fix bugs

applications/ColossalChat/coati/distributed/consumer.py

@@ -107,6 +107,37 @@ 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):
+        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
+        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}"
@@ -121,6 +152,38 @@ class BaseConsumer:
                     torch.cuda.reset_peak_memory_stats()
                     i = 0
                     for _ in range(self.num_recv_per_update):
+                        # 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()
+                        while len(effective_group_to_raw_group_mapping) > max(
+                            self.dp_size * self.batch_size
+                            - self.dp_size
+                            * self.minibatch_size
+                            * self.grpo_config.get("num_minibatch_during_rollout", 1),
+                            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()
+                            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}")
@@ -170,37 +233,20 @@ 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 = {}
+                        effective_group_to_raw_group_mapping = self.calculate_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
-                                )
                         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:
+                        while len(effective_group_to_raw_group_mapping) > self.dp_size * self.batch_size:
-                            # on each dp_rank, we use minibatch_size effective samples to form a batch
+                            self.profiler.log(
-                            batches = [
+                                f"Received {len(effective_group_to_raw_group_mapping)} effective groups, greater than {self.dp_size * self.batch_size}, start training after recv"
-                                self.buffer[effective_group_to_raw_group_mapping[i]]
+                            )
-                                for i in range(
+                            # always keep at least dp_size * batch_size effective samples in the buffer for training during the rollout times after each sync model
-                                    self.dp_rank * self.minibatch_size, (self.dp_rank + 1) * self.minibatch_size
+                            # on each dp_rank, we use minibatch_size effective samples to form a batch
+                            batch, raw_mini_batches_metric_dict = self.prepare_mini_batch(
+                                effective_group_to_raw_group_mapping
                             )
-                            ]
-                            # 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)
                             self.profiler.enter("step")
                             loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
                             self.profiler.exit("step")
@@ -209,12 +255,7 @@ class BaseConsumer:
                             ]
                             # 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 = {}
+                            effective_group_to_raw_group_mapping = self.calculate_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

applications/ColossalChat/coati/distributed/grpo_consumer.py

@@ -379,7 +379,7 @@ class GRPOConsumer(BaseConsumer):
                             reference_model_logits / self.generate_config["temperature"],
                             input_ids_forward_micro_batch,
                             num_action,
-                            self.plugin.shard_config,
+                            shard_config=self.plugin.shard_config,
                         )
                         per_token_kl = (
                             torch.exp(reference_action_log_probs - action_log_probs)

applications/ColossalChat/coati/distributed/profiling_utils.py

@@ -1,3 +1,7 @@
+import os
+import time
+
+
 class CustomProfiler:
     def __init__(self, name, disabled=True):
         self.disabled = disabled

applications/ColossalChat/profiling.sh

@@ -1,7 +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 $MAX_NEW_TOKENS -nb 1 --enable_profiling 2>&1| tee ibs_64_tbs_32_tmbs_2_pp_2_ptp_2_8192_GRPO_profile.txt
+# python profile_grpo.py --visualization actor_timelines_ibs_64_tbs_32_tmbs_2_pp_2_ptp_2_8192_GRPO_profile.png
+
+# 4K context length
 rm -rf *.prof
-MAX_NEW_TOKENS=$((8192-512))
+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 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 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 profile_grpo.py --visualization actor_timelines_ibs_64_tbs_32_tmbs_2_pp_2_ptp_2_8192_GRPO_profile.png
+python visualization.py --visualization actor_timelines_ibs_32_tbs_32_tmbs_4_zero_2_4096_GRPO_profile.png

applications/ColossalChat/rl_example.py

@@ -263,6 +263,7 @@ if __name__ == "__main__":
         grpo_config = {
             "lr": args.learning_rate,
             "train_microbatch_size": args.train_microbatch_size,
+            "num_minibatch_during_rollout": 1,  # number of mini batches to pop out from buffer and used for training during rollout of the producer after it syncs the model. Hint, set to a proper value close to the number of mini batches for training that takes roughly the same time as the rollout of the producer. A value that is too large or too small will cause bubble time on the trainer or the producer.
             "beta": args.kl_coeff,  # KL penalty coefficient
             "loss_variation": "sample_level",
             "reward_fn_type": args.reward_type,

applications/ColossalChat/visualization.py

@@ -74,8 +74,8 @@ for idx, (actor, func_dict) in enumerate(actors.items()):
         yticks.append(y_val)
         yticklabels.append(f"{actor}:{func}")
         for start, end in intervals:
-            if end - start < 100:
+            if end - start < 6:
-                end = start + 100  # Ensure minimum length of 100ms
+                end = start + 6  # Ensure minimum length of 100ms
             ax.plot(
                 [start, end],
                 [y_val, y_val],