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dataset.py

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+import torch
+from torch.utils.data import Dataset
+from tqdm import tqdm
+import copy
+import numpy as np
+import pdb
+import os
+import io
+import json
+import gzip
+import zstandard as zstd
+import numpy as np
+from tqdm import tqdm
+import pandas as pd
+
+
+class SemiNATForSingleRoundMaskInput(Dataset):
+    '''
+        Mask掉了所有的输入，只有输出的loss
+    '''
+
+    def __init__(self, tokenizer, datas, max_length, proc):
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.proc = proc
+        # 用 apply + 并行加速预处理
+        processed = self._vectorized_preprocess(datas)
+        self.input_ids = processed["input_ids"]
+        self.labels = processed["labels"]
+        self.attention_mask = processed["attention_mask"]
+        self.slice_indices = processed["slice_indices"]
+
+    def _vectorized_preprocess(self, datas):
+        # 批量预分配内存
+        input_ids = np.zeros((len(datas), self.max_length), dtype=np.int64)
+        attention_mask = np.zeros((len(datas), self.max_length),
+                                  dtype=np.int64)
+        labels = np.full((len(datas), self.max_length), -100, dtype=np.int64)
+        slice_indices = np.full((len(datas), self.max_length),
+                                -1,
+                                dtype=np.int64)
+
+        # 批量处理所有行的 messages
+        def process_row(row):
+            total_inputs = []
+            total_labels = []
+            sample_slice = []
+
+            # pdb.set_trace()
+            for msg in row['messages']:
+                # 批量分词（假设 msg['content'] 是文本列表）
+                inputs = self.tokenizer(msg['content'],
+                                        padding=False,
+                                        truncation=False,
+                                        add_special_tokens=False).input_ids
+                total_inputs.extend(inputs)
+
+                if msg['role'] == 'user':
+                    total_labels.extend(len(inputs) * [-100])
+                elif msg['role'] == 'assistant':
+                    total_labels.extend(inputs)
+                sample_slice.extend(msg['split_pos'])
+
+            # 截断或填充逻辑
+            seq_len = min(len(total_inputs), self.max_length)
+            # 输入和标签
+            input_ids = total_inputs[:self.max_length] + [
+                self.tokenizer.pad_token_id
+            ] * (self.max_length - seq_len)
+            labels = total_labels[:self.max_length] + [-100] * (
+                self.max_length - seq_len)
+            # attention_mask
+            attention_mask = [1] * seq_len + [0] * (self.max_length - seq_len)
+            # slice_indices
+            slice_arr = np.array(sample_slice[:self.max_length] + [-1] *
+                                 (self.max_length - len(sample_slice)))
+            slice_arr[slice_arr > self.max_length -
+                      1] = -1  # 过滤超长位置,这里-1是因为max length是1024，最大index是1023
+
+            return input_ids, labels, attention_mask, slice_arr
+
+        # 并行处理所有行（需安装 pandarallel）
+        try:
+            from pandarallel import pandarallel
+            pandarallel.initialize(nb_workers=self.proc, progress_bar=True)
+            processed = datas.parallel_apply(process_row, axis=1)
+        except ImportError:
+            processed = datas.progress_apply(process_row, axis=1)  # tqdm 进度条
+
+        # 合并结果
+        # pdb.set_trace()
+        for idx, (i_ids, lbl, attn, slc) in enumerate(processed):
+            input_ids[idx] = i_ids
+            labels[idx] = lbl
+            attention_mask[idx] = attn
+            slice_indices[idx] = slc
+
+        return {
+            "input_ids": input_ids,
+            "labels": labels,
+            "attention_mask": attention_mask,
+            "slice_indices": slice_indices
+        }
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, index):
+        # 直接返回预分配的张量，避免重复转换
+        return (torch.as_tensor(self.input_ids[index]),
+                torch.as_tensor(self.labels[index]),
+                torch.as_tensor(self.attention_mask[index]),
+                torch.as_tensor(self.slice_indices[index]))
