`cerebras_pytorch` package#

Creation Ops#

Can be used to lazily initialize tensors with known shape, dtype and value to avoid have them unnecessarily take up memory.

`full`#

`full_like`#

`ones`#

`ones_like`#

`zeros`#

`zeros_like`#

Checkpoint Saving/Loading utilities#

Data Utilities#

`utils.data.DataLoader`#

class cerebras_pytorch.utils.data.DataLoader#

Wrapper around torch.utils.data.DataLoader that facilitates moving data generated by the dataloader to a Cerebras system

Parameters

input_fn – A callable that returns a torch.utils.data.DataLoader instance or an iterable that returns a structure containing torch tensors.
*args – Any other positional or keyword arguments are passed into the input_fn when each worker instantiates their respective dataloaders
**kwargs –
Any other positional or keyword arguments are passed into the input_fn when each worker instantiates their respective dataloaders

__init__(*args: Any, **kwargs: Any) → None#

disable_dataloader_checkpointing()#

load_state_dict()#

serialize_state_dict()#

state_dict()#

Each worker will call this input function to construct their own dataloader object. This means that some data sharding scheme is required if the intent is for each worker to stream in a unique set of data.

`utils.data.SyntheticDataset`#

class cerebras_pytorch.utils.data.SyntheticDataset[source]#

A synthetic dataset that generates samples from a SampleSpec.

Constructs a SyntheticDataset instance.

A synthetic dataset can be used to generate samples on the fly with an expected dtype/shape but without needing to create a full-blown dataset. This is especially useful for compile validation.

Parameters

sample_spec –
Specification of the samples to generate. This can be a nested structure of one of the following types:
- torch.Tensor: A tensor to be cloned.
- Callable: A callable that takes the sample index and
  returns a tensor.
Supported data structures for holding the above leaf nodes are list, tuple, dict, OrderedDict, and NamedTuple.
num_samples – Total size of the dataset. If None, the dataset will generate samples indefinitely.

__init__(sample_spec: Union[torch.Tensor, Callable[[int], torch.Tensor], List[Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple]], Tuple[Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple], ...], Dict[str, Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple]], OrderedDict[str, Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple]], NamedTuple], num_samples: Optional[int] = None)[source]#

Constructs a SyntheticDataset instance.

A synthetic dataset can be used to generate samples on the fly with an expected dtype/shape but without needing to create a full-blown dataset. This is especially useful for compile validation.

Parameters

sample_spec –
Specification of the samples to generate. This can be a nested structure of one of the following types:
- torch.Tensor: A tensor to be cloned.
- Callable: A callable that takes the sample index and
  returns a tensor.
Supported data structures for holding the above leaf nodes are list, tuple, dict, OrderedDict, and NamedTuple.
num_samples – Total size of the dataset. If None, the dataset will generate samples indefinitely.

`utils.data.DataExecutor`#

class cerebras_pytorch.utils.data.DataExecutor#

Defines a single execution run on a Cerebras wafer scale cluster

Parameters

dataloader – the dataloader to use for the run
num_steps – the number of steps to run. Defaults to 1 if the backend was configured for compile or validate only
checkpoint_steps – the interval at which to schedule fetching checkpoints from the cluster
activation_steps – the interval at which to schedule fetching activations from the cluster
cs_config – optionally, a CSConfig object can be passed in to configure the cerebras wafer-scale cluster. if none provided the default configuration values will be used.
writer – The summary writer to be used to write any summarized scalars or tensors to tensorboard
profiler_activities – The list of activities to profile. By default the total samples, the client side rate and global rate are tracked and accessible via the profiler attribute

__init__(*args: Any, **kwargs: Any) → None#

Note

As of Cerebras Release 2.0, we don’t officially support multiple CS runs in a single process. This means that the above executor can only be run/iterated once. Any runs with different configurations must be run in separate processes.

`utils.data.RestartableDataLoader`#

class cerebras_pytorch.utils.data.RestartableDataLoader[source]#

Defines interface for the restartable dataloader protocol.

