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
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|---|---|---|---|---|---|---|---|
def decode(self, input, *args, **kwargs ) -> Union[str, List[str]]:
"""
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list.
The token sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
... |
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list.
The token sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
outputs... | decode | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def inference(self, inputs, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
Positional arguments.
... |
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.... | inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def save(self, dir, save_full_model=False, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full model.
kwa... |
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full model.
kwargs : Optional.
Keyword arguments.
Returns
... | save | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def get_max_length(self):
"""
Return max acceptable input length in terms of tokens.
"""
if "tokenizer" not in self.tokenizer.__dict__:
return self.tokenizer.model_max_length
else:
# for the multi-modality processor,
# the max length is stored ... |
Return max acceptable input length in terms of tokens.
| get_max_length | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def __init__(
self,
model_args: ModelArguments,
do_train: bool,
ds_config=None,
device: Optional[str]="gpu",
use_accelerator: bool=False,
hf_auto_model_additional_args: Optional[Dict]=None,
*args,
**kwargs
):
"""Initializes a HFModel in... | Initializes a HFModel instance.
Parameters
----------
model_args :
Dictionary with model arguments such as model name, path, revision, etc.
do_train : bool
To prepare the model for training or inference.
ds_config : optional
Deepspeed configu... | __init__ | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def __prepare_model_config(
self,
model_args: ModelArguments,
hf_auto_model_additional_args: Optional[Dict]=None,
):
"""Prepare model configuration for hf auto register,
Parameters
----------
model_args : ModelArguments
LMFlow model arguments.
... | Prepare model configuration for hf auto register,
Parameters
----------
model_args : ModelArguments
LMFlow model arguments.
hf_auto_model_additional_args : Optional[Dict], optional
Special configurations such as `num_labels` in `AutoModelForSequenceClassification`... | __prepare_model_config | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def __model_module_inject(
self,
model_args: ModelArguments,
) -> None:
"""Override some model modules with custom implementations.
Current implementations:
- Position interpolation (model_args.do_rope_scaling):
replace llama embeddings with condense emb... | Override some model modules with custom implementations.
Current implementations:
- Position interpolation (model_args.do_rope_scaling):
replace llama embeddings with condense embeddings.
| __model_module_inject | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def deactivate_model_for_inference(
self,
use_vllm: bool=False,
):
"""Deactivate the model and release the resources.
NOTE: Currently, VLLM doesn't have an official way to do this, and the
implementation below cannot release all gpu resources by our observation.
... | Deactivate the model and release the resources.
NOTE: Currently, VLLM doesn't have an official way to do this, and the
implementation below cannot release all gpu resources by our observation.
Thus this method is just a placeholder for future implementation. See:
[Github issue]... | deactivate_model_for_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def __init__(
self,
model_args: ModelArguments,
tune_strategy: str='normal',
ds_config=None,
device="gpu",
use_accelerator=False,
*args,
**kwargs
):
"""
Initializes a HFTextRegressionModel instance.
:param model_args: dictionary... |
Initializes a HFTextRegressionModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
:param tune_strategy: tuning strategy: normal, none, lora or adapter
:param ds_config: deepspeed configuration for distributed training
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def tokenize(
self,
dataset: Dataset,
add_special_tokens=True,
*args,
**kwargs
):
"""
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positional argume... |
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
tokenized_d... | tokenize | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def inference(
self,
inputs,
release_gpu: bool = False,
use_vllm: bool = False,
**kwargs
) -> Union[List[float], SequenceClassifierOutputWithPast]:
"""
Perform generation process of the model.
Parameters
------------
inputs :
... |
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generation or as model inputs to the model.
When using vllm inference, this should be a string or a list of strings.
When using normal... | inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def __inference(
self,
inputs,
**kwargs
):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
kwargs :... |
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
kwargs : Optional.
Keyword arguments.
Returns
-----... | __inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def __vllm_inference(
self,
inputs: Union[str, List[str]],
sampling_params: Optional['SamplingParams'] = None,
**kwargs,
) -> Union[List[List[str]], List[List[List[int]]]]:
"""Perform VLLM inference process of the model.
Parameters
----------
inputs ... | Perform VLLM inference process of the model.
Parameters
----------
inputs : Union[str, List[str]]
Prompt(s), string or a list of strings.
sampling_params : Optional[SamplingParams], optional
vllm SamplingParams object, by default None.
