code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def require_torch_gpu(test_case):
"""Decorator marking a test that requires CUDA and PyTorch."""
if torch_device != "cuda":
return unittest.skip("test requires CUDA")(test_case)
else:
return test_case | Decorator marking a test that requires CUDA and PyTorch. | require_torch_gpu | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def require_deepspeed(test_case):
"""
Decorator marking a test that requires deepspeed
"""
if not is_deepspeed_available():
return unittest.skip("test requires deepspeed")(test_case)
else:
return test_case |
Decorator marking a test that requires deepspeed
| require_deepspeed | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def require_bnb(test_case):
"""
Decorator marking a test that requires bitsandbytes
"""
if not is_bnb_available():
return unittest.skip("test requires bitsandbytes from https://github.com/facebookresearch/bitsandbytes")(
test_case
)
else:
return test_case |
Decorator marking a test that requires bitsandbytes
| require_bnb | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def require_bnb_non_decorator():
"""
Non-Decorator function that would skip a test if bitsandbytes is missing
"""
if not is_bnb_available():
raise SkipTest("Test requires bitsandbytes from https://github.com/facebookresearch/bitsandbytes") |
Non-Decorator function that would skip a test if bitsandbytes is missing
| require_bnb_non_decorator | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def set_seed(seed: int = 42):
"""
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch``
Args:
seed (:obj:`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
if is_torch_available():
torch.manual_seed(seed)
torch... |
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch``
Args:
seed (:obj:`int`): The seed to set.
| set_seed | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def get_gpu_count():
"""
Return the number of available gpus (regardless of whether torch or tf is used)
"""
if is_torch_available():
import torch
return torch.cuda.device_count()
else:
return 0 |
Return the number of available gpus (regardless of whether torch or tf is used)
| get_gpu_count | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def torch_assert_equal(actual, expected, **kwargs):
"""
compare two tensors or non-tensor numbers for their equality
"""
# assert_close was added around pt-1.9, it does better checks - e.g will check dimensions match
return torch.testing.assert_close(actual, expected, rtol=0.0, atol=0.0, **kwargs) |
compare two tensors or non-tensor numbers for their equality
| torch_assert_equal | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def torch_assert_close(actual, expected, **kwargs):
"""
compare two tensors or non-tensor numbers for their closeness.
"""
# assert_close was added around pt-1.9, it does better checks - e.g will check dimensions match
return torch.testing.assert_close(actual, expected, **kwargs) |
compare two tensors or non-tensor numbers for their closeness.
| torch_assert_close | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def require_torch_bf16(test_case):
"""Decorator marking a test that requires CUDA hardware supporting bf16 and PyTorch >= 1.9."""
if not is_torch_bf16_available():
return unittest.skip("test requires CUDA hardware supporting bf16 and PyTorch >= 1.9")(test_case)
else:
return test_case | Decorator marking a test that requires CUDA hardware supporting bf16 and PyTorch >= 1.9. | require_torch_bf16 | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def get_tests_dir(append_path=None):
"""
Args:
append_path: optional path to append to the tests dir path
Return:
The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
joined after the `tests` dir the former is provided.
"... |
Args:
append_path: optional path to append to the tests dir path
Return:
The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
joined after the `tests` dir the former is provided.
| get_tests_dir | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def ExtendSysPath(path: Union[str, os.PathLike]) -> Iterator[None]:
"""
Temporary add given path to `sys.path`.
Usage ::
with ExtendSysPath('/path/to/dir'):
mymodule = importlib.import_module('mymodule')
"""
path = os.fspath(path)
try:
sys.path.insert(0, path)
... |
Temporary add given path to `sys.path`.
Usage ::
with ExtendSysPath('/path/to/dir'):
mymodule = importlib.import_module('mymodule')
| ExtendSysPath | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def get_env(self):
"""
Return a copy of the ``os.environ`` object that sets up ``PYTHONPATH`` correctly. This is useful
for invoking external programs from the test suite - e.g. distributed training.
It always inserts ``.`` first, then ``./tests`` depending on the test suite type and
... |
Return a copy of the ``os.environ`` object that sets up ``PYTHONPATH`` correctly. This is useful
for invoking external programs from the test suite - e.g. distributed training.
It always inserts ``.`` first, then ``./tests`` depending on the test suite type and
finally the preset ``PYT... | get_env | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def get_auto_remove_tmp_dir(self, tmp_dir=None, before=None, after=None):
"""
Args:
tmp_dir (:obj:`string`, `optional`):
if :obj:`None`:
- a unique temporary path will be created
- sets ``before=True`` if ``before`` is :obj:`None`
... |
Args:
tmp_dir (:obj:`string`, `optional`):
if :obj:`None`:
- a unique temporary path will be created
- sets ``before=True`` if ``before`` is :obj:`None`
- sets ``after=True`` if ``after`` is :obj:`None`
else:
... | get_auto_remove_tmp_dir | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def mockenv_context(*remove, **update):
"""
Temporarily updates the ``os.environ`` dictionary in-place. Similar to mockenv
The ``os.environ`` dictionary is updated in-place so that the modification is sure to work in all situations.
