[Distributed RLHF] Integration of PP

.gitignore

@@ -164,3 +164,4 @@ coverage.xml
 applications/ColossalChat/logs
 applications/ColossalChat/tests/logs
 applications/ColossalChat/wandb
+applications/ColossalChat/model

applications/ColossalChat/coati/distributed/consumer.py

@@ -54,7 +54,6 @@ def __init__(
 
         self.model_config = model_config
         self.plugin_config = plugin_config
-        assert self.plugin_config.get("pp_size", 1) == 1, "pp_size > 1 is not supported now"
 
         self.device = get_current_device()
         self.lr_scheduler = None
@@ -95,7 +94,6 @@ def loop(self) -> None:
                     i = 0
                     for _ in range(self.num_recv_per_update):
                         # receive data from producers
-
                         for r in range(self.num_producers):
                             print(f"[T{dist.get_rank()}] Recv data episode {episode} step {step} from {r}")
                             self.buffer.extend(

applications/ColossalChat/coati/distributed/launch.py

@@ -47,6 +47,7 @@ def launch_distributed(
     master_addr: str = "localhost",
     master_port: int = 29500,
     core_algo: str = "GRPO",
+    project_name: Optional[str] = None,
 ):
 
     if core_algo not in ALGO_MAP:
@@ -108,6 +109,7 @@ def launch_distributed(
                 "train_microbatch_size": train_microbatch_size,
             },
             num_generations=num_generations,
+            project_name=project_name,
         )
         procs.append(consumer)
     ray.get([p.setup.remote() for p in procs])

applications/ColossalChat/rl_example.py

@@ -10,13 +10,44 @@
     parser.add_argument("-d", "--dataset", type=str, default="data.jsonl")
     parser.add_argument("-t", "--num-trainers", type=int, default=2)
     parser.add_argument("-i", "--num-inferencer", type=int, default=2)
-    parser.add_argument("-g", "--num-generations", type=int, default=8)
-    parser.add_argument("-ibs", "--inference-batch-size", type=int, default=64)
-    parser.add_argument("-imbs", "--inference-microbatch-size", type=int, default=8)
-    parser.add_argument("-tbs", "--train-batch-size", type=int, default=32)
-    parser.add_argument("-tMbs", "--train-minibatch-size", type=int, default=1)
-    parser.add_argument("-tmbs", "--train-microbatch-size", type=int, default=2)
-    parser.add_argument("-b", "--backend", type=str, default="transformers")
+    parser.add_argument("-g", "--num-generations", type=int, default=8, help="Number of generations per prompt.")
+    parser.add_argument("-p", "--project", type=str, default="GRPO", help="Project name.")
+    parser.add_argument(
+        "-ibs",
+        "--inference-batch-size",
+        type=int,
+        default=64,
+        help="Number of prompts to generate per inference step. It should be divisible by tbs, and the weights on the inference backend will be synced every ibs/tbs training steps of the policy model.",
+    )
+    parser.add_argument(
+        "-imbs",
+        "--inference-microbatch-size",
+        type=int,
+        default=8,
+        help="Effective batch size for the inference backend to run generation. Please select based on memory constraint.",
+    )
+    parser.add_argument(
+        "-tbs",
+        "--train-batch-size",
+        type=int,
+        default=32,
+        help="Number of unique prompts to update policy model per step per dp group. Gradient is accumulated across tbs * dp_size unique prompts, equivalently tbs * g * dp_size samples",
+    )
+    parser.add_argument(
+        "-tMbs",
+        "--train-minibatch-size",
+        type=int,
+        default=1,
+        help="Number of unique prompts in each training batch per dp group. The inference backend must generate tMbs * g * dp_size samples before forwarding. Satisfy tMbs * g >= tmbs",
+    )
+    parser.add_argument(
+        "-tmbs",
+        "--train-microbatch-size",
+        type=int,
+        default=2,
+        help="Effective batch size per dp group for forwarding and backwarding. Please select based on the availiable memory.",
+    )
+    parser.add_argument("-b", "--backend", type=str, default="transformers", choices=["transformers", "vllm"])
     parser.add_argument("-a", "--algo", type=str, default="GRPO", choices=["Simple", "GRPO", "EvalGRPO"])
     args = parser.parse_args()
 
@@ -29,11 +60,7 @@
     ray.init(address="local", namespace="ray-example")
 
     inference_model_config = dict(path=args.model)
-    train_model_config = dict(
-        path=args.model,
-        # use_flash_attention_2=True,
-        # use_cache=False
-    )
+    train_model_config = dict(path=args.model, use_flash_attention_2=True, use_cache=False)
     generate_config = dict(top_k=50, top_p=0.75, temperature=0.9)
 
     if args.backend == "transformers":
@@ -91,9 +118,17 @@
         generate_config=generate_config,
         num_generations=args.num_generations,
         train_model_config=train_model_config,
-        plugin_config={},
+        # plugin_config={}, # for zero
+        plugin_config={
+            "pp_size": 2,
+            "tp_size": 1,
+            "microbatch_size": args.train_microbatch_size // 2,
+            "zero_stage": 0,
+            "max_norm": 1.0,
+        },  # for pp
         inference_backend=args.backend,
         master_addr="localhost",
-        master_port=29505,
+        master_port=29506,
         core_algo=args.algo,
+        project_name=args.project,
     )

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -1411,8 +1411,10 @@ def execute_pipeline(
             )
 
         # run with gradients accumulation
-        if model.require_grad_sync == False or (
-            isinstance(optimizer, HybridParallelZeroOptimizer) and optimizer.require_grad_sync == False
+        if (
+            not torch.is_grad_enabled()
+            or model.require_grad_sync == False
+            or (isinstance(optimizer, HybridParallelZeroOptimizer) and optimizer.require_grad_sync == False)
         ):
             return outputs
 

colossalai/shardformer/modeling/qwen2.py

@@ -284,6 +284,7 @@ def qwen2_for_causal_lm_forward(
         hidden_states: Optional[torch.FloatTensor] = None,
         stage_index: Optional[List[int]] = None,
         shard_config: ShardConfig = None,
+        **kwargs,
     ):
         r"""
         Args: