fix grpo vllm lora

docs/source/Instruction/GRPO.md

@@ -17,7 +17,7 @@ pip install "trl>=0.15"
 奖励函数接受模型生成的文本 completions 以及其他数据集中的列作为参数，并对模型生成的文本进行打分。以下是一个示例，展示了如何实现一个简单的长度奖励函数。该函数会在模型生成的文本长度超过 1024 时，给予 1.0 的奖励信号；否则，奖励信号为 0.0。
 
 ```python
-from swift.plugin.orm import ORM, orms
+from swift.plugin import ORM, orms
 class DummyLengthRewardFunction(ORM)
     def __call__(completions, **kwargs):
         return [1.0 if len(completion) > 1024 else 0.0 for completion in completions]
@@ -134,7 +134,8 @@ swift rlhf \
     --num_generations 7 \
     --temperature 0.9 \
     --system 'examples/train/grpo/prompt.txt' \
-    --deepspeed zero2
+    --deepspeed zero2 \
+    --log_completions true
 ```
 
 单卡
@@ -167,5 +168,6 @@ swift rlhf \
     --dataset_num_proc 4 \
     --num_generations 4 \
     --temperature 0.9 \
-    --system 'examples/train/grpo/prompt.txt'
+    --system 'examples/train/grpo/prompt.txt' \
+    --log_completions true
 ```

docs/source_en/Instruction/GRPO.md

@@ -19,7 +19,7 @@ pip install "trl>=0.15"
 A reward function takes the text `completions` generated by a model and other columns from the dataset as parameters, and scores the model's generated text. Below is an example that demonstrates how to implement a simple length-based reward function. This function will give a reward signal of 1.0 if the length of the generated text exceeds 1024; otherwise, the reward signal will be 0.0.
 
 ```python
-from swift.plugin.orm import ORM, orms
+from swift.plugin import ORM, orms
 
 class DummyLengthRewardFunction(ORM):
     def __call__(self, completions, **kwargs):
@@ -138,7 +138,8 @@ swift rlhf \
     --num_generations 7 \
     --temperature 0.9 \
     --system 'examples/train/grpo/prompt.txt' \
-    --deepspeed zero2
+    --deepspeed zero2 \
+    --log_completions true
 ```
 
 Single-GPU
@@ -171,5 +172,6 @@ swift rlhf \
     --dataset_num_proc 4 \
     --num_generations 4 \
     --temperature 0.9 \
-    --system 'examples/train/grpo/prompt.txt'
+    --system 'examples/train/grpo/prompt.txt' \
+    --log_completions true
 ```

examples/train/grpo/full_lmdeploy.sh

@@ -34,4 +34,5 @@ swift rlhf \
     --num_generations 3 \
     --temperature 0.9 \
     --system 'examples/train/grpo/prompt.txt' \
-    --deepspeed zero3
+    --deepspeed zero3 \
+    --log_completions true

examples/train/grpo/full_vllm.sh

@@ -34,4 +34,5 @@ swift rlhf \
     --num_generations 7 \
     --temperature 0.9 \
     --system 'examples/train/grpo/prompt.txt' \
-    --deepspeed zero2
+    --deepspeed zero2 \
+    --log_completions true

examples/train/grpo/grpo.sh

@@ -5,7 +5,7 @@
 CUDA_VISIBLE_DEVICES=0 \
 swift rlhf \
     --rlhf_type grpo \
-    --model Qwen/Qwen2.5-7B-Instruct \
+    --model Qwen/Qwen2.5-7B \
     --reward_funcs accuracy format \
     --train_type lora \
     --lora_rank 8 \
@@ -30,4 +30,5 @@ swift rlhf \
     --dataset_num_proc 4 \
     --num_generations 4 \
     --temperature 0.9 \
-    --system 'examples/train/grpo/prompt.txt'
+    --system 'examples/train/grpo/prompt.txt' \
+    --log_completions true

examples/train/grpo/lora_vllm.sh

@@ -0,0 +1,40 @@
+# pip install math_verify # reward function
+# pip install "trl>=0.15"
+# GPU memory: 2 * 80GiB
+
+MASTER_PORT=29501 \
+CUDA_VISIBLE_DEVICES=0,1 \
+swift rlhf \
+    --rlhf_type grpo \
+    --model Qwen/Qwen2.5-7B \
+    --reward_funcs accuracy format \
+    --train_type lora \
+    --use_vllm true \
+    --vllm_device auto \
+    --vllm_gpu_memory_utilization 0.7 \
+    --vllm_max_model_len 8192 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --torch_dtype bfloat16 \
+    --dataset 'AI-MO/NuminaMath-TIR#1000' \
+    --max_completion_length 1024 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 16 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps 1 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --num_generations 16 \
+    --temperature 0.9 \
+    --deepspeed zero2 \
+    --system 'examples/train/grpo/prompt.txt' \
+    --log_completions true