Args:
remove: Environment variables to remove.
update: Di... |
Temporarily updates the ``os.environ`` dictionary in-place. Similar to mockenv
The ``os.environ`` dictionary is updated in-place so that the modification is sure to work in all situations.
Args:
remove: Environment variables to remove.
update: Dictionary of environment variables and values to... | mockenv_context | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def get_xdist_worker_id():
"""
when run under pytest-xdist returns the worker id (int), otherwise returns 0
"""
worker_id_string = os.environ.get("PYTEST_XDIST_WORKER", "gw0")
return int(worker_id_string[2:]) # strip "gw" |
when run under pytest-xdist returns the worker id (int), otherwise returns 0
| get_xdist_worker_id | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def pytest_addoption_shared(parser):
"""
This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there.
It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest`
option.
"""
option = "--make-repor... |
This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there.
It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest`
option.
| pytest_addoption_shared | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def pytest_terminal_summary_main(tr, id):
"""
Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current
directory. The report files are prefixed with the test suite name.
This function emulates --duration and -rA pytest arguments.
This function ... |
Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current
directory. The report files are prefixed with the test suite name.
This function emulates --duration and -rA pytest arguments.
This function is to be called from `conftest.py` via `pytest_te... | pytest_terminal_summary_main | python | huggingface/smollm | vision/m4/testing_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/testing_utils.py | Apache-2.0 |
def _compute_relaxed_vqa_accuracy(self, generated_texts_unique, answers_unique, normalize_text_fn):
"""
From https://aclanthology.org/2022.findings-acl.177.pdf
We use a relaxed accuracy measure for the numeric answers to allow a minor inaccuracy that may result from the automatic data extraction... |
From https://aclanthology.org/2022.findings-acl.177.pdf
We use a relaxed accuracy measure for the numeric answers to allow a minor inaccuracy that may result from the automatic data extraction process. We consider an answer to be correct if it is within 5% of the gold answer. For non-numeric answers, w... | _compute_relaxed_vqa_accuracy | python | huggingface/smollm | vision/m4/evaluation/custom_metrics/open_ended_vqa_metrics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/custom_metrics/open_ended_vqa_metrics.py | Apache-2.0 |
def vqa_normalize_text(text: str) -> str:
"""Process a text
Source: https://github.com/GT-Vision-Lab/VQA/blob/master/PythonEvaluationTools/vqaEvaluation/vqaEval.py
1. Conversion of characters to lower case
2. Replace breaking lines and tabulations by a white space
3. Replace punctuations by a white ... | Process a text
Source: https://github.com/GT-Vision-Lab/VQA/blob/master/PythonEvaluationTools/vqaEvaluation/vqaEval.py
1. Conversion of characters to lower case
2. Replace breaking lines and tabulations by a white space
3. Replace punctuations by a white space
4. Conversion of numbers written in let... | vqa_normalize_text | python | huggingface/smollm | vision/m4/evaluation/custom_metrics/utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/custom_metrics/utils.py | Apache-2.0 |
def check_is_number(string):
"""
Check if the given string is a number
"""
try:
_ = convert_to_number(string)
return True
except ValueError:
return False |
Check if the given string is a number
| check_is_number | python | huggingface/smollm | vision/m4/evaluation/custom_metrics/utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/custom_metrics/utils.py | Apache-2.0 |
def normalize_str_mmmu(string):
"""
Normalize the str to lower case and make them float numbers if possible.
"""
# check if characters in the string
# if number, numerize it.
string = string.strip()
if string.startswith("Answer: "):
string = string.replace("Answer: ", "")
is_nu... |
Normalize the str to lower case and make them float numbers if possible.
| normalize_str_mmmu | python | huggingface/smollm | vision/m4/evaluation/custom_metrics/utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/custom_metrics/utils.py | Apache-2.0 |
def extract_numbers_mmmu(string):
"""
Exact all forms of numbers from a string with regex.
"""
# Pattern for numbers with commas
pattern_commas = r"-?\b\d{1,3}(?:,\d{3})+\b"
# Pattern for scientific notation
pattern_scientific = r"-?\d+(?:\.\d+)?[eE][+-]?\d+"
# Pattern for simple numbers... |
Exact all forms of numbers from a string with regex.
| extract_numbers_mmmu | python | huggingface/smollm | vision/m4/evaluation/custom_metrics/utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/custom_metrics/utils.py | Apache-2.0 |
def parse_open_response_mmmu(response, normalize_text_fn):
"""
Parse the prediction from the generated response.
