DocuMint: Docstring Generation for Python using Small Language Models
Paper • 2405.10243 • Published
response stringlengths 1 33.1k | instruction stringlengths 22 582k |
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Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Gets a set of train, valid, and test dataloaders for a particular fold
Args:
accelerator (`Accelerator`):
The main `Accelerator` object
train_idxs (list of `int`):
The split indices for the training dataset
valid_idxs (list of `int`):
The split indices for the validation dataset
... | def get_fold_dataloaders(
accelerator: Accelerator, dataset: DatasetDict, train_idxs: List[int], valid_idxs: List[int], batch_size: int = 16
):
"""
Gets a set of train, valid, and test dataloaders for a particular fold
Args:
accelerator (`Accelerator`):
The main `Accelerator` object... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders. | def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
Th... |
A context manager under which models are initialized with all parameters on the meta device, therefore creating an
empty model. Useful when just initializing the model would blow the available RAM.
Args:
include_buffers (`bool`, *optional*):
Whether or not to also put all buffers on the meta device while i... | def init_empty_weights(include_buffers: bool = None):
"""
A context manager under which models are initialized with all parameters on the meta device, therefore creating an
empty model. Useful when just initializing the model would blow the available RAM.
Args:
include_buffers (`bool`, *optiona... |
A context manager under which models are initialized with all parameters on the specified device.
Args:
device (`torch.device`):
Device to initialize all parameters on.
include_buffers (`bool`, *optional*):
Whether or not to also put all buffers on the meta device while initializing.
Example:
... | def init_on_device(device: torch.device, include_buffers: bool = None):
"""
A context manager under which models are initialized with all parameters on the specified device.
Args:
device (`torch.device`):
Device to initialize all parameters on.
include_buffers (`bool`, *optional... |
Activates full CPU offload for a model. As a result, all parameters of the model will be offloaded and only one
copy of the state dict of the model will be kept. During the forward pass, parameters will be extracted from that
state dict and put on the execution device passed as they are needed, then offloaded again.
A... | def cpu_offload(
model: nn.Module,
execution_device: Optional[torch.device] = None,
offload_buffers: bool = False,
state_dict: Optional[Dict[str, torch.Tensor]] = None,
preload_module_classes: Optional[List[str]] = None,
):
"""
Activates full CPU offload for a model. As a result, all paramet... |
Offloads a model on the CPU and puts it back to an execution device when executed. The difference with
[`cpu_offload`] is that the model stays on the execution device after the forward and is only offloaded again when
the `offload` method of the returned `hook` is called. Useful for pipelines running a model in a loop.... | def cpu_offload_with_hook(
model: torch.nn.Module,
execution_device: Optional[Union[int, str, torch.device]] = None,
prev_module_hook: Optional[UserCpuOffloadHook] = None,
):
"""
Offloads a model on the CPU and puts it back to an execution device when executed. The difference with
[`cpu_offload`... |
Activates full disk offload for a model. As a result, all parameters of the model will be offloaded as
memory-mapped array in a given folder. During the forward pass, parameters will be accessed from that folder and
put on the execution device passed as they are needed, then offloaded again.
Args:
model (`torch.nn... | def disk_offload(
model: nn.Module,
offload_dir: Union[str, os.PathLike],
execution_device: Optional[torch.device] = None,
offload_buffers: bool = False,
preload_module_classes: Optional[List[str]] = None,
):
"""
Activates full disk offload for a model. As a result, all parameters of the mod... |
Dispatches a model according to a given device map. Layers of the model might be spread across GPUs, offloaded on
the CPU or even the disk.
