mirror of
https://github.com/twitter/the-algorithm-ml.git
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77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
import functools
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from tml.projects.twhin.models.config import TwhinModelConfig
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from tml.projects.twhin.models.models import TwhinModel
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from tml.optimizers.optimizer import get_optimizer_class, LRShim
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from tml.optimizers.config import get_optimizer_algorithm_config, LearningRate
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from tml.ml_logging.torch_logging import logging
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from torchrec.optim.optimizers import in_backward_optimizer_filter
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from torchrec.optim import keyed
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FUSED_OPT_KEY = "fused_opt"
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TRANSLATION_OPT_KEY = "operator_opt"
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def _lr_from_config(optimizer_config):
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"""Get the learning rate from an optimizer configuration.
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Args:
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optimizer_config: Optimizer configuration.
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Returns:
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Learning rate from the optimizer configuration.
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"""
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if optimizer_config.learning_rate is not None:
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return optimizer_config.learning_rate
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else:
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# treat None as constant lr
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lr_value = get_optimizer_algorithm_config(optimizer_config).lr
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return LearningRate(constant=lr_value)
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def build_optimizer(model: TwhinModel, config: TwhinModelConfig):
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"""Builds an optimizer for a Twhin model combining the embeddings optimizer with an optimizer for per-relation translations.
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Args:
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model: TwhinModel to build optimizer for.
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config: TwhinModelConfig for model.
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Returns:
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Optimizer for model.
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"""
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translation_optimizer_fn = functools.partial(
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get_optimizer_class(config.translation_optimizer),
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**get_optimizer_algorithm_config(config.translation_optimizer).dict(),
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)
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translation_optimizer = keyed.KeyedOptimizerWrapper(
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dict(in_backward_optimizer_filter(model.named_parameters())),
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optim_factory=translation_optimizer_fn,
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)
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lr_dict = {}
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for table in config.embeddings.tables:
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lr_dict[table.name] = _lr_from_config(table.optimizer)
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lr_dict[TRANSLATION_OPT_KEY] = _lr_from_config(config.translation_optimizer)
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logging.info(f"***** LR dict: {lr_dict} *****")
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logging.info(
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f"***** Combining fused optimizer {model.fused_optimizer} with operator optimizer: {translation_optimizer} *****"
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)
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optimizer = keyed.CombinedOptimizer(
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[
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(FUSED_OPT_KEY, model.fused_optimizer),
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(TRANSLATION_OPT_KEY, translation_optimizer),
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]
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)
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# scheduler = LRShim(optimizer, lr_dict)
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scheduler = None
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logging.info(f"***** Combined optimizer after init: {optimizer} *****")
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return optimizer, scheduler
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