Upload rl_code/verl/trainer/data_loader.py with huggingface_hub

rl_code/verl/trainer/data_loader.py

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+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# 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 typing import Optional
+
+import torch
+from torch.utils.data import RandomSampler, SequentialSampler
+from torchdata.stateful_dataloader import StatefulDataLoader
+from transformers import PreTrainedTokenizer, ProcessorMixin
+
+from ..utils.dataset import RLHFDataset, collate_fn
+from .config import DataConfig
+
+
+def create_dataloader(config: DataConfig, tokenizer: PreTrainedTokenizer, processor: Optional[ProcessorMixin]) -> None:
+    train_dataset = RLHFDataset(
+        data_path=config.train_files,
+        tokenizer=tokenizer,
+        processor=processor,
+        prompt_key=config.prompt_key,
+        answer_key=config.answer_key,
+        image_key=config.image_key,
+        video_key=config.video_key,
+        image_dir=config.image_dir,
+        video_fps=config.video_fps,
+        max_prompt_length=config.max_prompt_length,
+        truncation="right",
+        format_prompt=config.format_prompt,
+        min_pixels=config.min_pixels,
+        max_pixels=config.max_pixels,
+        filter_overlong_prompts=config.filter_overlong_prompts,
+        filter_overlong_prompts_workers=config.filter_overlong_prompts_workers,
+    )
+    # use sampler for better ckpt resume
+    if config.shuffle:
+        train_dataloader_generator = torch.Generator()
+        train_dataloader_generator.manual_seed(config.seed)
+        sampler = RandomSampler(data_source=train_dataset, generator=train_dataloader_generator)
+    else:
+        sampler = SequentialSampler(data_source=train_dataset)
+
+    if config.mini_rollout_batch_size is not None:
+        train_batch_size = config.mini_rollout_batch_size
+    else:
+        train_batch_size = config.rollout_batch_size
+
+    train_dataloader = StatefulDataLoader(
+        dataset=train_dataset,
+        batch_size=train_batch_size,
+        sampler=sampler,
+        num_workers=8,
+        collate_fn=collate_fn,
+        pin_memory=False,
+        drop_last=True,
+    )
+
+    val_dataset = RLHFDataset(
+        data_path=config.val_files,
+        tokenizer=tokenizer,
+        processor=processor,
+        prompt_key=config.prompt_key,
+        answer_key=config.answer_key,
+        image_key=config.image_key,
+        image_dir=config.image_dir,
+        max_prompt_length=config.max_prompt_length,
+        truncation="right",
+        format_prompt=config.format_prompt,
+        min_pixels=config.min_pixels,
+        max_pixels=config.max_pixels,
+        filter_overlong_prompts=config.filter_overlong_prompts,
+    )
+
+    if config.val_batch_size == -1:
+        val_batch_size = len(val_dataset)
+    else:
+        val_batch_size = config.val_batch_size
+
+    val_dataloader = StatefulDataLoader(
+        dataset=val_dataset,
+        batch_size=val_batch_size,
+        shuffle=False,
+        num_workers=8,
+        collate_fn=collate_fn,
+        pin_memory=False,
+        drop_last=False,
+    )
+
+    assert len(train_dataloader) >= 1
+    assert len(val_dataloader) >= 1
+    print(f"Size of train dataloader: {len(train_dataloader)}")
+    print(f"Size of val dataloader: {len(val_dataloader)}")
+    return train_dataloader, val_dataloader