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 __iter__(self) -> Iterator[tuple[str, Any]]:
"""Returns an iterator over field names and values.
Note: for an attribute to be a field, it must be declared in the
dataclass definition and have a type annotation.
"""
for param in dataclasses.fields(self):
yield par... | Returns an iterator over field names and values.
Note: for an attribute to be a field, it must be declared in the
dataclass definition and have a type annotation.
| __iter__ | python | oumi-ai/oumi | src/oumi/core/configs/base_config.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/base_config.py | Apache-2.0 |
def messages(self) -> list[Message]:
"""Returns the messages in oumi format.
This will include the judge system prompt, and any few-shot examples.
"""
messages = [Message(content=self.system_prompt, role=Role.SYSTEM)]
return messages + [e.message for e in self.examples] | Returns the messages in oumi format.
This will include the judge system prompt, and any few-shot examples.
| messages | python | oumi-ai/oumi | src/oumi/core/configs/judge_config.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/judge_config.py | Apache-2.0 |
def load(cls: type, filename: str) -> "JudgeAttribute[T]":
"""Loads the judge attribute config from a file."""
path = Path(filename)
if not path.exists():
raise FileNotFoundError(path)
return cls.model_validate_json(path.read_text()) | Loads the judge attribute config from a file. | load | python | oumi-ai/oumi | src/oumi/core/configs/judge_config.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/judge_config.py | Apache-2.0 |
def find_model_hf_config(
model_name: str,
*,
trust_remote_code: bool,
revision: Optional[str] = None,
**kwargs: dict[str, Any],
):
"""Finds HF model config by model name."""
hf_config, unused_kwargs = transformers.AutoConfig.from_pretrained(
model_name,
trust_remote_code=tru... | Finds HF model config by model name. | find_model_hf_config | python | oumi-ai/oumi | src/oumi/core/configs/internal/supported_models.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/internal/supported_models.py | Apache-2.0 |
def _create_default_vlm_config(
*,
supports_multiple_images: bool = False,
pixel_values_variable_shape: bool = False,
pixel_values_first_dim_action: InternalFeatureFirstDimAction = (
InternalFeatureFirstDimAction.DROP_IF_DUMMY
),
) -> InternalModelConfig:
"""Creates a default configurati... | Creates a default configuration for vision-language models.
This function provides a base configuration that can be used for most VLMs.
It sets up the basic visual features and configurations that VLMs typically need.
Args:
supports_multiple_images: Whether the model can process multiple images in... | _create_default_vlm_config | python | oumi-ai/oumi | src/oumi/core/configs/internal/supported_models.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/internal/supported_models.py | Apache-2.0 |
def _create_molmo_vlm_config() -> InternalModelConfig:
"""Creates a config for Molmo VLM model.
Molmo uses a specific set of features including image masks and input indices
for handling images in the model. The config is set up to handle these
features appropriately.
"""
config = InternalModel... | Creates a config for Molmo VLM model.
Molmo uses a specific set of features including image masks and input indices
for handling images in the model. The config is set up to handle these
features appropriately.
| _create_molmo_vlm_config | python | oumi-ai/oumi | src/oumi/core/configs/internal/supported_models.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/internal/supported_models.py | Apache-2.0 |
def get_all_models_map() -> Mapping[
str, # model type
_ModelTypeInfo,
]:
"""Creates a map of all supported models with their configurations.
This is the central registry of the non-standard models supported by the Oumi
framework. Each entry maps a model type (as defined in the HuggingFace model c... | Creates a map of all supported models with their configurations.
This is the central registry of the non-standard models supported by the Oumi
framework. Each entry maps a model type (as defined in the HuggingFace model config)
to its corresponding configuration and metadata.
Returns:
An immut... | get_all_models_map | python | oumi-ai/oumi | src/oumi/core/configs/internal/supported_models.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/internal/supported_models.py | Apache-2.0 |
def is_custom_model(model_name: str) -> bool:
"""Determines whether the model is a custom model defined in oumi registry."""
result: bool = len(model_name) > 0 and REGISTRY.contains(
name=model_name, type=RegistryType.MODEL
)
return result | Determines whether the model is a custom model defined in oumi registry. | is_custom_model | python | oumi-ai/oumi | src/oumi/core/configs/internal/supported_models.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/internal/supported_models.py | Apache-2.0 |
def find_internal_model_config_using_model_name(
model_name: str, trust_remote_code: bool
) -> Optional[InternalModelConfig]:
"""Finds an internal model config for supported models using model name.
Args:
model_name: The model name, either:
- A HuggingFace model ID (e.g., "meta-llama/Ll... | Finds an internal model config for supported models using model name.
Args:
model_name: The model name, either:
- A HuggingFace model ID (e.g., "meta-llama/Llama-2-7b-hf")
- A local path to a model directory
- A custom model name registered in Oumi
trust_remote_c... | find_internal_model_config_using_model_name | python | oumi-ai/oumi | src/oumi/core/configs/internal/supported_models.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/internal/supported_models.py | Apache-2.0 |
def find_internal_model_config(
model_params: ModelParams,
) -> Optional[InternalModelConfig]:
"""Finds an internal model config for supported models using `ModelParams`.
Args:
model_params: The model parameters.
Returns:
Model config, or `None` if model is not recognized.
"""
... | Finds an internal model config for supported models using `ModelParams`.
Args:
model_params: The model parameters.