Returns
--... | __vllm_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def __init__(
self,
model_args,
*args,
**kwargs
):
"""
Initializes a TextRegressionModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
"""
self.inference_func = None |
Initializes a TextRegressionModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/text_regression_model.py | Apache-2.0 |
def inference(self, inputs: Dataset):
"""
Gets regression results of a given dataset.
:inputs: Dataset object, only accept type "text_only".
"""
if self.inference_func is not None:
return self.inference_func(inputs)
else:
pass |
Gets regression results of a given dataset.
:inputs: Dataset object, only accept type "text_only".
| inference | python | OptimalScale/LMFlow | src/lmflow/models/text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/text_regression_model.py | Apache-2.0 |
def __init__(self,
config: Blip2Config,
image_encoder_name_or_path=None,
qformer_name_or_path=None,
language_model_name_or_path=None,
low_resource=False,):
'''
TODO update the docs
Args:
config:
... |
TODO update the docs
Args:
config:
# the below varaible are used to overwrite the model in config
image_encoder_name_or_path:
qformer_name_or_path:
language_model_name_or_path:
Returns:
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def register_prompt_cache(self, prompt_ids, prompt_keys_values):
"""
Udpate the prompt id and embedding for reuse in the future
Args:
prompt_ids (torch.LongTensor): The id of the prompt.
prompt_keys_values (torch.FloatTensor): The embedding of the prompt.
Return... |
Udpate the prompt id and embedding for reuse in the future
Args:
prompt_ids (torch.LongTensor): The id of the prompt.
prompt_keys_values (torch.FloatTensor): The embedding of the prompt.
Returns:
None
| register_prompt_cache | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def save_prompt_cache(self, path):
"""
Save prompt embedding and id.
Args:
path: The path to save the prompt embedding and id.
Returns:
None
"""
torch.save(
dict(
prompt_ids=self.prompt_ids,
prompt_key... |
Save prompt embedding and id.
Args:
path: The path to save the prompt embedding and id.
Returns:
None
| save_prompt_cache | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def generate(
self,
pixel_values: torch.FloatTensor,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
image_token_indexes: Optional[List] = [0],
one_sample_multiple_images: Optional[bool] = False,
images: Optional[to... |
Overrides `generate` function to be able to use the model as a conditional generator.
Args:
pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):
Input images to be processed.
input_ids (`torch.LongTensor` of shape (batch_size, s... | generate | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, images,
language_projection=None,
language_model=None,
**kwargs
):
'''
Copy from the LLAVA code base.
Should be polished.
'''
vision_tower = sel... |
Copy from the LLAVA code base.
Should be polished.
| prepare_inputs_labels_for_multimodal | python | OptimalScale/LMFlow | src/lmflow/models/vision_encoder/clip_encoder.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision_encoder/clip_encoder.py | Apache-2.0 |
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for ... | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/adabelief.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/adabelief.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group, base_lr in zip(self.param_... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/adabound.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/adabound.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/adamp.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/adamp.py | Apache-2.0 |
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
... | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/adamw_schedule_free.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/adamw_schedule_free.py | Apache-2.0 |
def step(self, closure: Callable=None):
"""
Performs a single optimization step.
Arguments:
closure (:obj:`Callable`, `optional`): A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
... |
Performs a single optimization step.
Arguments:
closure (:obj:`Callable`, `optional`): A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/dummy.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/dummy.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/lamb.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/lamb.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/lars.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/lars.py | Apache-2.0 |
def zeropower_via_newtonschulz5(G: Tensor, steps: int) -> Tensor:
"""
Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
of minimizing steps, it turns out to be emp... |
Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
zero even beyond t... | zeropower_via_newtonschulz5 | python | OptimalScale/LMFlow | src/lmflow/optim/muon.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/muon.py | Apache-2.0 |
def step(self, closure=None):
"""
Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
... |
Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/muon.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/muon.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/radam.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/radam.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/sgdp.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/sgdp.py | Apache-2.0 |
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
... | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/sgd_schedule_free.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/sgd_schedule_free.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/yogi.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/yogi.py | Apache-2.0 |
def convert_to_paired_dataset(
self,
source_dataset: Dataset,
sampling_paired_method: str="random",
length_penalty: float=0.0,
margin_scale: float=1.0,
use_fast: bool=False,
) -> Dataset:
"""Convert a scored one to multiple (text_to_scored_textlist) to a paire... | Convert a scored one to multiple (text_to_scored_textlist) to a paired dataset by rejection sampling.