Return a list of predicted strings or numbers
"""
def get_key_subresponses(response):
key_responses = []
response = response.strip().strip(".").lower()
sub_respon... |
Parse the prediction from the generated response.
Return a list of predicted strings or numbers
| parse_open_response_mmmu | python | huggingface/smollm | vision/m4/evaluation/custom_metrics/utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/custom_metrics/utils.py | Apache-2.0 |
def _split_to_single_caption(caption):
"""This function is mainly used in Localized Narratives where a paragraph can contain
multiple relevant captions to a single image. We split the paragraph into multiple
captions and then return each as an individual sample.
"""
extended = []
captions = capt... | This function is mainly used in Localized Narratives where a paragraph can contain
multiple relevant captions to a single image. We split the paragraph into multiple
captions and then return each as an individual sample.
| _split_to_single_caption | python | huggingface/smollm | vision/m4/evaluation/scripts/create_sample_evaluation_datasets_simplified.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/scripts/create_sample_evaluation_datasets_simplified.py | Apache-2.0 |
def fetch_training_run(training_run_name):
"""
Fetch training run. There can only be one corresponding training run.
If not, double check the tags (killed, failed, etc.)
"""
matching_runs = []
runs = api.runs(f"{args.wandb_entity}/{args.wandb_training_project}")
... |
Fetch training run. There can only be one corresponding training run.
If not, double check the tags (killed, failed, etc.)
| fetch_training_run | python | huggingface/smollm | vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | Apache-2.0 |
def fetch_evaluation_run(evaluation_run_name):
"""
Fetch evaluation run. There can only be one corresponding evaluation run at most.
If not, double check the tags (killed, failed, etc.)
"""
matching_runs = []
runs = api.runs(f"{args.wandb_entity}/{args.wandb_eval_project... |
Fetch evaluation run. There can only be one corresponding evaluation run at most.
If not, double check the tags (killed, failed, etc.)
| fetch_evaluation_run | python | huggingface/smollm | vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | Apache-2.0 |
def get_logged_eval_values(evaluation_run):
"""
If `evaluation_run` already exists, get the already logged values into a dictionary.
"""
logged_evaluation_values = defaultdict()
if evaluation_run is not None:
for row in evaluation_run.scan_history():
... |
If `evaluation_run` already exists, get the already logged values into a dictionary.
| get_logged_eval_values | python | huggingface/smollm | vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | Apache-2.0 |
def get_evaluations_values_from_json():
"""
Load all values from the json file
"""
evaluation_values = defaultdict(lambda: defaultdict())
for evaluation_jsonl_file in args.evaluation_jsonl_files:
with open(evaluation_jsonl_file, "r") as f:
for line in ... |
Load all values from the json file
| get_evaluations_values_from_json | python | huggingface/smollm | vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | Apache-2.0 |
def convert_training_run_to_dict(training_run):
"""
Get all the logged values from the training into a dictionary.
"""
training_history = training_run.scan_history()
d = defaultdict(dict)
for row in training_history:
if "num_opt_steps" not in row:
... |
Get all the logged values from the training into a dictionary.
| convert_training_run_to_dict | python | huggingface/smollm | vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | https://github.com/huggingface/smollm/blob/master/vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py | Apache-2.0 |
def from_pretrained(cls, *model_args, is_resume=False, new_model=False, **kwargs):
"""
Use this method when loading an already pretrained vloom model - either from a checkpoint or from hub.
For creating an untrained model use `pretrained_models` instead.
"""
# config is:
... |
Use this method when loading an already pretrained vloom model - either from a checkpoint or from hub.
For creating an untrained model use `pretrained_models` instead.
| from_pretrained | python | huggingface/smollm | vision/m4/models/custom_modules.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/custom_modules.py | Apache-2.0 |
def __init__(
self,
num_embeddings,
num_additional_embeddings,
embedding_dim,
partially_freeze=False,
device=None,
dtype=None,
padding_idx=None,
**kwargs,
) -> None:
"""
num_additional_embeddings: int. Number of additional embed... |
num_additional_embeddings: int. Number of additional embeddings. Only useful when you `partially_freeze=True`.
partially_freeze: bool. If True, the regular `weight` will be frozen. `additional_weight` is never frozen.
