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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
|
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# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
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# limitations under the License.
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#
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@@ -0,0 +1,39 @@
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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
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||||
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"""Tests for tunings.cancel()."""
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from ... import types as genai_types
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from .. import pytest_helper
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test_table: list[pytest_helper.TestTableItem] = [
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pytest_helper.TestTableItem(
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name="test_cancel_vertex",
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parameters=genai_types._CancelTuningJobParameters(
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name="projects/801452371447/locations/us-central1/tuningJobs/2470687341547618304"
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),
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exception_if_mldev="Not Found",
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),
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]
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pytestmark = pytest_helper.setup(
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file=__file__,
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globals_for_file=globals(),
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test_method="tunings.cancel",
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test_table=test_table,
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)
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pytest_plugins = ("pytest_asyncio",)
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@@ -0,0 +1,106 @@
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# Copyright 2025 Google LLC
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
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"""Tests for tunings.tune()."""
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import time
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from ... import _replay_api_client
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from ... import types as genai_types
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from .. import pytest_helper
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test_table: list[pytest_helper.TestTableItem] = []
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|
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pytestmark = pytest_helper.setup(
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file=__file__,
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globals_for_file=globals(),
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test_method="tunings.tune",
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test_table=test_table,
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)
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pytest_plugins = ("pytest_asyncio",)
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def test_tune_until_success(client):
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if client._api_client.vertexai:
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job = client.tunings.tune(
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base_model="gemini-2.5-flash-lite",
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training_dataset=genai_types.TuningDataset(
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gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",
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),
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config=genai_types.CreateTuningJobConfig(
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epoch_count=1,
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),
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)
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else:
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# Remove GenAI SDK test since it is deprecated:
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# https://ai.google.dev/gemini-api/docs/model-tuning
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return
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while not job.has_ended:
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# Skipping the sleep for when in replay mode.
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if client._api_client._mode not in ("replay", "auto"):
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time.sleep(300)
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job = client.tunings.get(name=job.name)
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assert job.has_ended
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assert job.has_succeeded
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def test_continuous_tuning(client):
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if not client._api_client.vertexai:
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return
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job = client.tunings.tune(
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base_model="gemini-2.5-flash",
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training_dataset=genai_types.TuningDataset(
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gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",
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),
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config=genai_types.CreateTuningJobConfig(
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epoch_count=1,
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),
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)
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while not job.has_ended:
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# Skipping the sleep for when in replay mode.
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if client._api_client._mode not in ("replay", "auto"):
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time.sleep(300)
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job = client.tunings.get(name=job.name)
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assert job.has_ended
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assert job.has_succeeded
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continuous_job = client.tunings.tune(
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base_model=job.tuned_model.model,
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training_dataset=genai_types.TuningDataset(
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gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",
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),
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config=genai_types.CreateTuningJobConfig(
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tuned_model_display_name="continuous tuning job",
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epoch_count=1,
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)
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)
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while not continuous_job.has_ended:
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# Skipping the sleep for when in replay mode.
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if client._api_client._mode not in ("replay", "auto"):
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time.sleep(300)
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continuous_job = client.tunings.get(name=continuous_job.name)
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assert continuous_job.has_ended
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assert continuous_job.has_succeeded
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@@ -0,0 +1,67 @@
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# Copyright 2025 Google LLC
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
|
||||
"""Tests for tunings.get()."""
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from ... import types as genai_types
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from .. import pytest_helper
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test_table: list[pytest_helper.TestTableItem] = [
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pytest_helper.TestTableItem(
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name="test_vertexai",
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parameters=genai_types._GetTuningJobParameters(
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name="projects/801452371447/locations/us-central1/tuningJobs/4303478340632707072"
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),
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exception_if_mldev="Not Found",
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),
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pytest_helper.TestTableItem(
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name="test_vertexai_with_pretuned_model",
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parameters=genai_types._GetTuningJobParameters(
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name="projects/801452371447/locations/us-central1/tuningJobs/8520221517529743360",
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),
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exception_if_mldev="Not Found",
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),
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pytest_helper.TestTableItem(
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name="test_mldev",
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parameters=genai_types._GetTuningJobParameters(
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name="tunedModels/generate-num-1896"
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),
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exception_if_vertex="Not Found",
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),
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||||
]
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pytestmark = pytest_helper.setup(
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file=__file__,
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globals_for_file=globals(),
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test_method="tunings.get",
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test_table=test_table,
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)
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pytest_plugins = ("pytest_asyncio",)
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def test_helper_properties(client):
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if client._api_client.vertexai:
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job = client.tunings.get(
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name="projects/801452371447/locations/us-central1/tuningJobs/4303478340632707072",
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)
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else:
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job = client.tunings.get(
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name="tunedModels/testdatasetexamples-model-j0fpgpaksvri"
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)
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assert job.has_ended
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assert job.has_succeeded
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@@ -0,0 +1,87 @@
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# Copyright 2025 Google LLC
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
|
||||
"""Tests for tunings.list()."""
