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Introduce save_strategy training argument (#10286)

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* Introduce save_strategy training argument

* deprecate EvaluationStrategy

* collapse EvaluationStrategy and LoggingStrategy into a single
  IntervalStrategy enum

* modify tests to use modified enum
This commit is contained in:
Tanmay Garg
2021-02-28 06:04:22 +05:30
committed by GitHub
parent aca6288ff4
commit 256482ac92
11 changed files with 81 additions and 46 deletions
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10
tests/test_trainer.py
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@@ -21,7 +21,7 @@ import unittest
import numpy as np
from transformers import AutoTokenizer, EvaluationStrategy, PretrainedConfig, TrainingArguments, is_torch_available
from transformers import AutoTokenizer, IntervalStrategy, PretrainedConfig, TrainingArguments, is_torch_available
from transformers.file_utils import WEIGHTS_NAME
from transformers.testing_utils import (
get_tests_dir,
@@ -852,7 +852,7 @@ class TrainerIntegrationTest(unittest.TestCase):
gradient_accumulation_steps=1,
per_device_train_batch_size=16,
load_best_model_at_end=True,
evaluation_strategy=EvaluationStrategy.EPOCH,
evaluation_strategy=IntervalStrategy.EPOCH,
compute_metrics=AlmostAccuracy(),
metric_for_best_model="accuracy",
)
@@ -867,7 +867,7 @@ class TrainerIntegrationTest(unittest.TestCase):
num_train_epochs=20,
gradient_accumulation_steps=1,
per_device_train_batch_size=16,
evaluation_strategy=EvaluationStrategy.EPOCH,
evaluation_strategy=IntervalStrategy.EPOCH,
compute_metrics=AlmostAccuracy(),
metric_for_best_model="accuracy",
)
@@ -1013,7 +1013,7 @@ class TrainerHyperParameterOptunaIntegrationTest(unittest.TestCase):
output_dir=tmp_dir,
learning_rate=0.1,
logging_steps=1,
evaluation_strategy=EvaluationStrategy.EPOCH,
evaluation_strategy=IntervalStrategy.EPOCH,
num_train_epochs=4,
disable_tqdm=True,
load_best_model_at_end=True,
@@ -1057,7 +1057,7 @@ class TrainerHyperParameterRayIntegrationTest(unittest.TestCase):
output_dir=tmp_dir,
learning_rate=0.1,
logging_steps=1,
evaluation_strategy=EvaluationStrategy.EPOCH,
evaluation_strategy=IntervalStrategy.EPOCH,
num_train_epochs=4,
disable_tqdm=True,
load_best_model_at_end=True,

9
tests/test_trainer_callback.py
View File

@@ -18,7 +18,7 @@ import unittest
from transformers import (
DefaultFlowCallback,
EvaluationStrategy,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
@@ -129,15 +129,12 @@ class TrainerCallbackTest(unittest.TestCase):
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log")
if (
trainer.args.evaluation_strategy == EvaluationStrategy.STEPS
and step % trainer.args.eval_steps == 0
):
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save")
expected_events.append("on_epoch_end")
if trainer.args.evaluation_strategy == EvaluationStrategy.EPOCH:
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events