[feat[ Support one-behind to reduce bubble time. Add profiling code (#6353)

* support n_behind, add profiling

* fix bugs

* fix visualization

* fix behind

* fix loop issue

* add profiling

* fix update

* update assert

* remove assert

---------

Co-authored-by: Tong Li <tong.li35271158@gmail.com>

applications/ColossalChat/coati/distributed/consumer.py

@@ -6,6 +6,7 @@ import ray
 import ray.util.collective as cc
 import torch
 import torch.distributed as dist
+from coati.distributed.profiling_utils import CustomProfiler
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM
 
@@ -36,6 +37,8 @@ class BaseConsumer:
         minibatch_size: int = 1,
         save_interval: int = 100,
         save_dir: str = "./model",
+        enable_profiling: bool = False,
+        n_behind: int = 0,
     ):
         self.num_producers = num_producers
         self.num_episodes = num_episodes
@@ -49,6 +52,7 @@ class BaseConsumer:
         self.minibatch_size = minibatch_size
         self.save_interval = save_interval
         self.save_dir = save_dir
+        self.enable_profiling = enable_profiling
         assert batch_size % minibatch_size == 0, "batch_size should be divisible by microbatch_size"
         self.num_microbatches = batch_size // minibatch_size
 
@@ -57,6 +61,7 @@ class BaseConsumer:
 
         self.device = get_current_device()
         self.lr_scheduler = None
+        self.n_behind = n_behind
 
     def setup(self) -> None:
         launch(self.rank, self.world_size, self.master_addr, self.master_port, local_rank=0)
@@ -94,6 +99,7 @@ class BaseConsumer:
 
         self.buffer = []
         self.recv_cnt = 0
+        self.profiler = CustomProfiler(f"C{self.rank}", disabled=not self.enable_profiling)
 
     def state_dict(self) -> Dict[str, torch.Tensor]:
         raise NotImplementedError
@@ -101,6 +107,41 @@ class BaseConsumer:
     def step(self, step_idx: int, **kwargs) -> Optional[float]:
         raise NotImplementedError
 