| convert_to_paired_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpov2_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpov2_aligner.py | Apache-2.0 |
def _calc_reward_with_length_penalty(
self,
rewards: List[float],
lengths: List[int],
length_penalty: float,
) -> List[float]:
"""When length_penalty > 0, penalize the longer sequence by subtracting
length_penalty * length from the reward. Vice versa when length_pe... | When length_penalty > 0, penalize the longer sequence by subtracting
length_penalty * length from the reward. Vice versa when length_penalty < 0.
| _calc_reward_with_length_penalty | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpov2_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpov2_aligner.py | Apache-2.0 |
def sampling_paired_idx_from_rewards(
self,
rewards: List[float],
sampling_paired_method: str="random",
use_fast: bool=False,
) -> Tuple[int, int]:
"""Prepare the dataset for DPO training by rejection sampling.
We implement different strategies to select pairs, includ... | Prepare the dataset for DPO training by rejection sampling.
We implement different strategies to select pairs, including
random: randomly select two instances
max_min: best v.s. worst
max_max: best v.s. second best
max_random: best v.s. random from the remaining
| sampling_paired_idx_from_rewards | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpov2_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpov2_aligner.py | Apache-2.0 |
def get_paired_dataset(
data_root: str,
data_dir: str,
sanity_check: bool = False,
cache_dir: Optional[str] = None,
num_proc=24,
) -> Dataset:
"""Load dataset and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
... | Load dataset and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
{
'prompt': List[str],
'chosen': List[str],
'rejected': List[str],
}
Prompts are structured as follows:
"Question: " + <prompt> + "
Answer: "
| get_paired_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpo_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpo_aligner.py | Apache-2.0 |
def evaluate(
self,
model,
dataset: Dataset,
metric = "accuracy",
verbose=True,
):
"""
Perform Evaluation for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform inference
dataset :... |
Perform Evaluation for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform inference
dataset : Dataset object.
| evaluate | python | OptimalScale/LMFlow | src/lmflow/pipeline/evaluator.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/evaluator.py | Apache-2.0 |
def _evaluate_nll(
self,
model,
dataset: Dataset,
verbose=True,
):
"""
Evaluates negative log likelihood of the model over a dataset.
NLL = -1/N sum_{i=1}^N sum_{j=1}^|w_i| ln(p(w_{i,j}|context_window)),
where N is the number of data samples, w_{i,j}... |
Evaluates negative log likelihood of the model over a dataset.
NLL = -1/N sum_{i=1}^N sum_{j=1}^|w_i| ln(p(w_{i,j}|context_window)),
where N is the number of data samples, w_{i,j} is the j-th token in
i-th sample. Here "context_window" = p(w_{i,start}, w_{i,start+1}, ...,
p_{i... | _evaluate_nll | python | OptimalScale/LMFlow | src/lmflow/pipeline/evaluator.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/evaluator.py | Apache-2.0 |
def group_text(self, tokenized_datasets, model_max_length):
"""
Groups texts together to form blocks of maximum length `model_max_length` and returns the processed data as
a dictionary.
"""
data_args = self.data_args
finetuner_args = self.finetuner_args
if data_a... |
Groups texts together to form blocks of maximum length `model_max_length` and returns the processed data as
a dictionary.
| group_text | python | OptimalScale/LMFlow | src/lmflow/pipeline/finetuner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/finetuner.py | Apache-2.0 |
def tune(self,
model: Union[HFDecoderModel, HFTextRegressionModel, HFEncoderDecoderModel],
dataset: Dataset,
transform_dataset_in_place=True,
data_collator=None):
"""
Perform tuning for a model
Parameters
------------
model : T... |
Perform tuning for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform tuning.
dataset:
dataset to train model.
| tune | python | OptimalScale/LMFlow | src/lmflow/pipeline/finetuner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/finetuner.py | Apache-2.0 |
def create_dataloader(self, dataset: Dataset):
r"""Batchlize dataset and format it to dataloader.
Args:
dataset (Dataset): the dataset object
Output:
dataloader (batchlize): the dataloader object
dataset_size (int): the length of the dataset
"""
... | Batchlize dataset and format it to dataloader.
