vision_eos|>"，以它作为 anchor； | |
| # # # label 由 chunk_gate_labels[turn_idx] 决定： | |
| # # # - 原来有 target 的 chunk → 1.0（需要开口） | |
| # # # - 原来没有 target 的 chunk → 0.0（不需要开口） | |
| # # if vision_tokens and source_len >= len(vision_tokens): | |
| # # L = len(vision_tokens) | |
| # # pos = -1 | |
| # # # 从后往前找，可以保证拿到最后一个 vision_eos | |
| # # for idx in range(source_len - L, -1, -1): | |
| # # if source_ids[idx : idx + L] == vision_tokens: | |
| # # pos = idx + L - 1 # pattern 最后一个 token 的位置 | |
| # # break | |
| # # if pos != -1: | |
| # # anchor_idx = base_len + pos | |
| # # anchor_idx_list.append(anchor_idx) | |
| # # gate_label_list.append(chunk_gate_labels[turn_idx]) | |
| # if self.template.efficient_eos: # false | |
| # input_ids += [self.tokenizer.eos_token_id] | |
| # labels += [self.tokenizer.eos_token_id] | |
| # return input_ids, labels, [[prompt, response]] #anchor_idx_list, gate_label_list, | |
| ################# 训 time head 的################### | |
| ################# 训正常SFT的(turn-taking)################### | |
| # for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs): | |
| # if total_length >= self.data_args.cutoff_len: | |
| # break | |
| # source_len, target_len = infer_seqlen( | |
| # len(source_ids), len(target_ids), self.data_args.cutoff_len - total_length | |
| # ) | |
| # source_ids = source_ids[:source_len] | |
| # target_ids = target_ids[:target_len] | |
| # ####total_length += source_len + target_len | |
| # if self.data_args.train_on_prompt: #prompt部分也要预测，也就是预训练的那种模式 | |
| # source_label = source_ids | |
| # elif self.template.efficient_eos: | |
| # source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1) | |
| # else: | |
| # source_label = [IGNORE_INDEX] * source_len ####### | |
| # if self.data_args.mask_history and turn_idx != 0: # train on the last turn only | |
| # target_label = [IGNORE_INDEX] * target_len | |
| # else: | |
| # if 151643 in target_ids: ##### 没生成完句子。 | |
| # target_label = target_ids[:-3]+[IGNORE_INDEX]*3 | |
| # else: | |
| # target_label = target_ids #[IGNORE_INDEX] * target_len if startswith_silence(target_ids) else target_ids | |
| # total_length += len(source_ids)+len(target_ids) | |
| # if self.data_args.mask_history: # false # reversed sequences | |
| # input_ids = source_ids + target_ids + input_ids | |
| # labels = source_label + target_label + labels | |
| # else: | |
| # base_len = len(input_ids) | |
| # input_ids += source_ids + target_ids | |
| # labels += source_label + target_label | |
| # is_assistant_turn = (target_len > 0) | |
| # if is_assistant_turn: | |
| # # 锚点 = 本轮 source 的最后一个 token（assistant\n 的 '\n' 位） | |
| # #anchor_idx = base_len + source_len - 1 | |
| # # 锚点 = 本轮source中最后一个<vision_eos>的位置 | |
| # anchor_idx = base_len + source_len - 6 | |
| # # gate 标签：仅当 target 以 <|silence|> 开头时视为沉默 | |
| # #is_sil = startswith_silence(target_ids) | |
| # #anchor_idx_list.append(anchor_idx) | |
| # #gate_label_list.append(0.0 if is_sil else 1.0) | |
| # if self.template.efficient_eos: #false | |
| # input_ids += [self.tokenizer.eos_token_id] | |
| # labels += [self.tokenizer.eos_token_id] | |
| # #print('prompt:',prompt,'input_ids:',input_ids) | |
| # return input_ids, labels, [[prompt,response]] #input_ids, labels, anchor_idx_list, gate_label_list, [[prompt,response]] | |
| ################# 训正常SFT的################### | |
| def preprocess_dataset(self, examples: dict[str, list[Any]]) -> 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_rank0( | |
| # "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]) | |
| # ) | |
| # continue | |
| input_ids, labels, anchor_idx_list, gate_label_list, messages = self._encode_data_example( ###### tokenize | |
| task=examples["_task"][i], | |
| prompt=examples["_prompt"][i], #query | |