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import pytest
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from ... import types as genai_types
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from .. import pytest_helper
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|
||||
|
||||
test_table: list[pytest_helper.TestTableItem] = [
|
||||
pytest_helper.TestTableItem(
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name='test_default',
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parameters=genai_types._ListTuningJobsParameters(),
|
||||
exception_if_mldev=(
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||||
'only supported in Gemini Enterprise Agent Platform'
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||||
),
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||||
),
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||||
pytest_helper.TestTableItem(
|
||||
name='test_with_config',
|
||||
parameters=genai_types._ListTuningJobsParameters(
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||||
config=genai_types.ListTuningJobsConfig(page_size=2)
|
||||
),
|
||||
exception_if_mldev=(
|
||||
'only supported in Gemini Enterprise Agent Platform'
|
||||
),
|
||||
),
|
||||
]
|
||||
|
||||
pytestmark = pytest_helper.setup(
|
||||
file=__file__,
|
||||
globals_for_file=globals(),
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||||
test_method='tunings.list',
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||||
test_table=test_table,
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||||
)
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||||
|
||||
pytest_plugins = ('pytest_asyncio',)
|
||||
|
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|
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def test_pager(client):
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if not client._api_client.vertexai:
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return
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||||
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tuning_jobs = client.tunings.list(config={'page_size': 2})
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||||
assert 'content-type' in tuning_jobs.sdk_http_response.headers
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assert tuning_jobs.name == 'tuning_jobs'
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assert tuning_jobs.page_size == 2
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assert len(tuning_jobs) <= 2
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# Iterate through all the pages. Then next_page() should raise an exception.
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for _ in tuning_jobs:
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||||
pass
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with pytest.raises(IndexError, match='No more pages to fetch.'):
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tuning_jobs.next_page()
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@pytest.mark.asyncio
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||||
async def test_async_pager(client):
|
||||
if not client._api_client.vertexai:
|
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return
|
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|
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tuning_jobs = await client.aio.tunings.list(config={'page_size': 2})
|
||||
|
||||
assert 'Content-Type' in tuning_jobs.sdk_http_response.headers
|
||||
assert tuning_jobs.name == 'tuning_jobs'
|
||||
assert tuning_jobs.page_size == 2
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||||
assert len(tuning_jobs) <= 2
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||||
|
||||
# Iterate through all the pages. Then next_page() should raise an exception.
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async for _ in tuning_jobs:
|
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pass
|
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with pytest.raises(IndexError, match='No more pages to fetch.'):
|
||||
await tuning_jobs.next_page()
|
||||
@@ -0,0 +1,561 @@
|
||||
# Copyright 2025 Google LLC
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
|
||||
"""Tests for tunings.tune()."""