Args:
dataset (Dataset): the dataset object
Output:
dataloader (batchlize): the dataloader object
dataset_size (int): the length of the dataset
| create_dataloader | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def inference(
self,
model,
dataset: Dataset,
max_new_tokens: int=100,
temperature: float=0.0,
prompt_structure: str='{input}',
remove_image_flag: bool=False,
chatbot_type: str="mini_gpt",
):
"""
Perform inference for a model
P... |
Perform inference for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform inference
dataset : Dataset object.
Returns:
output_dataset: Dataset object.
| inference | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def score_to_prob(scores: torch.Tensor,
temperature: float = 0.,
top_p: float = 1.,) -> torch.Tensor:
"""Convert scores (NOT softmaxed tensor) to probabilities with support for temperature, top-p sampling, and argmax.
Parameters
----------
sc... | Convert scores (NOT softmaxed tensor) to probabilities with support for temperature, top-p sampling, and argmax.
Parameters
----------
scores : torch.Tensor
Input scores.
temperature : float, optional
Temperature parameter for controlling randomness. Higher value... | score_to_prob | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def predict_next_token(model: HFDecoderModel, input_ids: torch.Tensor, num_new_tokens: int = 1):
"""Predict the next token given the input_ids.
"""
output = model.inference(input_ids,
use_accelerator=True,
max_new_tokens=num_new... | Predict the next token given the input_ids.
| predict_next_token | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def autoregressive_sampling(self,
input_ids: torch.Tensor,
model: HFDecoderModel,
temperature: float = 0.,
num_new_tokens: int = 5) -> Dict:
"""Ref: [arXiv:2211.17192v2](https://ar... | Ref: [arXiv:2211.17192v2](https://arxiv.org/abs/2211.17192) Section 2.2
| autoregressive_sampling | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def inference(
self,
model: HFDecoderModel,
input: str,
max_new_tokens: int=1024,
):
"""
Perform inference for a model
Parameters
------------
model : HFDecoderModel object.
TunableModel to perform inference
input : str.
... |
Perform inference for a model
Parameters
------------
model : HFDecoderModel object.
TunableModel to perform inference
input : str.
The input text (i.e., the prompt) for the model.
max_new_tokens : int.
The maximum numb... | inference | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def _initialize_trainer(self, model, tokenizer, training_args):
"""
This function takes the model and tokenizer as the input and initialize the trainer.
"""
trainer = RaftTrainer(
model=model,
args=training_args,
train_dataset=Dataset.from_dict({"text"... |
This function takes the model and tokenizer as the input and initialize the trainer.
| _initialize_trainer | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def _load_dataset(
self,
selected_dataset,
model,
tokenizer,
model_args,
data_args,
training_args,
):
'''
This function prepares the dataset for every iteration.
'''
raw_datasets = selected_dataset
if training_args.do_t... |
This function prepares the dataset for every iteration.
| _load_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def _load_input_dataset(self, dataset, tokenizer):
"""
Load input dataset (i.e. prompt/question dataset) for training.
Args:
dataset: A Dataset object.
The dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
... |
Load input dataset (i.e. prompt/question dataset) for training.
Args:
dataset: A Dataset object.
The dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
The dataloader for the dataset.
| _load_input_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def align(self, model, dataset, reward_model):
"""
Perform alignment for a model
Parameters
------------
model : BaseModel object.
dataset: Dataset object.
Input dataset for model to generate outputs. The input and output
will then be feed int... |
Perform alignment for a model
Parameters
------------
model : BaseModel object.
dataset: Dataset object.
Input dataset for model to generate outputs. The input and output
will then be feed into reward model to get the reward for
align... | align | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def __call__(self, batch: Dict[str, np.ndarray]):
"""batch: Dict[str, np.ndarray]
Example (batch size=2):
{'input': array(['...','...'], dtype=object),
'output': array([array(["...", "..."], dtype=object), array(['...','...'], dtype=object)], dtype=object... | batch: Dict[str, np.ndarray]
Example (batch size=2):
{'input': array(['...','...'], dtype=object),
'output': array([array(["...", "..."], dtype=object), array(['...','...'], dtype=object)], dtype=object),
'input_ids': array([[[128000, 128006, 882, ...... | __call__ | python | OptimalScale/LMFlow | src/lmflow/pipeline/rm_inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/rm_inferencer.py | Apache-2.0 |
def inference(
self,
model: HFDecoderModel,
dataset: Dataset,
enable_decode_inference_result: bool = True,
release_gpu: bool = False,
inference_args: Optional[InferencerArguments] = None,
enable_distributed_inference: bool = False,
**kwargs,
) -> Lis... | Perform inference using the provided model and dataset. Will save inference results if
`save_results` is set to True in `inferencer_args`.