Args:
model (`torch.nn.Module`):
The model to dispatch.
device_map (`Dict[str, Union[str, int, torch.device]]`):
A dictionary mapping module names in the m... | def dispatch_model(
model: nn.Module,
device_map: Dict[str, Union[str, int, torch.device]],
main_device: Optional[torch.device] = None,
state_dict: Optional[Dict[str, torch.Tensor]] = None,
offload_dir: Optional[Union[str, os.PathLike]] = None,
offload_index: Optional[Dict[str, str]] = None,
... |
Loads a (potentially sharded) checkpoint inside a model, potentially sending weights to a given device as they are
loaded and adds the various hooks that will make this model run properly (even if split across devices).
Args:
model (`torch.nn.Module`): The model in which we want to load a checkpoint.
checkpoin... | def load_checkpoint_and_dispatch(
model: nn.Module,
checkpoint: Union[str, os.PathLike],
device_map: Optional[Union[str, Dict[str, Union[int, str, torch.device]]]] = None,
max_memory: Optional[Dict[Union[int, str], Union[int, str]]] = None,
no_split_module_classes: Optional[List[str]] = None,
of... |
Saves the current states of the models, optimizers, scaler, and RNG generators to a given directory.
<Tip>
If `safe_serialization` is `True`, models will be saved with `safetensors` while the rest are saved using native
`pickle`.
</Tip>
Args:
output_dir (`str` or `os.PathLike`):
The name of the folder t... | def save_accelerator_state(
output_dir: str,
model_states: List[dict],
optimizers: list,
schedulers: list,
dataloaders: list,
process_index: int,
scaler: GradScaler = None,
save_on_each_node: bool = False,
safe_serialization: bool = True,
):
"""
Saves the current states of th... |
Loads states of the models, optimizers, scaler, and RNG generators from a given directory.
Args:
input_dir (`str` or `os.PathLike`):
The name of the folder to load all relevant weights and states.
models (`List[torch.nn.Module]`):
A list of model instances
optimizers (`List[torch.optim.Opti... | def load_accelerator_state(
input_dir,
models,
optimizers,
schedulers,
dataloaders,
process_index,
scaler=None,
map_location=None,
**load_model_func_kwargs,
):
"""
Loads states of the models, optimizers, scaler, and RNG generators from a given directory.
Args:
in... |
Saves the state of `obj` to `{path}/custom_checkpoint_{index}.pkl` | def save_custom_state(obj, path, index: int = 0, save_on_each_node: bool = False):
"""
Saves the state of `obj` to `{path}/custom_checkpoint_{index}.pkl`
"""
# Should this be the right way to get a qual_name type value from `obj`?
save_location = Path(path) / f"custom_checkpoint_{index}.pkl"
log... |
Loads the state of `obj` at `{path}/custom_checkpoint_{index}.pkl` | def load_custom_state(obj, path, index: int = 0):
"""
Loads the state of `obj` at `{path}/custom_checkpoint_{index}.pkl`
"""
load_location = f"{path}/custom_checkpoint_{index}.pkl"
logger.info(f"Loading the state of {get_pretty_name(obj)} from {load_location}")
obj.load_state_dict(torch.load(loa... |
Get the sampler associated to the dataloader
Args:
dataloader (`torch.utils.data.dataloader.DataLoader`):
The data loader to split across several devices.
Returns:
`torch.utils.data.Sampler`: The sampler associated to the dataloader | def get_sampler(dataloader):
"""
Get the sampler associated to the dataloader
Args:
dataloader (`torch.utils.data.dataloader.DataLoader`):
The data loader to split across several devices.
Returns:
`torch.utils.data.Sampler`: The sampler associated to the dataloader
"""
... |
Wraps a PyTorch `DataLoader` to generate batches for one of the processes only.
Depending on the value of the `drop_last` attribute of the `dataloader` passed, it will either stop the iteration
at the first batch that would be too small / not present on all processes or loop with indices from the beginning.