Returns:
Model config, or `None` if model is not recognized.
| find_internal_model_config | python | oumi-ai/oumi | src/oumi/core/configs/internal/supported_models.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/internal/supported_models.py | Apache-2.0 |
def _finalize_and_validate(self, validated: Optional[set[int]]) -> None:
"""Recursively finalizes and validates the parameters."""
if validated is None:
validated = set()
# If this object has already been validated, return immediately
if id(self) in validated:
re... | Recursively finalizes and validates the parameters. | _finalize_and_validate | python | oumi-ai/oumi | src/oumi/core/configs/params/base_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/base_params.py | Apache-2.0 |
def get_literal_value(self) -> Literal["first_exhausted", "all_exhausted"]:
"""Returns a literal value of the enum."""
if self.value == MixtureStrategy.FIRST_EXHAUSTED:
return "first_exhausted"
elif self.value == MixtureStrategy.ALL_EXHAUSTED:
return "all_exhausted"
... | Returns a literal value of the enum. | get_literal_value | python | oumi-ai/oumi | src/oumi/core/configs/params/data_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/data_params.py | Apache-2.0 |
def get_split(self, split: DatasetSplit) -> DatasetSplitParams:
"""A public getting for individual dataset splits."""
if split == DatasetSplit.TRAIN:
return self.train
elif split == DatasetSplit.TEST:
return self.test
elif split == DatasetSplit.VALIDATION:
... | A public getting for individual dataset splits. | get_split | python | oumi-ai/oumi | src/oumi/core/configs/params/data_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/data_params.py | Apache-2.0 |
def get_evaluation_backend(self) -> EvaluationBackend:
"""Returns the evaluation backend as an Enum."""
if not self.evaluation_backend:
raise ValueError(
"Missing `evaluation_backend`. When running evaluations, it is "
"necessary to specify the evaluation back... | Returns the evaluation backend as an Enum. | get_evaluation_backend | python | oumi-ai/oumi | src/oumi/core/configs/params/evaluation_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/evaluation_params.py | Apache-2.0 |
def to_torch(self) -> torch_fsdp.ShardingStrategy:
"""Convert the enum to the corresponding torch_fsdp.ShardingStrategy."""
strategy_map = {
ShardingStrategy.FULL_SHARD: torch_fsdp.ShardingStrategy.FULL_SHARD,
ShardingStrategy.SHARD_GRAD_OP: torch_fsdp.ShardingStrategy.SHARD_GRAD... | Convert the enum to the corresponding torch_fsdp.ShardingStrategy. | to_torch | python | oumi-ai/oumi | src/oumi/core/configs/params/fsdp_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/fsdp_params.py | Apache-2.0 |
def to_torch(self) -> torch_fsdp.StateDictType:
"""Converts to the corresponding torch.distributed.fsdp.StateDictType."""
state_dict_map = {
StateDictType.FULL_STATE_DICT: torch_fsdp.StateDictType.FULL_STATE_DICT,
StateDictType.SHARDED_STATE_DICT: (
torch_fsdp.Sta... | Converts to the corresponding torch.distributed.fsdp.StateDictType. | to_torch | python | oumi-ai/oumi | src/oumi/core/configs/params/fsdp_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/fsdp_params.py | Apache-2.0 |
def to_torch(self) -> Optional[torch_fsdp.BackwardPrefetch]:
"""Convert the enum to the corresponding torch_fsdp.BackwardPrefetch."""
map = {
BackwardPrefetch.BACKWARD_PRE: torch_fsdp.BackwardPrefetch.BACKWARD_PRE,
BackwardPrefetch.BACKWARD_POST: torch_fsdp.BackwardPrefetch.BACKW... | Convert the enum to the corresponding torch_fsdp.BackwardPrefetch. | to_torch | python | oumi-ai/oumi | src/oumi/core/configs/params/fsdp_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/fsdp_params.py | Apache-2.0 |
def to_hf_trainer_kwargs(self) -> dict[str, Any]:
"""Converts GrpoParams to TRL's GRPOConfig kwargs."""
result = {}
if len(self.model_init_kwargs) > 0:
result["model_init_kwargs"] = self.model_init_kwargs
if self.max_prompt_length is not None:
result["max_prompt_l... | Converts GrpoParams to TRL's GRPOConfig kwargs. | to_hf_trainer_kwargs | python | oumi-ai/oumi | src/oumi/core/configs/params/grpo_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/grpo_params.py | Apache-2.0 |
def __finalize_and_validate__(self):
"""Finalizes and validates final config params."""
# If the user didn't specify a LoRA adapter, check to see if the dir/repo
# specified by `model_name` contains an adapter, and set `adapter_name` if so.
if self.adapter_model is None:
# Th... | Finalizes and validates final config params. | __finalize_and_validate__ | python | oumi-ai/oumi | src/oumi/core/configs/params/model_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/model_params.py | Apache-2.0 |
def to_bits_and_bytes(self) -> BitsAndBytesConfig:
"""Creates a configuration for quantized models via BitsAndBytes.
The resulting configuration uses the instantiated peft parameters.
"""
quantization_config = BitsAndBytesConfig(
load_in_4bit=self.q_lora_bits == 4,
... | Creates a configuration for quantized models via BitsAndBytes.
The resulting configuration uses the instantiated peft parameters.
| to_bits_and_bytes | python | oumi-ai/oumi | src/oumi/core/configs/params/peft_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/peft_params.py | Apache-2.0 |
def _check_attribute_ids(self, attribute_ids: set[str], id: str):
"""Check if the attribute ID is already in the set."""
if id in attribute_ids:
raise ValueError(
f"GeneralSynthesisParams contains duplicate attribute IDs: {id}"
)
attribute_ids.add(id) | Check if the attribute ID is already in the set. | _check_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_dataset_source_attribute_ids(self, all_attribute_ids: set[str]) -> None:
"""Check attribute IDs from dataset sources for uniqueness."""
if self.input_data is None:
return
if len(self.input_data) == 0:
raise ValueError("GeneralSynthesisParams.input_data cannot ... | Check attribute IDs from dataset sources for uniqueness. | _check_dataset_source_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_document_source_attribute_ids(self, all_attribute_ids: set[str]) -> None:
"""Check attribute IDs from document sources for uniqueness."""
if self.input_documents is None:
return
if len(self.input_documents) == 0:
raise ValueError("GeneralSynthesisParams.input_... | Check attribute IDs from document sources for uniqueness. | _check_document_source_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_example_source_attribute_ids(self, all_attribute_ids: set[str]) -> None:
"""Check attribute IDs from example sources for uniqueness."""
if self.input_examples is None:
return
if len(self.input_examples) == 0:
raise ValueError("GeneralSynthesisParams.input_exam... | Check attribute IDs from example sources for uniqueness. | _check_example_source_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_permutable_attribute_ids(self, all_attribute_ids: set[str]) -> None:
"""Check attribute IDs from permutable attributes for uniqueness."""
