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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
import pytest
from pathlib import Path
from test_base import TestBase, servicePreparer
from devtools_testutils.aio import recorded_by_proxy_async
from devtools_testutils import is_live_and_not_recording
@pytest.mark.skipif(
condition=(not is_live_and_not_recording()),
reason="Skipped because we cannot record network calls with AOAI client",
)
class TestFineTuningAsync(TestBase):
async def _create_sft_finetuning_job_async(
self, openai_client, train_file_id, validation_file_id, model_type="openai", training_type="Standard"
):
"""Helper method to create a supervised fine-tuning job asynchronously."""
return await openai_client.fine_tuning.jobs.create(
training_file=train_file_id,
validation_file=validation_file_id,
model=self.test_finetuning_params["sft"][model_type]["model_name"],
method={
"type": "supervised",
"supervised": {
"hyperparameters": {
"n_epochs": self.test_finetuning_params["n_epochs"],
"batch_size": self.test_finetuning_params["batch_size"],
"learning_rate_multiplier": self.test_finetuning_params["learning_rate_multiplier"],
}
},
},
extra_body={"trainingType": training_type},
)
async def _create_dpo_finetuning_job_async(self, openai_client, train_file_id, validation_file_id):
"""Helper method to create a DPO fine-tuning job asynchronously."""
return await openai_client.fine_tuning.jobs.create(
training_file=train_file_id,
validation_file=validation_file_id,
model=self.test_finetuning_params["dpo"]["openai"]["model_name"],
method={
"type": "dpo",
"dpo": {
"hyperparameters": {
"n_epochs": self.test_finetuning_params["n_epochs"],
"batch_size": self.test_finetuning_params["batch_size"],
"learning_rate_multiplier": self.test_finetuning_params["learning_rate_multiplier"],
}
},
},
extra_body={"trainingType": "Standard"},
)
async def _create_rft_finetuning_job_async(self, openai_client, train_file_id, validation_file_id):
"""Helper method to create an RFT fine-tuning job asynchronously."""
grader = {
"name": "Response Quality Grader",
"type": "score_model",
"model": "o3-mini",
"input": [
{
"role": "user",
"content": "Evaluate the model's response based on correctness and quality. Rate from 0 to 10.",
}
],
"range": [0.0, 10.0],
}
return await openai_client.fine_tuning.jobs.create(
training_file=train_file_id,
validation_file=validation_file_id,
model=self.test_finetuning_params["rft"]["openai"]["model_name"],
method={
"type": "reinforcement",
"reinforcement": {
"grader": grader,
"hyperparameters": {
"n_epochs": self.test_finetuning_params["n_epochs"],
"batch_size": self.test_finetuning_params["batch_size"],
"learning_rate_multiplier": self.test_finetuning_params["learning_rate_multiplier"],
"eval_interval": 5,
"eval_samples": 2,
"reasoning_effort": "medium",
},
},
},
extra_body={"trainingType": "Standard"},
)
async def _upload_test_files_async(self, openai_client, job_type="sft"):
"""Helper method to upload training and validation files for fine-tuning tests asynchronously."""
test_data_dir = Path(__file__).parent.parent / "test_data" / "finetuning"
training_file_path = test_data_dir / self.test_finetuning_params[job_type]["training_file_name"]
validation_file_path = test_data_dir / self.test_finetuning_params[job_type]["validation_file_name"]
with open(training_file_path, "rb") as f:
train_file = await openai_client.files.create(file=f, purpose="fine-tune")
train_processed_file = await openai_client.files.wait_for_processing(train_file.id)
assert train_processed_file is not None
assert train_processed_file.id is not None
TestBase.assert_equal_or_not_none(train_processed_file.status, "processed")
print(f"[test_finetuning_{job_type}_async] Uploaded training file: {train_processed_file.id}")
with open(validation_file_path, "rb") as f:
validation_file = await openai_client.files.create(file=f, purpose="fine-tune")
validation_processed_file = await openai_client.files.wait_for_processing(validation_file.id)
assert validation_processed_file is not None
assert validation_processed_file.id is not None
TestBase.assert_equal_or_not_none(validation_processed_file.status, "processed")
print(f"[test_finetuning_{job_type}_async] Uploaded validation file: {validation_processed_file.id}")
return train_processed_file, validation_processed_file
async def _cleanup_test_files_async(self, openai_client, train_file, validation_file, job_type):
"""Helper method to clean up uploaded files after testing asynchronously."""
