mirror of https://github.com/InternLM/InternLM
195 lines
6.9 KiB
Python
195 lines
6.9 KiB
Python
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
# adopted from https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/engine
|
|
|
|
from typing import Any, Callable, Iterable, List, Optional
|
|
|
|
import torch
|
|
|
|
from internlm.core.engine import Engine
|
|
from internlm.utils.common import conditional_context
|
|
from internlm.utils.timeout import llm_timeout
|
|
|
|
from .base_scheduler import BaseScheduler, SchedulerHook
|
|
|
|
|
|
class NonPipelineScheduler(BaseScheduler):
|
|
"""A helper schedule class for no pipeline parallelism running environment.
|
|
During one process, it loads a batch of dataset and feeds it to the model.
|
|
After getting the output and calculating the loss, it will use :meth:`step`
|
|
to update the parameters if it is in training mode.
|
|
|
|
Args:
|
|
data_process_func (Callable, optional): The preprocessing function which receives a batch of data
|
|
and returns a tuple in the form of (data, label), and it will be executed in load_batch.
|
|
gradient_accumulation_steps(int, optional): the steps of gradient accumulation, 1 for disable
|
|
gradient accumulation.
|
|
|
|
Example:
|
|
# this shows an example of customized data_process_func
|
|
def data_process_func(dataloader_output):
|
|
item1, item2, item3 = dataloader_output
|
|
data = (item1, item2)
|
|
label = item3
|
|
return data, label
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
data_process_func: Callable = None,
|
|
gradient_accumulation_size: int = 1,
|
|
scheduler_hooks: Optional[List[SchedulerHook]] = None,
|
|
):
|
|
self._grad_accum_size = gradient_accumulation_size
|
|
self._grad_accum_offset = 0
|
|
|
|
self._hooks = scheduler_hooks
|
|
|
|
super().__init__(data_process_func)
|
|
|
|
def pre_processing(self, engine: Engine):
|
|
"""Performs actions before running the schedule.
|
|
|
|
Args:
|
|
engine (internlm.core.Engine): InternLM engine for training and inference.
|
|
"""
|
|
pass
|
|
|
|
def _call_hooks(self, func_name: str, *args, **kwargs) -> None:
|
|
for hook in self._hooks:
|
|
getattr(hook, func_name)(self, *args, **kwargs)
|
|
|
|
def _load_accum_batch(self, data: Any, label: Any):
|
|
"""Loads a batch of data and label for gradient accumulation.
|
|
|
|
Args:
|
|
data (Any): The data to be loaded.
|
|
label (Any): The label to be loaded.
|
|
"""
|
|
|
|
_data, _label = self._load_micro_batch(
|
|
data=data, label=label, offset=self._grad_accum_offset, micro_bsz=self._grad_accum_batch_size
|
|
)
|
|
self._grad_accum_offset += self._grad_accum_batch_size
|
|
|
|
if self.data_process_func:
|
|
_data["input_ids"] = self.data_process_func(_data["input_ids"], _data["cu_seqlens"])
|
|
_label = self.data_process_func(_label, _data["cu_seqlens"])
|
|
_data.pop("cu_seqlens")
|
|
_data.pop("indexes")
|
|
|
|
return _data, _label
|
|
|
|
def _train_one_batch(
|
|
self,
|
|
data: Any,
|
|
label: Any,
|
|
engine: Engine,
|
|
forward_only: bool = False,
|
|
return_loss: bool = True,
|
|
scale_loss: int = 1,
|
|
):
|
|
"""Trains one batch of data.
|
|
|
|
Args:
|
|
data (Any): The data to be trained.
|
|
label (Any): The label for the data.
|
|
engine (internlm.core.Engine): InternLM engine for training and inference.
|
|
forward_only (bool, optional): If True, the model is run for the forward pass, else back propagation will
|
|
be executed.
|
|
return_loss (bool, optional): Loss will be returned if True.
|
|
scale_loss (int, optional): The scale factor for the loss.
|
|
"""
|
|
|
|
# forward
|
|
with conditional_context(torch.no_grad(), enable=forward_only):
|
|
self._call_hooks("before_forward", data)
|
|
output = self._call_engine(engine, data)
|
|
self._call_hooks("after_forward", output)
|
|
|
|
self._call_hooks("post_helper_func", output, label)
|
|
|
|
if return_loss:
|
|
self._call_hooks("before_criterion", output, label)
|
|
loss = self._call_engine_criterion(engine, output, label)
|
|
self._call_hooks("after_criterion", loss)
|
|
loss /= scale_loss
|
|
|
|
# backward
|
|
if not forward_only:
|
|
self._call_hooks("before_backward", None, None)
|
|
engine.backward(loss)
|
|
self._call_hooks("after_backward", None)
|
|
|
|
if not return_loss:
|
|
loss = None
|
|
|
|
return output, loss
|
|
|
|
@llm_timeout(func_name="nopp_forward_backward_step")
|
|
def forward_backward_step(
|
|
self,
|
|
engine: Engine,
|
|
data_iter: Iterable,
|
|
forward_only: bool = False,
|
|
return_loss: bool = True,
|
|
return_output_label: bool = True,
|
|
):
|
|
"""The process function that loads a batch of dataset and feeds it to the model.
|
|
The returned labels and loss will None if :attr:`return_loss` is False.
|
|
|
|
Args:
|
|
engine (internlm.core.Engine): InternLM engine for training and inference.
|
|
data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader).
|
|
forward_only (bool, optional):
|
|
If True, the model is run for the forward pass, else back propagation will be executed.
|
|
return_loss (bool, optional): Loss will be returned if True.
|
|
return_output_label (bool, optional): Output and label will be returned if True.
|
|
|
|
Returns:
|
|
Tuple[:class:`torch.Tensor`]: A tuple of (output, label, loss), loss and label could be None.
|
|
"""
|
|
assert (
|
|
forward_only or return_loss
|
|
), "The argument 'return_loss' has to be True when 'forward_only' is False, but got False."
|
|
|
|
batch_data, batch_size = engine.load_batch(data_iter)
|
|
|
|
assert (
|
|
batch_size % self._grad_accum_size == 0
|
|
), f"batch_size:{batch_size} must be an integer multiple of gradient accumulation steps:{self._grad_accum_size}"
|
|
self._grad_accum_batch_size = batch_size // self._grad_accum_size
|
|
|
|
data, label = batch_data
|
|
|
|
loss = 0 if return_loss else None
|
|
outputs = []
|
|
labels = []
|
|
|
|
# reset accumulation microbatch offset
|
|
self._grad_accum_offset = 0
|
|
|
|
for _current_accum_step in range(self._grad_accum_size):
|
|
if _current_accum_step == self._grad_accum_size - 1:
|
|
engine.optimizer.skip_grad_reduce = False
|
|
else:
|
|
engine.optimizer.skip_grad_reduce = True
|
|
|
|
_data, _label = self._load_accum_batch(data, label)
|
|
|
|
_output, _loss = self._train_one_batch(
|
|
_data, _label, engine, forward_only, return_loss, self._grad_accum_size
|
|
)
|
|
|
|
if return_loss:
|
|
loss += _loss
|
|
if return_output_label:
|
|
outputs.append(_output)
|
|
labels.append(_label)
|
|
|
|
if not return_output_label:
|
|
outputs, labels = None, None
|
|
|
|
return outputs, labels, loss
|