+    def prepare_mini_batch(self, effective_group_to_raw_group_mapping: Dict[int, int]) -> Dict[str, torch.Tensor]:
+        """
+        Prepare a mini-batch from the effective group to raw group mapping.
+        This method is used to create a mini-batch for training.
+        """
+        batches = [
+            self.buffer[effective_group_to_raw_group_mapping[i]]
+            for i in range(self.dp_rank * self.minibatch_size, (self.dp_rank + 1) * self.minibatch_size)
+        ]
+        # every dp_rank will receive a complete mini-batch, no need to sync within step() later
+        # each mini-batch use the first self.dp_size * minibatch_size effective samples
+        raw_mini_batches = self.buffer[
+            : effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1
+        ]  # include the last effective sample
+        raw_mini_batches_metric_dict = {
+            "raw_train_mini_batch_reward": [t[1] for t in raw_mini_batches],
+            "raw_train_mini_batch_format_acc": [t[2] for t in raw_mini_batches],
+            "raw_train_mini_batch_ans_acc": [t[3] for t in raw_mini_batches],
+            "raw_train_mini_batch_response_len": [t[4] for t in raw_mini_batches],
+        }
+        batch = bind_batch([t[0] for t in batches])
+        batch = post_recv(batch)
+        return batch, raw_mini_batches_metric_dict
+
+    def calculate_effective_group_to_raw_group_mapping(self, step):
+        effective_group_to_raw_group_mapping = {}
+        for buffer_idx in range(len(self.buffer)):
+            if self.buffer[buffer_idx][0] is not None:
+                if self.n_behind == 0:
+                    effective_group_to_raw_group_mapping[len(effective_group_to_raw_group_mapping)] = buffer_idx
+                else:
+                    if self.buffer[buffer_idx][-1] <= step - self.n_behind:
+                        effective_group_to_raw_group_mapping[len(effective_group_to_raw_group_mapping)] = buffer_idx
+        return effective_group_to_raw_group_mapping
+
     def loop(self) -> None:
         print(
             f"Consumer{self.rank} num_update: {self.num_update_per_episode}, num_recv: {self.num_recv_per_update}, nmb: {self.num_microbatches}"
@@ -112,14 +153,53 @@ class BaseConsumer:
                 disable=self.rank != 0,
             ) as pbar:
                 for step in pbar:
+                    torch.cuda.reset_peak_memory_stats()
                     i = 0
+
+                    self.profiler.enter(f"rollout_episode_{episode}_step_{step}")
                     for _ in range(self.num_recv_per_update):
+                        if self.n_behind > 0:
+                            # after sync model, do not wait for more data to arrive as rollout takes time, use buffered data
+                            effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping(
+                                step=step
+                            )
+                            while len(effective_group_to_raw_group_mapping) >= self.dp_size * self.minibatch_size:
+                                self.profiler.log(
+                                    f"Still have {len(effective_group_to_raw_group_mapping)} effective groups, greater than {self.dp_size * self.minibatch_size}, start training"
+                                )
+                                batch, raw_mini_batches_metric_dict = self.prepare_mini_batch(
+                                    effective_group_to_raw_group_mapping
+                                )
+                                self.profiler.enter("step")
+                                loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
+                                self.profiler.exit("step")
+                                self.buffer = self.buffer[
+                                    effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1 :
+                                ]
+                                # recalculate the effective group to raw group mapping
+                                effective_group_to_raw_group_mapping_size_before = len(
+                                    effective_group_to_raw_group_mapping
+                                )
+                                effective_group_to_raw_group_mapping = (
+                                    self.calculate_effective_group_to_raw_group_mapping(step=step)
+                                )
+                                assert (
+                                    len(effective_group_to_raw_group_mapping)
+                                    == effective_group_to_raw_group_mapping_size_before
+                                    - self.dp_size * self.minibatch_size
+                                )
+                                if loss is not None:
+                                    pbar.set_postfix({"loss": loss})
+                                i += 1
+
                         # 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.profiler.enter(f"recv_broadcast_data_P{r}")
                             raw_batch = ray_broadcast_tensor_dict(
                                 None, src=0, device=self.device, group_name=f"sync_data_{r}"
                             )
+                            self.profiler.exit(f"recv_broadcast_data_P{r}")
                             # calculate group reward et al. filtering. As only the filtered group will be used for training (which is incomplete),
                             # we need to calculate the metrics before filtering here for logging
                             # [batch_size, num_generations, ...] -> [batch_size * num_generations, ...]
@@ -154,6 +234,7 @@ class BaseConsumer:
                                         format_acc[group_idx],
                                         ans_acc[group_idx],
                                         response_len[group_idx],
+                                        step,
                                     ]
                                 )
                             if effective_group_mask is not None:
@@ -161,56 +242,44 @@ class BaseConsumer:
                                     f"[T{dist.get_rank()}] Filter recv data: {len(raw_batch)} -> {torch.sum(effective_group_mask).cpu().item()} effective groups"
                                 )
                         # mapping the effective group to the raw group for indexing
-                        effective_group_to_raw_group_mapping = {}
-                        for buffer_idx in range(len(self.buffer)):
-                            if self.buffer[buffer_idx][0] is not None:
-                                effective_group_to_raw_group_mapping[len(effective_group_to_raw_group_mapping)] = (
-                                    buffer_idx
-                                )
+                        effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping(
+                            step=step
+                        )
                         print(
                             f"[T{dist.get_rank()}] Collect Effective Prompt: {len(effective_group_to_raw_group_mapping)}/{self.dp_size * self.minibatch_size}"
                         )
 
