colossalai.utils

colossalai.utils.checkpoint(function, activation_offload, *args)

Checkpoint the computation while preserve the rng states, modified from Pytorch torch.utils.checkpoint.

Parameters

• function – Describe the forward pass function. It should know how to handle the input tuples.

• args (list) – Tuple containing the parameters of the function

Returns

Output of running function with provided args.

colossalai.utils.print_rank_0(msg, logger=None)

Print messages and save logs(optional). This is executed only if you are the rank-0 gpu.

Parameters

• msg (str) – A string message to output.

• logger (colossalai.logging.DistributedLogger, optional) – The logger to record the message, defaults to None.

colossalai.utils.sync_model_param(model, parallel_mode)

Make sure data parameters are consistent during Data Parallel Mode.

Parameters

• model (torch.nn.Module) – A pyTorch model on whose parameters you check the consistency.

• parallel_mode (colossalai.context.ParallelMode) – Parallel mode to be checked.

Note

The parallel_mode should be concluded in ParallelMode. More details about ParallelMode could be found in parallel_mode

colossalai.utils.clip_grad_norm_fp32(parameters, max_norm, norm_type=2)

Clips gradient norm of an iterable of parameters whose gradients are in fp32.

This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_() and added functionality to handle model parallel parameters.

Note

the gradients are modified in place.

Parameters

• parameters (Iterable[torch.tensor] or torch.tensor) – An iterable of Tensors or a single Tensor that will have gradients normalized.

• max_norm (Union[float, int]) – Max norm of the gradients.

• norm_type (Union[float, int, 'inf']) – Type of the used p-norm. Can be 'inf' for infinity norm.

Returns

Total norm of the parameters.

Return type

float

colossalai.utils.get_current_device()

Returns currently selected device (gpu/cpu). If cuda available, return gpu, otherwise return cpu.

colossalai.utils.synchronize()

Similar to cuda.synchronize(). Waits for all kernels in all streams on a CUDA device to complete.

colossalai.utils.empty_cache()

Similar to cuda.empty_cache() Releases all unoccupied cached memory currently held by the caching allocator.

colossalai.utils.set_to_cuda(models)

Send model to gpu.

Parameters

models – nn.module or a list of module

colossalai.utils.report_memory_usage(message, logger=None, report_cpu=False)

Calculate and print RAM usage (in GB)

Parameters

• message (str) – A prefix message to add in the log.

• logger (colossalai.logging.DistributedLogger) – The logger used to record memory information.

• report_cpu (bool, optional) – Whether to report CPU memory.

Raises

EnvironmentError – Raise error if no distributed environment has been initialized.

colossalai.utils.colo_device_memory_capacity(device)

Get the capacity of the memory of the device

Parameters

device (torch.device) – a device

Returns

size in byte

Return type

int

colossalai.utils.colo_device_memory_used(device)

Get the device memory on device belonging to the current process.

Parameters

device (torch.device) – a device

Returns

memory size in bytes

Return type

int

colossalai.utils.colo_set_process_memory_fraction(ratio)

set how much cuda memory used on the gpu belonging to the current process.

Parameters

ratio (float) – a ratio between 0. ~ 1.

class colossalai.utils.Timer

A timer object which helps to log the execution times, and provides different tools to assess the times.

start()

Firstly synchronize cuda, reset the clock and then start the timer.

lap()

lap time and return elapsed time

stop(keep_in_history=False)

Stop the timer and record the start-stop time interval.

Parameters

keep_in_history (bool, optional) – Whether does it record into history each start-stop interval, defaults to False.

Returns

Start-stop interval.

Return type

int

get_history_mean()

Mean of all history start-stop time intervals.

Returns

Mean of time intervals

Return type

int

get_history_sum()

Add up all the start-stop time intervals.

Returns

Sum of time intervals.

Return type

int

get_elapsed_time()

Return the last start-stop time interval.

Returns

The last time interval.

Return type

int

Note

Use it only when timer is not in progress

reset()

Clear up the timer and its history

class colossalai.utils.MultiTimer(on=True)

An object contains multiple timers.

Parameters

on (bool, optional) – Whether the timer is enabled. Default is True.

start(name)

Start namely one of the timers.

Parameters

name (str) – Timer’s key.

stop(name, keep_in_history)

Stop namely one of the timers.

Parameters

• name (str) – Timer’s key.

• keep_in_history (bool) – Whether does it record into history each start-stop interval.

get_timer(name)

Get timer by its name (from multitimer)

Parameters

name (str) – Timer’s key.

Returns

Timer with the name you give correctly.

Return type

colossalai.utils.Timer

reset(name=None)

Reset timers.

Parameters

name (str, optional) – If name is designated, the named timer will be reset and others will not, defaults to None.

class colossalai.utils.DataParallelSampler(dataset, shuffle=False, seed=0, drop_last=False)

A data sampler for distributed data parallelism.