Args:
... | def prepare_data_loader(
dataloader: DataLoader,
device: Optional[torch.device] = None,
num_processes: Optional[int] = None,
process_index: Optional[int] = None,
split_batches: bool = False,
put_on_device: bool = False,
rng_types: Optional[List[Union[str, RNGType]]] = None,
dispatch_batc... |
Creates a `torch.utils.data.DataLoader` that will efficiently skip the first `num_batches`. | def skip_first_batches(dataloader, num_batches=0):
"""
Creates a `torch.utils.data.DataLoader` that will efficiently skip the first `num_batches`.
"""
dataset = dataloader.dataset
sampler_is_batch_sampler = False
if isinstance(dataset, IterableDataset):
new_batch_sampler = None
else... |
Adds a hook to a given module. This will rewrite the `forward` method of the module to include the hook, to remove
this behavior and restore the original `forward` method, use `remove_hook_from_module`.
<Tip warning={true}>
If the module already contains a hook, this will replace it with the new hook passed by defaul... | def add_hook_to_module(module: nn.Module, hook: ModelHook, append: bool = False):
"""
Adds a hook to a given module. This will rewrite the `forward` method of the module to include the hook, to remove
this behavior and restore the original `forward` method, use `remove_hook_from_module`.
<Tip warning={... |
Removes any hook attached to a module via `add_hook_to_module`.
Args:
module (`torch.nn.Module`): The module to attach a hook to.
recurse (`bool`, **optional**): Whether to remove the hooks recursively
Returns:
`torch.nn.Module`: The same module, with the hook detached (the module is modified in place, so... | def remove_hook_from_module(module: nn.Module, recurse=False):
"""
Removes any hook attached to a module via `add_hook_to_module`.
Args:
module (`torch.nn.Module`): The module to attach a hook to.
recurse (`bool`, **optional**): Whether to remove the hooks recursively
Returns:
... |
Recursively attaches `AlignDevicesHook` to all submodules of a given model to make sure they have the right
execution device
Args:
module (`torch.nn.Module`):
The module where we want to attach the hooks.
execution_device (`int`, `str` or `torch.device`):
The device on which inputs and model we... | def attach_execution_device_hook(
module: torch.nn.Module,
execution_device: Union[int, str, torch.device],
skip_keys: Optional[Union[str, List[str]]] = None,
preload_module_classes: Optional[List[str]] = None,
tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None,
):
"""... |
Recursively attaches `AlignDevicesHook` to all submodules of a given model that have direct parameters and/or
buffers.
Args:
module (`torch.nn.Module`):
The module where we want to attach the hooks.
execution_device (`torch.device`, *optional*):
The device on which inputs and model weights shou... | def attach_align_device_hook(
module: torch.nn.Module,
execution_device: Optional[torch.device] = None,
offload: bool = False,
weights_map: Optional[Mapping] = None,
offload_buffers: bool = False,
module_name: str = "",
skip_keys: Optional[Union[str, List[str]]] = None,
preload_module_cl... |
Recursively removes all hooks attached on the submodules of a given model.
Args:
module (`torch.nn.Module`): The module on which to remove all hooks. | def remove_hook_from_submodules(module: nn.Module):
"""
Recursively removes all hooks attached on the submodules of a given model.
Args:
module (`torch.nn.Module`): The module on which to remove all hooks.
"""
remove_hook_from_module(module)
for child in module.children():
remov... |
Attaches `AlignDevicesHook` to all blocks of a given model as needed.
Args:
module (`torch.nn.Module`):
The module where we want to attach the hooks.
execution_device (`torch.device` or `Dict[str, torch.device]`, *optional*):
The device on which inputs and model weights should be placed before ... | def attach_align_device_hook_on_blocks(
module: nn.Module,
execution_device: Optional[Union[torch.device, Dict[str, torch.device]]] = None,
offload: Union[bool, Dict[str, bool]] = False,
weights_map: Mapping = None,
offload_buffers: bool = False,
module_name: str = "",
skip_keys: Optional[Un... |
Calculates the device map for `model` with an offset for PiPPy | def generate_device_map(model, num_processes: int = 1, no_split_module_classes=None, max_memory: dict = None):
"""
Calculates the device map for `model` with an offset for PiPPy
"""
if num_processes == 1:
return infer_auto_device_map(model, no_split_module_classes=no_split_module_classes, clean_... |
Attaches the split points to the model based on `self.device_map` and generates a `PipelineStage`. Requires passing
in needed `args` and `kwargs` as the model needs on the CPU.
