Source code for oumi.core.configs.analyze_config
# Copyright 2025 - Oumi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Optional
from omegaconf import MISSING
from oumi.core.configs.base_config import BaseConfig
from oumi.core.configs.params.base_params import BaseParams
[docs]
class DatasetSource(Enum):
"""Source of the dataset for analysis.
.. deprecated::
This enum is deprecated and will be removed in a future release.
The dataset source is now automatically determined based on whether
a dataset is passed directly to DatasetAnalyzer.__init__().
"""
CONFIG = "config"
"""Load dataset from config parameters (dataset_name, dataset_path, etc.)"""
DIRECT = "direct"
"""Pass dataset directly to DatasetAnalyzer.__init__()"""
[docs]
@dataclass
class SampleAnalyzerParams(BaseParams):
"""Params for a single sample analyzer plugin."""
id: str = MISSING
"""Unique identifier for the analyzer."""
params: dict[str, Any] = field(default_factory=dict)
"""Analyzer-specific parameters passed to the analyzer constructor."""
[docs]
@dataclass
class AnalyzeConfig(BaseConfig):
"""Configuration for dataset analysis and aggregation."""
dataset_source: Optional[DatasetSource] = None
"""Source of the dataset for analysis.
.. deprecated::
This field is deprecated and will be removed in a future release.
The dataset source is now automatically determined based on whether
a dataset is passed directly to DatasetAnalyzer.__init__().
"""
dataset_format: Optional[str] = None
"""Format of the custom dataset.
.. deprecated::
This field is deprecated and will be removed in a future release.
The dataset format is now automatically detected from the file contents.
"""
dataset_name: Optional[str] = None
"""Dataset name."""
dataset_path: Optional[str] = None
"""Path to a custom dataset file (JSON or JSONL format).
If provided, this takes precedence over dataset_name for loading custom datasets.
"""
split: str = "train"
"""The split of the dataset to load.
This is typically one of "train", "test", or "validation". Defaults to "train".
"""
subset: Optional[str] = None
"""The subset of the dataset to load. If None, uses the base dataset."""
sample_count: Optional[int] = None
"""The number of examples to sample from the dataset.
If None, uses the full dataset. If specified, must be non-negative.
"""
output_path: str = "."
"""Directory path where output files will be saved.
Defaults to current directory ('.').
"""
analyzers: list[SampleAnalyzerParams] = field(default_factory=list)
"""List of analyzer configurations (plugin-style)."""
# Tokenizer configuration
tokenizer_name: Optional[str] = None
"""The name or path of the tokenizer to use for token counting metrics.
If None, no tokenizer will be used. This is typically a model identifier
from HuggingFace Hub (e.g., "openai-community/gpt2").
"""
tokenizer_kwargs: dict[str, Any] = field(default_factory=dict)
"""Additional keyword arguments to pass to the tokenizer constructor."""
tokenizer_config: Optional[dict[str, Any]] = None
"""Tokenizer configuration for building a tokenizer.
.. deprecated::
This field is deprecated and will be removed in a future release.
Use 'tokenizer_name' and 'tokenizer_kwargs' instead.
"""
# Processor parameters for vision-language datasets
processor_name: Optional[str] = None
"""Processor name for vision-language datasets.
If provided, the dataset will be treated as multimodal (vision-language).
"""
processor_kwargs: dict[str, Any] = field(default_factory=dict)
"""Processor-specific parameters."""
trust_remote_code: bool = False
"""Whether to trust remote code for tokenizer/processor loading."""
is_multimodal: Optional[bool] = None
"""Whether to treat the dataset as multimodal (vision-language).
.. deprecated::
This field is deprecated and will be removed in a future release.
Multimodality is now automatically detected based on whether
'processor_name' is provided.
"""
[docs]
def __post_init__(self):
"""Validates the configuration parameters."""
# Emit deprecation warnings for deprecated fields
if self.dataset_source is not None:
warnings.warn(
"The 'dataset_source' field is deprecated and will be removed in a "
"future release. The dataset source is now automatically determined "
"based on whether a dataset is passed directly to "
"DatasetAnalyzer.__init__(). This field is ignored.",
DeprecationWarning,
stacklevel=2,
)
if self.dataset_format is not None:
warnings.warn(
"The 'dataset_format' field is deprecated and will be removed in a "
"future release. The dataset format is now automatically detected "
"from the file contents. This field is ignored.",
DeprecationWarning,
stacklevel=2,
)
# Handle deprecated tokenizer_config field
if self.tokenizer_config is not None:
warnings.warn(
"The 'tokenizer_config' field is deprecated and will be removed in a "
"future release. Use 'tokenizer_name' and 'tokenizer_kwargs' instead. "
"Values from 'tokenizer_config' will be used for this run.",
DeprecationWarning,
stacklevel=2,
)
# Migrate values from tokenizer_config to new fields if not already set
if self.tokenizer_name is None and "model_name" in self.tokenizer_config:
self.tokenizer_name = self.tokenizer_config["model_name"]
if (
not self.tokenizer_kwargs
and "tokenizer_kwargs" in self.tokenizer_config
):
self.tokenizer_kwargs = self.tokenizer_config["tokenizer_kwargs"]
# trust_remote_code from tokenizer_config only applies if not explicitly set
if (
"trust_remote_code" in self.tokenizer_config
and self.tokenizer_config["trust_remote_code"]
and not self.trust_remote_code
):
self.trust_remote_code = self.tokenizer_config["trust_remote_code"]
# Handle deprecated is_multimodal field
if self.is_multimodal is not None:
warnings.warn(
"The 'is_multimodal' field is deprecated and will be removed in a "
"future release. Multimodality is now automatically detected based "
"on whether 'processor_name' is provided. This field is ignored.",
DeprecationWarning,
stacklevel=2,
)
# Validate sample_count
if self.sample_count is not None and self.sample_count <= 0:
raise ValueError("`sample_count` must be greater than 0.")
# Validate analyzer configurations
analyzer_ids = set()
for analyzer in self.analyzers:
# Validate analyzer ID
if not analyzer.id:
raise ValueError("Analyzer 'id' must be provided")
if analyzer.id in analyzer_ids:
raise ValueError(f"Duplicate analyzer ID found: '{analyzer.id}'")
analyzer_ids.add(analyzer.id)