-                        while len(effective_group_to_raw_group_mapping) >= self.dp_size * self.minibatch_size:
-                            # on each dp_rank, we use minibatch_size effective samples to form a batch
-                            batches = [
-                                self.buffer[effective_group_to_raw_group_mapping[i]]
-                                for i in range(
-                                    self.dp_rank * self.minibatch_size, (self.dp_rank + 1) * self.minibatch_size
+                        if self.n_behind == 0:
+                            # If n_behind is 0, we start training after receiving data from producers.
+                            while len(effective_group_to_raw_group_mapping) >= self.dp_size * self.minibatch_size:
+                                self.profiler.log(
+                                    f"Collect {len(effective_group_to_raw_group_mapping)} effective groups, greater than {self.dp_size * self.minibatch_size}, start training"
                                 )
-                            ]
-                            # every dp_rank will receive a complete mini-batch, no need to sync within step() later
-                            # each mini-batch use the first self.dp_size * minibatch_size effective samples
-                            raw_mini_batches = self.buffer[
-                                : effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1
-                            ]  # include the last effective sample
-                            raw_mini_batches_metric_dict = {
-                                "raw_train_mini_batch_reward": [t[1] for t in raw_mini_batches],
-                                "raw_train_mini_batch_format_acc": [t[2] for t in raw_mini_batches],
-                                "raw_train_mini_batch_ans_acc": [t[3] for t in raw_mini_batches],
-                                "raw_train_mini_batch_response_len": [t[4] for t in raw_mini_batches],
-                            }
-                            batch = bind_batch([t[0] for t in batches])
-                            batch = post_recv(batch)
-                            loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
-                            self.buffer = self.buffer[
-                                effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1 :
-                            ]
-                            # recalculate the effective group to raw group mapping
-                            effective_group_to_raw_group_mapping_size_before = len(effective_group_to_raw_group_mapping)
-                            effective_group_to_raw_group_mapping = {}
-                            for buffer_idx in range(len(self.buffer)):
-                                if self.buffer[buffer_idx][0] is not None:
-                                    effective_group_to_raw_group_mapping[len(effective_group_to_raw_group_mapping)] = (
-                                        buffer_idx
-                                    )
-                            assert (
-                                len(effective_group_to_raw_group_mapping)
-                                == effective_group_to_raw_group_mapping_size_before - self.dp_size * self.minibatch_size
-                            )
-                            if loss is not None:
-                                pbar.set_postfix({"loss": loss})
-                            i += 1
+                                batch, raw_mini_batches_metric_dict = self.prepare_mini_batch(
+                                    effective_group_to_raw_group_mapping
+                                )
+                                self.profiler.enter("step")
+                                loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
+                                self.profiler.exit("step")
+                                self.buffer = self.buffer[
+                                    effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1 :
+                                ]
+                                # recalculate the effective group to raw group mapping
+                                effective_group_to_raw_group_mapping_size_before = len(
+                                    effective_group_to_raw_group_mapping
+                                )
+                                effective_group_to_raw_group_mapping = (
+                                    self.calculate_effective_group_to_raw_group_mapping(step=step)
+                                )
+                                assert (
+                                    len(effective_group_to_raw_group_mapping)
+                                    == effective_group_to_raw_group_mapping_size_before
+                                    - self.dp_size * self.minibatch_size
+                                )
+                                if loss is not None:
+                                    pbar.set_postfix({"loss": loss})
+                                i += 1
+
                     if self.lr_scheduler is not None:
                         self.lr_scheduler.step()
                     if (step + 1) % self.save_interval == 0 or (step + 1) == self.num_update_per_episode:
@@ -221,13 +290,16 @@ class BaseConsumer:
                         if self.rank == 0:
                             print(f"Saved model checkpoint at step {step + 1} in folder {save_path}")
 