Users can pass in custom `num_chunks` as an optional hyper-parameter. By default will use
`AcceleratorState.num_processes` | def build_pipeline(model, split_points, args, kwargs, num_chunks):
"""
Attaches the split points to the model based on `self.device_map` and generates a `PipelineStage`. Requires passing
in needed `args` and `kwargs` as the model needs on the CPU.
Users can pass in custom `num_chunks` as an optional hy... |
Wraps `model` for pipeline parallel inference.
Args:
model (`torch.nn.Module`):
A model we want to split for pipeline-parallel inference
split_points (`str` or `List[str]`, defaults to 'auto'):
How to generate the split points and chunk the model across each GPU. 'auto' will find the best balan... | def prepare_pippy(
model,
split_points: Optional[Union[str, List[str]]] = "auto",
no_split_module_classes: Optional[List[str]] = None,
example_args: Optional[Tuple[Any]] = (),
example_kwargs: Optional[Dict[str, Any]] = None,
num_chunks: Optional[int] = None,
gather_output: Optional[bool] = F... |
Verify a `PartialState` can be initialized. | def test_launch():
"Verify a `PartialState` can be initialized."
_ = PartialState() |
Launches a training function, using several processes or multiple nodes if it's possible in the current environment
(TPU with multiple cores for instance).
<Tip warning={true}>
To use this function absolutely zero calls to a CUDA device must be made in the notebook session before calling. If
any have been made, you w... | def notebook_launcher(
function,
args=(),
num_processes=None,
mixed_precision="no",
use_port="29500",
master_addr="127.0.0.1",
node_rank=0,
num_nodes=1,
):
"""
Launches a training function, using several processes or multiple nodes if it's possible in the current environment
... |
Launches a training function using several processes on CPU for debugging purposes.
<Tip warning={true}>
This function is provided for internal testing and debugging, but it's not intended for real trainings. It will
only use the CPU.
</Tip>
Args:
function (`Callable`):
The training function to execute.... | def debug_launcher(function, args=(), num_processes=2):
"""
Launches a training function using several processes on CPU for debugging purposes.
<Tip warning={true}>
This function is provided for internal testing and debugging, but it's not intended for real trainings. It will
only use the CPU.
... |
Returns a `logging.Logger` for `name` that can handle multiprocessing.
If a log should be called on all processes, pass `main_process_only=False` If a log should be called on all
processes and in order, also pass `in_order=True`
Args:
name (`str`):
The name for the logger, such as `__file__`
log_level... | def get_logger(name: str, log_level: str = None):
"""
Returns a `logging.Logger` for `name` that can handle multiprocessing.
If a log should be called on all processes, pass `main_process_only=False` If a log should be called on all
processes and in order, also pass `in_order=True`
Args:
n... |
Checks if the `AcceleratorState` has been initialized from `Accelerator`. Same as `AcceleratorState.initialized`,
but works as a module method. | def is_initialized() -> bool:
"""
Checks if the `AcceleratorState` has been initialized from `Accelerator`. Same as `AcceleratorState.initialized`,
but works as a module method.
"""
return AcceleratorState._shared_state != {} |
Decorator to selectively run the decorated function on the main process only based on the `main_process_only`
attribute in a class.
Checks at function execution rather than initialization time, not triggering the initialization of the
`PartialState`. | def on_main_process(function):
"""
Decorator to selectively run the decorated function on the main process only based on the `main_process_only`
attribute in a class.
Checks at function execution rather than initialization time, not triggering the initialization of the
`PartialState`.