Note: there are a lot of other parameters to initialize a standard `nn.Embed... | __init__ | python | huggingface/smollm | vision/m4/models/custom_modules.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/custom_modules.py | Apache-2.0 |
def forward(self, input_ids):
"""
we have 2 embeddings, with different indices - one pretrained self.weight and another
self.additional_embedding.weight that is being trained.
in order to make a lookup of the input ids, we:
1. find out the indices of the entries belonging to the... |
we have 2 embeddings, with different indices - one pretrained self.weight and another
self.additional_embedding.weight that is being trained.
in order to make a lookup of the input ids, we:
1. find out the indices of the entries belonging to the 2nd embedding
2. extract those v... | forward | python | huggingface/smollm | vision/m4/models/custom_modules.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/custom_modules.py | Apache-2.0 |
def __init__(
self,
in_features: int,
out_features: int,
out_additional_features: int = 0,
bias: bool = True,
partially_freeze: bool = True,
device=None,
dtype=None,
) -> None:
"""
out_additional_features: int. Number of additional trai... |
out_additional_features: int. Number of additional trainable dimensions. Only makes sense when `partially_freeze=True`.
partially_freeze: bool. If True, the regular `weight` will be frozen and extra parameters (if any) will be trainable. If False, default to the regular behavior of nn.Linear.
| __init__ | python | huggingface/smollm | vision/m4/models/custom_modules.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/custom_modules.py | Apache-2.0 |
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.out_features,
self.out_additional_feature... | Overwriting `nn.Linear.extra_repr` to include new parameters. | extra_repr | python | huggingface/smollm | vision/m4/models/custom_modules.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/custom_modules.py | Apache-2.0 |
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
... |
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
| to_dict | python | huggingface/smollm | vision/m4/models/idefics/configuration_idefics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/idefics/configuration_idefics.py | Apache-2.0 |
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.a... |
Make causal mask used for bi-directional self-attention.
| _make_causal_mask | python | huggingface/smollm | vision/m4/models/idefics/modeling_idefics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/idefics/modeling_idefics.py | Apache-2.0 |
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1) | Rotates half the hidden dims of the input. | rotate_half | python | huggingface/smollm | vision/m4/models/idefics/modeling_idefics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/idefics/modeling_idefics.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[boo... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values... | forward | python | huggingface/smollm | vision/m4/models/idefics/modeling_idefics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/idefics/modeling_idefics.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[b... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values... | forward | python | huggingface/smollm | vision/m4/models/idefics/modeling_idefics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/idefics/modeling_idefics.py | Apache-2.0 |
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
"""
output_embeddings = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getattr(sel... |
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
| tie_weights | python | huggingface/smollm | vision/m4/models/idefics/modeling_idefics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/idefics/modeling_idefics.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pix... |
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `... | forward | python | huggingface/smollm | vision/m4/models/idefics/modeling_idefics.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/idefics/modeling_idefics.py | Apache-2.0 |
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_... |
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
| repeat_kv | python | huggingface/smollm | vision/m4/models/perceiver/perceiver.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/perceiver/perceiver.py | Apache-2.0 |
def __init__(self, config) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.perceiver_config.resampler_n... | Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents` | __init__ | python | huggingface/smollm | vision/m4/models/perceiver/perceiver.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/perceiver/perceiver.py | Apache-2.0 |
def forward(
self,
latents: torch.Tensor,
context: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cach... |
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
:param context: Tensor of shape [bsz, seq, embed_dim] representing long-form context to resample.
:param latents: Tensor of shape [bsz, n_latents, embed_dim] representing fixed length latents to c... | forward | python | huggingface/smollm | vision/m4/models/perceiver/perceiver.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/perceiver/perceiver.py | Apache-2.0 |
def _flash_attention_forward(
self,
query_states,
key_states,
value_states,
attention_mask,
query_length,
dropout=0.0,
softmax_scale=None,
use_sliding_windows=False,
):
"""
Calls the forward method of Flash Attention - if the in... |
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be pa... | _flash_attention_forward | python | huggingface/smollm | vision/m4/models/perceiver/perceiver.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/perceiver/perceiver.py | Apache-2.0 |
def forward(
self,
latents: torch.Tensor,
context: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
... |
Args:
latents (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
context (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(... | forward | python | huggingface/smollm | vision/m4/models/perceiver/perceiver.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/perceiver/perceiver.py | Apache-2.0 |
def __init__(
self,
config,
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a... |
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a Tensor of shape [bsz, n_latents, embed_dim].
:param embed_dim... | __init__ | python | huggingface/smollm | vision/m4/models/perceiver/perceiver.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/perceiver/perceiver.py | Apache-2.0 |
def retrieve_idx_closest_examples(ref_embedding, embeddings_to_compare, num_examples):
"Returns the indices of the `num_examples` closest embeddings in ascending order"
sim = np.dot(embeddings_to_compare, ref_embedding)
# We can achieve linear complexity because we don't need to sort... | Returns the indices of the `num_examples` closest embeddings in ascending order | retrieve_idx_closest_examples | python | huggingface/smollm | vision/m4/models/vgpt2/evaluation_captioning_in_context_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/evaluation_captioning_in_context_vgpt2.py | Apache-2.0 |
def prepare_dataset(self, exs: Dict, **kwargs) -> Dict:
"""
Prepare batch of examples.