|
||||
|
||||
from ... import types as genai_types
|
||||
from .. import pytest_helper
|
||||
import pytest
|
||||
|
||||
|
||||
VERTEX_HTTP_OPTIONS = {
|
||||
'api_version': 'v1beta1',
|
||||
'base_url': 'https://us-central1-autopush-aiplatform.sandbox.googleapis.com/',
|
||||
}
|
||||
|
||||
evaluation_config=genai_types.EvaluationConfig(
|
||||
metrics=[
|
||||
genai_types.Metric(name="bleu", prompt_template="test prompt template")
|
||||
],
|
||||
output_config=genai_types.OutputConfig(
|
||||
gcs_destination=genai_types.GcsDestination(
|
||||
output_uri_prefix="gs://sararob_test/"
|
||||
)
|
||||
),
|
||||
)
|
||||
|
||||
test_table: list[pytest_helper.TestTableItem] = [
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_dataset_gcs_uri",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_pretuned_model",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="projects/801452371447/locations/us-central1/models/9030969596621881344",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_pretuned_model_with_checkpoint_id",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="projects/801452371447/locations/us-central1/models/9030969596621881344",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
pre_tuned_model_checkpoint_id="3",
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_simple_dpo",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="Model display name",
|
||||
epoch_count=1,
|
||||
method="PREFERENCE_TUNING",
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_dpo_with_beta",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="Model display name",
|
||||
epoch_count=1,
|
||||
method=genai_types.TuningMethod.PREFERENCE_TUNING,
|
||||
beta=0.5,
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_non_pretuned_model_with_checkpoint_id",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
pre_tuned_model_checkpoint_id="3",
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_dataset_gcs_uri_all_parameters",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="Model display name",
|
||||
epoch_count=1,
|
||||
learning_rate_multiplier=1.0,
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_validation_data.jsonl",
|
||||
),
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_dataset_vertex_dataset_resource",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
vertex_dataset_resource="projects/801452371447/locations/us-central1/datasets/5779918772206829568",
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_dataset_dataset_resource_all_parameters",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
vertex_dataset_resource="projects/801452371447/locations/us-central1/datasets/5779918772206829568",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="Model display name",
|
||||
epoch_count=1,
|
||||
learning_rate_multiplier=1.0,
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
vertex_dataset_resource="projects/801452371447/locations/us-central1/datasets/1168232753779441664",
|
||||
),
|
||||
labels={"testlabelkey": "testlabelvalue"},
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_distillation",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="meta/llama3_1@llama-3.1-8b-instruct",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-openai-opposites.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
method="DISTILLATION",
|
||||
base_teacher_model="deepseek-ai/deepseek-v3.1-maas",
|
||||
epoch_count=20,
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-val-openai-opposites.jsonl",
|
||||
),
|
||||
output_uri="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test-folder",
|
||||
http_options=VERTEX_HTTP_OPTIONS,
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_oss_sft",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="meta/llama3_1@llama-3.1-8b-instruct",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-openai-opposites.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
epoch_count=20,
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-val-openai-opposites.jsonl",
|
||||
),
|
||||
custom_base_model="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test-folder/postprocess/node-0/checkpoints/final",
|
||||
output_uri="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test",
|
||||
http_options=VERTEX_HTTP_OPTIONS,
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_oss_sft_hyperparams",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="meta/llama3_1@llama-3.1-8b-instruct",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-openai-opposites.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
epoch_count=20,
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-val-openai-opposites.jsonl",
|
||||
),
|
||||
learning_rate=2.5e-4,
|
||||
tuning_mode="TUNING_MODE_FULL",
|
||||
custom_base_model="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test-folder/postprocess/node-0/checkpoints/final",
|
||||
output_uri="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test",
|
||||
http_options=VERTEX_HTTP_OPTIONS,
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_oss_distillation",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="meta/llama3_1@llama-3.1-8b-instruct",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-openai-opposites.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
method="DISTILLATION",
|
||||
base_teacher_model="deepseek-ai/deepseek-v3.1-maas",
|
||||
epoch_count=20,
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-val-openai-opposites.jsonl",
|
||||
),
|
||||
custom_base_model="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test-folder/postprocess/node-0/checkpoints/final",