Parameters
----------
model : HFDecoderModel
LMFlow HFDecoderModel object
dataset : Dataset
LMFlow Dataset object
... | inference | python | OptimalScale/LMFlow | src/lmflow/pipeline/vllm_inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/vllm_inferencer.py | Apache-2.0 |
def __call__(self, batch: Dict[str, np.ndarray]):
"""batch: Dict[str, np.ndarray], {"item": array(['...', '...', '...', ...])}
"""
batched_inference_res = self.model.inference(
inputs=batch['item'],
sampling_params=self.sampling_par... | batch: Dict[str, np.ndarray], {"item": array(['...', '...', '...', ...])}
| __call__ | python | OptimalScale/LMFlow | src/lmflow/pipeline/vllm_inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/vllm_inferencer.py | Apache-2.0 |
def tokenize_batch_element(
self,
prompt: str,
chosen: str,
rejected: str,
) -> Dict:
"""Tokenize a single batch element.
At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
in case the prompt + chosen or prompt + rejecte... | Tokenize a single batch element.
At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
in case the prompt + chosen or prompt + rejected responses is/are too long. First
we truncate the prompt; if we're still too long, we truncate the chosen/rejected.
... | tokenize_batch_element | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/dpov2_dataprocessor.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/dpov2_dataprocessor.py | Apache-2.0 |
def dpo_loss(
self,
policy_chosen_logps: torch.FloatTensor,
policy_rejected_logps: torch.FloatTensor,
reference_chosen_logps: torch.FloatTensor,
reference_rejected_logps: torch.FloatTensor,
reference_free: bool = False,
margin: Optional[torch.FloatTensor] = None,
... | Compute the DPO loss for a batch of policy and reference model log probabilities.
Args:
policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)
policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shap... | dpo_loss | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/dpov2_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/dpov2_trainer.py | Apache-2.0 |
def get_batch_metrics(
self,
model,
batch: Dict[str, Union[List, torch.LongTensor]],
train_eval: Literal["train", "eval"] = "train",
):
"""Compute the DPO loss and other metrics for the given batch of inputs for train or test."""
metrics = {}
(
pol... | Compute the DPO loss and other metrics for the given batch of inputs for train or test. | get_batch_metrics | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/dpov2_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/dpov2_trainer.py | Apache-2.0 |
def _save_checkpoint(self, _, trial, metrics=None):
""" Don't save base model, optimizer etc.
but create checkpoint folder (needed for saving adapter) """
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
run_dir = self._get_output_dir(trial=trial)
outp... | Don't save base model, optimizer etc.
but create checkpoint folder (needed for saving adapter) | _save_checkpoint | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/peft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/peft_trainer.py | Apache-2.0 |
def on_epoch_end(self, args: TrainingArguments, state: TrainerState,
control: TrainerControl, **kwargs):
""" Save intermediate model adapters in case of interrupted training """
folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
self._save(kwargs['... | Save intermediate model adapters in case of interrupted training | on_epoch_end | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/peft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/peft_trainer.py | Apache-2.0 |
def _get_collator_with_removed_columns(
self, data_collator: Callable, description: Optional[str] = None
) -> Callable:
"""Wrap the data collator in a callable removing unused columns."""
if not self.args.remove_unused_columns:
return data_collator
self._set_signature_col... | Wrap the data collator in a callable removing unused columns. | _get_collator_with_removed_columns | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def get_train_dataloader(self) -> DataLoader:
"""
Returns the training [`~torch.utils.data.DataLoader`].
Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
training if necessary) otherwise.
Subclass and override this meth... |
Returns the training [`~torch.utils.data.DataLoader`].
Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
training if necessary) otherwise.
