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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# adopted from https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/initialize
from typing import Callable, Iterable, List, Optional, Tuple
from torch import nn
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
from internlm.core.context import global_context as gpc
from internlm.core.context import ParallelMode
from internlm.core.engine import Engine, KDEngine
from internlm.core.gradient_handler import PipelineSharedModuleGradientHandler
from internlm.core.scheduler import (InterleavedPipelineScheduler, KDNonPipelineScheduler, KDPipelineScheduler,
NonPipelineScheduler, PipelineScheduler, SchedulerHook)
from internlm.core.scheduler.pipeline_scheduler import get_tensor_shape
from internlm.core.trainer import Trainer
from internlm.data.utils import unpack_data
from internlm.solver.beta2_scheduler import Beta2Scheduler
from internlm.solver.optimizer.hybrid_zero_optim import BaseOptimizer
from internlm.utils.common import get_current_device
def initialize_kd_trainer(
model: nn.Module,
teacher: nn.Module,
optimizer: Optimizer,
criterion: Optional[_Loss] = None,
kd_criterion: Optional[_Loss] = None,
train_dataloader: Optional[Iterable] = None,
test_dataloader: Optional[Iterable] = None,
lr_scheduler: Optional[_LRScheduler] = None,
beta2_scheduler: Optional[Beta2Scheduler] = None,
scheduler_hooks: Optional[List[SchedulerHook]] = None,
) -> Tuple[Trainer, DataLoader, DataLoader, _LRScheduler]:
"""Core function to wrap the essential training components with our functionality based on the config which is
loaded into gpc.config.
Args:
model (:class:`torch.nn.Module` or `Callable`): Your model instance or a function to build the model.
optimizer (:class:`BaseOptimizer`): Your optimizer for training.
criterion (:class:`torch.nn.modules.loss._Loss`, optional): Your criterion instance.
train_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for training.
test_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for testing.
lr_scheduler (:class:`torch.nn.lr_scheduler._LRScheduler`, optional): Your lr scheduler instance, optional.
Returns:
Tuple (trainer, train_dataloader, test_dataloader, lr_scheduler):
A tuple of ``(trainer, train_dataloader, test_dataloader, lr_scheduler)``
where only ``trainer`` could not be None.
"""
if isinstance(model, nn.Module):
# first sync model across dp ranks
model.to(get_current_device())
elif isinstance(model, Callable):
model = model().to(get_current_device())
# clip grad norm
clip_grad_norm = gpc.config.hybrid_zero_optimizer.get("clip_grad_norm", 0.0)
assert isinstance(optimizer, BaseOptimizer), "optimizer must be instance of BaseOptimizer"
# gradient handler, only support PipelineSharedModuleGradientHandler now
if gpc.is_using_pp():
gpc.config.gradient_handler = [dict(type="PipelineSharedModuleGradientHandler")]
gradient_handler_cfg = gpc.config.get("gradient_handler", [])
gradient_handlers = []
assert isinstance(gradient_handler_cfg, list), f"gradient_handler must be list but got {type(gradient_handler_cfg)}"
for config in gradient_handler_cfg:
if isinstance(config, dict) and config.get("type") == "PipelineSharedModuleGradientHandler":
handler = PipelineSharedModuleGradientHandler(model=model, optimizer=optimizer)
gradient_handlers.append(handler)
# initialize scheduler for trainer
scheduler = None
if gpc.config.model.use_flash_attn:
data_fn = None
else:
data_fn = unpack_data
if gpc.is_using_pp():
gpc.config.NUM_MICRO_BATCHES = gpc.config.data.micro_num
tensor_shape = get_tensor_shape()
use_interleaved = (
hasattr(gpc.config, "model") and hasattr(gpc.config.model,
"num_chunks") and gpc.config.model.num_chunks > 1
)
scatter_gather = gpc.is_initialized(ParallelMode.TENSOR)
if use_interleaved:
raise NotImplementedError('InterleavedPipelineScheduler for KD is not implemented')
else:
scheduler = KDPipelineScheduler(
data_process_func=data_fn,
num_microbatches=gpc.config.NUM_MICRO_BATCHES,
dtype=gpc.config.model["dtype"],
tensor_shape=tensor_shape,
scatter_gather_tensors=scatter_gather,
scheduler_hooks=scheduler_hooks,
)
else:
scheduler = KDNonPipelineScheduler(
data_process_func=data_fn,
gradient_accumulation_size=gpc.config.data.gradient_accumulation,
scheduler_hooks=scheduler_hooks,
)
# initialize engine for trainer
engine = KDEngine(
model=model,
teacher=teacher,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
beta2_scheduler=beta2_scheduler,
criterion=criterion,
kd_criterion=kd_criterion,
gradient_handlers=gradient_handlers,
clip_grad_norm=clip_grad_norm,
)
trainer = Trainer(engine, scheduler)
return trainer, train_dataloader, test_dataloader, lr_scheduler
def initialize_trainer(
model: nn.Module,
optimizer: Optimizer,
criterion: Optional[_Loss] = None,
train_dataloader: Optional[Iterable] = None,
test_dataloader: Optional[Iterable] = None,
lr_scheduler: Optional[_LRScheduler] = None,
beta2_scheduler: Optional[Beta2Scheduler] = None,
scheduler_hooks: Optional[List[SchedulerHook]] = None,
) -> Tuple[Trainer, DataLoader, DataLoader, _LRScheduler]:
"""Core function to wrap the essential training components with our functionality based on the config which is
loaded into gpc.config.