-                    if episode != self.num_episodes - 1 or step != self.num_update_per_episode - 1:
+                    if (episode != self.num_episodes - 1 or step != self.num_update_per_episode - 1) and (
+                        episode != 0 or step >= self.n_behind
+                    ):
                         if self.pp_size > 1:
                             print(
                                 f"[T{dist.get_rank()}] Sync model PP stage {self.pp_rank} episode {episode} step {step}"
                             )
                         else:
                             print(f"[T{dist.get_rank()}] Sync model episode {episode} step {step}")
+                        self.profiler.enter("sync_model")
                         torch.cuda.empty_cache()
                         state_dict = self.state_dict()
                         if self.pp_size > 1:
@@ -245,6 +317,13 @@ class BaseConsumer:
                                 )
                         del state_dict
                         torch.cuda.empty_cache()
+                        self.profiler.exit("sync_model")
+                    self.profiler.log(f"Peak memory usage: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB")
+                    self.profiler.exit(f"rollout_episode_{episode}_step_{step}")
+
+    def __del__(self):
+        if hasattr(self, "profiler"):
+            self.profiler.close()
 
 
 @ray.remote

applications/ColossalChat/coati/distributed/grpo_consumer.py

@@ -38,6 +38,8 @@ class GRPOConsumer(BaseConsumer):
         project_name: str = None,
         run_name: str = None,
         wandb_group_name: str = None,
+        enable_profiling: bool = False,
+        n_behind: int = 0,
     ):
         print(f"Using GRPO config: {grpo_config}")
         if (
@@ -63,6 +65,8 @@ class GRPOConsumer(BaseConsumer):
             minibatch_size,
             save_interval=save_interval,
             save_dir=save_dir,
+            enable_profiling=enable_profiling,
+            n_behind=n_behind,
         )
         path = model_config.pop("path")
         self.policy_model = AutoModelForCausalLM.from_pretrained(path, **model_config)

applications/ColossalChat/coati/distributed/launch.py

@@ -57,6 +57,8 @@ def launch_distributed(
     eval_generation_config: Optional[Dict[str, Any]] = None,
     log_rollout_interval: int = 20,
     rollout_save_dir: str = "./rollout",
+    enable_profiling: bool = False,
+    n_behind: int = 0,
 ):
     if core_algo not in ALGO_MAP:
         raise NotImplementedError(f"{core_algo} is not supported yet.")
@@ -132,6 +134,8 @@ def launch_distributed(
             wandb_group_name=wandb_group_name,
             log_rollout_interval=log_rollout_interval,
             rollout_log_file=rollout_log_file,
+            enable_profiling=enable_profiling,
+            n_behind=n_behind,
         )
         producer_procs.append(producer)
     ray.get([p.setup.remote() for p in producer_procs])
@@ -171,6 +175,8 @@ def launch_distributed(
             project_name=project_name,
             run_name=run_name,
             wandb_group_name=wandb_group_name,
+            enable_profiling=enable_profiling,
+            n_behind=n_behind,
         )
         consumer_procs.append(consumer)
     ray.get([p.setup.remote() for p in consumer_procs])

applications/ColossalChat/coati/distributed/producer.py

@@ -9,6 +9,7 @@ import torch
 import tqdm
 import wandb
 from coati.dataset.loader import RawConversationDataset, collate_fn_grpo
+from coati.distributed.profiling_utils import CustomProfiler
 from coati.distributed.reward.reward_fn import boxed_math_reward_fn, code_reward_fn, math_reward_fn
 from coati.distributed.reward.verifiable_reward import VerifiableReward
 from ray.util.collective import allreduce
@@ -52,6 +53,8 @@ class BaseProducer:
         wandb_group_name: str = None,
         log_rollout_interval: int = 20,
         rollout_log_file: str = "./rollout_log.jsonl",
+        enable_profiling: bool = False,
+        n_behind: int = 0,
     ):
         self.producer_idx = producer_idx
         self.num_producers = num_producers
@@ -62,6 +65,7 @@ class BaseProducer:
         assert batch_size % microbatch_size == 0
         self.num_microbatches = batch_size // microbatch_size
         self.latest_eval_step = -1
+        self.profiler = CustomProfiler(f"P{self.producer_idx}", disabled=not enable_profiling)
 