__init__(*args, **kwargs)#

aggregate_state_dict(worker_states: List[Dict[str, Any]]) → Dict[str, Any][source]#

Use this method to specify how to combine the list of CSX Worker state dicts. Each CSX Worker state in the worker_states list is to be specified in state_dict

Returns: The consolidated state dict that will be saved in a checkpoint.

Usage:

def aggregate_state_dict(self, worker_states):
    return {
        "worker_0": worker_states[0],
        "worker_1": worker_states[1]
    }

deaggregate_state_dict(aggregated_state_dict: Dict[str, Any]) → Dict[str, Any][source]#

Use this method to specify how to load an individual CSX Worker state given a consolidated list of state dicts, as specified in aggregate_state_dict.

Returns: The state dict will be passed to the above-defined load_state_dict method.

Usage:

def deaggregate_state_dict(self, aggregated_state_dict):
    return {
        "worker_0": aggregated_state_dict.get("worker_0", {})
    }

load_state_dict(state_dict: Dict[str, Any]) → None[source]#

Use this method to load CSX Worker state for the dataloader instance, as captured from a previous run.

Parameters: state_dict – dict holding worker state info, specified in deaggregate_state_dict

Usage:

def load_state_dict(self, state_dict):
    wrk_state_dict = state_dict.get("worker_0", {})

    worker_step = wrk_state_dict.get("worker_step", 0)
    worker_id = wrk_state_dict.get("worker_id")

    print(f"WRK {worker_id} loaded step: {worker_step}")

state_dict() → Dict[str, Any][source]#

Use this method to specify what state information should be saved by each CSX Worker.

Returns: dict holding state information for the CSX Worker

In order to access Cerebras internal data checkpoint info per CSX Worker at some checkpoint step, follow the steps in the example below. Cerebras internal data checkpoint format is recorded in the DataLoaderCheckpoint dataclass.

Usage:

import cerebras_pytorch as cstorch
...
def state_dict(self) -> Dict[str, Any]:
    worker_state = cstorch.distributed.get_worker_state()
    state_dict = {}
    if worker_state:
        state_dict["worker_step"] = worker_state.worker_step
        state_dict["worker_id"] = worker_state.global_worker_id
    return state_dict

Note

The call to get_worker_state is well-defined only inside of the state_dict method; using this anywhere else will result in a RuntimeError exception. See linked docs for more details.

`utils.data.DataLoaderCheckpoint`#

class cerebras_pytorch.utils.data.DataLoaderCheckpoint#

Dataclass representing the Cerebras internal dataloader checkpoint format. Each CSX Worker captures its state information via this class at a checkpoint step.

Parameters

global_worker_id (int) – ID of this worker amongst all other workers across all boxes
local_worker_id (int) – ID of this worker amongst all other workers across the same box
total_num_workers (int) – The total number of workers for the run across all boxes
num_workers_per_csx (int) – The total number of workers per box for the run
num_csx (int) – The total number of CSXs (boxes) for the run
wse_id (int) – ID of the Wafer-Scale Engine (CSX) to which this worker streams data
appliance_step (int) – The appliance step at which this checkpoint state info is captured
worker_step (int) – The worker step at which this state info is captured. Note that this is simply equal to appliance_step if num_workers_per_csx = 1; for the multi-worker scenario, the appliance step is distributed across workers on a single box in a round-robin fashion based on the local worker id
samples_streamed (int) – The total number of samples streamed by this worker at checkpoint step. This is simply worker_step * per_box_batch_size

Note

appliance_step, worker_step and samples_streamed are the attributes that vary across different steps; whereas the other attributes provide constant state information for the current run.