|
||||
output_uri="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test",
|
||||
http_options=VERTEX_HTTP_OPTIONS,
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_oss_distillation_hyperparams",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="qwen/qwen3@qwen3-4b",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://nathreya-oss-tuning-sdk-test/distillation-openai-opposites.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
method="DISTILLATION",
|
||||
base_teacher_model="deepseek-ai/deepseek-r1-0528-maas",
|
||||
learning_rate=1e-4,
|
||||
batch_size=4,
|
||||
output_uri="gs://nathreya-oss-tuning-sdk-test/ayushagra-distillation-test",
|
||||
tuning_mode="TUNING_MODE_FULL",
|
||||
http_options=VERTEX_HTTP_OPTIONS,
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_encryption_spec",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
encryption_spec=genai_types.EncryptionSpec(
|
||||
kms_key_name="projects/vertex-sdk-dev/locations/us-central1/keyRings/ayush-test-key/cryptoKeys/ayush-key",
|
||||
),
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_reinforcement",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="Model display name",
|
||||
epoch_count=1,
|
||||
method="REINFORCEMENT_TUNING",
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
learning_rate_multiplier=1.0,
|
||||
batch_size=4,
|
||||
samples_per_prompt=4,
|
||||
evaluate_interval=100,
|
||||
checkpoint_interval=100,
|
||||
max_output_tokens=2048,
|
||||
reward_config=genai_types.SingleReinforcementTuningRewardConfig(
|
||||
autorater_scorer=genai_types.ReinforcementTuningAutoraterScorer(
|
||||
autorater_config=genai_types.AutoraterConfig(
|
||||
autorater_model="test-model"
|
||||
)
|
||||
)
|
||||
),
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_tune_reinforcement_composite",
|
||||
parameters=genai_types.CreateTuningJobParameters(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
|
||||
),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="Model display name",
|
||||
epoch_count=1,
|
||||
method="REINFORCEMENT_TUNING",
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
learning_rate_multiplier=1.0,
|
||||
composite_reward_config=genai_types.CompositeReinforcementTuningRewardConfig(
|
||||
weighted_reward_configs=[
|
||||
genai_types.CompositeReinforcementTuningRewardConfigWeightedRewardConfig(
|
||||
weight=1.0,
|
||||
reward_config=genai_types.SingleReinforcementTuningRewardConfig(
|
||||
autorater_scorer=genai_types.ReinforcementTuningAutoraterScorer(
|
||||
autorater_config=genai_types.AutoraterConfig(
|
||||
autorater_model="test-model"
|
||||
)
|
||||
)
|
||||
)
|
||||
)
|
||||
]
|
||||
),
|
||||
),
|
||||
),
|
||||
exception_if_mldev="parameter is only supported in Gemini Enterprise Agent Platform mode",
|
||||
),
|
||||
]
|
||||
|
||||
pytestmark = pytest_helper.setup(
|
||||
file=__file__,
|
||||
globals_for_file=globals(),
|
||||
test_method="tunings.tune",
|
||||
test_table=test_table,
|
||||
)
|
||||
|
||||
pytest_plugins = ("pytest_asyncio",)
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
"config.getoption('--private')",
|
||||
reason="Skipping in pre-public tests"
|
||||
)
|
||||
def test_eval_config(client):
|
||||
"""Tests tuning with eval config."""
|
||||
|
||||
if client._api_client.vertexai:
|
||||
tuning_job = client.tunings.tune(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl"),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="tuning job with eval config",
|
||||
epoch_count=1,
|
||||
learning_rate_multiplier=1.0,
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl"
|
||||
),
|
||||
evaluation_config=evaluation_config,
|
||||
),
|
||||
)
|
||||
|
||||
assert tuning_job.state == genai_types.JobState.JOB_STATE_PENDING
|
||||
assert tuning_job.evaluation_config == evaluation_config
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
"config.getoption('--private')",
|
||||
reason="Skipping in pre-public tests"
|
||||
)
|
||||
def test_eval_config_with_metrics(client):
|
||||
"""Tests tuning with eval config metrics."""
|
||||
if client._api_client.vertexai:
|
||||
evaluation_config=genai_types.EvaluationConfig(
|
||||
metrics=[
|
||||
genai_types.Metric(
|
||||
name="prompt-relevance",
|
||||
prompt_template="How well does the response address the prompt?: PROMPT: {request}\n RESPONSE: {response}\n",
|
||||
return_raw_output=True,
|
||||
judge_model_system_instruction="You are a cat. Make all evaluations from this perspective.",
|
||||
),
|
||||
genai_types.Metric(name='bleu'),
|
||||
genai_types.Metric(name='rouge_1'),
|
||||
],
|
||||
output_config=genai_types.OutputConfig(
|
||||
gcs_destination=genai_types.GcsDestination(
|
||||
output_uri_prefix="gs://sararob_test/"
|
||||
)
|
||||
),
|
||||
autorater_config=genai_types.AutoraterConfig(
|
||||
sampling_count=1,
|
||||
autorater_model="test-model",
|
||||
),
|
||||
)
|
||||
tuning_job = client.tunings.tune(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl"),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="tuning job with eval config",
|
||||
epoch_count=1,
|
||||
learning_rate_multiplier=1.0,
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl"
|
||||
),
|
||||
evaluation_config=evaluation_config,
|
||||
),
|
||||
)
|
||||
assert tuning_job.state == genai_types.JobState.JOB_STATE_PENDING
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
"config.getoption('--private')",
|
||||
reason="Skipping in pre-public tests"
|
||||
)
|
||||
def test_eval_config_with_unified_metrics(client):
|
||||
"""Tests tuning with eval config metrics."""
