/
test_language_models.py
2816 lines (2552 loc) · 106 KB
/
test_language_models.py
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# -*- coding: utf-8 -*-
# Copyright 2023 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.
#
# pylint: disable=protected-access,bad-continuation
import json
import pytest
from importlib import reload
from unittest import mock
from urllib import request as urllib_request
from typing import Tuple
import pandas as pd
from google.cloud import storage
from google.cloud import aiplatform
from google.cloud.aiplatform import _streaming_prediction
from google.cloud.aiplatform import base
from google.cloud.aiplatform import initializer
from google.cloud.aiplatform.utils import gcs_utils
import constants as test_constants
from google.cloud.aiplatform.compat.services import (
model_garden_service_client,
endpoint_service_client,
model_service_client,
pipeline_service_client,
)
from google.cloud.aiplatform.compat.services import prediction_service_client
from google.cloud.aiplatform.compat.types import (
artifact as gca_artifact,
prediction_service as gca_prediction_service,
context as gca_context,
endpoint as gca_endpoint,
pipeline_job as gca_pipeline_job,
pipeline_state as gca_pipeline_state,
deployed_model_ref_v1,
)
from google.cloud.aiplatform.compat.types import (
publisher_model as gca_publisher_model,
model as gca_model,
)
from vertexai.preview import (
language_models as preview_language_models,
)
from vertexai import language_models
from vertexai.language_models import _language_models
from vertexai.language_models import (
_evaluatable_language_models,
)
from google.cloud.aiplatform_v1 import Execution as GapicExecution
from google.cloud.aiplatform.compat.types import (
encryption_spec as gca_encryption_spec,
)
_TEST_PROJECT = "test-project"
_TEST_LOCATION = "us-central1"
# CMEK encryption
_TEST_ENCRYPTION_KEY_NAME = "key_1234"
_TEST_ENCRYPTION_SPEC = gca_encryption_spec.EncryptionSpec(
kms_key_name=_TEST_ENCRYPTION_KEY_NAME
)
_TEXT_BISON_PUBLISHER_MODEL_DICT = {
"name": "publishers/google/models/text-bison",
"version_id": "001",
"open_source_category": "PROPRIETARY",
"launch_stage": gca_publisher_model.PublisherModel.LaunchStage.GA,
"publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/text-bison@001",
"predict_schemata": {
"instance_schema_uri": "gs://google-cloud-aiplatform/schema/predict/instance/text_generation_1.0.0.yaml",
"parameters_schema_uri": "gs://google-cloud-aiplatfrom/schema/predict/params/text_generation_1.0.0.yaml",
"prediction_schema_uri": "gs://google-cloud-aiplatform/schema/predict/prediction/text_generation_1.0.0.yaml",
},
}
_CHAT_BISON_PUBLISHER_MODEL_DICT = {
"name": "publishers/google/models/chat-bison",
"version_id": "001",
"open_source_category": "PROPRIETARY",
"launch_stage": gca_publisher_model.PublisherModel.LaunchStage.GA,
"publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/chat-bison@001",
"predict_schemata": {
"instance_schema_uri": "gs://google-cloud-aiplatform/schema/predict/instance/chat_generation_1.0.0.yaml",
"parameters_schema_uri": "gs://google-cloud-aiplatfrom/schema/predict/params/chat_generation_1.0.0.yaml",
"prediction_schema_uri": "gs://google-cloud-aiplatform/schema/predict/prediction/chat_generation_1.0.0.yaml",
},
}
_CODECHAT_BISON_PUBLISHER_MODEL_DICT = {
"name": "publishers/google/models/codechat-bison",
"version_id": "001",
"open_source_category": "PROPRIETARY",
"launch_stage": gca_publisher_model.PublisherModel.LaunchStage.GA,
"publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/codechat-bison@001",
"predict_schemata": {
"instance_schema_uri": "gs://google-cloud-aiplatform/schema/predict/instance/codechat_generation_1.0.0.yaml",
"parameters_schema_uri": "gs://google-cloud-aiplatfrom/schema/predict/params/codechat_generation_1.0.0.yaml",
"prediction_schema_uri": "gs://google-cloud-aiplatform/schema/predict/prediction/codechat_generation_1.0.0.yaml",
},
}