Subclass and override this method if you want to inject some custom behavior.
| get_train_dataloader | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
"""
Returns the evaluation [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (`torch.utils.data.Dataset`, *opt... |
Returns the evaluation [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (`torch.utils.data.Dataset`, *optional*):
If provided, will override `self.eval_dataset`. If it is a [`~datasets.... | get_eval_dataloader | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
"""
Returns the test [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
test_dataset (`torch.utils.data.Dataset`, *optional*):
... |
Returns the test [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
test_dataset (`torch.utils.data.Dataset`, *optional*):
The test dataset to use. If it is a [`~datasets.Dataset`], columns not accept... | get_test_dataloader | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def create_optimizer_and_scheduler(self, num_training_steps: int):
"""
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and... |
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or
`create_scheduler`... | create_optimizer_and_scheduler | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def create_optimizer(self):
"""
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
"""
opt_model = se... |
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
| create_optimizer | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any, Any]:
"""
Returns the optimizer class and optimizer parameters based on the training arguments.
Args:
args (`transformers.training_args.TrainingArguments`):
The training arguments for the training... |
Returns the optimizer class and optimizer parameters based on the training arguments.
Args:
args (`transformers.training_args.TrainingArguments`):
The training arguments for the training session.
| get_optimizer_cls_and_kwargs | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):
"""
Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.
Args:
num_training_steps (int): The number o... |
Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.
Args:
num_training_steps (int): The number of training steps to do.
| create_scheduler | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def num_examples(self, dataloader: DataLoader) -> int:
"""
Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When
dataloader.dataset does not exist or has no length, estimates as best it can
"""
try:
dataset = dataloader.d... |
Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When
dataloader.dataset does not exist or has no length, estimates as best it can
| num_examples | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def train(
self,
resume_from_checkpoint: Optional[Union[str, bool]] = None,
trial: Union["optuna.Trial", Dict[str, Any]] = None,
ignore_keys_for_eval: Optional[List[str]] = None,
is_first_time = False,
**kwargs,
):
"""
Main training entry point.
... |
Main training entry point.
Args:
resume_from_checkpoint (`str` or `bool`, *optional*):
If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a
`bool` and equals `True`, load the last checkpoint in *args.output_dir* as... | train | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _inner_training_loop(
self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None
):
'''
0 This function serves to train one time
1 Update the self.train_dataset before calling this function
'''
# 1 Get dataloader
s... |
0 This function serves to train one time
1 Update the self.train_dataset before calling this function
| _inner_training_loop | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _load_optimizer_and_scheduler(self, checkpoint):
"""If optimizer and scheduler states exist, load them."""
if checkpoint is None:
return
if self.deepspeed:
# deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init
return
... | If optimizer and scheduler states exist, load them. | _load_optimizer_and_scheduler | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def hyperparameter_search(
self,
hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
n_trials: int = 20,
direction: str = "minimize",
backend: Optional[Union["str", HPSearchBacke... |
Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined
by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise.
<Tip warning={true}>
To... | hyperparameter_search | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def log(self, logs: Dict[str, float]) -> None:
"""
Log `logs` on the various objects watching training.
Subclass and override this method to inject custom behavior.
Args:
logs (`Dict[str, float]`):
The values to log.
"""
if self.state.epoch is ... |
Log `logs` on the various objects watching training.
Subclass and override this method to inject custom behavior.
Args:
logs (`Dict[str, float]`):
The values to log.
| log | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any]:
"""
Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
"""
if isinstance(data, Mapping):
return type(data)({k: self._prepare_input(v) ... |
Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
| _prepare_input | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
"""
Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and
handling potential state.
"""
inputs = self._prepare_input(... |
Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and
handling potential state.
| _prepare_inputs | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def autocast_smart_context_manager(self, cache_enabled: Optional[bool] = True):
"""
A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired
arguments, depending on the situation.
"""
if self.use_cuda_amp or self.use_cpu_amp:
... |
A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired
arguments, depending on the situation.
| autocast_smart_context_manager | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
"""
Perform a training step on a batch of inputs.
Subclass and override to inject custom behavior.
Args:
model (`nn.Module`):
The model to train.
... |
Perform a training step on a batch of inputs.
Subclass and override to inject custom behavior.
Args:
model (`nn.Module`):
The model to train.
inputs (`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
... | training_step | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def compute_loss(self, model, inputs, return_outputs=False):
"""
How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for custom behavior.
"""
if self.label_smoother is not None and "labels" in inputs:
... |
How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for custom behavior.
| compute_loss | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def is_world_process_zero(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be `True` for one process).
"""
# Special case for SageMaker ModelParallel since there process_in... |
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be `True` for one process).
| is_world_process_zero | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
"""
Will save the model, so you can reload it using `from_pretrained()`.