|
||||
if client._api_client.vertexai:
|
||||
evaluation_config=genai_types.EvaluationConfig(
|
||||
metrics=[
|
||||
genai_types.UnifiedMetric(
|
||||
pointwise_metric_spec=genai_types.PointwiseMetricSpec(
|
||||
metric_prompt_template=(
|
||||
"How well does the response address the prompt?: "
|
||||
"PROMPT: {request}\n RESPONSE: {response}\n"
|
||||
),
|
||||
system_instruction=(
|
||||
"You are a cat. Make all evaluations from this perspective."
|
||||
),
|
||||
custom_output_format_config=genai_types.CustomOutputFormatConfig(
|
||||
return_raw_output=True
|
||||
),
|
||||
)
|
||||
),
|
||||
genai_types.UnifiedMetric(
|
||||
bleu_spec=genai_types.BleuSpec(use_effective_order=True)
|
||||
),
|
||||
genai_types.UnifiedMetric(
|
||||
rouge_spec=genai_types.RougeSpec(rouge_type="rouge1")
|
||||
),
|
||||
],
|
||||
output_config=genai_types.OutputConfig(
|
||||
gcs_destination=genai_types.GcsDestination(
|
||||
output_uri_prefix="gs://sararob_test/"
|
||||
)
|
||||
),
|
||||
autorater_config=genai_types.AutoraterConfig(
|
||||
sampling_count=1,
|
||||
autorater_model="test-model",
|
||||
),
|
||||
inference_generation_config=genai_types.GenerationConfig(
|
||||
temperature=0.5,
|
||||
max_output_tokens=1024,
|
||||
),
|
||||
)
|
||||
tuning_job = client.tunings.tune(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl"),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="tuning job with eval config",
|
||||
epoch_count=1,
|
||||
learning_rate_multiplier=1.0,
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl"
|
||||
),
|
||||
evaluation_config=evaluation_config,
|
||||
),
|
||||
)
|
||||
assert tuning_job.state == genai_types.JobState.JOB_STATE_PENDING
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
"config.getoption('--private')",
|
||||
reason="Skipping in pre-public tests"
|
||||
)
|
||||
def test_eval_config_with_metrics_dict(client):
|
||||
"""Tests tuning with eval config metrics."""
|
||||
if client._api_client.vertexai:
|
||||
evaluation_config=genai_types.EvaluationConfig(
|
||||
metrics = [
|
||||
{
|
||||
"name": "prompt-relevance",
|
||||
"prompt_template": "How well does the response address the prompt?: PROMPT: {request}\n RESPONSE: {response}\n",
|
||||
"return_raw_output": True,
|
||||
"judge_model_system_instruction": "You are a cat. Make all evaluations from this perspective.",
|
||||
},
|
||||
{"name": "bleu"},
|
||||
{"name": "rouge_1"},
|
||||
{
|
||||
"bleu_spec": {
|
||||
"use_effective_order": True
|
||||
}
|
||||
},
|
||||
],
|
||||
output_config=genai_types.OutputConfig(
|
||||
gcs_destination=genai_types.GcsDestination(
|
||||
output_uri_prefix="gs://sararob_test/"
|
||||
)
|
||||
),
|
||||
autorater_config=genai_types.AutoraterConfig(
|
||||
sampling_count=1,
|
||||
autorater_model="test-model",
|
||||
),
|
||||
)
|
||||
tuning_job = client.tunings.tune(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl"),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="tuning job with eval config",
|
||||
epoch_count=1,
|
||||
learning_rate_multiplier=1.0,
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl"
|
||||
),
|
||||
evaluation_config=evaluation_config,
|
||||
),
|
||||
)
|
||||
assert tuning_job.state == genai_types.JobState.JOB_STATE_PENDING
|
||||
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
"config.getoption('--private')",
|
||||
reason="Skipping in pre-public tests"
|
||||
)
|
||||
@pytest.mark.asyncio
|
||||
async def test_eval_config_async(client):
|
||||
"""Tests tuning with eval config."""