+
+
+class SemiNATForSingleRound(Dataset):
+
+    def __init__(self, tokenizer, datas, max_length, proc):
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.proc = proc
+        # 用 apply + 并行加速预处理
+        processed = self._vectorized_preprocess(datas)
+        self.input_ids = processed["input_ids"]
+        self.labels = processed["labels"]
+        self.attention_mask = processed["attention_mask"]
+        self.slice_indices = processed["slice_indices"]
+
+    def _vectorized_preprocess(self, datas):
+        # 批量预分配内存
+        input_ids = np.zeros((len(datas), self.max_length), dtype=np.int64)
+        attention_mask = np.zeros((len(datas), self.max_length),
+                                  dtype=np.int64)
+        labels = np.full((len(datas), self.max_length), -100, dtype=np.int64)
+        slice_indices = np.full((len(datas), self.max_length),
+                                -1,
+                                dtype=np.int64)
+
+        # 批量处理所有行的 messages
+        def process_row(row):
+            total_inputs = []
+            sample_slice = []
+
+            for msg in row['messages']:
+                # 批量分词（假设 msg['content'] 是文本列表）
+                inputs = self.tokenizer(msg['content'],
+                                        padding=False,
+                                        truncation=False,
+                                        add_special_tokens=False).input_ids
+                total_inputs.extend(inputs)
+                # 直接使用列表扩展 slice
+                sample_slice.extend(msg['split_pos'])
+
+            # 截断或填充逻辑
+            seq_len = min(len(total_inputs), self.max_length)
+            # 输入和标签
+            input_ids = total_inputs[:self.max_length] + [
+                self.tokenizer.pad_token_id
+            ] * (self.max_length - seq_len)
+            labels = total_inputs[:self.max_length] + [-100] * (
+                self.max_length - seq_len)
+            # attention_mask
+            attention_mask = [1] * seq_len + [0] * (self.max_length - seq_len)
+            # slice_indices
+            slice_arr = np.array(sample_slice[:self.max_length] + [-1] *
+                                 (self.max_length - len(sample_slice)))
+            slice_arr[slice_arr > self.max_length] = -1  # 过滤超长位置
+
+            return input_ids, labels, attention_mask, slice_arr
+
+        # 并行处理所有行（需安装 pandarallel）
+        try:
+            from pandarallel import pandarallel
+            pandarallel.initialize(nb_workers=self.proc, progress_bar=True)
+            processed = datas.parallel_apply(process_row, axis=1)
+        except:
+            processed = datas.progress_apply(process_row, axis=1)  # tqdm 进度条
+
+        # 合并结果
+        for idx, (i_ids, lbl, attn, slc) in enumerate(processed):
+            input_ids[idx] = i_ids
+            labels[idx] = lbl
+            attention_mask[idx] = attn
+            slice_indices[idx] = slc
+
+        return {
+            "input_ids": input_ids,
+            "labels": labels,
+            "attention_mask": attention_mask,
+            "slice_indices": slice_indices
+        }
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, index):
+        # 直接返回预分配的张量，避免重复转换
+        return (torch.as_tensor(self.input_ids[index]),
+                torch.as_tensor(self.labels[index]),
+                torch.as_tensor(self.attention_mask[index]),
+                torch.as_tensor(self.slice_indices[index]))
+
+
+class SemiNATDatasetForPretrain(Dataset):
+    # data_path is jsonl.zstd or json.gz file
+    def __init__(self,
+                 tokenizer,
+                 data_path,
+                 max_length,
+                 proc,
+                 cache_path=None):
+        if data_path.endswith('.zstd'):
+            data = pd.DataFrame([
+                json.loads(line) for line in self._decompress_zst_to_string(
+                    data_path).splitlines()
+            ])
+        else:  # json.gz file, each line a json
+            with gzip.open(data_path, 'rt', encoding='utf-8') as f:
+                data = pd.DataFrame([json.loads(line) for line in f])
+
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.proc = proc
+