_CODE_GENERATION_BISON_PUBLISHER_MODEL_DICT = {
"name": "publishers/google/models/code-bison",
"version_id": "001",
"open_source_category": "PROPRIETARY",
"launch_stage": gca_publisher_model.PublisherModel.LaunchStage.GA,
"publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/code-bison@001",
"predict_schemata": {
"instance_schema_uri": "gs://google-cloud-aiplatform/schema/predict/instance/code_generation_1.0.0.yaml",
"parameters_schema_uri": "gs://google-cloud-aiplatfrom/schema/predict/params/code_generation_1.0.0.yaml",
"prediction_schema_uri": "gs://google-cloud-aiplatform/schema/predict/prediction/code_generation_1.0.0.yaml",
},
}
_CODE_COMPLETION_BISON_PUBLISHER_MODEL_DICT = {
"name": "publishers/google/models/code-gecko",
"version_id": "001",
"open_source_category": "PROPRIETARY",
"launch_stage": gca_publisher_model.PublisherModel.LaunchStage.GA,
"publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/code-gecko@001",
"predict_schemata": {
"instance_schema_uri": "gs://google-cloud-aiplatform/schema/predict/instance/code_generation_1.0.0.yaml",
"parameters_schema_uri": "gs://google-cloud-aiplatfrom/schema/predict/params/code_generation_1.0.0.yaml",
"prediction_schema_uri": "gs://google-cloud-aiplatform/schema/predict/prediction/code_generation_1.0.0.yaml",
},
}
_TEXT_EMBEDDING_GECKO_PUBLISHER_MODEL_DICT = {
"name": "publishers/google/models/textembedding-gecko",
"version_id": "001",
"open_source_category": "PROPRIETARY",
"launch_stage": gca_publisher_model.PublisherModel.LaunchStage.GA,
"publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/textembedding-gecko@001",
"predict_schemata": {
"instance_schema_uri": "gs://google-cloud-aiplatform/schema/predict/instance/text_embedding_1.0.0.yaml",
"parameters_schema_uri": "gs://google-cloud-aiplatfrom/schema/predict/params/text_generation_1.0.0.yaml",
"prediction_schema_uri": "gs://google-cloud-aiplatform/schema/predict/prediction/text_embedding_1.0.0.yaml",
},
}
_TEST_TEXT_GENERATION_PREDICTION = {
"safetyAttributes": {
"categories": ["Violent"],
"blocked": False,
"scores": [0.10000000149011612],
},
"content": """
Ingredients:
* 3 cups all-purpose flour
Instructions:
1. Preheat oven to 350 degrees F (175 degrees C).""",
}
_TEST_TEXT_GENERATION_PREDICTION_STREAMING = [
{
"content": "1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.",
},
{
"content": " 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31.",
"safetyAttributes": {"blocked": False, "categories": None, "scores": None},
},
{
"content": " 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45.",
"citationMetadata": {
"citations": [
{
"title": "THEATRUM ARITHMETICO-GEOMETRICUM",
"publicationDate": "1727",
"endIndex": 181,
"startIndex": 12,
}
]
},
"safetyAttributes": {
"blocked": True,
"categories": ["Finance"],
"scores": [0.1],
},
},
]
_TEST_CHAT_GENERATION_PREDICTION1 = {
"safetyAttributes": [
{
"scores": [],
"blocked": False,
"categories": [],
}
],
"candidates": [
{
"author": "1",
"content": "Chat response 1",
}
],
}
_TEST_CHAT_GENERATION_PREDICTION2 = {
"safetyAttributes": [
{
"scores": [],
"blocked": False,
"categories": [],
}
],
"candidates": [
{
"author": "1",
"content": "Chat response 2",
}
],
}
_TEST_CHAT_PREDICTION_STREAMING = [
{
"candidates": [
{
"author": "1",
"content": "1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.",
}
],
"safetyAttributes": [{"blocked": False, "categories": None, "scores": None}],
},
{
"candidates": [
{
"author": "1",
"content": " 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.",
}
],
"safetyAttributes": [
{
"blocked": True,
"categories": ["Finance"],
"scores": [0.1],
}
],
},
]
_TEST_CODE_GENERATION_PREDICTION = {
"safetyAttributes": {
"categories": [],
"blocked": False,
"scores": [],
},
"content": """
```python
def is_leap_year(year):
\"\"\"
Returns True if the given year is a leap year, False otherwise.
Args:
year: The year to check.
Returns:
True if the year is a leap year, False otherwise.