if self.permutable_attributes is None:
return
if len(self.permutable_attributes) == 0:
raise ValueError(
"... | Check attribute IDs from permutable attributes for uniqueness. | _check_permutable_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_generated_attribute_ids(self, all_attribute_ids: set[str]) -> None:
"""Check attribute IDs from generated attributes for uniqueness."""
if self.generated_attributes is None:
return
if len(self.generated_attributes) == 0:
raise ValueError(
"Gene... | Check attribute IDs from generated attributes for uniqueness. | _check_generated_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_transformed_attribute_ids(self, all_attribute_ids: set[str]) -> None:
"""Check attribute IDs from transformed attributes for uniqueness."""
if self.transformed_attributes is None:
return
if len(self.transformed_attributes) == 0:
raise ValueError(
... | Check attribute IDs from transformed attributes for uniqueness. | _check_transformed_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_combination_sampling_sample_rates(self) -> None:
"""Validate that the combination sample rates are <= 1.0."""
if self.combination_sampling is None:
return
if len(self.combination_sampling) == 0:
raise ValueError(
"GeneralSynthesisParams.combina... | Validate that the combination sample rates are <= 1.0. | _check_combination_sampling_sample_rates | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def _check_passthrough_attribute_ids(self) -> None:
"""Validate that passthrough attributes are non-empty when defined."""
if self.passthrough_attributes is None:
return
if len(self.passthrough_attributes) == 0:
raise ValueError(
"GeneralSynthesisParams.p... | Validate that passthrough attributes are non-empty when defined. | _check_passthrough_attribute_ids | python | oumi-ai/oumi | src/oumi/core/configs/params/synthesis_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/synthesis_params.py | Apache-2.0 |
def to_hf(self):
"""Converts Oumi config to HuggingFace's TrainingArguments."""
save_strategy: str = "no"
if self.save_epoch:
save_strategy = "epoch"
if self.save_steps > 0:
save_strategy = "steps"
dataloader_num_workers = 0
if isinstance(self.dat... | Converts Oumi config to HuggingFace's TrainingArguments. | to_hf | python | oumi-ai/oumi | src/oumi/core/configs/params/training_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/training_params.py | Apache-2.0 |
def _get_hf_report_to(self) -> list[str]:
"""Gets the list of reporting tools enabled for the current instance.
Returns:
list: A list of reporting tools enabled.
Possible values are "wandb", "tensorboard", or "none".
"""
report_to = []
if self.enable_... | Gets the list of reporting tools enabled for the current instance.
Returns:
list: A list of reporting tools enabled.
Possible values are "wandb", "tensorboard", or "none".
| _get_hf_report_to | python | oumi-ai/oumi | src/oumi/core/configs/params/training_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/training_params.py | Apache-2.0 |
def telemetry_dir(self) -> Optional[Path]:
"""Returns the telemetry stats output directory."""
result: Optional[Path] = None
if self.telemetry.telemetry_dir:
result = Path(self.telemetry.telemetry_dir)
if self.output_dir:
output_dir = Path(self.output_dir)
... | Returns the telemetry stats output directory. | telemetry_dir | python | oumi-ai/oumi | src/oumi/core/configs/params/training_params.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/configs/params/training_params.py | Apache-2.0 |
def __init__(
self,
*,
dataset_name: Optional[str] = None,
dataset_path: Optional[str] = None,
split: Optional[str] = None,
tokenizer: Optional[BaseTokenizer] = None,
return_tensors: bool = False,
**kwargs,
) -> None:
"""Initializes a new insta... | Initializes a new instance of the BaseExperimentalDpoDataset class. | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_dpo_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_dpo_dataset.py | Apache-2.0 |
def transform_preference(self, samples: dict) -> dict:
"""Transform the samples to the Oumi format."""
prompt = samples[_PROMPT_KEY]
chosen_chat = samples[_CHOSEN_KEY]
rejected_chat = samples[_REJECTED_KEY]
chosen_chat_response = self._extract_from_chat_format(chosen_chat)
... | Transform the samples to the Oumi format. | transform_preference | python | oumi-ai/oumi | src/oumi/core/datasets/base_dpo_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_dpo_dataset.py | Apache-2.0 |
def _extract_from_chat_format(self, sample: dict) -> str:
"""Extract the last 'assistant' turn in the chat."""
for turn in sample[::-1]:
if turn[_ROLE] == _ASSISTANT:
return turn[_CONTENT]
raise ValueError("No chat turn was found with an 'assistant' role.") | Extract the last 'assistant' turn in the chat. | _extract_from_chat_format | python | oumi-ai/oumi | src/oumi/core/datasets/base_dpo_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_dpo_dataset.py | Apache-2.0 |
def __init__(
self,
*,
dataset_name: Optional[str] = None,
dataset_path: Optional[str] = None,
split: Optional[str] = None,
**kwargs,
) -> None:
"""Initializes a new instance of the BaseExperimentalGrpoDataset class."""
super().__init__(
da... | Initializes a new instance of the BaseExperimentalGrpoDataset class. | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_grpo_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_grpo_dataset.py | Apache-2.0 |
def _transform_grpo_example(self, example: Union[dict, pd.Series]) -> dict:
"""Validate and transform the GRPO sample into Python `dict`."""
for required_key in (_PROMPT_KEY, _COMPLETION_KEY):
if required_key not in example:
raise ValueError(
f"Example doe... | Validate and transform the GRPO sample into Python `dict`. | _transform_grpo_example | python | oumi-ai/oumi | src/oumi/core/datasets/base_grpo_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_grpo_dataset.py | Apache-2.0 |
def transform_conversation(self, sample: Union[dict, pd.Series]) -> Conversation:
"""Converts the input sample to a Conversation.
Args:
sample (Union[dict, pd.Series]): The input example.
Returns:
Conversation: The resulting conversation.
"""
# Contains... | Converts the input sample to a Conversation.
Args:
sample (Union[dict, pd.Series]): The input example.
