mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-04-27 19:36:13 +00:00
26d859f68e
* update help information * update style * fix * minor fix * support PP training * add pp support * remove unused code * address conversation * fix memory leakage support tp+pp * move empty cache * move empty cache * add DAPO support * remove format reward * fix filtering, still buggy * small fix * add DAPO support * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * tested multi-node training; fix bind_batch bug * fix conversation; support sleep mode * support reusing excessive samples * add dynamic batching control flag * add dynamic batching control flag * refactored * fix logging --------- Co-authored-by: Tong Li <tong.li35271158@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
147 lines
4.2 KiB
Python
Executable File
147 lines
4.2 KiB
Python
Executable File
"""
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Training utilities for Coati.
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"""
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from typing import Any
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import torch
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import torch.distributed as dist
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from torch.utils._pytree import tree_map
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from torch.utils.data import DataLoader
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from colossalai.booster import Plugin
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class AnnealingScheduler:
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def __init__(self, start, end, warmup_steps=100, annealing_step=2000):
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self.start = start
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self.end = end
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self.warmup_steps = warmup_steps
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self.step = 0
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self.annealing_step = annealing_step
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def get_temperature(self):
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if self.step <= self.warmup_steps:
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return self.start # Stop annealing after warm-up steps
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elif self.step >= self.annealing_step:
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return self.end
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# Linear annealing
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temp = self.start - (self.step / self.annealing_step) * (self.start - self.end)
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return temp
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def step_forward(self):
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self.step += 1
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class CycledDataLoader:
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"""
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A data loader that cycles through the data when it reaches the end.
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Args:
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dataloader (DataLoader): The original data loader.
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Attributes:
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dataloader (DataLoader): The original data loader.
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count (int): The number of times the data loader has been cycled.
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dataloader_iter (iterable): The iterator for the data loader.
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Methods:
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next(): Returns the next batch of data from the data loader, cycling through the data if necessary.
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"""
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def __init__(
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self,
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dataloader: DataLoader,
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) -> None:
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self.dataloader = dataloader
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self.count = 0
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self.dataloader_iter = None
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def next(self):
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"""
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Returns the next batch of data from the data loader, cycling through the data if necessary.
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Returns:
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Any: The next batch of data from the data loader.
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"""
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# defer initialization
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if self.dataloader_iter is None:
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self.dataloader_iter = iter(self.dataloader)
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self.count += 1
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try:
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return next(self.dataloader_iter)
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except StopIteration:
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self.count = 0
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self.dataloader_iter = iter(self.dataloader)
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return next(self.dataloader_iter)
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def is_rank_0() -> bool:
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"""
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Check if the current process is the rank 0 process in a distributed training setup.
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Returns:
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bool: True if the current process is the rank 0 process, False otherwise.
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"""
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return not dist.is_initialized() or dist.get_rank() == 0
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def to_device(x: Any, device: torch.device) -> Any:
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"""
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Move the input tensor or nested structure of tensors to the specified device.
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Args:
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x (Any): The input tensor or nested structure of tensors.
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device (torch.device): The target device to move the tensors to.
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Returns:
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Any: The tensor or nested structure of tensors moved to the target device.
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"""
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def _to(t: Any):
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if isinstance(t, torch.Tensor):
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return t.to(device)
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return t
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return tree_map(_to, x)
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def all_reduce_mean(tensor: torch.Tensor, plugin: Plugin = None) -> torch.Tensor:
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"""
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Perform all-reduce operation on the given tensor and compute the mean across all processes.
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Args:
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tensor (torch.Tensor): The input tensor to be reduced.
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Returns:
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torch.Tensor: The reduced tensor with mean computed across all processes.
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"""
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# All reduce mean across DP group
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if plugin is not None:
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dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM, group=plugin.dp_group)
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tensor.div_(plugin.dp_size)
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else:
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dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
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tensor.div_(dist.get_world_size())
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return tensor
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def all_reduce_sum(tensor: torch.Tensor, plugin: Plugin = None) -> torch.Tensor:
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"""
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Performs an all-reduce operation to sum the values of the given tensor across all processes.
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Args:
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tensor (torch.Tensor): The input tensor to be reduced.
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Returns:
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torch.Tensor: The reduced tensor with the sum of values across all processes.
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"""
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# All reduce sum across DP group
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if plugin is not None:
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dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM, group=plugin.dp_group)
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else:
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dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
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return tensor
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