mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-04-27 19:36:13 +00:00
220 lines
8.9 KiB
Python
220 lines
8.9 KiB
Python
import os
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from contextlib import nullcontext
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from typing import Any, Dict, Optional
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import ray
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import ray.util.collective as cc
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import torch
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import torch.distributed as dist
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from tqdm import tqdm
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from transformers import AutoModelForCausalLM
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from colossalai.booster import Booster
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from colossalai.booster.plugin import HybridParallelPlugin
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from colossalai.initialize import launch
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.utils import get_current_device
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from .comm import ray_broadcast_tensor_dict
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from .utils import bind_batch, pad_batch, post_recv, unbind_batch
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class BaseConsumer:
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def __init__(
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self,
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num_producers: int,
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num_episodes: int,
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rank: int,
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world_size: int,
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master_addr: str,
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master_port: int,
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num_update_per_episode: int,
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num_recv_per_update: int,
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batch_size: int,
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model_config: Dict[str, Any],
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plugin_config: Dict[str, Any],
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minibatch_size: int = 1,
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save_interval: int = 100,
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save_dir: str = "./model",
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):
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self.num_producers = num_producers
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self.num_episodes = num_episodes
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self.rank = rank
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self.world_size = world_size
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self.master_addr = master_addr
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self.master_port = master_port
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self.num_update_per_episode = num_update_per_episode
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self.num_recv_per_update = num_recv_per_update
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self.batch_size = batch_size
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self.minibatch_size = minibatch_size
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self.save_interval = save_interval
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self.save_dir = save_dir
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assert batch_size % minibatch_size == 0, "batch_size should be divisible by microbatch_size"
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self.num_microbatches = batch_size // minibatch_size
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self.model_config = model_config
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self.plugin_config = plugin_config
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self.device = get_current_device()
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self.lr_scheduler = None
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def setup(self) -> None:
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for i in range(self.num_producers):
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cc.init_collective_group(self.world_size + 1, self.rank + 1, group_name=f"sync_data_{i}")
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if self.rank == 0:
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cc.init_collective_group(self.num_producers + 1, self.num_producers, group_name="sync_model")
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launch(self.rank, self.world_size, self.master_addr, self.master_port, local_rank=0)
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plugin_config = dict(tp_size=1, pp_size=1, precision="bf16", zero_stage=2)
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if self.plugin_config.get("pp_size", 1) > 1 and "num_microbatches" not in self.plugin_config:
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plugin_config["microbatch_size"] = self.minibatch_size
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plugin_config.update(self.plugin_config)
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self.plugin = HybridParallelPlugin(**plugin_config)
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self.booster = Booster(plugin=self.plugin)
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self.dp_rank = dist.get_rank(self.plugin.dp_group)
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self.tp_rank = dist.get_rank(self.plugin.tp_group)
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self.dp_size = dist.get_world_size(self.plugin.dp_group)
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self.buffer = []
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self.recv_cnt = 0
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def state_dict(self) -> Dict[str, torch.Tensor]:
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raise NotImplementedError
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def step(self, step_idx: int, **kwargs) -> Optional[float]:
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raise NotImplementedError
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def loop(self) -> None:
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print(
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f"Consumer{self.rank} num_update: {self.num_update_per_episode}, num_recv: {self.num_recv_per_update}, nmb: {self.num_microbatches}"
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)
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for episode in range(self.num_episodes):
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with tqdm(range(self.num_update_per_episode), desc=f"Episode {episode}", disable=self.rank != 0) as pbar:
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for step in pbar:
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i = 0
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for _ in range(self.num_recv_per_update):
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# receive data from producers
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for r in range(self.num_producers):
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print(f"[T{dist.get_rank()}] Recv data episode {episode} step {step} from {r}")
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self.buffer.extend(
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unbind_batch(
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ray_broadcast_tensor_dict(
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None, src=0, device=self.device, group_name=f"sync_data_{r}"
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)
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)
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)
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while len(self.buffer) >= self.dp_size * self.minibatch_size:
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batches = self.buffer[
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self.dp_rank * self.minibatch_size : (self.dp_rank + 1) * self.minibatch_size
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]
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batch = pad_batch(
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batches
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) # when `imbs` is smaller than `tMbs`, samples may have differ in size, need to pad before stacking
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batch = bind_batch(batches)
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batch = post_recv(batch)
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loss, num_excessive_prompts = self.step(i, pbar, **batch)
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self.buffer = (
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self.buffer[
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(self.dp_rank + 1) * self.minibatch_size
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- num_excessive_prompts : (self.dp_rank + 1) * self.minibatch_size
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]
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+ self.buffer[self.dp_size * self.minibatch_size :]
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)
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if loss is not None:
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pbar.set_postfix({"loss": loss})
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i += 1
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if self.lr_scheduler is not None:
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self.lr_scheduler.step()
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if (step + 1) % self.save_interval == 0 or (step + 1) == self.num_update_per_episode:
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if self.rank == 0:
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print(f"Start saving policy model at step {step + 1}.")