Will only save from the main process.
"""
if output_dir is None:
output_dir = self.args.output_dir
... |
Will save the model, so you can reload it using `from_pretrained()`.
Will only save from the main process.
| save_model | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def evaluate(
self,
eval_dataset: Optional[Dataset] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
) -> Dict[str, float]:
"""
Run evaluation and returns metrics.
The calling script will be responsible for providing a method t... |
Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `compute_metrics` argument).
You can also subclass and override this method to inject custom behavior.
Args:
... | evaluate | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def predict(
self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "test"
) -> PredictionOutput:
"""
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels... |
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `evaluate()`.
Args:
test_dataset (`Dataset`):
Dataset t... | predict | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def evaluation_loop(
self,
dataloader: DataLoader,
description: str,
prediction_loss_only: Optional[bool] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
) -> EvalLoopOutput:
"""
Prediction/evaluation loop, shared by `... |
Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
Works both with or without labels.
| evaluation_loop | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _nested_gather(self, tensors, name=None):
"""
Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
concatenating them to `gathered`
"""
if tensors is None:
return
if is_torch_tpu_available():
if na... |
Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
concatenating them to `gathered`
| _nested_gather | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _pad_across_processes(self, tensor, pad_index=-100):
"""
Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so
they can safely be gathered.
"""
if isinstance(tensor, (list, tuple)):
return type(tensor)(self._... |
Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so
they can safely be gathered.
| _pad_across_processes | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def prediction_step(
self,
model: nn.Module,
inputs: Dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[List[str]] = None,
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Perform an ev... |
Perform an evaluation step on `model` using `inputs`.
Subclass and override to inject custom behavior.
Args:
model (`nn.Module`):
The model to evaluate.
inputs (`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
... | prediction_step | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
"""
For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
... |
For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
model or subclass and override this method.
Args:
inputs (`D... | floating_point_ops | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def init_git_repo(self, at_init: bool = False):
"""
Initializes a git repo in `self.args.hub_model_id`.
Args:
at_init (`bool`, *optional*, defaults to `False`):
Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is
... |
Initializes a git repo in `self.args.hub_model_id`.
Args:
at_init (`bool`, *optional*, defaults to `False`):
Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is
`True` and `at_init` is `True`, the path to the rep... | init_git_repo | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def prediction_loop(
self,
dataloader: DataLoader,
description: str,
prediction_loss_only: Optional[bool] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
) -> EvalLoopOutput:
"""
Prediction/evaluation loop, shared by `... |
Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
Works both with or without labels.
| prediction_loop | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _gather_and_numpify(self, tensors, name):
"""
Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
concatenating them to `gathered`
"""
if tensors is None:
return
if is_torch_tpu_available():
tenso... |
Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
concatenating them to `gathered`
| _gather_and_numpify | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def _add_sm_patterns_to_gitignore(self) -> None:
"""Add SageMaker Checkpointing patterns to .gitignore file."""
# Make sure we only do this on the main process
if not self.is_world_process_zero():
return
patterns = ["*.sagemaker-uploading", "*.sagemaker-uploaded"]
#... | Add SageMaker Checkpointing patterns to .gitignore file. | _add_sm_patterns_to_gitignore | python | OptimalScale/LMFlow | src/lmflow/pipeline/utils/raft_trainer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/utils/raft_trainer.py | Apache-2.0 |
def text_to_textlist_tokenize_function(
examples,
data_args: DatasetArguments,
tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
column_names,
add_special_tokens,
use_truncation,
) -> Dict:
"""For rm inference, and don't need attn mask and labels.
NOTE: input_ids here refe... | For rm inference, and don't need attn mask and labels.
NOTE: input_ids here refers to the tokenized input_ids of the input **and** output
| text_to_textlist_tokenize_function | python | OptimalScale/LMFlow | src/lmflow/tokenization/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/tokenization/hf_text_regression_model.py | Apache-2.0 |
def make_shell_args_from_dataclass(
dataclass_objects: List,
format: str="subprocess",
skip_default: bool=True,
ignored_args_list: Optional[List[str]]=None,
) -> Union[str, List[str]]:
"""Return a string or a list of strings that can be used as shell arguments.
Parameters
----------
da... | Return a string or a list of strings that can be used as shell arguments.