         self.train_dataset_config = train_dataset_config
         self.model_config = model_config
@@ -75,6 +79,7 @@ class BaseProducer:
         self.log_rollout_interval = log_rollout_interval
         self.latest_rollout_log_step = -1
         self.grpo_config = grpo_config
+        self.n_behind = n_behind
         reward_model_kwargs = {
             k: v
             for k, v in grpo_config.items()
@@ -268,11 +273,14 @@ class BaseProducer:
                             self.wandb_run.log(to_log_msg, step=self.consumer_global_step)
                         self.eval_mode = False
                         self.latest_eval_step = self.consumer_global_step
+                self.profiler.enter("rollout")
                 outputs = self.rollout(**batch)
+                self.profiler.exit("rollout")
                 outputs["temperature"] = torch.tensor(
                     [self.model.generate_config["temperature"]] * outputs["input_ids"].size(0)
                 ).to(outputs["input_ids"].device)
                 bs, num_gen = outputs["input_ids"].size(0), outputs["input_ids"].size(1)
+                self.profiler.enter("calculate_reward")
                 if self.grpo_config["reward_fn_type"] == "code":
                     test_cases = []
                     for prompt_id in range(bs):
@@ -310,14 +318,19 @@ class BaseProducer:
                     outputs.pop("gt_answer")
                 if "test_cases" in outputs:
                     outputs.pop("test_cases")
+                self.profiler.exit("calculate_reward")
 
                 print(f"[P{self.producer_idx}] Send data {[(k, v.shape) for k, v in outputs.items()]}")
                 outputs = pre_send(outputs)
+                self.profiler.enter("send_broadcast_data")
                 ray_broadcast_tensor_dict(
                     outputs, src=0, device=self.device, group_name=f"sync_data_{self.producer_idx}"
                 )
-                if (i + 1) % self.num_microbatches == 0 and (
-                    episode != self.num_episodes - 1 or i != num_valid_microbatches - 1
+                self.profiler.exit("send_broadcast_data")
+                if (
+                    (i + 1) % self.num_microbatches == 0
+                    and (episode != self.num_episodes - 1 or i != num_valid_microbatches - 1)
+                    and (episode != 0 or (i + 1) > self.n_behind * self.num_microbatches)
                 ):
                     if isinstance(self.model, BACKEND_MAP["vllm"]) and self.model.model_config.get(
                         "enable_sleep_mode", False
@@ -325,7 +338,7 @@ class BaseProducer:
                         self.model.llm.sleep()  # revict KV_cache to avoid OOM
                     # don't sync model for last iteration
                     torch.cuda.empty_cache()
-
+                    self.profiler.enter("sync_model")
                     if self.consumer_pp_size > 1:
                         for pp_idx in range(self.consumer_pp_size):
                             print(
@@ -347,6 +360,7 @@ class BaseProducer:
                         if "consumer_global_step" in state_dict:
                             self.consumer_global_step = state_dict.pop("consumer_global_step").item()
                         self.load_state_dict(state_dict)
+                    self.profiler.exit("sync_model")
                     del state_dict
                     torch.cuda.empty_cache()
                     if isinstance(self.model, BACKEND_MAP["vllm"]) and self.model.model_config.get(
@@ -364,6 +378,9 @@ class BaseProducer:
                             "temperature"
                         ] + ratio * 0.9
 
+    def __del__(self):
+        self.profiler.close()
+
 
 @ray.remote
 class SimpleProducer(BaseProducer):
@@ -392,6 +409,8 @@ class SimpleProducer(BaseProducer):
         wandb_group_name: str = None,
         log_rollout_interval: int = 20,
         rollout_log_file: str = "./rollout_log.jsonl",
+        enable_profiling: bool = False,
+        n_behind: int = 0,
     ):
         super().__init__(
             producer_idx,
@@ -415,6 +434,8 @@ class SimpleProducer(BaseProducer):
             wandb_group_name=wandb_group_name,
             log_rollout_interval=log_rollout_interval,
             rollout_log_file=rollout_log_file,
+            enable_profiling=enable_profiling,
+            n_behind=n_behind,
         )
         self.model = self.backend_cls(model_config, generate_config, self.tokenizer, num_generations)
         self.eval_generation_config = copy.deepcopy(self.model.generate_config)

