[grpo] Refactor GRPOVllmEngine

swift/llm/infer/infer_engine/grpo_vllm_engine.py

@@ -1,23 +1,15 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import os
-from copy import copy, deepcopy
-from typing import Any, Dict, Iterator, List, Optional, Union
+from typing import Any, Dict, Optional
 
 import torch
-from packaging import version
 
-from swift.llm import InferRequest, Template, VllmEngine, get_model_tokenizer
-from swift.plugin import Metric
-from ..protocol import ChatCompletionResponse, ChatCompletionStreamResponse, RequestConfig
-from .patch import patch_auto_config, patch_auto_tokenizer
-from .utils import AdapterRequest, patch_vllm_memory_leak
+from swift.llm import Template, VllmEngine
 
 try:
     # After setting the environment variables, import vllm. This way of writing allows lint to pass.
     os.environ['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'
     os.environ['VLLM_ENGINE_ITERATION_TIMEOUT_S'] = '3600'
-    import vllm
-    from vllm import AsyncEngineArgs, AsyncLLMEngine, SamplingParams, EngineArgs, LLM
 except Exception:
     raise
 
@@ -56,23 +48,15 @@ def __init__(
         engine_kwargs: Optional[Dict[str, Any]] = None,
         template: Optional[Template] = None,
     ) -> None:
-        os.environ['VLLM_USE_V1'] = os.environ.get('VLLM_USE_V1', '0')
-        if engine_kwargs is None:
-            engine_kwargs = {}
-        patch_vllm_memory_leak()
-        self.use_async_engine = use_async_engine
-        self.processor = get_model_tokenizer(
-            model_id_or_path,
-            torch_dtype,
-            load_model=False,
-            download_model=True,
+        assert not use_async_engine  # TODO
+        super().__init__(
+            model_id_or_path=model_id_or_path,
+            torch_dtype=torch_dtype,
+            use_async_engine=use_async_engine,
             model_type=model_type,
             use_hf=use_hf,
             hub_token=hub_token,
-            revision=revision)[1]
-        self._post_init(template)
-
-        self._prepare_engine_kwargs(
+            revision=revision,
             gpu_memory_utilization=gpu_memory_utilization,
             tensor_parallel_size=tensor_parallel_size,
             pipeline_parallel_size=pipeline_parallel_size,
@@ -81,78 +65,15 @@ def __init__(
             disable_custom_all_reduce=disable_custom_all_reduce,
             enforce_eager=enforce_eager,
             limit_mm_per_prompt=limit_mm_per_prompt,
+            device=device,
+            seed=seed,
             enable_lora=enable_lora,
             max_loras=max_loras,
             max_lora_rank=max_lora_rank,
             enable_prefix_caching=enable_prefix_caching,
-            device=device,
-            seed=seed,
-            distributed_executor_backend=distributed_executor_backend,
             enable_sleep_mode=enable_sleep_mode,
+            distributed_executor_backend=distributed_executor_backend,
             quantization=quantization,
-            **engine_kwargs,
+            engine_kwargs=engine_kwargs,
+            template=template,
         )
-        self._prepare_engine()
-        self._load_generation_config()
-
-    def _prepare_engine(self) -> None:
-        with patch_auto_tokenizer(self.tokenizer), patch_auto_config(self.config):
-            engine = LLM(**self.engine_args.__dict__)
-        self.engine = engine
-
-    @property
-    def inner_model(self):
-        return self.engine.llm_engine.model_executor.driver_worker.model_runner.model
-
-    @property
-    def inner_model_executor(self):
-        return self.engine.llm_engine.model_executor
-
-    def infer(
-        self,
-        infer_requests: List[InferRequest],
-        request_config: Optional[RequestConfig] = None,
-        metrics: Optional[List[Metric]] = None,
-        *,
-        template: Optional[Template] = None,
-        use_tqdm: Optional[bool] = None,
-        adapter_request: Optional[AdapterRequest] = None,
-    ) -> List[Union[ChatCompletionResponse, Iterator[ChatCompletionStreamResponse]]]:
-        request_config = deepcopy(request_config or RequestConfig())
-        if template is None:
-            template = self.default_template
-        template.set_mode('vllm')
-        batched_inputs, error_list = self._batch_encode(
-            infer_requests, template=template, strict=getattr(self, 'strict', True))
-        self.set_default_max_tokens(request_config, batched_inputs)
-
-        prompts = []
-        for inputs in batched_inputs:
-            llm_inputs = {'prompt_token_ids': inputs['input_ids']}
-            mm_data = {}
-            for key in ['images', 'audios', 'videos']:
-                media_data = inputs.get(key) or []
-                if media_data:
-                    if version.parse(vllm.__version__) < version.parse('0.6'):
-                        assert len(media_data) == 1, (
-                            f'The current version of vllm only supports single {key}. Please upgrade to vllm >= 0.6.0')
-                        mm_data = {key.rstrip('s'): media_data[0]}
-                    else:
-                        mm_data = {key.rstrip('s'): media_data[0] if len(media_data) == 1 else media_data}
-            if mm_data:
-                llm_inputs['multi_modal_data'] = mm_data
-            prompts.append(llm_inputs)
-
-        generation_configs = []
-        seed = request_config.seed
-        assert seed >= 0, 'Seed is needed for GRPOVllmEngine.'
-        for i, _ in enumerate(prompts):
-            request_config = copy(request_config)
-            request_config.seed = seed + i
-            generation_config = self._prepare_generation_config(request_config)
-            self._add_stop_words(generation_config, request_config, template.template_meta)
-            generation_configs.append(generation_config)
-        outputs = self.engine.generate(prompts, generation_configs, use_tqdm=False)
-        return [
-            self._create_chat_completion_response(result, template, generation_configs[0], '') for result in outputs
-        ]