Each example (X, y) where y is among (y1, y2, ..., yN) - the labels options -
is turned into [(X, y1), (X, y2), ... (X, yN)].
"""
support_dataset: Dataset = kwargs["support_dataset"]
... |
Prepare batch of examples.
Each example (X, y) where y is among (y1, y2, ..., yN) - the labels options -
is turned into [(X, y1), (X, y2), ... (X, yN)].
| prepare_dataset | python | huggingface/smollm | vision/m4/models/vgpt2/evaluation_classification_in_context_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/evaluation_classification_in_context_vgpt2.py | Apache-2.0 |
def retrieve_idx_closest_examples(ref_embedding, embeddings_to_compare, num_examples):
"Returns the indices of the `num_examples` closest embeddings in ascending order"
sim = np.dot(embeddings_to_compare, ref_embedding)
# We can achieve linear complexity because we don't need to sort... | Returns the indices of the `num_examples` closest embeddings in ascending order | retrieve_idx_closest_examples | python | huggingface/smollm | vision/m4/models/vgpt2/evaluation_classification_in_context_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/evaluation_classification_in_context_vgpt2.py | Apache-2.0 |
def prepare_dataset(self, exs: Dict, **kwargs) -> Dict:
"""
Prepare batch of examples.
Each example (X, y) where y is among (y1, y2, ..., yN) - the labels options -
is turned into [(X, y1), (X, y2), ... (X, yN)].
"""
support_dataset: Dataset = kwargs["support_dataset"]
... |
Prepare batch of examples.
Each example (X, y) where y is among (y1, y2, ..., yN) - the labels options -
is turned into [(X, y1), (X, y2), ... (X, yN)].
| prepare_dataset | python | huggingface/smollm | vision/m4/models/vgpt2/evaluation_classification_vqa_in_context_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/evaluation_classification_vqa_in_context_vgpt2.py | Apache-2.0 |
def retrieve_idx_closest_examples(ref_embedding, embeddings_to_compare, num_examples):
"Returns the indices of the `num_examples` closest embeddings in ascending order"
sim = np.dot(embeddings_to_compare, ref_embedding)
# We can achieve linear complexity because we don't need to sort... | Returns the indices of the `num_examples` closest embeddings in ascending order | retrieve_idx_closest_examples | python | huggingface/smollm | vision/m4/models/vgpt2/evaluation_classification_vqa_in_context_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/evaluation_classification_vqa_in_context_vgpt2.py | Apache-2.0 |
def retrieve_idx_closest_examples(ref_embedding, embeddings_to_compare, num_examples):
"Returns the indices of the `num_examples` closest embeddings in ascending order"
sim = np.dot(embeddings_to_compare, ref_embedding)
# We can achieve linear complexity because we don't need to sort... | Returns the indices of the `num_examples` closest embeddings in ascending order | retrieve_idx_closest_examples | python | huggingface/smollm | vision/m4/models/vgpt2/evaluation_open_ended_vqa_in_context_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/evaluation_open_ended_vqa_in_context_vgpt2.py | Apache-2.0 |
def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
"""Load tf checkpoints in a pytorch model"""
try:
import re
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see... | Load tf checkpoints in a pytorch model | load_tf_weights_in_gpt2 | python | huggingface/smollm | vision/m4/models/vgpt2/modeling_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/modeling_vgpt2.py | Apache-2.0 |
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
"""
output_embeddings = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getattr(sel... |
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
| tie_weights | python | huggingface/smollm | vision/m4/models/vgpt2/modeling_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/modeling_vgpt2.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] =... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-10... | forward | python | huggingface/smollm | vision/m4/models/vgpt2/modeling_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/modeling_vgpt2.py | Apache-2.0 |
def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_val... |
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
| _reorder_cache | python | huggingface/smollm | vision/m4/models/vgpt2/modeling_vgpt2.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vgpt2/modeling_vgpt2.py | Apache-2.0 |
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
... |
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
| to_dict | python | huggingface/smollm | vision/m4/models/vllama3/configuration_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/configuration_vllama3.py | Apache-2.0 |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... |
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
| _dynamic_frequency_update | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1) | Rotates half the hidden dims of the input. | rotate_half | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.... | Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
positio... | apply_rotary_pos_emb | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_... |
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
| repeat_kv | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def _flash_attention_forward(
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
):
"""
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then com... |
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be pa... | _flash_attention_forward | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
qu... | forward | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
"""
def get_lowest_module(module):
if len(list(module.children())) == 0:
# ... |
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
| enable_input_require_grads | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def inputs_merger(
self,
input_ids: torch.LongTensor = None,
inputs_embeds: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
):
"""
This method aims at merging the token embeddings with the image hidden states into one single sequence... |
This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM.