applications/ColossalChat/coati/distributed/profiling_utils.py

@@ -0,0 +1,37 @@
+import os
+import time
+
+
+class CustomProfiler:
+    def __init__(self, name, disabled=True):
+        self.disabled = disabled
+        if not disabled:
+            self.name = name
+            self.pid = os.getpid()
+            self.file = open(f"{name}.prof", "w")
+
+    def _log(self, message):
+        if self.disabled:
+            return
+        current_time = time.time()
+        self.file.write(f"{current_time} {self.name} {self.pid}:: {message}\n")
+        self.file.flush()
+
+    def log(self, message):
+        if self.disabled:
+            return
+        current_time = time.time()
+        self.file.write(f"[Log]: {current_time} {self.name} {self.pid}:: {message}\n")
+        self.file.flush()
+
+    def enter(self, event_name):
+        self._log(f"Enter {event_name}")
+
+    def exit(self, event_name):
+        self._log(f"Exit {event_name}")
+
+    def close(self):
+        if self.disabled:
+            return
+        self.file.close()
+        print(f"Profiler data written to {self.name}.prof")

applications/ColossalChat/profiling.sh

@@ -0,0 +1,13 @@
+export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+
+# 8K context length
+# rm -rf *.prof
+# MAX_NEW_TOKENS=$((8192-512))
+# python rl_example.py --dataset /mnt/nfs/yeanbang/experiments/RLHF/grpo/train-alignment-samll.jsonl --model /home/share/data/model/Qwen2.5-Math-7B/ -t 4 -i 4 -b vllm -a GRPO -ibs 16 -tbs 16 -e 1 -rt boxed -si 100 -s "Please reason step by step, and put your final answer within \\boxed{}." -tmbs 4 -p GRPO-Math-Profile -ei -5 -zero 1 -pp 2 -mnt $MAX_NEW_TOKENS -nb 1 --enable_profiling 2>&1| tee ibs_64_tbs_32_tmbs_2_pp_2_ptp_2_8192_GRPO_profile.txt
+# python profile_grpo.py --visualization actor_timelines_ibs_64_tbs_32_tmbs_2_pp_2_ptp_2_8192_GRPO_profile.png
+
+# 4K context length
+rm -rf *.prof
+MAX_NEW_TOKENS=$((4096-512))
+python rl_example.py --dataset /mnt/nfs/yeanbang/experiments/RLHF/grpo/train-alignment-samll.jsonl --model /home/share/data/model/Qwen2.5-Math-7B/ -t 4 -i 4 -b vllm -a GRPO -ibs 8 -tbs 8 -e 1 -rt boxed -si 100 -s "Please reason step by step, and put your final answer within \\boxed{}." -tMbs 4 -tmbs 4 -p GRPO-Math-Profile -ei -5 -zero 2 -mnt $MAX_NEW_TOKENS -nb 1 --enable_profiling 2>&1| tee ibs_32_tbs_32_tmbs_4_zero_2_4096_GRPO_profile.txt
+python visualization.py --visualization actor_timelines_ibs_32_tbs_32_tmbs_4_zero_2_4096_GRPO_profile.png