swift/llm/infer/infer_engine/vllm_engine.py

@@ -19,7 +19,7 @@
                         ChatCompletionStreamResponse, ChatMessage, DeltaMessage, RequestConfig, random_uuid)
 from .infer_engine import InferEngine
 from .patch import patch_auto_config, patch_auto_tokenizer
-from .utils import AdapterRequest, InferStreamer, patch_npu_vllm
+from .utils import AdapterRequest, InferStreamer, patch_npu_vllm, patch_vllm_memory_leak
 
 try:
     # After setting the environment variables, import vllm. This way of writing allows lint to pass.
@@ -70,6 +70,7 @@ def __init__(
     ) -> None:
         if engine_kwargs is None:
             engine_kwargs = {}
+        patch_vllm_memory_leak()
         self.use_async_engine = use_async_engine
         self.processor = get_model_tokenizer(
             model_id_or_path,

swift/llm/infer/rollout.py

@@ -86,7 +86,6 @@ def _register_rl_rollout_app(self):
         self.app.post('/infer/', response_model=None)(self.infer)
 
     def __init__(self, args: Union[List[str], DeployArguments, None] = None):
-        os.environ['VLLM_USE_V1'] = os.environ.get('VLLM_USE_V1', '1')
         super().__init__(args)
         safe_set_start_method()
         self.app = FastAPI(lifespan=self.lifespan)

swift/trainers/rlhf_trainer/grpo_trainer.py

@@ -411,11 +411,6 @@ def split_llm(name):
     def prepare_vllm(self, model):
         from swift.tuners import Swift
         from swift.llm.infer.infer_engine import GRPOVllmEngine
-        if self.vllm_tensor_parallel_size > 1:
-            vllm_kwargs = {'distributed_executor_backend': 'external_launcher'}
-        else:
-            vllm_kwargs = {}
-
         max_num_seqs = (
             self.args.per_device_train_batch_size * self.vllm_tensor_parallel_size
             * self.args.gradient_accumulation_steps)
@@ -436,7 +431,8 @@ def prepare_vllm(self, model):
                 max_model_len=self.args.vllm_max_model_len,
                 seed=self.accelerator.process_index // self.vllm_tensor_parallel_size,
                 template=self.template,
-                **vllm_kwargs)
+                distributed_executor_backend='external_launcher',
+            )
         return engine
 
     @contextmanager