Returns:
Conversation: The resulting conversation.
| transform_conversation | python | oumi-ai/oumi | src/oumi/core/datasets/base_grpo_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_grpo_dataset.py | Apache-2.0 |
def __init__(
self,
*,
dataset_name: Optional[str] = None,
dataset_path: Optional[str] = None,
subset: Optional[str] = None,
split: Optional[str] = None,
trust_remote_code: bool = False,
stream: bool = True,
**kwargs,
) -> None:
"""Init... | Initializes a new instance of the BaseIterableDataset class. | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_iterable_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_iterable_dataset.py | Apache-2.0 |
def to_hf(self, return_iterable: bool = True) -> datasets.IterableDataset:
"""Converts the dataset to a Hugging Face dataset."""
if not return_iterable:
raise NotImplementedError("Only returning IterableDataset is supported.")
return datasets.IterableDataset.from_generator(self.__ite... | Converts the dataset to a Hugging Face dataset. | to_hf | python | oumi-ai/oumi | src/oumi/core/datasets/base_iterable_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_iterable_dataset.py | Apache-2.0 |
def _load_data(self) -> Iterable[Any]:
"""Loads the dataset from the specified source."""
if self.dataset_path:
result = self._load_local_dataset(self.dataset_path)
else:
result = self._load_hf_hub_dataset()
return result | Loads the dataset from the specified source. | _load_data | python | oumi-ai/oumi | src/oumi/core/datasets/base_iterable_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_iterable_dataset.py | Apache-2.0 |
def __init__(
self,
*,
dataset_name: Optional[str],
dataset_path: Optional[str] = None,
subset: Optional[str] = None,
split: Optional[str] = None,
trust_remote_code: bool = False,
transform_num_workers: Optional[Union[str, int]] = None,
**kwargs,
... | Initializes a new instance of the BaseDataset class. | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def __getitem__(self, idx: int) -> dict:
"""Gets the item at the specified index.
Args:
idx (int): The index of the item to retrieve.
Returns:
dict: The item at the specified index.
"""
sample = self.raw(idx)
processed = self.transform(sample)
... | Gets the item at the specified index.
Args:
idx (int): The index of the item to retrieve.
Returns:
dict: The item at the specified index.
| __getitem__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def _as_generator_over_shards(
self, shards: list[_ExamplesIndicesRange]
) -> Generator[dict[str, Any], None, None]:
"""Returns a sharded generator for the dataset."""
for shard in shards:
for idx in range(shard.start_index, shard.end_index):
yield self[idx] | Returns a sharded generator for the dataset. | _as_generator_over_shards | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def _detect_features_and_estimate_element_size_bytes(
self, samples_iter: Iterable[dict[str, Any]]
) -> _InferredFeatureMap:
"""Returns an estimate of max element size in bytes."""
samples_list = list(samples_iter)
def _dummy_generator():
yield from samples_list
... | Returns an estimate of max element size in bytes. | _detect_features_and_estimate_element_size_bytes | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def _compute_effective_transform_num_workers(self) -> int:
"""Returns an effective number of dataset transform workers.
Guaranteed to be a positive integer (>= 1). 1 if no parallelism is used.
"""
num_proc = None
if self.transform_num_workers is not None:
if isinstan... | Returns an effective number of dataset transform workers.
Guaranteed to be a positive integer (>= 1). 1 if no parallelism is used.
| _compute_effective_transform_num_workers | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def to_hf(
self, return_iterable: bool = False
) -> Union[datasets.Dataset, datasets.IterableDataset]:
"""Converts the dataset to a Hugging Face dataset.
Args:
return_iterable: Whether to return an iterable dataset.
Iterable datasets aren't cached to disk, which ... | Converts the dataset to a Hugging Face dataset.
Args:
return_iterable: Whether to return an iterable dataset.
Iterable datasets aren't cached to disk, which can sometimes be
advantageous. For example, if transformed examples are very large
(e.g., if `... | to_hf | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def _load_data(self) -> pd.DataFrame:
"""Loads the dataset from the specified source.
Returns:
dict: The loaded dataset.
"""
if self.dataset_path:
result = self._load_local_dataset(self.dataset_path)
else:
result = self._load_hf_hub_dataset()
... | Loads the dataset from the specified source.
Returns:
dict: The loaded dataset.
| _load_data | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def _load_local_dataset(self, path: str) -> pd.DataFrame:
"""Loads the dataset from the specified local source.
Returns:
dict: The loaded dataset.
"""
dataset_path = Path(path)
if not dataset_path.exists():
raise FileNotFoundError(f"File not found: {data... | Loads the dataset from the specified local source.
Returns:
dict: The loaded dataset.
| _load_local_dataset | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def _load_hf_hub_dataset(self) -> pd.DataFrame:
"""Loads the dataset from the specified Hugging Face Hub source.
Returns:
dict: The loaded dataset.
"""
splits_or_dataset = datasets.load_dataset(
path=self.dataset_name,
name=self.dataset_subset,
... | Loads the dataset from the specified Hugging Face Hub source.
Returns:
dict: The loaded dataset.
| _load_hf_hub_dataset | python | oumi-ai/oumi | src/oumi/core/datasets/base_map_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_map_dataset.py | Apache-2.0 |
def __init__(
self,
*,
tokenizer: BaseTokenizer,
seq_length: int,
dataset_text_field: str = "text",
append_concat_token: bool = True,
add_special_tokens: bool = True,
skip_last: bool = True,
**kwargs,
):
"""Initializes a new instance of... | Initializes a new instance of the BasePretrainingDataset class. | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_pretraining_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_pretraining_dataset.py | Apache-2.0 |
def __iter__(self):
"""Iterates over the dataset and yields samples of a specified sequence length.
The underlying dataset is a stream of documents. Each document is expected to
contain a text field `self._dataset_text_field` that will be tokenized.
Training samples are then yielded in ... | Iterates over the dataset and yields samples of a specified sequence length.
The underlying dataset is a stream of documents. Each document is expected to
contain a text field `self._dataset_text_field` that will be tokenized.
Training samples are then yielded in sequences of length `self.seq_l... | __iter__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_pretraining_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_pretraining_dataset.py | Apache-2.0 |
def tokenize(self, text: str) -> list[int]:
"""Tokenizes the given text.
Should not apply any padding/truncation to allow for packing.