applications/ColossalChat/rl_example.py

@@ -67,6 +67,27 @@ if __name__ == "__main__":
         default=2,
         help="Effective batch size per dp group for forwarding and backwarding. Please select based on the availiable memory.",
     )
+    parser.add_argument(
+        "-tp",
+        "--tensor-parallel-size",
+        type=int,
+        default=1,
+        help="Tensor parallel size for the trainer (consumer). Please check the generation arguments documentation for your backend.",
+    )
+    parser.add_argument(
+        "-pp",
+        "--pipeline-parallel-size",
+        type=int,
+        default=1,
+        help="Pipeline parallel size for the trainer (consumer). Please check the generation arguments documentation for your backend.",
+    )
+    parser.add_argument(
+        "-zero",
+        "--zero-stage",
+        type=int,
+        default=0,
+        help="Zero stage for the trainer (consumer). Please check the generation arguments documentation for your backend.",
+    )
     parser.add_argument(
         "--ray_dir", type=str, default=None, help="Custom temperary directory for storing ray cluster data, Optional"
     )
@@ -97,6 +118,13 @@ if __name__ == "__main__":
     parser.add_argument("-s", "--system-prompt", type=str, default=None, help="System prompt for data construction.")
     parser.add_argument("-mnt", "--max-new-tokens", type=int, default=1024 * 4 - 512, help="Max length for generation.")
     parser.add_argument("-mpt", "--max-prompt-tokens", type=int, default=512, help="Max length for prompt.")
+    parser.add_argument(
+        "-ptp",
+        "--producer-tensor-parallel-size",
+        type=int,
+        default=1,
+        help="Tensor parallel size for the producer. Please check the generation arguments documentation for your backend.",
+    )
 
     # GRPO parameters
     parser.add_argument("-a", "--algo", type=str, default="GRPO", choices=["DAPO", "GRPO"])
@@ -117,6 +145,13 @@ if __name__ == "__main__":
         default=100,
         help="Interval for evaluation. Evaluate every ei training steps.",
     )
+    parser.add_argument(
+        "-nb",
+        "--n-behind",
+        type=int,
+        default=0,
+        help="Number of producer batches to rollout to fill the data buffer before trainer starts to decrease bubble time",
+    )
 
     # Logging/Checkpointing parameters
     parser.add_argument("-si", "--save-interval", type=int, default=100, help="Interval for saving checkpoints.")
@@ -128,32 +163,7 @@ if __name__ == "__main__":
         "-rsd", "--rollout-save-dir", type=str, default="./rollouts", help="Directory for saving rollout loggings."
     )
     parser.add_argument(
-        "-tp",
-        "--tensor-parallel-size",
-        type=int,
-        default=1,
-        help="Tensor parallel size for the trainer (consumer). Please check the generation arguments documentation for your backend.",
-    )
-    parser.add_argument(
-        "-pp",
-        "--pipeline-parallel-size",
-        type=int,
-        default=1,
-        help="Pipeline parallel size for the trainer (consumer). Please check the generation arguments documentation for your backend.",
-    )
-    parser.add_argument(
-        "-zero",
-        "--zero-stage",
-        type=int,
-        default=0,
-        help="Zero stage for the trainer (consumer). Please check the generation arguments documentation for your backend.",
-    )
-    parser.add_argument(
-        "-ptp",
-        "--producer-tensor-parallel-size",
-        type=int,
-        default=1,
-        help="Tensor parallel size for the producer. Please check the generation arguments documentation for your backend.",
+        "--enable_profiling", action="store_true", default=False, help="Enable profiling for the training process."
     )
     args = parser.parse_args()
 
@@ -236,14 +246,25 @@ if __name__ == "__main__":
                 tensor_parallel_size=args.producer_tensor_parallel_size,
             )
         )
-        generate_config.update(
-            dict(
-                max_tokens=args.max_new_tokens,  # max new tokens
-                ignore_eos=True if args.reward_type == "think_answer_tags" else False,
-                include_stop_str_in_output=True,
-                stop=["</answer>"] if args.reward_type == "think_answer_tags" else None,
+        if args.enable_profiling:
+            # If profiling is enabled, we force model to generate to max_new_tokens
+            generate_config.update(
+                dict(
+                    max_tokens=args.max_new_tokens,  # max new tokens
+                    ignore_eos=True,
+                    include_stop_str_in_output=True,
+                    stop=None,
+                )
+            )
+        else:
+            generate_config.update(
+                dict(
+                    max_tokens=args.max_new_tokens,  # max new tokens
+                    ignore_eos=True if args.reward_type == "think_answer_tags" else False,
+                    include_stop_str_in_output=True,
+                    stop=["</answer>"] if args.reward_type == "think_answer_tags" else None,
+                )
             )
-        )
         eval_generation_config = {"temperature": 0.6}  # used to update generation config for evaluation
     else:
         raise ValueError(f"Unsupported backend: {args.backend}")
@@ -353,4 +374,6 @@ if __name__ == "__main__":
         eval_generation_config=eval_generation_config,
         log_rollout_interval=20,
         rollout_save_dir=args.rollout_save_dir,
+        enable_profiling=args.enable_profiling,
+        n_behind=args.n_behind,
     )