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save_path = os.path.join(self.save_dir, f"modeling-episode-{episode}-step-{step + 1}")
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self.booster.save_model(self.policy_model, save_path, shard=True)
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if self.rank == 0:
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print(f"Saved model checkpoint at step {step + 1} in folder {save_path}")
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if episode != self.num_episodes - 1 or step != self.num_update_per_episode - 1:
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print(f"[T{dist.get_rank()}] Sync model episode {episode} step {step}")
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torch.cuda.empty_cache()
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state_dict = self.state_dict()
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if self.rank == 0:
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ray_broadcast_tensor_dict(
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state_dict, src=self.num_producers, device=self.device, group_name="sync_model"
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)
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del state_dict
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torch.cuda.empty_cache()
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@ray.remote
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class SimpleConsumer(BaseConsumer):
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def __init__(
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self,
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num_producers,
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num_episodes,
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rank,
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world_size,
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master_addr,
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master_port,
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num_update_per_episode,
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num_recv_per_update,
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batch_size,
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model_config,
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plugin_config,
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minibatch_size=1,
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save_interval: int = 100,
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save_dir="./model",
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):
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super().__init__(
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num_producers,
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num_episodes,
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rank,
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world_size,
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master_addr,
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master_port,
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num_update_per_episode,
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num_recv_per_update,
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batch_size,
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model_config,
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plugin_config,
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minibatch_size,
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)
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path = model_config.pop("path")
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self.model = AutoModelForCausalLM.from_pretrained(path, **model_config)
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self.model.train()
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self.model.gradient_checkpointing_enable()
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self.optimizer = HybridAdam(self.model.parameters(), lr=1e-3)
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self.accum_loss = torch.zeros(1, device=self.device)
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def setup(self):
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super().setup()
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self.model, self.optimizer, *_ = self.booster.boost(self.model, self.optimizer)
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def step(self, step_idx: int, pbar: Any, **kwargs) -> Optional[float]:
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labels = kwargs["input_ids"].clone()
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labels[kwargs["attention_mask"] == 0] = -100
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kwargs["labels"] = labels
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assert kwargs.pop("action_mask").shape == kwargs.pop("action_log_probs").shape
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need_update = (step_idx + 1) % self.num_microbatches == 0
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ctx = nullcontext() if need_update else self.booster.no_sync(self.model, self.optimizer)
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with ctx:
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out = self.model(**kwargs)
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loss = out.loss / self.num_microbatches
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self.accum_loss.add_(loss.data)
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self.booster.backward(loss, self.optimizer)
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if need_update:
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self.optimizer.step()
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self.optimizer.zero_grad()
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loss_scalar = self.accum_loss.item()
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self.accum_loss.zero_()
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return loss_scalar
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def state_dict(self):
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self.model._force_wait_all_gather()
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model = self.model.unwrap()
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state_dict = model.state_dict()
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return state_dict
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