The merging happens as follows:
- The text token sequence is: `tok_1 tok_2 tok_3 <fake_token_around_image> <image> <image> ... <image> <fake_toke... | inputs_merger | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
"""
def get_lowest_module(module):
if len(list(module.children())) == 0:
# ... |
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
| enable_input_require_grads | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
"""
lm_head, additional_fc = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getat... |
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
| tie_weights | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = Non... |
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `... | forward | python | huggingface/smollm | vision/m4/models/vllama3/modeling_vllama3.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vllama3/modeling_vllama3.py | Apache-2.0 |
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
... |
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
| to_dict | python | huggingface/smollm | vision/m4/models/vmistral/configuration_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/configuration_vmistral.py | Apache-2.0 |
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1) | Rotates half the hidden dims of the input. | rotate_half | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_... |
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
| repeat_kv | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def _flash_attention_forward(
self,
query_states,
key_states,
value_states,
attention_mask,
query_length,
dropout=0.0,
softmax_scale=None,
use_sliding_windows=False,
):
"""
Calls the forward method of Flash Attention - if the in... |
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be pa... | _flash_attention_forward | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
"""
def get_lowest_module(module):
if len(list(module.children())) == 0:
# ... |
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
| enable_input_require_grads | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def inputs_merger(
self,
input_ids: torch.LongTensor = None,
inputs_embeds: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
):
"""
This method aims at merging the token embeddings with the image hidden states into one single sequence... |
This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM.
The merging happens as follows:
- The text token sequence is: `tok_1 tok_2 tok_3 <fake_token_around_image> <image> <image> ... <image> <fake_toke... | inputs_merger | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
"""
def get_lowest_module(module):
if len(list(module.children())) == 0:
# ... |
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
| enable_input_require_grads | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
"""
lm_head, additional_fc = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getat... |
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
| tie_weights | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pix... |
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `... | forward | python | huggingface/smollm | vision/m4/models/vmistral/modeling_vmistral.py | https://github.com/huggingface/smollm/blob/master/vision/m4/models/vmistral/modeling_vmistral.py | Apache-2.0 |
def should_process(path, control_file_path, args):
"""Heuristics to decide whether to cleanup this opt_step-XXX checkpoint or not"""
s3_completed_path = path / finished_uploading_file_name
eval_completed_paths = [
path / "run_evals_0_shots_done",
path / "run_evals_4_shots_done",
pat... | Heuristics to decide whether to cleanup this opt_step-XXX checkpoint or not | should_process | python | huggingface/smollm | vision/m4/scripts/cleanup-checkpoints.py | https://github.com/huggingface/smollm/blob/master/vision/m4/scripts/cleanup-checkpoints.py | Apache-2.0 |
def should_process(path, force, control_file_path):
"""Heuristics to decide whether to convert this opt_step-XXX checkpoint or not"""
target_dir = path / "unwrapped_model"
config_file = target_dir / "config.json"
# check if target directory exists
if not target_dir.exists():
print(f"[N... | Heuristics to decide whether to convert this opt_step-XXX checkpoint or not | should_process | python | huggingface/smollm | vision/m4/scripts/convert-checkpoints.py | https://github.com/huggingface/smollm/blob/master/vision/m4/scripts/convert-checkpoints.py | Apache-2.0 |
def should_process(path, force, control_file_path, finished_uploading_file_path, args):
"""Heuristics to decide whether to upload this opt_step-XXX checkpoint or not"""
# check if checkpoint is fully saved
finished_saving_path = path / "finished-saving" # defined in from trainer.py
if not finished_sav... | Heuristics to decide whether to upload this opt_step-XXX checkpoint or not | should_process | python | huggingface/smollm | vision/m4/scripts/s3-upload-checkpoints.py | https://github.com/huggingface/smollm/blob/master/vision/m4/scripts/s3-upload-checkpoints.py | Apache-2.0 |
def check_eval_crash(path):
"""Heuristics to decide whether to restart this opt_step-XXX checkpoint evaluation or not"""
eval_start_paths = map(
lambda x: path / x,
[
"start_run_evals_0_shots",
"start_run_evals_4_shots",
"start_run_evals_perplexity_validation"... | Heuristics to decide whether to restart this opt_step-XXX checkpoint evaluation or not | check_eval_crash | python | huggingface/smollm | vision/m4/scripts/schedule-evals.py | https://github.com/huggingface/smollm/blob/master/vision/m4/scripts/schedule-evals.py | Apache-2.0 |
def _strip_html_tree(self, selectolax_tree):
"""
Strips all nodes with tags NOT in INTERESTING_TAGS_SET and has
counterintuitively nothing to do with the STRIP_TAGS list
"""
strip_tags_l = [
node.tag
for node in selectolax_tree.root.traverse()
... |
Strips all nodes with tags NOT in INTERESTING_TAGS_SET and has
counterintuitively nothing to do with the STRIP_TAGS list
| _strip_html_tree | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/dom_tree_simplificator.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/dom_tree_simplificator.py | Apache-2.0 |
def _remove_empty_leaves(self, selectolax_tree):
"""
Function used to remove empty leaves iteratively, so it also ends up also removing nodes
that are higher up in the tree.