applications/ColossalChat/visualization.py

@@ -0,0 +1,100 @@
+# Re-import required libraries due to kernel reset
+import argparse
+from collections import defaultdict
+
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+
+# Argument parser for command line arguments
+parser = argparse.ArgumentParser(description="Process profiling logs and generate a timeline plot.")
+parser.add_argument("--visualization", type=str, default="actor_timelines.png", help="Path to the visualization file.")
+args = parser.parse_args()
+
+# Raw log lines
+log_lines = []
+
+import glob
+
+files = glob.glob("*.prof")
+for file in files:
+    with open(file, "r") as f:
+        log_lines += f.readlines()
+
+# Parse logs and collect function intervals grouped by actor
+actors = defaultdict(lambda: defaultdict(list))
+current_entries = {}
+
+# First, collect all timestamps to find the minimum
+all_timestamps = []
+parsed_lines = []
+
+for line in log_lines:
+    if line.startswith("[Log]"):
+        continue
+    parts = line.split()
+    timestamp = float(parts[0])
+    actor = parts[1]
+    action = parts[3]
+    func_name = parts[4]
+    parsed_lines.append((timestamp, actor, action, func_name))
+    all_timestamps.append(timestamp)
+
+if not all_timestamps:
+    raise ValueError("No valid log entries found.")
+
+min_timestamp = min(all_timestamps)
+
+for timestamp, actor, action, func_name in parsed_lines:
+    rel_timestamp = timestamp - min_timestamp
+    key = (actor, func_name)
+    if action == "Enter":
+        current_entries[key] = rel_timestamp
+    elif action == "Exit":
+        start_time = current_entries.pop(key, None)
+        if start_time is not None:
+            actors[actor][func_name].append((start_time, rel_timestamp))
+
+# Plotting setup
+fig, ax = plt.subplots(figsize=(12, 6))
+colors = cm.get_cmap("tab10", len(actors))
+
+actor_offsets = {}
+base_offset = 0
+function_spacing = 0.9
+
+yticks = []
+yticklabels = []
+
+for idx, (actor, func_dict) in enumerate(actors.items()):
+    actor_offsets[actor] = base_offset
+    color = colors(idx)
+    for j, (func, intervals) in enumerate(func_dict.items()):
+        print(actor, func, intervals)
+        y_val = base_offset + j * function_spacing
+        yticks.append(y_val)
+        yticklabels.append(f"{actor}:{func}")
+        for start, end in intervals:
+            if end - start < 1:
+                end = start + 1  # Ensure all lines are at least 3 units long
+            ax.plot(
+                [start, end],
+                [y_val, y_val],
+                color=color,
+                linewidth=2,
+                label=actor if j == 0 else "",
+            )
+    base_offset += len(func_dict) * function_spacing + 1
+
+# Formatting
+ax.set_yticks(yticks)
+ax.set_yticklabels(yticklabels)
+ax.set_xlabel("Time")
+ax.set_title("Timeline per Actor")
+# Remove duplicate labels in legend
+handles, labels = ax.get_legend_handles_labels()
+unique = dict(zip(labels, handles))
+ax.legend(unique.values(), unique.keys())
+plt.tight_layout()
+plt.grid(True)
+plt.savefig(args.visualization, dpi=600)  # Increase dpi for higher resolution
+print(f"Plot saved as {args.visualization}")