"""
return self.tokenizer.encode(
text=text,
return_tensors=None,
max_length=None,
padding=False,
... | Tokenizes the given text.
Should not apply any padding/truncation to allow for packing.
| tokenize | python | oumi-ai/oumi | src/oumi/core/datasets/base_pretraining_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_pretraining_dataset.py | Apache-2.0 |
def _create_training_sample(self, tokens: list) -> dict[str, torch.Tensor]:
"""Creates a training sample from the given tokens."""
input_ids = torch.tensor(tokens)
attention_mask = torch.ones_like(input_ids)
return {
"input_ids": input_ids,
"attention_mask": atten... | Creates a training sample from the given tokens. | _create_training_sample | python | oumi-ai/oumi | src/oumi/core/datasets/base_pretraining_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_pretraining_dataset.py | Apache-2.0 |
def __init__(
self,
*,
dataset_name: Optional[str] = None,
dataset_path: Optional[str] = None,
split: Optional[str] = None,
tokenizer: Optional[BaseTokenizer] = None,
task: Literal["sft", "generation", "auto"] = "auto",
return_tensors: bool = False,
... | Initializes a new instance of the BaseSftDataset class. | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/base_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_sft_dataset.py | Apache-2.0 |
def transform(self, sample: pd.Series) -> dict:
"""Preprocesses the inputs in the given sample."""
conversation = self.transform_conversation(sample)
if self._return_conversations:
# This may require `use_torchdata=True` for TRL_SFT trainer,
# but compatible with TRL_GRPO... | Preprocesses the inputs in the given sample. | transform | python | oumi-ai/oumi | src/oumi/core/datasets/base_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_sft_dataset.py | Apache-2.0 |
def tokenize(
self,
sample: Union[dict, pd.Series, Conversation],
tokenize: bool = True,
) -> dict:
"""Applies the chat template carried by the tokenizer to the input example.
Args:
sample (Dict): Mapping `messages` to a List containing the (ordered)
... | Applies the chat template carried by the tokenizer to the input example.
Args:
sample (Dict): Mapping `messages` to a List containing the (ordered)
messages exchanged within a single chat dialogue.
Each item of example["messages"] is a dict mapping the `content` of t... | tokenize | python | oumi-ai/oumi | src/oumi/core/datasets/base_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/base_sft_dataset.py | Apache-2.0 |
def __init__(
self,
base_dataset: BaseSftDataset,
max_seq_len: int,
show_progress: bool = True,
split_samples: bool = False,
concat_token_id: Optional[int] = None,
pad_token_id: Optional[int] = None,
enable_padding: bool = True,
**kwargs,
):
... | Initialize the PackedSftDataset.
Args:
base_dataset: The base SFT dataset to pack samples from.
max_seq_len: Maximum sequence length for packed samples.
show_progress: Whether to show progress bar during packing.
Defaults to True.
split_samples: W... | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def _load_data(self) -> None:
"""Pack the base dataset into constant-length samples."""
buffer = self._get_empty_buffer()
iterator = range(len(self.base_dataset))
for idx in tqdm(
iterator,
desc="Packing dataset",
dynamic_ncols=True,
disa... | Pack the base dataset into constant-length samples. | _load_data | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
"""Get a pack from the dataset by index."""
if idx >= len(self):
raise IndexError(f"Index {idx} is out of bounds for PackedSftDataset")
return self._data[idx] | Get a pack from the dataset by index. | __getitem__ | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def _append_packed_sample_to_dataset(self, buffer: dict[str, list]) -> None:
"""Creates a fixed length training sample from the buffer and add to dataset."""
buffer_len = self._get_sample_len(buffer)
if buffer_len > self._max_seq_len:
raise ValueError(
"Buffer is too... | Creates a fixed length training sample from the buffer and add to dataset. | _append_packed_sample_to_dataset | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def _append_sample_to_buffer(
self, sample: dict[str, list], buffer: dict[str, list]
) -> None:
"""Append a single training sample to the buffer.
If concat token is enabled, and if and only if we actually concatenate
two samples, we add the concat token in between the two samples
... | Append a single training sample to the buffer.
If concat token is enabled, and if and only if we actually concatenate
two samples, we add the concat token in between the two samples
| _append_sample_to_buffer | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def _split_sample(
self, sample: dict[str, list], cutoff: int
) -> tuple[dict[str, list], dict[str, list]]:
"""Split a sample into two parts at the cutoff point.
Args:
sample: Dictionary containing lists to split
cutoff: Index at which to split the lists
Ret... | Split a sample into two parts at the cutoff point.
Args:
sample: Dictionary containing lists to split
cutoff: Index at which to split the lists
Returns:
Tuple of two dictionaries containing the split lists
| _split_sample | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def _get_empty_buffer(self) -> dict[str, list]:
"""Get an empty buffer with all required fields."""
return {
"input_ids": [],
"labels": [],
} | Get an empty buffer with all required fields. | _get_empty_buffer | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def _get_potential_sample_len(
self, sample: dict[str, list], buffer: dict[str, list]
) -> int:
"""Get the length of the samples in the buffer."""
buffer_len = self._get_sample_len(buffer)
sample_len = self._get_sample_len(sample)
# In case we don't need to add a concat toke... | Get the length of the samples in the buffer. | _get_potential_sample_len | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def _check_dataset_compatibility(self) -> None:
"""Check the base dataset for errors."""
if len(self.base_dataset) == 0:
raise ValueError("Base dataset is empty. Cannot pack empty dataset.")
keys = set(self.base_dataset[0].keys())
if "input_ids" not in keys:
rai... | Check the base dataset for errors. | _check_dataset_compatibility | python | oumi-ai/oumi | src/oumi/core/datasets/packed_sft_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/packed_sft_dataset.py | Apache-2.0 |
def __init__(
self,
tokenizer: Optional[BaseTokenizer],
dataset: datasets.Dataset,
dataset_text_field: Optional[str] = None,
formatting_func: Optional[Callable] = None,
infinite: bool = False,
seq_length: int = 1024,
sequence_buffer_size: int = 1024,
... | Iterable dataset that returns constant length chunks of tokens.