+        if cache_path and os.path.exists(cache_path):
+            print(f"[INFO] Loading cached data from {cache_path}")
+            cached = torch.load(cache_path)
+            self.input_ids = cached["input_ids"]
+            self.labels = cached["labels"]
+            self.attention_mask = cached["attention_mask"]
+            self.slice_indices = cached["slice_indices"]
+        else:
+            processed = self._vectorized_preprocess(data)
+            self.input_ids = processed["input_ids"]
+            self.labels = processed["labels"]
+            self.attention_mask = processed["attention_mask"]
+            self.slice_indices = processed["slice_indices"]
+        if type(self.input_ids) != torch.Tensor:
+            self.input_ids = torch.tensor(self.input_ids, dtype=torch.long)
+            self.labels = torch.tensor(self.labels, dtype=torch.long)
+            self.attention_mask = torch.tensor(self.attention_mask,
+                                               dtype=torch.long)
+            self.slice_indices = torch.tensor(self.slice_indices,
+                                              dtype=torch.long)
+
+    def _decompress_zst_to_string(self, input_file):
+        with open(input_file, 'rb') as f:
+            dctx = zstd.ZstdDecompressor()
+            with dctx.stream_reader(f) as reader:
+                text_stream = io.TextIOWrapper(reader, encoding='utf-8')
+                return text_stream.read()  # 读取为字符串
+
+    def _vectorized_preprocess(self, data):
+        input_ids = np.zeros((len(data), self.max_length), dtype=np.int64)
+        attention_mask = np.zeros((len(data), self.max_length), dtype=np.int64)
+        labels = np.full((len(data), self.max_length), -100, dtype=np.int64)
+        slice_indices = np.full((len(data), self.max_length),
+                                -1,
+                                dtype=np.int64)
+
+        def process_row(row):
+            inputs = self.tokenizer(row['text'],
+                                    padding=False,
+                                    truncation=False,
+                                    add_special_tokens=False).input_ids
+            # slice to 8-token segments. that is, sample_slice is [1, 9, 17, 25, ...]
+            sample_slice = (np.arange(0, len(inputs), 8) + 1).tolist()
+            # add the end
+            if len(inputs) % 8 != 1:
+                sample_slice.append(len(inputs))
+
+            # 截断或填充逻辑
+            seq_len = min(len(inputs), self.max_length)
+            # 输入和标签
+            input_ids = inputs[:self.max_length] + [
+                self.tokenizer.pad_token_id
+            ] * (self.max_length - seq_len)
+            labels = [
+                50279  # <EOS>
+            ] + inputs[:self.max_length -
+                       1] + [-100] * (self.max_length - 1 - seq_len)
+            # attention_mask
+            attention_mask = [1] * seq_len + [0] * (self.max_length - seq_len)
+            # slice_indices
+            slice_arr = np.array(sample_slice[:self.max_length] + [-1] *
+                                 (self.max_length - len(sample_slice)))
+            slice_arr[slice_arr > self.max_length] = -1  # 过滤超长位置
+
+            return input_ids, labels, attention_mask, slice_arr
+
+        try:
+            from pandarallel import pandarallel
+            pandarallel.initialize(nb_workers=self.proc, progress_bar=True)
+            processed = data.parallel_apply(process_row, axis=1)
+        except ImportError:
+            processed = data.progress_apply(process_row, axis=1)  # tqdm 进度条
+
+        # 合并结果
+        for idx, (i_ids, lbl, attn, slc) in enumerate(processed):
+            input_ids[idx] = i_ids
+            labels[idx] = lbl
+            attention_mask[idx] = attn
+            slice_indices[idx] = slc
+
+        return {
+            "input_ids": input_ids,
+            "labels": labels,
+            "attention_mask": attention_mask,
+            "slice_indices": slice_indices
+        }
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, index):
+        return (self.input_ids[index], self.labels[index],
+                self.attention_mask[index], self.slice_indices[index])