ColossalAI/applications/ColossalChat/coati/distributed/consumer.py

import os
import time
from contextlib import nullcontext
from typing import Any, Dict, Optional

import ray
import ray.util.collective as cc
import torch
import torch.distributed as dist
from tqdm import tqdm
from transformers import AutoModelForCausalLM

from colossalai.booster import Booster
from colossalai.booster.plugin import HybridParallelPlugin
from colossalai.initialize import launch
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device

from .comm import ray_broadcast_tensor_dict
from .utils import CustomProfiler, bind_batch, post_recv, unbind_batch


class BaseConsumer:
    def __init__(
        self,
        num_producers: int,
        num_episodes: int,
        rank: int,
        world_size: int,
        master_addr: str,
        master_port: int,
        num_update_per_episode: int,
        num_recv_per_update: int,
        batch_size: int,
        model_config: Dict[str, Any],
        plugin_config: Dict[str, Any],
        minibatch_size: int = 1,
        save_interval: int = 100,
        save_dir: str = "./model",
    ):
        self.num_producers = num_producers
        self.num_episodes = num_episodes
        self.rank = rank
        self.world_size = world_size
        self.master_addr = master_addr
        self.master_port = master_port
        self.num_update_per_episode = num_update_per_episode
        self.num_recv_per_update = num_recv_per_update
        self.batch_size = batch_size
        self.minibatch_size = minibatch_size
        self.save_interval = save_interval
        self.save_dir = save_dir
        assert batch_size % minibatch_size == 0, "batch_size should be divisible by microbatch_size"
        self.num_microbatches = batch_size // minibatch_size

        self.model_config = model_config
        self.plugin_config = plugin_config

        self.device = get_current_device()
        self.lr_scheduler = None

    def setup(self) -> None:
        launch(self.rank, self.world_size, self.master_addr, self.master_port, local_rank=0)

        plugin_config = dict(tp_size=1, pp_size=1, precision="bf16", zero_stage=2)
        if (
            self.plugin_config.get("pp_size", 1) > 1
            and "num_microbatches" not in self.plugin_config
            and "microbatch_size" not in self.plugin_config
        ):
            plugin_config["microbatch_size"] = max(1, self.minibatch_size // plugin_config.get("pp_size", 1))
        plugin_config.update(self.plugin_config)
        self.plugin = HybridParallelPlugin(**plugin_config)
        self.booster = Booster(plugin=self.plugin)
        self.dp_rank = dist.get_rank(self.plugin.dp_group)
        self.tp_rank = dist.get_rank(self.plugin.tp_group)
        self.pp_rank = dist.get_rank(self.plugin.pp_group)

        self.dp_size = dist.get_world_size(self.plugin.dp_group)
        self.tp_size = dist.get_world_size(self.plugin.tp_group)
        self.pp_size = dist.get_world_size(self.plugin.pp_group)

        torch.cuda.reset_peak_memory_stats()

        # Init Hybrid ray process group
        for i in range(self.num_producers):
            cc.init_collective_group(self.world_size + 1, self.rank + 1, group_name=f"sync_data_{i}")
        if self.pp_size > 1:
            # use hybrid tp + pp
            if self.tp_rank == 0 and self.dp_rank == 0:
                cc.init_collective_group(
                    self.num_producers + 1, self.num_producers, group_name=f"sync_model_{self.pp_rank}"
                )
        else:
            if self.rank == 0:
                cc.init_collective_group(self.num_producers + 1, self.num_producers, group_name="sync_model")

        self.buffer = []
        self.recv_cnt = 0
        self.profiler = CustomProfiler(f"C{self.rank}")

    def state_dict(self) -> Dict[str, torch.Tensor]:
        raise NotImplementedError

    def step(self, step_idx: int, **kwargs) -> Optional[float]:
        raise NotImplementedError