"""
... |
Returns a list of all supported available trackers in the system | def get_available_trackers():
"Returns a list of all supported available trackers in the system"
return _available_trackers |
Takes in a list of potential tracker types and checks that:
- The tracker wanted is available in that environment
- Filters out repeats of tracker types
- If `all` is in `log_with`, will return all trackers in the environment
- If a tracker requires a `logging_dir`, ensures that `logging_dir` is not `No... | def filter_trackers(
log_with: List[Union[str, LoggerType, GeneralTracker]],
logging_dir: Union[str, os.PathLike] = None,
):
"""
Takes in a list of potential tracker types and checks that:
- The tracker wanted is available in that environment
- Filters out repeats of tracker types
... |
Verifies that the model is on the hub and returns the model info. | def verify_on_hub(repo: str, token: str = None):
"Verifies that the model is on the hub and returns the model info."
try:
return model_info(repo, token=token)
except GatedRepoError:
return "gated"
except RepositoryNotFoundError:
return "repo" |
Checks what library spawned `error` when a model is not found | def check_has_model(error):
"""
Checks what library spawned `error` when a model is not found
"""
if is_timm_available() and isinstance(error, RuntimeError) and "Unknown model" in error.args[0]:
return "timm"
elif (
is_transformers_available()
and isinstance(error, OSError)
... |
Creates an empty model from its parent library on the `Hub` to calculate the overall memory consumption.
Args:
model_name (`str`):
The model name on the Hub
library_name (`str`):
The library the model has an integration with, such as `transformers`. Will be used if `model_name` has no
m... | def create_empty_model(model_name: str, library_name: str, trust_remote_code: bool = False, access_token: str = None):
"""
Creates an empty model from its parent library on the `Hub` to calculate the overall memory consumption.
Args:
model_name (`str`):
The model name on the Hub
... |
Creates a pretty table from a list of rows, minimal version of `tabulate`. | def create_ascii_table(headers: list, rows: list, title: str):
"Creates a pretty table from a list of rows, minimal version of `tabulate`."
sep_char, in_between = "│", "─"
column_widths = []
for i in range(len(headers)):
column_values = [row[i] for row in rows] + [headers[i]]
max_column_... |
Given an amount of `bytes` and `mixed_precision`, calculates how much training memory is needed for a batch size of
1.
Args:
bytes (`int`):
The size of the model being trained.
mixed_precision (`str`):
The mixed precision that would be ran.
msamp_config (`str`):
The msamp config to ... | def estimate_training_usage(bytes: int, mixed_precision: str, msamp_config: str = None) -> dict:
"""
Given an amount of `bytes` and `mixed_precision`, calculates how much training memory is needed for a batch size of
1.
Args:
bytes (`int`):
The size of the model being trained.
... |
Creates an empty model and gathers the data for the sizes | def gather_data(args):
"Creates an empty model and gathers the data for the sizes"
try:
model = create_empty_model(
args.model_name, library_name=args.library_name, trust_remote_code=args.trust_remote_code
)
except (RuntimeError, OSError) as e:
library = check_has_model(e... |
Finds all cases of - after the first two characters and changes them to _ | def clean_option(option):
"Finds all cases of - after the first two characters and changes them to _"
if option.startswith("--"):
return option[2:].replace("-", "_") |
Creates and saves a basic cluster config to be used on a local machine with potentially multiple GPUs. Will also
set CPU if it is a CPU-only machine.
Args:
mixed_precision (`str`, *optional*, defaults to "no"):
Mixed Precision to use. Should be one of "no", "fp16", or "bf16"
save_location (`str`, *opti... | def write_basic_config(mixed_precision="no", save_location: str = default_json_config_file, use_xpu: bool = False):
"""
Creates and saves a basic cluster config to be used on a local machine with potentially multiple GPUs. Will also
set CPU if it is a CPU-only machine.