Parameters
----------
dataclass_objects : List
A list of dataclass objects.
format : str, optional
Return format, can be "shell" or "subprocess", by default "subprocess".
skip_default : bool, optional
... | make_shell_args_from_dataclass | python | OptimalScale/LMFlow | src/lmflow/utils/common.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/common.py | Apache-2.0 |
def create_copied_dataclass(
original_dataclass,
field_prefix: str,
class_prefix: str,
new_default: Dict=None
):
"""Create a copied dataclass with new field names and default values.
Parameters
----------
original_dataclass : dataclass
field_prefix : str
The prefix to add... | Create a copied dataclass with new field names and default values.
Parameters
----------
original_dataclass : dataclass
field_prefix : str
The prefix to add to the **field** names of the copied dataclass.
class_prefix : str
The prefix to add to the **class** name of the copied datac... | create_copied_dataclass | python | OptimalScale/LMFlow | src/lmflow/utils/common.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/common.py | Apache-2.0 |
def remove_dataclass_attr_prefix(data_instance, prefix: str) -> Dict:
"""Remove the prefix from the attribute names of a dataclass instance.
Parameters
----------
data_instance : dataclass
prefix : str
The prefix to remove from the attribute names of the dataclass instance.
Returns
... | Remove the prefix from the attribute names of a dataclass instance.
Parameters
----------
data_instance : dataclass
prefix : str
The prefix to remove from the attribute names of the dataclass instance.
Returns
-------
Dict
| remove_dataclass_attr_prefix | python | OptimalScale/LMFlow | src/lmflow/utils/common.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/common.py | Apache-2.0 |
def add_dataclass_attr_prefix(data_instance, prefix: str) -> Dict:
"""Add the prefix to the attribute names of a dataclass instance.
Parameters
----------
data_instance : dataclass
prefix : str
The prefix to add to the attribute names of the dataclass instance.
Returns
-------
... | Add the prefix to the attribute names of a dataclass instance.
Parameters
----------
data_instance : dataclass
prefix : str
The prefix to add to the attribute names of the dataclass instance.
Returns
-------
Dict
| add_dataclass_attr_prefix | python | OptimalScale/LMFlow | src/lmflow/utils/common.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/common.py | Apache-2.0 |
def set_random_seed(seed: int):
"""
Set the random seed for `random`, `numpy`, `torch`, `torch.cuda`.
Parameters
------------
seed : int
The default seed.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
to... |
Set the random seed for `random`, `numpy`, `torch`, `torch.cuda`.
Parameters
------------
seed : int
The default seed.
| set_random_seed | python | OptimalScale/LMFlow | src/lmflow/utils/data_utils.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/data_utils.py | Apache-2.0 |
def load_data(file_name: str):
"""
Load data with file name.
Parameters
------------
file_name : str.
The dataset file name.
Returns
------------
inputs : list.
The input texts of the dataset.
outputs : list.
The output texts file datasets.
len :... |
Load data with file name.
Parameters
------------
file_name : str.
The dataset file name.
Returns
------------
inputs : list.
The input texts of the dataset.
outputs : list.
The output texts file datasets.
len : int.
The length of the datase... | load_data | python | OptimalScale/LMFlow | src/lmflow/utils/data_utils.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/data_utils.py | Apache-2.0 |
def batchlize(examples: list, batch_size: int, random_shuffle: bool):
"""
Convert examples to a dataloader.
Parameters
------------
examples : list.
Data list.
batch_size : int.
random_shuffle : bool
If true, the dataloader shuffle the training data.
Returns
--... |
Convert examples to a dataloader.
Parameters
------------
examples : list.
Data list.
batch_size : int.
random_shuffle : bool
If true, the dataloader shuffle the training data.
Returns
------------
dataloader:
Dataloader with batch generator.
| batchlize | python | OptimalScale/LMFlow | src/lmflow/utils/data_utils.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/data_utils.py | Apache-2.0 |
def preview_file(file_path: str, chars: int = 100):
"""
Returns the first and last specified number of characters from a file
without loading the entire file into memory, working with any file type.
Args:
file_path (str): Path to the file to be previewed
chars (int, optional): Numbe... |
Returns the first and last specified number of characters from a file
without loading the entire file into memory, working with any file type.
Args:
file_path (str): Path to the file to be previewed
chars (int, optional): Number of characters to show from start and end. Defaults to 100... | preview_file | python | OptimalScale/LMFlow | src/lmflow/utils/data_utils.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/utils/data_utils.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 Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
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.