examples/train/grpo/multi_node/multi_node1.sh

@@ -36,4 +36,5 @@ swift rlhf \
     --num_generations 7 \
     --temperature 0.9 \
     --system 'examples/train/grpo/prompt.txt' \
-    --deepspeed zero2
+    --deepspeed zero2 \
+    --log_completions true

examples/train/grpo/multi_node/multi_node2.sh

@@ -34,4 +34,5 @@ swift rlhf \
     --num_generations 7 \
     --temperature 0.9 \
     --system 'examples/train/grpo/prompt.txt' \
-    --deepspeed zero2
+    --deepspeed zero2 \
+    --log_completions true

examples/train/grpo/plugin/plugin.py

@@ -1,7 +1,7 @@
 import re
 from typing import List
 
-from swift.plugin.orm import ORM, orms
+from swift.plugin import ORM, orms
 from swift.utils import get_logger
 
 logger = get_logger()

examples/train/grpo/plugin/run_external_rm.sh

@@ -31,4 +31,5 @@ swift rlhf \
     --dataset_num_proc 4 \
     --num_generations 4 \
     --temperature 0.9 \
-    --system 'examples/train/grpo/prompt.txt'
+    --system 'examples/train/grpo/prompt.txt' \
+    --log_completions true

swift/llm/__init__.py

@@ -6,7 +6,7 @@
 if TYPE_CHECKING:
     # Recommend using `xxx_main`
     from .infer import (VllmEngine, RequestConfig, LmdeployEngine, PtEngine, InferEngine, infer_main, deploy_main,
-                        InferClient, run_deploy, AdapterRequest, prepare_model_template)
+                        InferClient, run_deploy, AdapterRequest, prepare_model_template, BaseInferEngine)
     from .export import (export_main, merge_lora, quantize_model, export_to_ollama)
     from .eval import eval_main
     from .app import app_main
@@ -35,7 +35,7 @@
         'rlhf': ['rlhf_main'],
         'infer': [
             'deploy_main', 'VllmEngine', 'RequestConfig', 'LmdeployEngine', 'PtEngine', 'infer_main', 'InferClient',
-            'run_deploy', 'InferEngine', 'AdapterRequest', 'prepare_model_template'
+            'run_deploy', 'InferEngine', 'AdapterRequest', 'prepare_model_template', 'BaseInferEngine'
         ],
         'export': ['export_main', 'merge_lora', 'quantize_model', 'export_to_ollama'],
         'app': ['app_main'],

swift/llm/infer/__init__.py

@@ -9,7 +9,7 @@
     from .protocol import RequestConfig
     from .utils import prepare_model_template
     from .infer_engine import (InferEngine, VllmEngine, LmdeployEngine, PtEngine, InferClient,
-                               prepare_generation_config, AdapterRequest)
+                               prepare_generation_config, AdapterRequest, BaseInferEngine)
 else:
     _extra_objects = {k: v for k, v in globals().items() if not k.startswith('_')}
     _import_structure = {
@@ -19,7 +19,7 @@
         'utils': ['prepare_model_template'],
         'infer_engine': [
             'InferEngine', 'VllmEngine', 'LmdeployEngine', 'PtEngine', 'InferClient', 'prepare_generation_config',
-            'AdapterRequest'
+            'AdapterRequest', 'BaseInferEngine'
         ],
     }
 

swift/llm/sampling/base.py

@@ -1,8 +1,7 @@
 from typing import Any, Dict, List
 
 from swift.llm import SamplingArguments
-from swift.plugin.orm import orms
-from swift.plugin.prm import prms
+from swift.plugin import orms, prms
 from swift.utils import get_logger
 
 logger = get_logger()

swift/plugin/__init__.py

@@ -11,6 +11,8 @@
     from .optimizer import optimizers_map
     from .tools import get_tools_prompt, get_tools_keyword
     from .tuner import Tuner, extra_tuners
+    from .prm import prms, PRM
+    from .orm import orms, ORM
 
 else:
     _extra_objects = {k: v for k, v in globals().items() if not k.startswith('_')}
@@ -22,6 +24,8 @@
         'optimizer': ['optimizers_map'],
         'tools': ['get_tools_prompt', 'get_tools_keyword'],
         'tuner': ['Tuner', 'extra_tuners'],
+        'prm': ['prms', 'PRM'],
+        'orm': ['orms', 'ORM']
     }
 
     import sys

swift/trainers/rlhf_arguments.py

@@ -49,9 +49,6 @@ class GRPOConfig(GRPOArgumentsMixin, SwiftArgumentsMixin, HfGRPOConfig):
     repetition_penalty: Optional[float] = None
 
     def __post_init__(self):
-        if self.use_lmdeploy:
-            # In case trl GRPOTrainer need use_vllm
-            self.use_vllm = True
         super().__post_init__()
         if self.cosine_max_len is None:
             self.cosine_max_len = self.max_completion_length