Args:
model (:class:`torch.nn.Module` or `Callable`): Your model instance or a function to build the model.
optimizer (:class:`BaseOptimizer`): Your optimizer for training.
criterion (:class:`torch.nn.modules.loss._Loss`, optional): Your criterion instance.
train_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for training.
test_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for testing.
lr_scheduler (:class:`torch.nn.lr_scheduler._LRScheduler`, optional): Your lr scheduler instance, optional.
Returns:
Tuple (trainer, train_dataloader, test_dataloader, lr_scheduler):
A tuple of ``(trainer, train_dataloader, test_dataloader, lr_scheduler)``
where only ``trainer`` could not be None.
"""
if isinstance(model, nn.Module):
# first sync model across dp ranks
model.to(get_current_device())
elif isinstance(model, Callable):
model = model().to(get_current_device())
# clip grad norm
clip_grad_norm = gpc.config.hybrid_zero_optimizer.get("clip_grad_norm", 0.0)
assert isinstance(optimizer, BaseOptimizer), "optimizer must be instance of BaseOptimizer"
# gradient handler, only support PipelineSharedModuleGradientHandler now
if gpc.is_using_pp():
gpc.config.gradient_handler = [dict(type="PipelineSharedModuleGradientHandler")]
gradient_handler_cfg = gpc.config.get("gradient_handler", [])
gradient_handlers = []
assert isinstance(gradient_handler_cfg, list), f"gradient_handler must be list but got {type(gradient_handler_cfg)}"
for config in gradient_handler_cfg:
if isinstance(config, dict) and config.get("type") == "PipelineSharedModuleGradientHandler":
handler = PipelineSharedModuleGradientHandler(model=model, optimizer=optimizer)
gradient_handlers.append(handler)
# initialize scheduler for trainer
scheduler = None
if gpc.config.model.use_flash_attn:
data_fn = None
else:
data_fn = unpack_data
if gpc.is_using_pp():
gpc.config.NUM_MICRO_BATCHES = gpc.config.data.micro_num
tensor_shape = get_tensor_shape()
use_interleaved = (
hasattr(gpc.config, "model") and hasattr(gpc.config.model, "num_chunks") and gpc.config.model.num_chunks > 1
)
scatter_gather = gpc.is_initialized(ParallelMode.TENSOR)
if use_interleaved:
if isinstance(model, nn.Sequential):
model = nn.ModuleList([model])
communication_overlap = gpc.config.parallel["pipeline"].get("interleaved_overlap", False)
scheduler = InterleavedPipelineScheduler(
num_microbatches=gpc.config.NUM_MICRO_BATCHES,
num_chunks=gpc.config.model.num_chunks,
dtype=gpc.config.model["dtype"],
tensor_shape=tensor_shape,
scatter_gather_tensors=scatter_gather,
scheduler_hooks=scheduler_hooks,
communication_overlap=communication_overlap,
)
else:
scheduler = PipelineScheduler(
data_process_func=data_fn,
num_microbatches=gpc.config.NUM_MICRO_BATCHES,
dtype=gpc.config.model["dtype"],
tensor_shape=tensor_shape,
scatter_gather_tensors=scatter_gather,
scheduler_hooks=scheduler_hooks,
)
else:
scheduler = NonPipelineScheduler(
data_process_func=data_fn,
gradient_accumulation_size=gpc.config.data.gradient_accumulation,
scheduler_hooks=scheduler_hooks,
)
# initialize engine for trainer
engine = Engine(
model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
beta2_scheduler=beta2_scheduler,
criterion=criterion,
gradient_handlers=gradient_handlers,
clip_grad_norm=clip_grad_norm,
)
trainer = Trainer(engine, scheduler)
return trainer, train_dataloader, test_dataloader, lr_scheduler
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