    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}"
        )
        start_time = time.time()
        total_step = 0
        for episode in range(self.num_episodes):
            with tqdm(
                range(self.num_update_per_episode),
                desc=f"Episode {episode} with rollout step(s)",
                disable=self.rank != 0,
            ) as pbar:
                for step in pbar:
                    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.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, ...]
                            raw_batch = {
                                k: v.view(-1, self.num_generations, v.size(-1)) if k != "temperature" else v
                                for k, v in raw_batch.items()
                            }
                            # [batch_size, num_generations] -> [batch_size]
                            reward = raw_batch["reward"][:, :, 0]
                            format_acc = raw_batch["format_acc"][:, :, 0]
                            ans_acc = raw_batch["ans_acc"][:, :, 0]
                            response_len = (
                                raw_batch["response_idx"][:, :, 1] - raw_batch["response_idx"][:, :, 0] + 1
                            ).type(torch.float32)
                            effective_group_mask = None
                            if self.filter_range is not None and self.grpo_config.get("dynamic_batching", True):
                                # filter the group based on the reward and accuracy
                                group_ans_acc_mean = ans_acc.mean(dim=1)
                                effective_group_mask = torch.logical_and(
                                    group_ans_acc_mean > self.filter_range[0], group_ans_acc_mean < self.filter_range[1]
                                )
                            raw_batch = unbind_batch(raw_batch)  # List[Dict[str, torch.Tensor]]
                            for group_idx, group_with_reward in enumerate(raw_batch):
                                self.buffer.append(
                                    [
                                        (
                                            group_with_reward
                                            if effective_group_mask is None or effective_group_mask[group_idx]
                                            else None
                                        ),
                                        reward[group_idx],
                                        format_acc[group_idx],
                                        ans_acc[group_idx],
                                        response_len[group_idx],
                                    ]
                                )
                            if effective_group_mask is not None:
                                print(
                                    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
                                )
                        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
                                )
                            ]
                            # 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)
                            torch.cuda.empty_cache()
                            torch.cuda.reset_peak_memory_stats()
                            self.profiler.enter("step")
                            loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
                            total_step += 1
                            self.profiler.exit("step")
                            self.profiler.log(
                                f"step_{self.global_step}: peak_memory: {torch.cuda.max_memory_allocated() / 1024 / 1024:.2f}MB"
                            )
                            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
                    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:
                        if self.rank == 0:
                            print(f"Start saving policy model at step {step + 1}.")
                        save_path = os.path.join(self.save_dir, f"modeling-episode-{episode}-step-{step + 1}")
                        self.booster.save_model(self.policy_model, save_path, shard=True)
                        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 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:
                            if self.tp_rank == 0 and self.dp_rank == 0:
                                ray_broadcast_tensor_dict(
                                    state_dict,
                                    src=self.num_producers,
                                    device=self.device,
                                    group_name=f"sync_model_{self.pp_rank}",
                                )
                        else:
                            if self.rank == 0:
                                ray_broadcast_tensor_dict(
                                    state_dict, src=self.num_producers, device=self.device, group_name="sync_model"
                                )
                        del state_dict
                        torch.cuda.empty_cache()
                        self.profiler.exit("sync_model")
        print(f"[T{self.rank}] Peak memory usage: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB")
        print(f"Average running time per step: {(time.time() - start_time) / total_step:.2f} seconds")

    def __del__(self):
        if hasattr(self, "profiler"):
            self.profiler.close()


@ray.remote  # (runtime_env={ "nsight": "default"})
class SimpleConsumer(BaseConsumer):
    def __init__(
        self,
        num_producers,
        num_episodes,
        rank,
        world_size,
        master_addr,
        master_port,
        num_update_per_episode,
        num_recv_per_update,
        batch_size,
        model_config,
        plugin_config,
        minibatch_size=1,
        save_interval: int = 100,
        save_dir="./model",
    ):
        super().__init__(
            num_producers,
            num_episodes,
            rank,
            world_size,
            master_addr,
            master_port,
            num_update_per_episode,
            num_recv_per_update,
            batch_size,
            model_config,
            plugin_config,
            minibatch_size,
            save_interval,
            save_dir,
        )
        path = model_config.pop("path")
        self.model = AutoModelForCausalLM.from_pretrained(path, **model_config)
        self.model.train()
        self.model.gradient_checkpointing_enable()
        self.optimizer = HybridAdam(self.model.parameters(), lr=1e-3)
        self.accum_loss = torch.zeros(1, device=self.device)

    def setup(self):
        super().setup()
        self.model, self.optimizer, *_ = self.booster.boost(self.model, self.optimizer)

    def step(self, step_idx: int, pbar: Any, **kwargs) -> Optional[float]:
        labels = kwargs["input_ids"].clone()
        labels[kwargs["attention_mask"] == 0] = -100
        kwargs["labels"] = labels
        assert kwargs.pop("action_mask").shape == kwargs.pop("action_log_probs").shape

        need_update = (step_idx + 1) % self.num_microbatches == 0

        ctx = nullcontext() if need_update else self.booster.no_sync(self.model, self.optimizer)
        with ctx:
            out = self.model(**kwargs)
            loss = out.loss / self.num_microbatches
            self.accum_loss.add_(loss.data)
            self.booster.backward(loss, self.optimizer)
        if need_update:
            self.optimizer.step()
            self.optimizer.zero_grad()
            loss_scalar = self.accum_loss.item()
            self.accum_loss.zero_()
            return loss_scalar

    def state_dict(self):
        self.model._force_wait_all_gather()
        model = self.model.unwrap()
        state_dict = model.state_dict()
        return state_dict