Args:
mixed_precision (`s... |
Update an existing config file with the latest defaults while maintaining the old configuration. | def update_config(args):
"""
Update an existing config file with the latest defaults while maintaining the old configuration.
"""
config_file = args.config_file
if config_file is None and Path(default_config_file).exists():
config_file = default_config_file
elif not Path(config_file).ex... |
Context manager to hide the terminal cursor | def hide():
"Context manager to hide the terminal cursor"
try:
hide_cursor()
yield
finally:
show_cursor() |
Mark the function with the key code so it can be handled in the register | def mark(key: str):
"""
Mark the function with the key code so it can be handled in the register
"""
def decorator(func):
handle = getattr(func, "handle_key", [])
handle += [key]
func.handle_key = handle
return func
return decorator |
Mark the function with the key codes so it can be handled in the register | def mark_multiple(*keys: List[str]):
"""
Mark the function with the key codes so it can be handled in the register
"""
def decorator(func):
handle = getattr(func, "handle_key", [])
handle += keys
func.handle_key = handle
return func
return decorator |
Adds KeyHandler metaclass to the class | def register(cls):
"""Adds KeyHandler metaclass to the class"""
return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy()) |
Gets raw characters from inputs | def get_raw_chars():
"Gets raw characters from inputs"
if os.name == "nt":
import msvcrt
encoding = "mbcs"
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(WIN_CH_BUFFER) == 0:
# Read the keystroke
ch = msvcrt... |
Gets a character from the keyboard and returns the key code | def get_character():
"Gets a character from the keyboard and returns the key code"
char = get_raw_chars()
if ord(char) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(char) == KEYMAP["esc"]:
combo = get_raw_chars()
if ord(combo) == KEYMAP["mod_int"]:
... |
Extracts a function from `lines` of segmented source code with the name `name`.
Args:
lines (`List[str]`):
Source code of a script seperated by line.
name (`str`):
The name of the function to extract. Should be either `training_function` or `main` | def get_function_contents_by_name(lines: List[str], name: str):
"""
Extracts a function from `lines` of segmented source code with the name `name`.
Args:
lines (`List[str]`):
Source code of a script seperated by line.
name (`str`):
The name of the function to extract... |
Filters `lines` and removes any entries that start with a comment ('#') or is just a newline ('
')
Args:
lines (`List[str]`):
Source code of a script seperated by line.
| def clean_lines(lines: List[str]):
"""
Filters `lines` and removes any entries that start with a comment ('#') or is just a newline ('\n')
Args:
lines (`List[str]`):
Source code of a script seperated by line.
"""
return [line for line in lines if not line.lstrip().startswith("#"... |
Tests whether the additional code inside of `feature_filename` was implemented in `base_filename`. This should be
used when testing to see if `complete_*_.py` examples have all of the implementations from each of the
`examples/by_feature/*` scripts.
It utilizes `nlp_example.py` to extract out all of the repeated train... | def compare_against_test(base_filename: str, feature_filename: str, parser_only: bool, secondary_filename: str = None):
"""
Tests whether the additional code inside of `feature_filename` was implemented in `base_filename`. This should be
used when testing to see if `complete_*_.py` examples have all of the ... |
Wraps around `kwargs` to help simplify launching from `subprocess`.
Example:
```python
# returns ['accelerate', 'launch', '--num_processes=2', '--device_count=2']
get_launch_command(num_processes=2, device_count=2)
``` | def get_launch_command(**kwargs) -> list:
"""
Wraps around `kwargs` to help simplify launching from `subprocess`.
Example:
```python
# returns ['accelerate', 'launch', '--num_processes=2', '--device_count=2']
get_launch_command(num_processes=2, device_count=2)
```
"""
command = ["ac... |
Decorator that skips a test unconditionally | def skip(test_case):
"Decorator that skips a test unconditionally"
return unittest.skip("Test was skipped")(test_case) |
Decorator marking a test as slow. Slow tests are skipped by default. Set the RUN_SLOW environment variable to a
truthy value to run them. | def slow(test_case):
"""
Decorator marking a test as slow. Slow tests are skipped by default. Set the RUN_SLOW environment variable to a
truthy value to run them.