Parameters

• dataset (torch.utils.data.Dataset) – The Dataset for sampling.

• shuffle (bool, optional) – Whether to shuffle data, defaults to False.

• seed (int, optional) – The random seed used for sampling, defaults to 0.

• drop_last (bool, optional) – Set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller, defaults to False.

set_epoch(epoch)

Sets the epoch for this sampler. When shuffle=True, this ensures all replicas use a different random ordering for each epoch. Otherwise, the next iteration of this sampler will yield the same ordering.

Parameters

epoch (int) – Epoch number.

colossalai.utils.get_dataloader(dataset, shuffle=False, seed=1024, add_sampler=True, drop_last=False, pin_memory=False, num_workers=0, **kwargs)

Set up a deterministic dataloader (also configure seed workers, samplers and whether shuffle or not)

Note

When pipeline parallel is enabled, shuffle cannot be True as it will result in mismatch between input data on the 1st stage and label on the last stage.

Parameters

• dataset (torch.utils.data.Dataset) – The dataset to be loaded.

• shuffle (bool, optional) – Whether to shuffle the dataset. Defaults to False.

• seed (int, optional) – Random worker seed for sampling, defaults to 1024.

• add_sampler – Whether to add DistributedDataParallelSampler to the dataset. Defaults to True.

• drop_last (bool, optional) – Set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller, defaults to False.

• pin_memory (bool, optional) – Whether to pin memory address in CPU memory. Defaults to False.

• num_workers (int, optional) – Number of worker threads for this dataloader. Defaults to 0.

• kwargs (dict) – optional parameters for torch.utils.data.DataLoader, more details could be found in DataLoader.

Returns

A DataLoader used for training or testing.

Return type

torch.utils.data.DataLoader

colossalai.utils.load_checkpoint(file, model, optimizer=None, lr_scheduler=None, strict=True)

Loads training states from a checkpoint file.

Parameters

• file – a file-like object (has to implement read(), readline(), tell(), and seek()), or a string or os.PathLike object containing a file name.

• model (torch.nn.Module) – Model to load saved weights and buffers.

• optimizer (Union[torch.optim.Optimizer, colossalai.nn.optimizer]) – Optimizer to recuperate.

• lr_scheduler (torch.optim.lr_scheduler._LRScheduler, optional) – lr_scheduler to recuperate, defaults to None.

• strict (bool, optional) – Whether to strictly enforce that the keys in state_dict of the checkpoint match the names of parameters and buffers in model, defaults to True.

Returns

The saved epoch number.

Return type

int

Raises

RuntimeError – Raise error if the model/optimizer cannot successfully be recuperated

colossalai.utils.save_checkpoint(file, epoch, model, optimizer=None, lr_scheduler=None, **kwargs)

Stores the checkpoint to disk. Saves all the training components’ parameters or buffers, such as model, optimizer, lr_scheduler etc. into a checkpoint dictionary.

Parameters

• file – a file-like object (has to implement write and flush) or a string or os.PathLike object containing a file name.

• epoch (int) – Epoch number (indicates how many epochs have you trained this model).

• model (torch.nn.Module) – Model to be saved.

• optimizer (Union[torch.optim.Optimizer, colossalai.nn.optimizer]) – Optimizer to be saved.

• lr_scheduler (Union[torch.optim.lr_scheduler, colossalai.nn.lr_scheduler], optional) – lr_scheduler to be saved, defaults to None.

• pickle_module – module used for pickling metadata and objects

• pickle_protocol – can be specified to override the default protocol

colossalai.utils.colo_get_cpu_memory_capacity()

Get the cpu memory capacity. We may not use all of it. :returns: _description_ :rtype: int

• colossalai.utils.data_sampler
  • colossalai.utils.data_sampler.base_sampler
  • colossalai.utils.data_sampler.data_parallel_sampler
• colossalai.utils.gradient_accumulation
• colossalai.utils.memory_utils
  • colossalai.utils.memory_utils.memory_monitor
  • colossalai.utils.memory_utils.utils
• colossalai.utils.multi_tensor_apply
  • colossalai.utils.multi_tensor_apply.multi_tensor_apply
• colossalai.utils.profiler
  • colossalai.utils.profiler.comm_profiler
  • colossalai.utils.profiler.mem_profiler
  • colossalai.utils.profiler.pcie_profiler
  • colossalai.utils.profiler.prof_utils
• colossalai.utils.tensor_detector
  • colossalai.utils.tensor_detector.tensor_detector

• colossalai.utils.activation_checkpoint
• colossalai.utils.checkpointing
• colossalai.utils.common
• colossalai.utils.cuda
• colossalai.utils.moe
• colossalai.utils.timer