mirror of
https://github.com/facebookresearch/blt.git
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fc3399ef40
- Create a common class to use in all inheritance for states - Add a limit iterator that we can use in evals - Modify ArrowFileIterator behavior to not do arrow path inference if file_format='json' - Make EvalArgs valid - Move testing iterators to a common directory to allow usage in multiple test files - Make it so that SequenceIterator can take a None rng_state, to disable all rng ops (for eval mainly) Test Plan: - `pytest bytelatent` - `python -m bytelatent.train config=../internal-blt/configs/entropy_model.yaml logging.wandb=null eval=null`
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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from typing import Any
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import numpy as np
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from pydantic import ConfigDict
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from bytelatent.data.iterators.abstract_iterator import (
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PydanticIteratorState,
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StatefulIterator,
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)
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from bytelatent.data.iterators.sequence_iterator import SequenceIteratorState
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class SamplingIteratorState(PydanticIteratorState):
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model_config = ConfigDict(extra="forbid")
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rng_state: dict[str, Any]
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source_to_weight: dict[str, float]
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source_to_iterator_state: dict[str, SequenceIteratorState]
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def build(self) -> "SamplingIterator":
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return SamplingIterator(
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rng_state=self.rng_state,
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source_to_weight=self.source_to_weight,
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source_to_iterator={
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source: state.build()
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for source, state in self.source_to_iterator_state.items()
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},
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)
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class SamplingIterator(StatefulIterator):
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def __init__(
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self,
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*,
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rng_state: dict[str, Any],
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source_to_weight: dict[str, float],
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source_to_iterator: dict[str, StatefulIterator],
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):
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self.rng = np.random.default_rng()
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self.rng.bit_generator.state = rng_state
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self.source_to_weight = source_to_weight
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self.source_to_iterator = source_to_iterator
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def get_state(self) -> SamplingIteratorState:
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return SamplingIteratorState(
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rng_state=self.rng.bit_generator.state,
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source_to_weight=self.source_to_weight,
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source_to_iterator_state={
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source: iterator.get_state()
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for source, iterator in self.source_to_iterator.items()
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},
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)
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def create_iter(self):
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n_sources = len(self.source_to_weight)
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possible_sources = []
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weights = []
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for source, w in self.source_to_weight.items():
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possible_sources.append(source)
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weights.append(w)
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source_to_python_iter = {
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source: self.source_to_iterator[source].create_iter()
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for source in possible_sources
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}
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while True:
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norm_weights = np.array(weights) / np.array(weights).sum()
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source_choice = possible_sources[self.rng.choice(n_sources, p=norm_weights)]
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yield next(source_to_python_iter[source_choice])
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