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Explicitly Type CorpusSample in ES #369

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Explicitly Type CorpusSample in ES #369

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Explicitly Type CorpusSample in ES
boomanaiden154 Sep 16, 2024
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boomanaiden154 Sep 16, 2024
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11 changes: 9 additions & 2 deletions compiler_opt/es/blackbox_learner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -82,6 +82,12 @@ class BlackboxLearnerConfig:
step_size: float


@dataclasses.dataclass(frozen=True)
class CorpusSample:
"""A sample of a corpus."""
modules: List[corpus.ModuleSpec]


def _prune_skipped_perturbations(perturbations: List[npt.NDArray[np.float32]],
rewards: List[Optional[float]]):
"""Remove perturbations that were skipped during the training step.
Expand Down Expand Up @@ -250,8 +256,9 @@ def _get_results(
perturbations: List[bytes]) -> List[concurrent.futures.Future]:
if not self._samples:
for _ in range(self._config.total_num_perturbations):
sample = self._train_corpus.sample(
self._config.num_ir_repeats_within_worker)
sample = CorpusSample(
self._train_corpus.sample(
self._config.num_ir_repeats_within_worker))
self._samples.append(sample)
# add copy of sample for antithetic perturbation pair
if self._config.est_type == (
Expand Down
6 changes: 3 additions & 3 deletions compiler_opt/es/blackbox_learner_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,6 @@
import concurrent.futures
import gin
import tempfile
from typing import List
import numpy as np
import numpy.typing as npt
import tensorflow as tf
Expand All @@ -45,8 +44,9 @@ def __init__(self, arg, *, kwarg):
self._kwarg = kwarg
self.function_value = 0.0

def compile(self, policy: bytes, samples: List[corpus.ModuleSpec]) -> float:
if policy and samples:
def compile(self, policy: bytes,
samples: blackbox_learner.CorpusSample) -> float:
if policy and samples.modules:
self.function_value += 1.0
return self.function_value
else:
Expand Down