Args:
tokenizer (`BaseTokenizer`):
The tokenizer used for converting strings to tokens.
dataset (`dataset.Dataset`):
Dataset of text samples.
dataset_text_field (`str`, **optional... | __init__ | python | oumi-ai/oumi | src/oumi/core/datasets/pretraining_async_text_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/pretraining_async_text_dataset.py | Apache-2.0 |
def _add_example_to_queue(self, example):
"""Adds a single example to the queue."""
# Shuffle by using a priority queue with random priority values
# Note that the tensors themselves are identical,
# Only the order they are returned is shuffled.
priority = _SMALLEST_PRIORITY_VALU... | Adds a single example to the queue. | _add_example_to_queue | python | oumi-ai/oumi | src/oumi/core/datasets/pretraining_async_text_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/pretraining_async_text_dataset.py | Apache-2.0 |
def __iter__(self):
"""Iterates through the dataset with most work on a separate thread."""
# Set worker thread to daemon so it dies when the program finishes.
worker_thread = threading.Thread(
target=self._dataset_iterator_worker, args=(), daemon=True
)
worker_thread... | Iterates through the dataset with most work on a separate thread. | __iter__ | python | oumi-ai/oumi | src/oumi/core/datasets/pretraining_async_text_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/pretraining_async_text_dataset.py | Apache-2.0 |
def transform(self, sample: dict) -> dict:
"""Transforms an Oumi conversation into a dictionary of inputs for a model.
Args:
sample (dict): A dictionary representing a single conversation example.
Returns:
dict: A dictionary of inputs for a model.
"""
co... | Transforms an Oumi conversation into a dictionary of inputs for a model.
Args:
sample (dict): A dictionary representing a single conversation example.
Returns:
dict: A dictionary of inputs for a model.
| transform | python | oumi-ai/oumi | src/oumi/core/datasets/vision_language_dataset.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/datasets/vision_language_dataset.py | Apache-2.0 |
def evaluate(self, config: EvaluationConfig, **kwargs) -> list[EvaluationResult]:
"""Evaluates a model using the provided evaluation configuration.
Args:
config: The desired configuration for evaluation.
kwargs: Additional keyword arguments required by evaluator backends.
... | Evaluates a model using the provided evaluation configuration.
Args:
config: The desired configuration for evaluation.
kwargs: Additional keyword arguments required by evaluator backends.
Returns:
List of evaluation results (one per task, in the same order with `tas... | evaluate | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def evaluate_task(
self,
task_params: EvaluationTaskParams,
config: EvaluationConfig,
**kwargs,
) -> EvaluationResult:
"""Evaluates a model using the provided configuration on a specific task.
Args:
task_params: The task parameters for evaluation.
... | Evaluates a model using the provided configuration on a specific task.
Args:
task_params: The task parameters for evaluation.
config: The desired evaluation configuration for evaluation.
kwargs: Additional keyword arguments required by evaluator backends.
Returns:
... | evaluate_task | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def save_output(
self,
task_params: EvaluationTaskParams,
evaluation_result: EvaluationResult,
base_output_dir: str,
config: Optional[EvaluationConfig],
) -> None:
"""Saves the evaluation's output to the specified output directory.
Args:
task_para... | Saves the evaluation's output to the specified output directory.
Args:
task_params: The task parameters used for this evaluation.
evaluation_result: The evaluation result.
base_output_dir: The directory where the evaluation results will be saved.
config: The eval... | save_output | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def _get_custom_evaluation_fn(task_name: Optional[str]) -> Callable:
"""Retrieve the evaluation function of the custom task."""
if not task_name:
raise ValueError(
"Missing `task_name` for custom Oumi evaluation. Please specify the "
"task name, which should b... | Retrieve the evaluation function of the custom task. | _get_custom_evaluation_fn | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def _get_backend_task_params(
task_params: EvaluationTaskParams,
) -> Union[LMHarnessTaskParams, AlpacaEvalTaskParams]:
"""Returns the evaluation backend-specific task parameters."""
if task_params.get_evaluation_backend() == EvaluationBackend.LM_HARNESS:
target_class = LMHarness... | Returns the evaluation backend-specific task parameters. | _get_backend_task_params | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def _get_init_kwargs_for_task_params_class(
task_params: EvaluationTaskParams,
target_class: type[EvaluationTaskParams],
) -> dict[str, Any]:
"""Returns the init keyword arguments for a `target_class` of name *TaskParams.
Given a target class of name <evaluation backend>TaskParams, ... | Returns the init keyword arguments for a `target_class` of name *TaskParams.
Given a target class of name <evaluation backend>TaskParams, which subclasses
`EvaluationTaskParams`, this method returns a 'flattened' dict with all
arguments needed to instantiate it. The dict includes all the parame... | _get_init_kwargs_for_task_params_class | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def _validate_custom_kwargs(
custom_kwargs: dict[str, Any],
evaluation_fn: Callable,
evaluation_fn_name: str,
) -> None:
"""Validates the keyword arguments of the custom evaluation function."""
# Ensure that user-provided keyword arguments, which are passed into method
... | Validates the keyword arguments of the custom evaluation function. | _validate_custom_kwargs | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def _add_reserved_keys_into_custom_kwargs(
self,
custom_kwargs: dict[str, Any],
evaluation_fn: Callable,
task_params: EvaluationTaskParams,
config: EvaluationConfig,
) -> None:
"""Adds reserved keys into the keyword arguments, if needed.
Reserved keys are key... | Adds reserved keys into the keyword arguments, if needed.
Reserved keys are keys that, if defined in the custom evaluation function
(`evaluation_fn`), are automatically populated by the Evaluator. This function
is responsible to add them into the keyword arguments.
| _add_reserved_keys_into_custom_kwargs | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def _add_inference_engine_if_needed(
self,
evaluation_function: Callable,
kwargs: dict[str, Any],
config: EvaluationConfig,
) -> None:
"""Adds an inference engine to the keyword arguments (`kwargs`), if needed."""
