Initial commit

verl/workers/sharding_manager/fsdp_vllm.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.
+
+import os
+import logging
+import torch
+from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp.api import ShardingStrategy, ShardedStateDictConfig, StateDictType, FullStateDictConfig
+from torch.distributed.device_mesh import DeviceMesh
+
+from verl.third_party.vllm import LLM
+from verl.third_party.vllm import parallel_state as vllm_ps
+from verl import DataProto
+from verl.utils.torch_functional import (broadcast_dict_tensor, allgather_dict_tensors)
+from verl.utils.debug import log_gpu_memory_usage
+
+from .base import BaseShardingManager
+
+logger = logging.getLogger(__file__)
+logger.setLevel(os.getenv('VERL_PPO_LOGGING_LEVEL', 'WARN'))
+
+
+class FSDPVLLMShardingManager(BaseShardingManager):
+
+    def __init__(self,
+                 module: FSDP,
+                 inference_engine: LLM,
+                 model_config,
+                 full_params: bool = False,
+                 device_mesh: DeviceMesh = None):
+        self.module = module
+        self.inference_engine = inference_engine
+        self.model_config = model_config
+        self.device_mesh = device_mesh
+
+        # Full params
+        self.full_params = full_params
+        if full_params:
+            FSDP.set_state_dict_type(self.module,
+                                     state_dict_type=StateDictType.FULL_STATE_DICT,
+                                     state_dict_config=FullStateDictConfig())
+        else:
+            FSDP.set_state_dict_type(self.module,
+                                     state_dict_type=StateDictType.SHARDED_STATE_DICT,
+                                     state_dict_config=ShardedStateDictConfig())
+
+        # Note that torch_random_states may be different on each dp rank
+        self.torch_random_states = torch.cuda.get_rng_state()
+        # get a random rng states
+        if self.device_mesh is not None:
+            gen_dp_rank = self.device_mesh['dp'].get_local_rank()
+            torch.cuda.manual_seed(gen_dp_rank + 1000)  # make sure all tp ranks have the same random states
+            self.gen_random_states = torch.cuda.get_rng_state()
+            torch.cuda.set_rng_state(self.torch_random_states)
+        else:
+            self.gen_random_states = None
+
+    def __enter__(self):
+        log_gpu_memory_usage('Before state_dict() in sharding manager memory', logger=logger)
+        params = self.module.state_dict()
+        log_gpu_memory_usage('After state_dict() in sharding manager memory', logger=logger)
+        # Copy, not share memory
+        load_format = 'hf' if self.full_params else 'dtensor'
+        self.inference_engine.sync_model_weights(params, load_format=load_format)
+        log_gpu_memory_usage('After sync model weights in sharding manager', logger=logger)
+
+        del params
+        torch.cuda.empty_cache()
+        log_gpu_memory_usage('After del state_dict and empty_cache in sharding manager', logger=logger)
+
+        # TODO: offload FSDP model weights
+        # self.module.cpu()
+        # torch.cuda.empty_cache()
+        # if torch.distributed.get_rank() == 0:
+        # print(f'after model to cpu in sharding manager memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB')
+
+        # important: need to manually set the random states of each tp to be identical.
+        if self.device_mesh is not None:
+            self.torch_random_states = torch.cuda.get_rng_state()
+            torch.cuda.set_rng_state(self.gen_random_states)
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        log_gpu_memory_usage('Before vllm offload in sharding manager', logger=logger)
+        self.inference_engine.offload_model_weights()
+        log_gpu_memory_usage('After vllm offload in sharding manager', logger=logger)
+
+        # self.module.to('cuda')
+        # if torch.distributed.get_rank() == 0:
+        #     print(f'after actor module to cuda in sharding manager memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB')
+
+        self.module.train()
+
+        # add empty cache after each compute
+        torch.cuda.empty_cache()
+
+        # restore random states
+        if self.device_mesh is not None:
+            self.gen_random_states = torch.cuda.get_rng_state()
+            torch.cuda.set_rng_state(self.torch_random_states)
+
+    def preprocess_data(self, data: DataProto) -> DataProto:
+        # TODO: Current impl doesn't consider FSDP with torch micro-dp
+        data.batch = allgather_dict_tensors(data.batch.contiguous(),
+                                            size=vllm_ps.get_tensor_model_parallel_world_size(),
+                                            group=vllm_ps.get_tensor_model_parallel_group(),
+                                            dim=0)
+
+        return data
+
+    def postprocess_data(self, data: DataProto) -> DataProto:
+        # TODO: Current impl doesn't consider FSDP with torch micro-dp
+        broadcast_dict_tensor(data.batch,
+                              src=vllm_ps.get_tensor_model_parallel_src_rank(),
+                              group=vllm_ps.get_tensor_model_parallel_group())
+        dp_rank = torch.distributed.get_rank()
+        dp_size = torch.distributed.get_world_size()  # not consider torch micro-dp
+        tp_size = vllm_ps.get_tensor_model_parallel_world_size()
+        if tp_size > 1:
+            # TODO: shall we build a micro_dp group for vllm when integrating with vLLM?
+            local_prompts = data.chunk(chunks=tp_size)
+            data = local_prompts[dp_rank % tp_size]
+        return data