update

swift/llm/train/sft.py

@@ -226,21 +226,20 @@ def _encode_dataset(self, train_dataset, val_dataset):
         template = self.template
         args = self.args
         is_grpo = hasattr(args, 'rlhf_type') and args.rlhf_type == 'grpo'
-        if args.lazy_tokenize:
-            train_dataset = LazyLLMDataset(
-                train_dataset, template.encode, strict=args.strict, random_state=args.data_seed)
-            if val_dataset is not None and not args.predict_with_generate:
-                val_dataset = LazyLLMDataset(
-                    val_dataset, template.encode, strict=args.strict, random_state=args.data_seed)
-        elif is_grpo:
-            pass
-        else:
-            preprocessor_cls = PackingPreprocessor if args.packing else EncodePreprocessor
-            preprocessor = preprocessor_cls(template=template)
-            train_dataset = preprocessor(train_dataset, num_proc=args.dataset_num_proc, strict=args.strict)
-            if val_dataset is not None and not args.predict_with_generate:
-                val_dataset = preprocessor(val_dataset, num_proc=args.dataset_num_proc, strict=args.strict)
         if not is_grpo:
+            if args.lazy_tokenize:
+                train_dataset = LazyLLMDataset(
+                    train_dataset, template.encode, strict=args.strict, random_state=args.data_seed)
+                if val_dataset is not None and not args.predict_with_generate:
+                    val_dataset = LazyLLMDataset(
+                        val_dataset, template.encode, strict=args.strict, random_state=args.data_seed)
+            else:
+                preprocessor_cls = PackingPreprocessor if args.packing else EncodePreprocessor
+                preprocessor = preprocessor_cls(template=template)
+                train_dataset = preprocessor(train_dataset, num_proc=args.dataset_num_proc, strict=args.strict)
+                if val_dataset is not None and not args.predict_with_generate:
+                    val_dataset = preprocessor(val_dataset, num_proc=args.dataset_num_proc, strict=args.strict)
+
             inputs = train_dataset[0] if hasattr(train_dataset, '__len__') else next(iter(train_dataset))
             template.print_inputs(inputs, tokenizer_kwargs=inputs.pop('tokenizer_kwargs', None) or {})
             if isinstance(train_dataset, HfDataset):

swift/trainers/rlhf_arguments.py

@@ -1,5 +1,4 @@
-from dataclasses import dataclass, field
-from typing import List
+from dataclasses import dataclass
 
 from trl import CPOConfig as HfCPOConfig
 from trl import DPOConfig as HfDPOConfig

swift/trainers/rlhf_trainer/grpo_trainer.py

@@ -1,17 +1,16 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 # Part of the implementation is borrowed from huggingface/trl.
 from collections import defaultdict
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 from unittest.mock import patch
 
 import torch
 import torch.nn as nn
 from accelerate.utils import broadcast_object_list, gather, gather_object
 from accelerate.utils.other import is_compiled_module
-from transformers import GenerationConfig, PreTrainedModel
+from transformers import PreTrainedModel
 from trl import GRPOTrainer as HFGRPOTrainer
 from trl.models import unwrap_model_for_generation
-from trl.trainer.utils import pad
 
 from swift.llm import InferRequest, RequestConfig, to_device
 from swift.plugin.orm import orms
@@ -102,7 +101,7 @@ def __init__(self,
                     logger.warning(
                         f'The requested device {vllm_device} is also used for training. This may lead to unexpected '
                         'behavior. It is recommended to use a dedicated device for vLLM.')
-                from swift.llm import VllmEngine  # , PtEngine
+                from swift.llm import VllmEngine
                 world_size_patch = patch('torch.distributed.get_world_size', return_value=1)
                 profiling_patch = patch(
                     'vllm.worker.worker.Worker._assert_memory_footprint_increased_during_profiling', return_value=None)