# Check if the evaluation function requires an inferen... | Adds an inference engine to the keyword arguments (`kwargs`), if needed. | _add_inference_engine_if_needed | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def _get_inference_engine(self, config: EvaluationConfig) -> BaseInferenceEngine:
"""Returns the inference engine based on the evaluation configuration."""
if not self._inference_engine:
self._inference_engine = build_inference_engine(
engine_type=config.inference_engine,
... | Returns the inference engine based on the evaluation configuration. | _get_inference_engine | python | oumi-ai/oumi | src/oumi/core/evaluation/evaluator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/evaluator.py | Apache-2.0 |
def bootstrap(
y_true: list[int],
y_pred: list[int],
metric_fn: Callable[..., float],
alpha: float = 0.95,
n_iter: int = 1000,
sample_prop=1.0,
) -> tuple[float, float]:
"""Perform bootstrap resampling to calculate confidence intervals for a metric.
Args:
y_true (list): True lab... | Perform bootstrap resampling to calculate confidence intervals for a metric.
Args:
y_true (list): True labels.
y_pred (list): Predicted labels.
metric_fn (callable): A function that computes a performance metric. The
required signature for this function is:
`metric_f... | bootstrap | python | oumi-ai/oumi | src/oumi/core/evaluation/metrics.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/metrics.py | Apache-2.0 |
def f1_score(
y_true: list[int],
y_pred: list[int],
average: str = "binary",
pos_label: int = 1,
populate_ci: bool = True,
alpha: float = 0.95,
n_iter: int = 1000,
sample_prop=1.0,
) -> Metric:
"""Calculate the F1 score and its confidence interval."""
# Ensure that our inputs are... | Calculate the F1 score and its confidence interval. | f1_score | python | oumi-ai/oumi | src/oumi/core/evaluation/metrics.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/metrics.py | Apache-2.0 |
def evaluate(
task_params: AlpacaEvalTaskParams,
config: EvaluationConfig,
inference_engine: BaseInferenceEngine,
) -> EvaluationResult:
"""Evaluates a model using the Alpaca Eval framework.
For detailed documentation on the AlpacaEval framework, we refer you to the
following readme: https://gi... | Evaluates a model using the Alpaca Eval framework.
For detailed documentation on the AlpacaEval framework, we refer you to the
following readme: https://github.com/tatsu-lab/alpaca_eval.
Args:
task_params: The AlpacaEval parameters to use for evaluation.
config: The desired configuration f... | evaluate | python | oumi-ai/oumi | src/oumi/core/evaluation/backends/alpaca_eval.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/backends/alpaca_eval.py | Apache-2.0 |
def _generate_lm_harness_model_args(
lm_harness_model: str,
is_multimodal: bool,
device: str,
model_params: ModelParams,
generation_params: GenerationParams,
inference_engine_type: InferenceEngineType,
inference_remote_params: Optional[RemoteParams],
) -> dict[str, Any]:
"""Converts Oumi... | Converts Oumi's ModelParams to LM Harness model arguments. | _generate_lm_harness_model_args | python | oumi-ai/oumi | src/oumi/core/evaluation/backends/lm_harness.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/backends/lm_harness.py | Apache-2.0 |
def _apply_to_all_tasks(
task_dict: dict[Union[str, ConfigurableGroup], Union[Task, dict]],
fn: Callable,
fn_kwargs: Optional[dict[str, Any]] = None,
) -> None:
"""Apply the provided function `fn` to all tasks in the `task_dict`."""
fn_kwargs = fn_kwargs or {}
for task_obj in task_dict.values():... | Apply the provided function `fn` to all tasks in the `task_dict`. | _apply_to_all_tasks | python | oumi-ai/oumi | src/oumi/core/evaluation/backends/lm_harness.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/backends/lm_harness.py | Apache-2.0 |
def _get_task_dict(
task_params: LMHarnessTaskParams,
) -> dict[Union[str, ConfigurableGroup], Union[Task, dict]]:
"""Get a dictionary of LM Harness tasks, given Oumi's `task_params`."""
if not task_params.task_name:
raise ValueError("The `task_name` must be specified for LM Harness evaluation.")
... | Get a dictionary of LM Harness tasks, given Oumi's `task_params`. | _get_task_dict | python | oumi-ai/oumi | src/oumi/core/evaluation/backends/lm_harness.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/backends/lm_harness.py | Apache-2.0 |
def _set_random_seeds(random_seed, numpy_random_seed, torch_random_seed) -> None:
"""Setting random seeds for reproducibility and consistency with LM Harness."""
if random_seed is not None:
random.seed(random_seed)
if numpy_random_seed is not None:
np.random.seed(numpy_random_seed)
if ... | Setting random seeds for reproducibility and consistency with LM Harness. | _set_random_seeds | python | oumi-ai/oumi | src/oumi/core/evaluation/backends/lm_harness.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/backends/lm_harness.py | Apache-2.0 |
def evaluate(
task_params: LMHarnessTaskParams,
config: EvaluationConfig,
random_seed: Optional[int] = 0,
numpy_random_seed: Optional[int] = 1234,
torch_random_seed: Optional[int] = 1234,
) -> EvaluationResult:
"""Evaluates a model using the LM Evaluation Harness framework (EleutherAI).
For... | Evaluates a model using the LM Evaluation Harness framework (EleutherAI).
For detailed documentation, we refer you to the following readme:
https://github.com/EleutherAI/lm-evaluation-harness
Args:
task_params: The LM Harness parameters to use for evaluation.
config: The evaluation configu... | evaluate | python | oumi-ai/oumi | src/oumi/core/evaluation/backends/lm_harness.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/backends/lm_harness.py | Apache-2.0 |
def check_prerequisites(
evaluation_backend: EvaluationBackend,
task_name: Optional[str] = None,
) -> None:
"""Check whether the evaluation backend prerequisites are satisfied.
Args:
evaluation_backend: The evaluation backend that the task will run.
task_name (for LM Harness backend onl... | Check whether the evaluation backend prerequisites are satisfied.
Args:
evaluation_backend: The evaluation backend that the task will run.
task_name (for LM Harness backend only): The name of the task to run.
Raises:
RuntimeError: If the evaluation backend prerequisites are not satisfi... | check_prerequisites | python | oumi-ai/oumi | src/oumi/core/evaluation/utils/platform_prerequisites.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/utils/platform_prerequisites.py | Apache-2.0 |
def _find_non_existing_output_dir_from_base_dir(base_dir: Path) -> Path:
"""Finds a new output directory, if the provided `base_dir` already exists.