|
||||
if client._api_client.vertexai:
|
||||
tuning_job = await client.aio.tunings.tune(
|
||||
base_model="gemini-2.5-flash",
|
||||
training_dataset=genai_types.TuningDataset(gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl"),
|
||||
config=genai_types.CreateTuningJobConfig(
|
||||
tuned_model_display_name="tuning job with eval config",
|
||||
epoch_count=1,
|
||||
learning_rate_multiplier=1.0,
|
||||
adapter_size="ADAPTER_SIZE_ONE",
|
||||
validation_dataset=genai_types.TuningValidationDataset(
|
||||
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl"
|
||||
),
|
||||
evaluation_config=evaluation_config,
|
||||
),
|
||||
)
|
||||
|
||||
assert tuning_job.state == genai_types.JobState.JOB_STATE_PENDING
|
||||
+187
@@ -0,0 +1,187 @@
|
||||
# Copyright 2025 Google LLC
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
|
||||
"""Tests for tunings.validate_reward()."""
|
||||
|
||||
from ... import types as genai_types
|
||||
from .. import pytest_helper
|
||||
|
||||
test_table: list[pytest_helper.TestTableItem] = [
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_validate_reward_single_autorater",
|
||||
parameters=genai_types._ValidateRewardParameters(
|
||||
parent="projects/801452371447/locations/us-central1",
|
||||
sample_response=genai_types.Content(
|
||||
parts=[genai_types.Part(text="The answer is 42.")]
|
||||
),
|
||||
example=genai_types.ReinforcementTuningExample(
|
||||
contents=[
|
||||
genai_types.Content(
|
||||
parts=[
|
||||
genai_types.Part(text="What is the answer to life?")
|
||||
]
|
||||
)
|
||||
],
|
||||
),
|
||||
single_reward_config=genai_types.SingleReinforcementTuningRewardConfig(
|
||||
autorater_scorer=genai_types.ReinforcementTuningAutoraterScorer(
|
||||
autorater_config=genai_types.AutoraterConfig(
|
||||
autorater_model="test-model"
|
||||
)
|
||||
)
|
||||
),
|
||||
),
|
||||
exception_if_mldev=(
|
||||
"only supported in Gemini Enterprise Agent Platform mode"
|
||||
),
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_validate_reward_code_execution",
|
||||
parameters=genai_types._ValidateRewardParameters(
|
||||
parent="projects/801452371447/locations/us-central1",
|
||||
sample_response=genai_types.Content(
|
||||
parts=[genai_types.Part(text="print('hello')")]
|
||||
),
|
||||
example=genai_types.ReinforcementTuningExample(
|
||||
contents=[
|
||||
genai_types.Content(
|
||||
parts=[
|
||||
genai_types.Part(
|
||||
text="Write a hello world program."
|
||||
)
|
||||
]
|
||||
)
|
||||
],
|
||||
references={"reference": "hello"},
|
||||
),
|
||||
single_reward_config=genai_types.SingleReinforcementTuningRewardConfig(
|
||||
reward_name="codeExecReward",
|
||||
parse_response_config=genai_types.ReinforcementTuningParseResponseConfig(
|
||||
parse_type="IDENTITY",
|
||||
),
|
||||
code_execution_reward_scorer=genai_types.ReinforcementTuningCodeExecutionRewardScorer(
|
||||
python_code_snippet=(
|
||||
"reward = 1.0 if response == references['reference']"
|
||||
" else 0.0"
|
||||
),
|
||||
),
|
||||
),
|
||||
),
|
||||
exception_if_mldev=(
|
||||
"only supported in Gemini Enterprise Agent Platform mode"
|
||||
),
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_validate_reward_string_match",
|
||||
parameters=genai_types._ValidateRewardParameters(
|
||||
parent="projects/801452371447/locations/us-central1",
|
||||
sample_response=genai_types.Content(
|
||||
parts=[genai_types.Part(text="42")]
|
||||
),
|
||||
example=genai_types.ReinforcementTuningExample(
|
||||
contents=[
|
||||
genai_types.Content(
|
||||
parts=[genai_types.Part(text="What is 6 times 7?")]