`get_worker_state`#

`utils.CSConfig`#

class cerebras_pytorch.utils.CSConfig#

Contains config details for the Cerebras Wafer Scale Cluster

Parameters

mgmt_address (Optional[str]) – Address to connect to appliance. If not provided, query the cluster management node for it. Default: None.
credentials_path (Optional[str]) – Credentials for connecting to appliance. If not provided, query the cluster management node for it. Default: None.
num_csx (int) – Number of Cerebras Systems to run on. Default: 1.
max_wgt_servers (int) – Number of weight servers to support run. Default: 24.
max_act_per_csx (int) – Number of activation servers per system. Default: 1.
num_workers_per_csx (int) – Number of streaming workers per system. Default: 1.
transfer_processes (int) – Number of processes to transfer data to/from appliance. Default: 5.
job_time_sec (int) – Time limit for the appliance jobs, not including the queue time. Default: None.
mount_dirs (List[str]) – Local storage to mount to appliance (ex. training data). Default: None.
python_paths (List[str]) – A list of path that worker pods respect as PYTHONPATH in addition to the PYTHONPATH set in the container image. Default: None.
job_labels (List[str]) – A list of equal-sign-separated key-value pairs that get applied as part of job metadata. Default: None.
debug_args (DebugArgs) – Optional debugging arguments object. Default: None.
precision_opt_level (int) – The precision optimization level. Default: 1.

`numpy` utilities#

`from_numpy`#

`to_numpy`#

Tensorboard utilities#

Note

Scalars summarized using this API are only visible in Tensorboard if a SummaryWriter was passed to the DataExecutor object.

Note

Tensors summarized using this API are only visible if a SummaryWriter was passed to the DataExecutor object.

class cerebras_pytorch.utils.tensorboard.SummaryWriter#

Thin wrapper around torch.utils.tensorboard.SummaryWriter

Additional features include the ability to add a tensor summary

Parameters

base_step – The base step to use in summarize_{scalar,tensor} functions
*args – Any other positional and keyword arguments are forwarded directly to the base class
**kwargs –
Any other positional and keyword arguments are forwarded directly to the base class

__init__(*args: Any, **kwargs: Any) → None#

add_tensor()#

class cerebras_pytorch.utils.tensorboard.SummaryReader#

Class for reading summaries saved using the SummaryWriter

Parameters

log_dir – The directory at which the event files can be found
kwargs – The remaining keyword arguments are forwarded to the internal EventAccumulator object

__init__(*args: Any, **kwargs: Any) → None#

read_scalar()#

read_tensor()#

reload()#

scalar_names()#

tensor_names()#

Dataloader benchmark utilities#

cerebras_pytorch.utils.benchmark.benchmark_dataloader(input_fn: Callable[[...], Iterable], num_epochs: Optional[int] = None, steps_per_epoch: Optional[int] = None, sampling_frequency: Optional[int] = None, profile_activities: Optional[List[str]] = None, print_metrics: bool = True) → cerebras_pytorch.utils.benchmark.utils.dataloader.Metrics[source]#

Utility to benchmark a dataloader.

Parameters

input_fn – Function that creates and returns a dataloader.
num_epochs – Number of epochs to iterate over the dataloader. If None, the dataloader is only iterated for one epoch.
steps_per_epoch – Number of steps to iterate over the dataloader in each epoch. If None, the dataloader is iterated in its entirety.
sampling_frequency – Frequency at which to sample metrics. If None, a default value of 100 (i.e. every 100 steps) is used. First step of each epoch is always sampled.
profile_activities – List of optional activities to profile. If None, no extra activities are profiled. Note that these may incur additional overhead and could affect overall performance of the dataloader, especially if the sampling frequency is high.
print_metrics – Whether to pretty print the final metrics to console.

Returns

Metrics for the dataloader experiment.

class cerebras_pytorch.utils.benchmark.utils.dataloader.Metrics[source]#

Metrics for a single dataloader experiment.