await openai_client.files.delete(train_file.id)
print(f"[test_finetuning_{job_type}_async] Deleted training file: {train_file.id}")
await openai_client.files.delete(validation_file.id)
print(f"[test_finetuning_{job_type}_async] Deleted validation file: {validation_file.id}")
@servicePreparer()
@recorded_by_proxy_async
async def test_sft_finetuning_create_job_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "sft")
fine_tuning_job = await self._create_sft_finetuning_job_async(
openai_client, train_file.id, validation_file.id
)
print(f"[test_finetuning_sft_async] Created fine-tuning job: {fine_tuning_job.id}")
TestBase.validate_fine_tuning_job(fine_tuning_job)
TestBase.assert_equal_or_not_none(fine_tuning_job.training_file, train_file.id)
TestBase.assert_equal_or_not_none(fine_tuning_job.validation_file, validation_file.id)
assert fine_tuning_job.method is not None, "Method should not be None for SFT job"
TestBase.assert_equal_or_not_none(fine_tuning_job.method.type, "supervised")
print(f"[test_finetuning_sft_async] SFT method validation passed - type: {fine_tuning_job.method.type}")
await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_sft_async] Cancelled job: {fine_tuning_job.id}")
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "sft")
@servicePreparer()
@recorded_by_proxy_async
async def test_finetuning_retrieve_job_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "sft")
fine_tuning_job = await self._create_sft_finetuning_job_async(
openai_client, train_file.id, validation_file.id
)
print(f"[test_finetuning_sft_async] Created job: {fine_tuning_job.id}")
retrieved_job = await openai_client.fine_tuning.jobs.retrieve(fine_tuning_job.id)
print(f"[test_finetuning_sft_async] Retrieved job: {retrieved_job.id}")
TestBase.validate_fine_tuning_job(retrieved_job, expected_job_id=fine_tuning_job.id)
TestBase.assert_equal_or_not_none(retrieved_job.training_file, train_file.id)
TestBase.assert_equal_or_not_none(retrieved_job.validation_file, validation_file.id)
await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_sft_async] Cancelled job: {fine_tuning_job.id}")
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "sft")
@servicePreparer()
@recorded_by_proxy_async
async def test_finetuning_list_jobs_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "sft")
fine_tuning_job = await self._create_sft_finetuning_job_async(
openai_client, train_file.id, validation_file.id
)
print(f"[test_finetuning_sft_async] Created job: {fine_tuning_job.id}")
jobs_list_async = openai_client.fine_tuning.jobs.list()
jobs_list = []
async for job in jobs_list_async:
jobs_list.append(job)
print(f"[test_finetuning_sft_async] Listed {len(jobs_list)} jobs")
assert len(jobs_list) > 0
job_ids = [job.id for job in jobs_list]
assert fine_tuning_job.id in job_ids
await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_sft_async] Cancelled job: {fine_tuning_job.id}")
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "sft")
@servicePreparer()
@recorded_by_proxy_async
async def test_finetuning_cancel_job_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "sft")
fine_tuning_job = await self._create_sft_finetuning_job_async(
openai_client, train_file.id, validation_file.id
)
print(f"[test_finetuning_sft_async] Created job: {fine_tuning_job.id}")
cancelled_job = await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_sft_async] Cancelled job: {cancelled_job.id}")
TestBase.validate_fine_tuning_job(cancelled_job, expected_job_id=fine_tuning_job.id)
TestBase.assert_equal_or_not_none(cancelled_job.status, "cancelled")
retrieved_job = await openai_client.fine_tuning.jobs.retrieve(fine_tuning_job.id)
print(f"[test_finetuning_sft_async] Verified cancellation persisted for job: {retrieved_job.id}")
TestBase.validate_fine_tuning_job(
retrieved_job, expected_job_id=fine_tuning_job.id, expected_status="cancelled"
)
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "sft")
@servicePreparer()
@recorded_by_proxy_async
async def test_dpo_finetuning_create_job_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "dpo")
fine_tuning_job = await self._create_dpo_finetuning_job_async(
openai_client, train_file.id, validation_file.id
)
print(f"[test_finetuning_dpo_async] Created DPO fine-tuning job: {fine_tuning_job.id}")
print(fine_tuning_job)
TestBase.validate_fine_tuning_job(fine_tuning_job)
TestBase.assert_equal_or_not_none(fine_tuning_job.training_file, train_file.id)
TestBase.assert_equal_or_not_none(fine_tuning_job.validation_file, validation_file.id)