|
||||
)
|
||||
],
|
||||
references={"answer": "42"},
|
||||
system_instruction=genai_types.Content(
|
||||
parts=[genai_types.Part(text="You are a math tutor.")]
|
||||
),
|
||||
),
|
||||
single_reward_config=genai_types.SingleReinforcementTuningRewardConfig(
|
||||
reward_name="stringMatchReward",
|
||||
parse_response_config=genai_types.ReinforcementTuningParseResponseConfig(
|
||||
parse_type="REGEX_EXTRACT",
|
||||
regex_extract_expression=r"(\d+)",
|
||||
),
|
||||
string_match_reward_scorer=genai_types.ReinforcementTuningStringMatchRewardScorer(
|
||||
correct_answer_reward=1.0,
|
||||
wrong_answer_reward=-1.0,
|
||||
string_match_expression=genai_types.ReinforcementTuningStringMatchRewardScorerStringMatchExpression(
|
||||
match_operation="EXACT_MATCH",
|
||||
expression="{{references.answer}}",
|
||||
),
|
||||
),
|
||||
),
|
||||
),
|
||||
exception_if_mldev=(
|
||||
"only supported in Gemini Enterprise Agent Platform mode"
|
||||
),
|
||||
),
|
||||
pytest_helper.TestTableItem(
|
||||
name="test_validate_reward_composite",
|
||||
parameters=genai_types._ValidateRewardParameters(
|
||||
parent="projects/801452371447/locations/us-central1",
|
||||
sample_response=genai_types.Content(
|
||||
parts=[genai_types.Part(text="The answer is 42.")]
|
||||
),
|
||||
example=genai_types.ReinforcementTuningExample(
|
||||
contents=[
|
||||
genai_types.Content(
|
||||
parts=[
|
||||
genai_types.Part(text="What is the answer to life?")
|
||||
]
|
||||
)
|
||||
],
|
||||
),
|
||||
composite_reward_config=genai_types.CompositeReinforcementTuningRewardConfig(
|
||||
weighted_reward_configs=[
|
||||
genai_types.CompositeReinforcementTuningRewardConfigWeightedRewardConfig(
|
||||
weight=0.7,
|
||||
reward_config=genai_types.SingleReinforcementTuningRewardConfig(
|
||||
reward_name="autoraterReward",
|
||||
autorater_scorer=genai_types.ReinforcementTuningAutoraterScorer(
|
||||
autorater_config=genai_types.AutoraterConfig(
|
||||
autorater_model="test-model"
|
||||
),
|
||||
autorater_prompt=(
|
||||
"Rate the response: {{response}}"
|
||||
),
|
||||
exact_match_scorer=genai_types.ReinforcementTuningAutoraterScorerExactMatchScorer(
|
||||
correct_answer_reward=1.0,
|
||||
wrong_answer_reward=0.0,
|
||||
expression="good",
|
||||
),
|
||||
),
|
||||
),
|
||||
),
|
||||
genai_types.CompositeReinforcementTuningRewardConfigWeightedRewardConfig(
|
||||
weight=0.3,
|
||||
reward_config=genai_types.SingleReinforcementTuningRewardConfig(
|
||||
reward_name="codeReward",
|
||||
code_execution_reward_scorer=genai_types.ReinforcementTuningCodeExecutionRewardScorer(
|
||||
python_code_snippet="reward = 1.0",
|
||||
),
|
||||
),
|
||||
),
|
||||
]
|
||||
),
|
||||
),
|
||||
exception_if_mldev=(
|
||||
"only supported in Gemini Enterprise Agent Platform mode"
|
||||
),
|
||||
),
|
||||
]
|
||||
|
||||
pytestmark = pytest_helper.setup(
|
||||
file=__file__,
|
||||
globals_for_file=globals(),
|
||||
test_method="tunings.validate_reward",
|
||||
test_table=test_table,
|
||||
)
|
||||
|
||||
pytest_plugins = ("pytest_asyncio",)
|
||||
Reference in new issue
Block a user