"""
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case) |
Decorator marking a test that must be only ran on the CPU. These tests are skipped when a GPU is available. | def require_cpu(test_case):
"""
Decorator marking a test that must be only ran on the CPU. These tests are skipped when a GPU is available.
"""
return unittest.skipUnless(torch_device == "cpu", "test requires only a CPU")(test_case) |
Decorator marking a test that requires a hardware accelerator backend. These tests are skipped when there are no
hardware accelerator available. | def require_non_cpu(test_case):
"""
Decorator marking a test that requires a hardware accelerator backend. These tests are skipped when there are no
hardware accelerator available.
"""
return unittest.skipUnless(torch_device != "cpu", "test requires a GPU")(test_case) |
Decorator marking a test that requires CUDA. These tests are skipped when there are no GPU available or when
TorchXLA is available. | def require_cuda(test_case):
"""
Decorator marking a test that requires CUDA. These tests are skipped when there are no GPU available or when
TorchXLA is available.
"""
return unittest.skipUnless(is_cuda_available() and not is_torch_xla_available(), "test requires a GPU")(test_case) |
Decorator marking a test that requires XPU. These tests are skipped when there are no XPU available. | def require_xpu(test_case):
"""
Decorator marking a test that requires XPU. These tests are skipped when there are no XPU available.
"""
return unittest.skipUnless(is_xpu_available(), "test requires a XPU")(test_case) |
Decorator marking a test that should be skipped for XPU. | def require_non_xpu(test_case):
"""
Decorator marking a test that should be skipped for XPU.
"""
return unittest.skipUnless(torch_device != "xpu", "test requires a non-XPU")(test_case) |
Decorator marking a test that requires MLU. These tests are skipped when there are no MLU available. | def require_mlu(test_case):
"""
Decorator marking a test that requires MLU. These tests are skipped when there are no MLU available.
"""
return unittest.skipUnless(is_mlu_available(), "test require a MLU")(test_case) |
Decorator marking a test that requires NPU. These tests are skipped when there are no NPU available. | def require_npu(test_case):
"""
Decorator marking a test that requires NPU. These tests are skipped when there are no NPU available.
"""
return unittest.skipUnless(is_npu_available(), "test require a NPU")(test_case) |
Decorator marking a test that requires MPS backend. These tests are skipped when torch doesn't support `mps`
backend. | def require_mps(test_case):
"""
Decorator marking a test that requires MPS backend. These tests are skipped when torch doesn't support `mps`
backend.
"""
return unittest.skipUnless(is_mps_available(), "test requires a `mps` backend support in `torch`")(test_case) |
Decorator marking a test that requires transformers and datasets. These tests are skipped when they are not. | def require_huggingface_suite(test_case):
"""
Decorator marking a test that requires transformers and datasets. These tests are skipped when they are not.
"""
return unittest.skipUnless(
is_transformers_available() and is_datasets_available(),
"test requires the Hugging Face suite",
... |
Decorator marking a test that requires transformers. These tests are skipped when they are not. | def require_transformers(test_case):
"""
Decorator marking a test that requires transformers. These tests are skipped when they are not.
"""
return unittest.skipUnless(is_transformers_available(), "test requires the transformers library")(test_case) |
End of preview. Expand in Data Studio
DocuMint Dataset
The DocuMint Dataset is a collection of 100,000 Python functions and their corresponding docstrings, extracted from popular open-source repositories in the Free and open-source software (FLOSS) ecosystem. This dataset was created to train the DocuMint model, a fine-tuned variant of Google's CodeGemma-2B that generates high-quality docstrings for Python code functions. For more information on the model and its training procedure, please refer to the model card.