\"\"\"
# A year is a leap year if it is divisible by 4, but not divisible by 100,
# unless it is also divisible by 400.
return (year % 4 == 0 and year % 100 != 0) or year % 400 == 0
```""",
}
_TEST_CODE_COMPLETION_PREDICTION = {
"safetyAttributes": {
"categories": [],
"blocked": False,
"scores": [],
},
"content": """
return s[::-1]
def reverse_string_2(s):""",
}
_TEXT_EMBEDDING_VECTOR_LENGTH = 768
_TEST_TEXT_EMBEDDING_PREDICTION = {
"embeddings": {
"values": list([1.0] * _TEXT_EMBEDDING_VECTOR_LENGTH),
"statistics": {"truncated": False, "token_count": 4.0},
}
}
_TEST_TEXT_BISON_TRAINING_DF = pd.DataFrame(
{
"input_text": [
"Basketball teams in the Midwest.",
"How to bake gluten-free bread?",
"Want to buy a new phone.",
],
"output_text": [
"There are several basketball teams located in the Midwest region of the United States. Here are some of them:",
"Baking gluten-free bread can be a bit challenging because gluten is the protein that gives bread its structure and elasticity.",
"Great! There are many factors to consider when buying a new phone, including your budget, preferred operating system, desired features, and more. Here are some general steps to follow to help you make an informed decision:",
],
},
)
_TEST_PIPELINE_SPEC = {
"components": {},
"pipelineInfo": {"name": "evaluation-llm-text-generation-pipeline"},
"root": {
"dag": {"tasks": {}},
"inputDefinitions": {
"parameters": {
"api_endpoint": {
"defaultValue": "aiplatform.googleapis.com/ui",
"isOptional": True,
"parameterType": "STRING",
},
"dataset_name": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"dataset_uri": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"default_context": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"enable_early_stopping": {
"defaultValue": True,
"isOptional": True,
"parameterType": "BOOLEAN",
},
"encryption_spec_key_name": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"evaluation_data_uri": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"evaluation_interval": {
"defaultValue": 20,
"isOptional": True,
"parameterType": "NUMBER_INTEGER",
},
"evaluation_output_root_dir": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"large_model_reference": {
"defaultValue": "text-bison@001",
"isOptional": True,
"parameterType": "STRING",
},
"learning_rate": {
"defaultValue": -1,
"isOptional": True,
"parameterType": "NUMBER_DOUBLE",
},
"learning_rate_multiplier": {
"defaultValue": 1,
"isOptional": True,
"parameterType": "NUMBER_DOUBLE",
},
"location": {"parameterType": "STRING"},
"model_display_name": {"parameterType": "STRING"},
"project": {"parameterType": "STRING"},
"tensorboard_resource_id": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"tpu_training_skip_cmek": {
"defaultValue": False,
"isOptional": True,
"parameterType": "BOOLEAN",
},
"train_steps": {
"defaultValue": 300,
"isOptional": True,
"parameterType": "NUMBER_INTEGER",
},
"tuning_method": {
"defaultValue": "tune_v2",
"isOptional": True,
"parameterType": "STRING",
},
}
},
},
"schemaVersion": "2.1.0",
"sdkVersion": "kfp-2.0.0-beta.14",
}
_TEST_PIPELINE_SPEC_JSON = json.dumps(
_TEST_PIPELINE_SPEC,
)
_TEST_PIPELINE_JOB = json.dumps(
{
"runtimeConfig": {"parameterValues": {}},
"pipelineSpec": json.loads(_TEST_PIPELINE_SPEC_JSON),
}
)
_TEST_TEXT_GENERATION_METRICS = {
"bleu": 3.9311041439597427,
"rougeLSum": 19.014677479620463,
}
_TEST_TEXT_CLASSIFICATION_METRICS = {"auPrc": 0.9, "auRoc": 0.8, "logLoss": 0.5}
_TEST_EVAL_DATA = [
{
"prompt": "Basketball teams in the Midwest.",
"ground_truth": "There are several basketball teams located in the Midwest region of the United States. Here are some of them:",
},
{
"prompt": "How to bake gluten-free bread?",
"ground_truth": "Baking gluten-free bread can be a bit challenging because gluten is the protein that gives bread its structure and elasticity.",
},
]
_TEST_EVAL_DATA_DF = pd.DataFrame(_TEST_EVAL_DATA)
_TEST_ARTIFACT_ID = "123456"
_TEST_ARTIFACT_NAME = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/metadataStores/default/artifacts/{_TEST_ARTIFACT_ID}"
_TEST_EVAL_PIPELINE_SPEC = {
"components": {},
