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import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
]
)
class MultiNodeTest(unittest.TestCase):
def setUp(self):
if self.framework == "pytorch":
subprocess.run(
f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
encoding="utf-8",
check=True,
)
assert hasattr(self, "env")
def create_estimator(self, instance_count):
# configuration for running training on smdistributed Model Parallel
mpi_options = {
"enabled": True,
"processes_per_host": 8,
}
smp_options = {
"enabled": True,
"parameters": {
"microbatches": 4,
"placement_strategy": "spread",
"pipeline": "interleaved",
"optimize": "speed",
"partitions": 4,
"ddp": True,
},
}
distribution = {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}
name_extension = "trainer" if self.script == "run_glue.py" else "smtrainer"
# creates estimator
return HuggingFace(
entry_point=self.script,
source_dir=self.env.test_path,
role=self.env.role,
image_uri=self.env.image_uri,
base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}",
instance_count=instance_count,
instance_type=self.instance_type,
debugger_hook_config=False,
hyperparameters={
**self.env.hyperparameters,
"model_name_or_path": self.model_name_or_path,
"max_steps": 500,
},
metric_definitions=self.env.metric_definitions,
distribution=distribution,
py_version="py36",
)
def save_results_as_csv(self, job_name):
TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")
# @parameterized.expand([(2,), (4,),])
@parameterized.expand([(1,)])
def test_scripz(self, instance_count):
# create estimator
estimator = self.create_estimator(instance_count)
# run training
estimator.fit()
# result dataframe
result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()
# extract kpis
eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
# get train time from SageMaker job, this includes starting, preprocessing, stopping
train_runtime = (
Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
assert all(t <= self.results["eval_loss"] for t in eval_loss)
# dump tests result into json file to share in PR
with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)
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