Why is this function useful?
Users may repeatedly run the same evaluation script, which will overwrite the
results of the existing output director... | Finds a new output directory, if the provided `base_dir` already exists.
Why is this function useful?
Users may repeatedly run the same evaluation script, which will overwrite the
results of the existing output directory. When this happens, we could fail, to
avoid corrupting previous evalua... | _find_non_existing_output_dir_from_base_dir | python | oumi-ai/oumi | src/oumi/core/evaluation/utils/save_utils.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/utils/save_utils.py | Apache-2.0 |
def save_evaluation_output(
backend_name: str,
task_params: EvaluationTaskParams,
evaluation_result: EvaluationResult,
base_output_dir: Optional[str],
config: Optional[EvaluationConfig],
) -> None:
"""Writes configuration settings and evaluation outputs to files.
Args:
backend_name:... | Writes configuration settings and evaluation outputs to files.
Args:
backend_name: The name of the evaluation backend used (e.g., "lm_harness").
task_params: Oumi task parameters used for this evaluation.
evaluation_result: The evaluation results to save.
base_output_dir: The direct... | save_evaluation_output | python | oumi-ai/oumi | src/oumi/core/evaluation/utils/save_utils.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/evaluation/utils/save_utils.py | Apache-2.0 |
def transform_conversation(
self, conversation: Conversation, options: Optional[FeatureGeneratorOptions]
) -> dict:
"""Transforms a single Oumi conversation into a dictionary of model inputs.
Args:
conversation: An input conversation.
options: Options for the feature... | Transforms a single Oumi conversation into a dictionary of model inputs.
Args:
conversation: An input conversation.
options: Options for the feature generator.
Returns:
dict: A dictionary of inputs for a model.
| transform_conversation | python | oumi-ai/oumi | src/oumi/core/feature_generators/base_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/base_feature_generator.py | Apache-2.0 |
def transform_conversations(
self,
conversations: list[Conversation],
options: Optional[FeatureGeneratorOptions],
) -> dict:
"""Transforms a list of Oumi conversations into a dictionary of model inputs.
Args:
conversations: A list of input conversations.
... | Transforms a list of Oumi conversations into a dictionary of model inputs.
Args:
conversations: A list of input conversations.
options: Options for the feature generator.
Returns:
dict: A dictionary of inputs for a model.
| transform_conversations | python | oumi-ai/oumi | src/oumi/core/feature_generators/base_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/base_feature_generator.py | Apache-2.0 |
def _prepare_simple_model(
self, conversation: Conversation
) -> tuple[Image.Image, str]:
"""Prepares the images and prompt for a simple model.
Simple models only use the last image and text turn in the conversation. They
don't use the chat template, so the prompt is just the last t... | Prepares the images and prompt for a simple model.
Simple models only use the last image and text turn in the conversation. They
don't use the chat template, so the prompt is just the last text turn.
| _prepare_simple_model | python | oumi-ai/oumi | src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | Apache-2.0 |
def _prepare_instruct_model(
self, conversation: Conversation
) -> tuple[list[Image.Image], str]:
"""Prepares the images and prompt for an instruct model.
Instruct models use the chat template to generate the prompt, and can include
multiple images and text turns.
"""
... | Prepares the images and prompt for an instruct model.
Instruct models use the chat template to generate the prompt, and can include
multiple images and text turns.
| _prepare_instruct_model | python | oumi-ai/oumi | src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | Apache-2.0 |
def _load_image(self, image_item: ContentItem) -> Image.Image:
"""Loads an image from a message.
Args:
image_item (`ContentItem`): A content item representing an image.
Returns:
Image.Image: A PIL image.
"""
if self._image_processor is None:
... | Loads an image from a message.
Args:
image_item (`ContentItem`): A content item representing an image.
Returns:
Image.Image: A PIL image.
| _load_image | python | oumi-ai/oumi | src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | Apache-2.0 |
def transform_conversations(
self,
conversations: list[Conversation],
options: Optional[FeatureGeneratorOptions],
) -> dict:
"""Transforms a list of Oumi conversations into a dictionary of model inputs.
Args:
conversations: An input conversation.
opti... | Transforms a list of Oumi conversations into a dictionary of model inputs.
Args:
conversations: An input conversation.
options: Options for the feature generator.
Returns:
dict: A dictionary of inputs for a model.
| transform_conversations | python | oumi-ai/oumi | src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | Apache-2.0 |
def _truncate_text_in_content_items(self, messages: list[Message]) -> list[Message]:
"""Truncates text contents in Messages to `max_length` total tokens.
Note that we have to truncate plain texts *before* we apply chat template
as the final processed prompt is generally unsafe to truncate at ar... | Truncates text contents in Messages to `max_length` total tokens.
Note that we have to truncate plain texts *before* we apply chat template
as the final processed prompt is generally unsafe to truncate at arbitrary
offset: it may break invariants (e.g., prompt contains `N` images tokens)
... | _truncate_text_in_content_items | python | oumi-ai/oumi | src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | Apache-2.0 |
def _truncate_text_pieces(self, text_pieces: list[str]) -> list[str]:
"""Truncates text pieces to total length not exceeding `max_length`."""
if not (
self._truncation and self._max_length is not None and self._max_length > 0
):
return copy.deepcopy(text_pieces)
... | Truncates text pieces to total length not exceeding `max_length`. | _truncate_text_pieces | python | oumi-ai/oumi | src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | Apache-2.0 |
def _apply_completion_only_masking(self, inputs: Any) -> None:
"""Apply masking to keep only assistant responses for loss computation."""
labels = inputs.get("labels")
input_ids = inputs.get("input_ids")
if labels is None or input_ids is None:
raise ValueError(
... | Apply masking to keep only assistant responses for loss computation. | _apply_completion_only_masking | python | oumi-ai/oumi | src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | https://github.com/oumi-ai/oumi/blob/master/src/oumi/core/feature_generators/vision_language_conversation_feature_generator.py | Apache-2.0 |
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