assert fine_tuning_job.method is not None, "Method should not be None for DPO job"
TestBase.assert_equal_or_not_none(fine_tuning_job.method.type, "dpo")
print(f"[test_finetuning_dpo_async] DPO method validation passed - type: {fine_tuning_job.method.type}")
await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_dpo_async] Cancelled job: {fine_tuning_job.id}")
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "dpo")
@servicePreparer()
@recorded_by_proxy_async
async def test_rft_finetuning_create_job_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "rft")
fine_tuning_job = await self._create_rft_finetuning_job_async(
openai_client, train_file.id, validation_file.id
)
print(f"[test_finetuning_rft_async] Created RFT fine-tuning job: {fine_tuning_job.id}")
TestBase.validate_fine_tuning_job(fine_tuning_job)
TestBase.assert_equal_or_not_none(fine_tuning_job.training_file, train_file.id)
TestBase.assert_equal_or_not_none(fine_tuning_job.validation_file, validation_file.id)
assert fine_tuning_job.method is not None, "Method should not be None for RFT job"
TestBase.assert_equal_or_not_none(fine_tuning_job.method.type, "reinforcement")
print(f"[test_finetuning_rft_async] RFT method validation passed - type: {fine_tuning_job.method.type}")
await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_rft_async] Cancelled job: {fine_tuning_job.id}")
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "rft")
@servicePreparer()
@recorded_by_proxy_async
async def test_finetuning_list_events_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "sft")
fine_tuning_job = await self._create_sft_finetuning_job_async(
openai_client, train_file.id, validation_file.id
)
print(f"[test_finetuning_sft_async] Created job: {fine_tuning_job.id}")
TestBase.validate_fine_tuning_job(fine_tuning_job)
TestBase.assert_equal_or_not_none(fine_tuning_job.training_file, train_file.id)
TestBase.assert_equal_or_not_none(fine_tuning_job.validation_file, validation_file.id)
await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_sft_async] Cancelled job: {fine_tuning_job.id}")
events_list_async = openai_client.fine_tuning.jobs.list_events(fine_tuning_job.id)
events_list = []
async for event in events_list_async:
events_list.append(event)
print(f"[test_finetuning_sft_async] Listed {len(events_list)} events for job: {fine_tuning_job.id}")
# Verify that events exist (at minimum, job creation event should be present)
assert len(events_list) > 0, "Fine-tuning job should have at least one event"
# Verify events have required attributes
for event in events_list:
assert event.id is not None, "Event should have an ID"
assert event.object is not None, "Event should have an object type"
assert event.created_at is not None, "Event should have a creation timestamp"
assert event.level is not None, "Event should have a level"
assert event.message is not None, "Event should have a message"
assert event.type is not None, "Event should have a type"
print(f"[test_finetuning_sft_async] Successfully validated {len(events_list)} events")
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "sft")
@servicePreparer()
@recorded_by_proxy_async
async def test_sft_finetuning_create_job_oss_model_async(self, **kwargs):
project_client = self.create_async_client(**kwargs)
openai_client = project_client.get_openai_client()
async with project_client:
train_file, validation_file = await self._upload_test_files_async(openai_client, "sft")
fine_tuning_job = await self._create_sft_finetuning_job_async(
openai_client, train_file.id, validation_file.id, "oss", "GlobalStandard"
)
print(f"[test_finetuning_sft_oss_async] Created fine-tuning job: {fine_tuning_job.id}")
TestBase.validate_fine_tuning_job(
fine_tuning_job, expected_model=self.test_finetuning_params["sft"]["oss"]["model_name"]
)
TestBase.validate_fine_tuning_job(fine_tuning_job)
TestBase.assert_equal_or_not_none(fine_tuning_job.training_file, train_file.id)
TestBase.assert_equal_or_not_none(fine_tuning_job.validation_file, validation_file.id)
assert fine_tuning_job.method is not None, "Method should not be None for SFT job"
TestBase.assert_equal_or_not_none(fine_tuning_job.method.type, "supervised")
print(f"[test_finetuning_sft_oss_async] SFT method validation passed - type: {fine_tuning_job.method.type}")
await openai_client.fine_tuning.jobs.cancel(fine_tuning_job.id)
print(f"[test_finetuning_sft_oss_async] Cancelled job: {fine_tuning_job.id}")
await self._cleanup_test_files_async(openai_client, train_file, validation_file, "sft_oss")
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