"""
modification = True
while modification:
nodes_to_remove = [
node
... |
Function used to remove empty leaves iteratively, so it also ends up also removing nodes
that are higher up in the tree.
| _remove_empty_leaves | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/dom_tree_simplificator.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/dom_tree_simplificator.py | Apache-2.0 |
def _get_clip_scores(self, media_info, image):
"""If possible, modifies `media_info`to add clip scores on available texts"""
texts = []
for text_key in ["formatted_filename", "alt_text", "extracted_text"]:
if text_key in media_info and media_info[text_key] != "":
text... | If possible, modifies `media_info`to add clip scores on available texts | _get_clip_scores | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/pair_extractor.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/pair_extractor.py | Apache-2.0 |
def split_on_whitespace(
text,
new_line=False,
tab=False,
):
"""This method also removes concatenated spaces."""
sep = [" "] + new_line * ["\n"] + tab * ["\t"]
sep = "|".join(sep)
split_text = re.split(sep, text)
split_text = PairFiltering.remove_empty... | This method also removes concatenated spaces. | split_on_whitespace | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/pair_filtering.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/pair_filtering.py | Apache-2.0 |
def strip(text, strip_characters):
"""Way faster than text.strip(strip_characters)
since strip_characters is a set instead of a str,
and it contains a lot of elements (all the emojis)."""
if not text:
return text
beg_ind = 0
end_ind = len(text)
for i i... | Way faster than text.strip(strip_characters)
since strip_characters is a set instead of a str,
and it contains a lot of elements (all the emojis). | strip | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/pair_filtering.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/pair_filtering.py | Apache-2.0 |
def get_words_from_text(text, lower_case, strip_words, strip_characters):
"""Get words from a text. Non reversible since the text
is split on multiple characters, words are stripped of
special characters and characters are converted to lower case.
Useful to compute ratios, like the stopw... | Get words from a text. Non reversible since the text
is split on multiple characters, words are stripped of
special characters and characters are converted to lower case.
Useful to compute ratios, like the stopword ratio. | get_words_from_text | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/pair_filtering.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/pair_filtering.py | Apache-2.0 |
def split_on_whitespace(
text,
new_line=False,
tab=False,
):
"""This method also removes concatenated spaces."""
sep = [" "] + new_line * ["\n"] + tab * ["\t"]
sep = "|".join(sep)
split_text = re.split(sep, text)
split_text = FilteringFunctions.remove_... | This method also removes concatenated spaces. | split_on_whitespace | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/web_document_filtering.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/web_document_filtering.py | Apache-2.0 |
def strip(text, strip_characters):
"""Way faster than text.strip(strip_characters)
since strip_characters is a set instead of a str,
and it contains a lot of elements (all the emojis)."""
if not text:
return text
beg_ind = 0
end_ind = len(text)
for i i... | Way faster than text.strip(strip_characters)
since strip_characters is a set instead of a str,
and it contains a lot of elements (all the emojis). | strip | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/web_document_filtering.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/web_document_filtering.py | Apache-2.0 |
def get_words_from_text(text, lower_case=True, strip_words=True, strip_characters=SPECIAL_CHARACTERS):
"""Get words from a text. Non reversible since the text
is split on multiple characters, words are stripped of
special characters and characters are converted to lower case.
Useful to c... | Get words from a text. Non reversible since the text
is split on multiple characters, words are stripped of
special characters and characters are converted to lower case.