"pipelineInfo": {"name": "evaluation-llm-text-generation-pipeline"},
"root": {
"dag": {"tasks": {}},
"inputDefinitions": {
"parameters": {
"batch_predict_accelerator_count": {
"defaultValue": 0.0,
"isOptional": True,
"parameterType": "NUMBER_INTEGER",
},
"batch_predict_accelerator_type": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"batch_predict_gcs_source_uris": {
"defaultValue": [],
"isOptional": True,
"parameterType": "LIST",
},
"batch_predict_gcs_destination_output_uri": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"batch_predict_predictions_format": {
"defaultValue": "jsonl",
"isOptional": True,
"parameterType": "STRING",
},
"enable_web_access": {
"defaultValue": True,
"isOptional": True,
"parameterType": "BOOLEAN",
},
"encryption_spec_key_name": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"evaluation_display_name": {
"defaultValue": "evaluation-text-generation",
"isOptional": True,
"parameterType": "STRING",
},
"location": {
"defaultValue": "us-central1",
"isOptional": True,
"parameterType": "STRING",
},
"machine_type": {
"defaultValue": "e2-highmem-16",
"isOptional": True,
"parameterType": "STRING",
},
"model_name": {"parameterType": "STRING"},
"evaluation_task": {"parameterType": "STRING"},
"network": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"nlp_task": {
"defaultValue": "text-generation",
"isOptional": True,
"parameterType": "STRING",
},
"predictions_format": {
"defaultValue": "jsonl",
"isOptional": True,
"parameterType": "STRING",
},
"predictions_gcs_source": {
"defaultValue": [],
"isOptional": True,
"parameterType": "LIST",
},
"project": {"parameterType": "STRING"},
"root_dir": {"parameterType": "STRING"},
"service_account": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
}
},
},
"schemaVersion": "2.1.0",
"sdkVersion": "kfp-2.0.0-beta.14",
}
_TEST_EVAL_PIPELINE_SPEC_JSON = json.dumps(
_TEST_EVAL_PIPELINE_SPEC,
)
_TEST_EVAL_PIPELINE_JOB = json.dumps(
{
"runtimeConfig": {"parameterValues": {}},
"pipelineSpec": json.loads(_TEST_EVAL_PIPELINE_SPEC_JSON),
}
)
# Eval classification spec
_TEST_EVAL_CLASSIFICATION_PIPELINE_SPEC = {
"components": {},
"pipelineInfo": {"name": "evaluation-llm-text-generation-pipeline"},
"root": {
"dag": {"tasks": {}},
"inputDefinitions": {
"parameters": {
"batch_predict_accelerator_count": {
"defaultValue": 0.0,
"isOptional": True,
"parameterType": "NUMBER_INTEGER",
},
"batch_predict_accelerator_type": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"batch_predict_gcs_source_uris": {
"defaultValue": [],
"isOptional": True,
"parameterType": "LIST",
},
"batch_predict_gcs_destination_output_uri": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"batch_predict_predictions_format": {
"defaultValue": "jsonl",
"isOptional": True,
"parameterType": "STRING",
},
"enable_web_access": {
"defaultValue": True,
"isOptional": True,
"parameterType": "BOOLEAN",
},
"encryption_spec_key_name": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"evaluation_display_name": {
"defaultValue": "evaluation-text-generation",
"isOptional": True,
"parameterType": "STRING",
},
"location": {
"defaultValue": "us-central1",
"isOptional": True,
"parameterType": "STRING",
},
"machine_type": {
"defaultValue": "e2-highmem-16",
"isOptional": True,
"parameterType": "STRING",
},
"model_name": {"parameterType": "STRING"},
"evaluation_task": {"parameterType": "STRING"},
"network": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"nlp_task": {
"defaultValue": "text-generation",
"isOptional": True,
"parameterType": "STRING",
},
"predictions_format": {
"defaultValue": "jsonl",
"isOptional": True,
"parameterType": "STRING",
},
"predictions_gcs_source": {
"defaultValue": [],
"isOptional": True,
"parameterType": "LIST",
},
"evaluation_class_labels": {
"defaultValue": [],
"isOptional": True,
"parameterType": "LIST",
},
"target_field_name": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
"project": {"parameterType": "STRING"},
"root_dir": {"parameterType": "STRING"},
"service_account": {
"defaultValue": "",
"isOptional": True,
"parameterType": "STRING",
},
}
},