| response=examples["_response"][i], #ans | |
| system=examples["_system"][i], | |
| tools=examples["_tools"][i], | |
| images=examples["_images"][i] or [], | |
| videos=examples["_videos"][i] or [], | |
| audios=examples["_audios"][i] or [], | |
| ) | |
| model_inputs["input_ids"].append(input_ids) #[151544, 8948, xxxxx] | |
| model_inputs["attention_mask"].append([1] * len(input_ids)) #[[1,1,1,.....]] | |
| model_inputs["labels"].append(labels) #[-100,xxxxx] | |
| model_inputs["images"].append(examples["_images"][i]) | |
| model_inputs["videos"].append(examples["_videos"][i]) | |
| model_inputs["audios"].append(examples["_audios"][i]) | |
| model_inputs['messages'].append(messages) | |
| model_inputs['anchor_idx_list'].append(anchor_idx_list) | |
| model_inputs['gate_label_list'].append(gate_label_list) | |
| return model_inputs | |
| def print_data_example(self, example: dict[str, list[int]]) -> None: | |
| valid_labels = list(filter(lambda x: x != IGNORE_INDEX, example["labels"])) | |
| print("input_ids:\n{}".format(example["input_ids"])) | |
| #print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False))) | |
| #print("label_ids:\n{}".format(example["labels"])) | |
| #print(f"labels:\n{self.tokenizer.decode(valid_labels, skip_special_tokens=False)}") | |
| class PackedSupervisedDatasetProcessor(SupervisedDatasetProcessor): | |
| def preprocess_dataset(self, examples: dict[str, list[Any]]) -> 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>` | |
| print('PackedSupervisedDatasetProcessor') | |
| valid_num = 0 | |
| batch_input_ids, batch_labels, batch_images, batch_videos, batch_audios = [], [], [], [], [] | |
| 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_rank0( | |
| "Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]) | |
| ) | |
| continue | |
| input_ids, labels,_ = self._encode_data_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 [], | |
| audios=examples["_audios"][i] or [], | |
| ) | |
| length = len(input_ids) | |
| if length > self.data_args.cutoff_len: | |
| logger.warning_rank0(f"Dropped lengthy example with length {length} > {self.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 []) | |
| batch_audios.append(examples["_audios"][i] or []) | |
| valid_num += 1 | |
| model_inputs = defaultdict(list) | |
| knapsacks = greedy_knapsack(lengths, self.data_args.cutoff_len) | |
| for knapsack in knapsacks: | |
| packed_input_ids, packed_attention_masks, packed_position_ids, packed_labels = [], [], [], [] | |
| packed_images, packed_videos, packed_audios = [], [], [] | |
| for i, length in enumerate(knapsack): | |
| index = length2indexes[length].pop() | |
| packed_input_ids += batch_input_ids[index] | |
| packed_position_ids += list(range(len(batch_input_ids[index]))) # NOTE: pad_to_multiple_of ignore this | |
| packed_labels += batch_labels[index] | |
| packed_images += batch_images[index] | |
| packed_videos += batch_videos[index] | |
| packed_audios += batch_audios[index] | |
| if self.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) < self.data_args.cutoff_len + 1: # avoid flash_attn drops attn mask | |
| pad_length = self.data_args.cutoff_len - len(packed_input_ids) + 1 | |
| packed_input_ids += [self.tokenizer.pad_token_id] * pad_length | |
| packed_position_ids += [0] * pad_length | |
| packed_labels += [IGNORE_INDEX] * pad_length | |
| if self.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) != self.data_args.cutoff_len + 1: | |
| 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["position_ids"].append(packed_position_ids) | |
| model_inputs["labels"].append(packed_labels) | |
| model_inputs["images"].append(packed_images or None) | |
| model_inputs["videos"].append(packed_videos or None) | |
| model_inputs["audios"].append(packed_audios or None) | |
| return model_inputs | |