Useful to compute ratios, like the stopword ratio. | get_words_from_text | python | huggingface/smollm | vision/m4/sourcing/data_collection/processors/web_document_filtering.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/processors/web_document_filtering.py | Apache-2.0 |
def compute_clip_score(texts, image, num_max_words=NUM_MAX_WORDS):
"""
Args
texts: List[str]
images: (:obj:`PIL.Image.Image`, :obj:`np.ndarray`, :obj:`torch.Tensor`):
The image to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case o... |
Args
texts: List[str]
images: (:obj:`PIL.Image.Image`, :obj:`np.ndarray`, :obj:`torch.Tensor`):
The image to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), w... | compute_clip_score | python | huggingface/smollm | vision/m4/sourcing/data_collection/utils/clip_utils.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/utils/clip_utils.py | Apache-2.0 |
def check_image_quality(media_info):
"""
Args_ : Media Node
Returns :
img_has_good_quality: Boolean indictating there is an image with good quality (defined by its height, width, and aspect ratio)
w: image width
h: image height
"""
w, h = media_info["original_widt... |
Args_ : Media Node
Returns :
img_has_good_quality: Boolean indictating there is an image with good quality (defined by its height, width, and aspect ratio)
w: image width
h: image height
| check_image_quality | python | huggingface/smollm | vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | Apache-2.0 |
def check_text(media_info):
"""
Args_ : Media Node
Returns :
has_text: Boolean indictating if there is a text that corresponds to the media
txt_dict: Dictionary mapping each text_length to its text type (filename, alt-text, extracted_text)
Note:
All variables are set ... |
Args_ : Media Node
Returns :
has_text: Boolean indictating if there is a text that corresponds to the media
txt_dict: Dictionary mapping each text_length to its text type (filename, alt-text, extracted_text)
Note:
All variables are set to 0 if they don't exist in the med... | check_text | python | huggingface/smollm | vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | Apache-2.0 |
def check_CLIP(media_info):
"""
Args_ : Media Node
Returns :
clip_score_max_per_img: Max CLIP score per Image
clip_nbr_per_img: Number of CLIP scores for a given image
clip_dict: Dictionary mapping each CLIP score to its text type (filename, alt-text, extracted_text).
... |
Args_ : Media Node
Returns :
clip_score_max_per_img: Max CLIP score per Image
clip_nbr_per_img: Number of CLIP scores for a given image
clip_dict: Dictionary mapping each CLIP score to its text type (filename, alt-text, extracted_text).
Note:
All variables ar... | check_CLIP | python | huggingface/smollm | vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | Apache-2.0 |
def update_df_metrics_and_lists_for_extraction_method(
media_info, aggregate_metrics_df, image_centric_df, text_centric_df, extraction_method_name
):
"""_summary_
Given a Media_Node and the Extraction_Method_Name used to get this Media_Node,
this function uses the Media_Node's values to update the 2D Da... | _summary_
Given a Media_Node and the Extraction_Method_Name used to get this Media_Node,
this function uses the Media_Node's values to update the 2D Dataframes' numbers
and append values to the 3D Dataframes' lists.
| update_df_metrics_and_lists_for_extraction_method | python | huggingface/smollm | vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | Apache-2.0 |
def get_extraction_evaluation_metrics(
num_docs_to_consider=100,
use_clip_scores=True,
):
"""_summary_
Args:
num_docs_to_consider (int, optional): _description_. Defaults to 100.
use_clip_scores (bool, optional): _description_. Defaults to True.
Returns:
_type_: _descriptio... | _summary_
Args:
num_docs_to_consider (int, optional): _description_. Defaults to 100.
use_clip_scores (bool, optional): _description_. Defaults to True.
Returns:
_type_: _description_
| get_extraction_evaluation_metrics | python | huggingface/smollm | vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | Apache-2.0 |
def display_bar_charts(self, header, list_metric_to_compare):
"""
Given a list of metrics to compare, makes one bar chart per metric and compares over
all extraction methods.
Each bar chart has its own column, so it is better to put no more than 3 metrics.
"""
charts = []... |
Given a list of metrics to compare, makes one bar chart per metric and compares over
all extraction methods.
Each bar chart has its own column, so it is better to put no more than 3 metrics.
| display_bar_charts | python | huggingface/smollm | vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/data_collection/visualization/pair_stat_dashboard.py | Apache-2.0 |
def cached_path(
url_or_filename,
compute_cache_path,
download_config=None,
**download_kwargs,
) -> str:
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's alr... |
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's already a local path,
make sure the file exists and then return the path.
Return:
Local path (string)
Raises:... | cached_path | python | huggingface/smollm | vision/m4/sourcing/pmd/cache_path.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/pmd/cache_path.py | Apache-2.0 |
def get_from_cache(
url,
compute_cache_path,
cache_dir=None,
force_download=False,
proxies=None,
etag_timeout=10.0, # reduce timeout
resume_download=False,
user_agent=None,
local_files_only=False,
use_etag=True,
max_retries=0,
use_auth_token=None,
ignore_url_params=F... |
Given a URL, look for the corresponding file in the local cache.
If it's not there, download it. Then return the path to the cached file.
Return:
Local path (string)
Raises:
FileNotFoundError: in case of non-recoverable file
(non-existent or no cache on disk)
Conne... | get_from_cache | python | huggingface/smollm | vision/m4/sourcing/pmd/cache_path.py | https://github.com/huggingface/smollm/blob/master/vision/m4/sourcing/pmd/cache_path.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.