[core][rlhf] add colocate example for RLHF (#12984)

Signed-off-by: youkaichao <youkaichao@gmail.com>

Author: youkaichao

.buildkite/test-pipeline.yaml

@@ -128,7 +128,7 @@ steps:
   - tests/spec_decode/e2e/test_integration_dist_tp4
   - tests/compile
   - examples/offline_inference/rlhf.py
-  - examples/offline_inference/ray_placement.py
+  - examples/offline_inference/rlhf_colocate.py
   commands:
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
@@ -137,7 +137,7 @@ steps:
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - python3 ../examples/offline_inference/rlhf.py
-  - RAY_DEDUP_LOGS=0 python3 ../examples/offline_inference/ray_placement.py
+  - RAY_DEDUP_LOGS=0 python3 ../examples/offline_inference/rlhf_colocate.py
 
 - label: Metrics, Tracing Test # 10min
   num_gpus: 2 

examples/offline_inference/ray_placement.py renamed to examples/offline_inference/rlhf_colocate.py

@@ -1,13 +1,18 @@
 # SPDX-License-Identifier: Apache-2.0
 """
-a simple demonstration to show how to control
-the placement of the vLLM workers with Ray.
-The key is to set VLLM_RAY_PER_WORKER_GPUS and
-VLLM_RAY_BUNDLE_INDICES properly.
+a simple demonstration to show how to co-locate
+vLLM worker with training actors on the same GPUs,
+for RLHF-like applications.
+The key points:
+- Control the placement of the vLLM workers with Ray, by setting
+    VLLM_RAY_PER_WORKER_GPUS and VLLM_RAY_BUNDLE_INDICES properly.
+- Use cuda-ipc to pass tensors, since NCCL does not work when we have
+    multiple processes on the same GPU.
 """
 import os
 
 import ray
+import torch
 from ray.util.placement_group import placement_group
 from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
 
@@ -19,7 +24,33 @@ class MyWorker(Worker):
 
     def report_device_id(self) -> str:
         from vllm.platforms import current_platform
-        return current_platform.get_device_uuid(self.device.index)
+        self.device_uuid = current_platform.get_device_uuid(self.device.index)
+        return self.device_uuid
+
+    def update_weights_from_ipc_handles(self, ipc_handles):
+        handles = ipc_handles[self.device_uuid]
+        device_id = self.device.index
+        weights = []
+        for name, handle in handles.items():
+            func, args = handle
+            list_args = list(args)
+            # the key is to change device id to the current device id
+            # in case two processes have different CUDA_VISIBLE_DEVICES
+            list_args[6] = device_id
+            tensor = func(*list_args)
+            weights.append((name, tensor))
+        self.model_runner.model.load_weights(weights=weights)
+        torch.cuda.synchronize()
+
+    def check_weights_changed(self):
+        """
+        Check if the weights are updated to 0.
+        """
+        weights_updated = True
+        for name, p in self.model_runner.model.named_parameters():
+            weights_updated = weights_updated and torch.allclose(
+                p, torch.zeros_like(p))
+        return weights_updated
 
 
 class MyLLM(LLM):
@@ -40,12 +71,32 @@ def __init__(self, *args, bundle_indices: list, **kwargs):
 
 class RayTrainingActor:
 
-    def report_device_id(self) -> str:
+    def __init__(self):
+        # ray will set CUDA_VISIBLE_DEVICES to the assigned GPUs
+        from transformers import AutoModelForCausalLM
+        self.model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m")
+        self.model.to("cuda:0")
+        for name, p in self.model.named_parameters():
+            p.data.zero_()
+        torch.cuda.synchronize()
         # the argument for get_device_uuid is the index
         # of the GPU in the visible devices.
-        # ray will set CUDA_VISIBLE_DEVICES to the assigned GPUs
         from vllm.platforms import current_platform
-        return current_platform.get_device_uuid(0)
+        self.device_uuid = current_platform.get_device_uuid(0)
+
+    def report_device_id(self) -> str:
+        return self.device_uuid
+
+    def get_weight_ipc_handles(self):
+        from torch.multiprocessing.reductions import reduce_tensor
+        data = {}
+        for name, p in self.model.named_parameters():
+            # the training actor might only have a subset of the weights
+            # and need to all-gather the weights from all the actors.
+            # for demonstration, here we assume all training actors have
+            # the full weights.
+            data[name] = reduce_tensor(p.detach())
+        return {self.device_uuid: data}
 
 
 # ray manages 4 GPUs
@@ -78,6 +129,8 @@ def report_device_id(self) -> str:
         ),
     )(RayTrainingActor).remote()
     training_actors.append(training_actor)
+
+for bundle_index, training_actor in enumerate(training_actors):
     device_id = ray.get(training_actor.report_device_id.remote())
     print(f"training actor {bundle_index} is on {device_id}")
     training_actor_device_ids.append(device_id)
@@ -119,3 +172,18 @@ def report_device_id(self) -> str:
 # the last two training actors should be
 # on the same GPUs as the second inference engine
 assert training_actor_device_ids[2:] == inference_engine_device_ids[1]
+
+print("gather all the IPC handles from the training actors")
+ipc_handles = {}
+for actor in training_actors:
+    ipc_handles.update(ray.get(actor.get_weight_ipc_handles.remote()))
+
+print("update the weights of the inference engines")
+for llm in inference_engines:
+    ray.get(
+        llm.collective_rpc.remote("update_weights_from_ipc_handles",
+                                  args=(ipc_handles, )))
+print("check if the weights are updated")
+for llm in inference_engines:
+    assert ray.get(
+        llm.collective_rpc.remote("check_weights_changed", args=tuple()))