Dataset Description
The dataset consists of JSON-formatted entries, each containing a Python function definition (as the instruction) and its associated docstring (as the response).
The functions were sourced from well-established and actively maintained projects, filtered based on metrics such as the number of contributors (> 50), commits (> 5k), stars (> 35k), and forks (> 10k).
Data Sources
- Released by: Bibek Poudel, Adam Cook, Sekou Traore, Shelah Ameli (University of Tennessee, Knoxville)
- Repository: GitHub
- Paper: DocuMint: Docstring Generation for Python using Small Language Models
Dataset Structure
Each entry in the dataset follows this structure:
{
"instruction": "def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):\n \"\"\"\n Creates a set of `DataLoader`s for the `glue` dataset,\n using \"bert-base-cased\" as the tokenizer.\n\n Args:\n accelerator (`Accelerator`):\n An `Accelerator` object\n batch_size (`int`, *optional*):\n The batch size for the train and validation DataLoaders.\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n datasets = load_dataset(\"glue\", \"mrpc\")\n\n def tokenize_function(examples):\n # max_length=None => use the model max length (it's actually the default)\n outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n return outputs\n\n # Apply the method we just defined to all the examples in all the splits of the dataset\n # starting with the main process first:\n with accelerator.main_process_first():\n tokenized_datasets = datasets.map(\n tokenize_function,\n batched=True,\n remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n )\n\n # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n # transformers library\n tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n\n def collate_fn(examples):\n # For Torchxla, it's best to pad everything to the same length or training will be very slow.\n max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None\n # When using mixed precision we want round multiples of 8/16\n if accelerator.mixed_precision == \"fp8\":\n pad_to_multiple_of = 16\n\t elif accelerator.mixed_precision != \"no\":\n pad_to_multiple_of = 8\n\t\t else:\n pad_to_multiple_of = None\n\n return tokenizer.pad(\n examples,\n padding=\"longest\",\n max_length=max_length,\n pad_to_multiple_of=pad_to_multiple_of,\n return_tensors=\"pt\",\n )\n\n # Instantiate dataloaders.\n train_dataloader = DataLoader(\n tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size, drop_last=True\n )\n eval_dataloader = DataLoader(\n tokenized_datasets[\"validation\"],\n shuffle=False,\n collate_fn=collate_fn,\n batch_size=EVAL_BATCH_SIZE,\n drop_last=(accelerator.mixed_precision == \"fp8\"),\n )\n\n return train_dataloader, eval_dataloader",
"response": "Creates a set of `DataLoader`s for the `glue` dataset,\nusing \"bert-base-cased\" as the tokenizer.\n\nArgs:\n accelerator (`Accelerator`):\n An `Accelerator` object\n batch_size (`int`, *optional*):\n The batch size for the train and validation DataLoaders."
}
Dataset Usecases
The DocuMint dataset can be used for various purposes related to code documentation and natural language processing tasks. Some potential usecases include:
- Training and evaluating models for automatic docstring generation
- Studying the characteristics and patterns of high-quality docstrings
- Analyzing the relationship between code structure and its corresponding documentation
- Developing tools for assisting developers in writing effective docstrings
- Conducting research on the challenges and best practices in code documentation
Researchers, developers, and organizations interested in improving code documentation quality and automating the process of docstring generation can benefit from this dataset.
Citation
BibTeX:
@article{poudel2024documint,
title={DocuMint: Docstring Generation for Python using Small Language Models},
author={Poudel, Bibek and Cook, Adam and Traore, Sekou and Ameli, Shelah},
journal={arXiv preprint arXiv:2405.10243},
year={2024}
}
Model Card Contact
- For questions or more information, please contact:
{bpoudel3,acook46,staore1,oameli}@vols.utk.edu
- Downloads last month
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Size of downloaded dataset files:
155 MB
Size of the auto-converted Parquet files:
55.7 MB
Number of rows:
100,392