Parameters

dataloader_build_time – Time to build the dataloader.
epoch_metrics – List of metrics for each epoch.
batch_specs – Mapping between unique batch specs found and their occurences.
total_time – Total time to iterate through all epochs.
global_rate – Overall global rate in steps/second.
is_partial – Whether the metrics are partial. This can happen if the benchmark is interrupted in the middle of execution.
start_time_ns – Time at which the experiment started.
end_time_ns – Time at which the experiment ended.

batch_specs: Dict[cerebras_pytorch.utils.benchmark.utils.dataloader.BatchSpec, cerebras_pytorch.utils.benchmark.utils.dataloader.BatchSpecOccurence]#

dataloader_build_time: numpy.timedelta64#

end_time_ns: int = 0#

epoch_metrics: List[cerebras_pytorch.utils.benchmark.utils.dataloader.EpochMetrics]#

global_rate: float = 0.0#

property global_sample_rate: Optional[float]#

Returns the overall global rate in samples/second.

Note that this value only exists if all batches have the exact same structure, dtypes, and shapes. Otherwise, this value is None.

is_partial: bool = True#

start_time_ns: int#

property total_steps: int#: Returns the total number of steps across all epochs.

total_time: numpy.timedelta64#

class cerebras_pytorch.utils.benchmark.utils.dataloader.EpochMetrics[source]#

Metrics for a single epoch of a dataloader experiment.

Parameters

iterator_creation – Time to create the dataloader iterator.
iteration_time – Time to iterate the entire epoch excluding the creation of the iterator.
total_steps – Total number of steps in the epoch.
batch_metrics – List of metrics for batches generated in the epoch.
start_time_ns – Time at which the epoch started.
end_time_ns – Time at which the epoch ended.

batch_metrics: List[cerebras_pytorch.utils.benchmark.utils.dataloader.BatchMetrics]#

end_time_ns: int = 0#

iteration_time: numpy.timedelta64#

iterator_creation: numpy.timedelta64#

start_time_ns: int#

total_steps: int = 0#

property total_time: numpy.timedelta64#: Returns the total time to create and iterate the epoch.

class cerebras_pytorch.utils.benchmark.utils.dataloader.BatchMetrics[source]#

Metrics for a single batch of a dataloader experiment.

Parameters

epoch_step – Epoch step at which the batch was generated.
global_step – Global step at which the batch was generated.
local_rate – Local rate (in steps/second) at the sampling frequency. This is the instantaneous rate (relative to previous batch) at which the batch was generated.
global_rate – global rate (in steps/second) at the sampling frequency. This is the global rate since the start of the iterating epochs.
profile_activities – Dictionary of profile activities and their values.
sampling_time_ns – Time at which the batch was sampled.

epoch_step: int#

global_rate: float#

global_step: int#

local_rate: float#

profile_activities: Dict[str, Any]#

sampling_time_ns: int#

Cerebras PyTorch API

cerebras_pytorch.amp

cerebras_pytorch package#

Backend related functions#

backend#

current_backend#

current_torch_device#

use_cs#

Compile related functions#

compile#

trace#

Creation Ops#

full#

full_like#

ones#

ones_like#

zeros#

zeros_like#

Checkpoint Saving/Loading utilities#

Data Utilities#

utils.data.DataLoader#

utils.data.SyntheticDataset#

utils.data.DataExecutor#

utils.data.RestartableDataLoader#

utils.data.DataLoaderCheckpoint#

get_worker_state#

utils.CSConfig#

numpy utilities#

from_numpy#

to_numpy#

Step Closure related utilities#

step_closure#

checkpoint_closure#

Tensorboard utilities#

Dataloader benchmark utilities#

`cerebras_pytorch` package#

`backend`#

`current_backend`#

`current_torch_device`#

`use_cs`#

`compile`#

`trace`#

`full`#

`full_like`#

`ones`#

`ones_like`#

`zeros`#

`zeros_like`#

`utils.data.DataLoader`#

`utils.data.SyntheticDataset`#

`utils.data.DataExecutor`#

`utils.data.RestartableDataLoader`#

`utils.data.DataLoaderCheckpoint`#

`get_worker_state`#

`utils.CSConfig`#

`numpy` utilities#

`from_numpy`#

`to_numpy`#

`step_closure`#

`checkpoint_closure`#