},
"schemaVersion": "2.1.0",
"sdkVersion": "kfp-2.0.0-beta.14",
}
_TEST_EVAL_CLASSIFICATION_PIPELINE_SPEC_JSON = json.dumps(
_TEST_EVAL_CLASSIFICATION_PIPELINE_SPEC,
)
_TEST_EVAL_CLASSIFICATION_PIPELINE_JOB = json.dumps(
{
"runtimeConfig": {"parameterValues": {}},
"pipelineSpec": json.loads(_TEST_EVAL_PIPELINE_SPEC_JSON),
}
)
@pytest.fixture
def mock_pipeline_bucket_exists():
def mock_create_gcs_bucket_for_pipeline_artifacts_if_it_does_not_exist(
output_artifacts_gcs_dir=None,
service_account=None,
project=None,
location=None,
credentials=None,
):
output_artifacts_gcs_dir = (
output_artifacts_gcs_dir
or gcs_utils.generate_gcs_directory_for_pipeline_artifacts(
project=project,
location=location,
)
)
return output_artifacts_gcs_dir
with mock.patch(
"google.cloud.aiplatform.utils.gcs_utils.create_gcs_bucket_for_pipeline_artifacts_if_it_does_not_exist",
wraps=mock_create_gcs_bucket_for_pipeline_artifacts_if_it_does_not_exist,
) as mock_context:
yield mock_context
def make_pipeline_job(state):
return gca_pipeline_job.PipelineJob(
name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_NAME,
state=state,
create_time=test_constants.PipelineJobConstants._TEST_PIPELINE_CREATE_TIME,
service_account=test_constants.ProjectConstants._TEST_SERVICE_ACCOUNT,
network=test_constants.TrainingJobConstants._TEST_NETWORK,
job_detail=gca_pipeline_job.PipelineJobDetail(
pipeline_run_context=gca_context.Context(
name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_NAME,
),
task_details=[
gca_pipeline_job.PipelineTaskDetail(
task_id=456,
task_name="tune-large-model-20230724214903",
execution=GapicExecution(
name="projects/123/locations/europe-west4/metadataStores/default/executions/...",
display_name="tune-large-model-20230724214903",
schema_title="system.Run",
metadata={
"output:model_resource_name": "projects/123/locations/us-central1/models/456",
"output:endpoint_resource_name": "projects/123/locations/us-central1/endpoints/456",
},
),
),
],
),
)
def make_eval_pipeline_job(state):
return gca_pipeline_job.PipelineJob(
name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_NAME,
state=state,
create_time=test_constants.PipelineJobConstants._TEST_PIPELINE_CREATE_TIME,
service_account=test_constants.ProjectConstants._TEST_SERVICE_ACCOUNT,
network=test_constants.TrainingJobConstants._TEST_NETWORK,
job_detail=gca_pipeline_job.PipelineJobDetail(
pipeline_run_context=gca_context.Context(
name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_NAME,
),
task_details=[
gca_pipeline_job.PipelineTaskDetail(
task_id=456,
task_name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_ID,
outputs={
"evaluation_metrics": gca_pipeline_job.PipelineTaskDetail.ArtifactList(
artifacts=[
gca_artifact.Artifact(
name="test-metric-artifact",
metadata=_TEST_TEXT_GENERATION_METRICS,
),
],
)
},
),
gca_pipeline_job.PipelineTaskDetail(
task_id=789,
task_name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_ID,
outputs={
"evaluation_metrics": gca_pipeline_job.PipelineTaskDetail.ArtifactList(
artifacts=[
gca_artifact.Artifact(
display_name="evaluation_metrics",
uri="gs://test-bucket/evaluation_metrics",
),
]
)
},
),
],
),
)
def make_eval_classification_pipeline_job(state):
return gca_pipeline_job.PipelineJob(
name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_NAME,
state=state,
create_time=test_constants.PipelineJobConstants._TEST_PIPELINE_CREATE_TIME,
service_account=test_constants.ProjectConstants._TEST_SERVICE_ACCOUNT,
network=test_constants.TrainingJobConstants._TEST_NETWORK,
job_detail=gca_pipeline_job.PipelineJobDetail(
pipeline_run_context=gca_context.Context(
name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_NAME,
),
task_details=[
gca_pipeline_job.PipelineTaskDetail(
task_id=456,
task_name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_ID,
outputs={
"evaluation_metrics": gca_pipeline_job.PipelineTaskDetail.ArtifactList(
artifacts=[
gca_artifact.Artifact(
name="test-metric-artifact",
metadata=_TEST_TEXT_CLASSIFICATION_METRICS,
),
],
)
},
),
gca_pipeline_job.PipelineTaskDetail(
task_id=789,
task_name=test_constants.PipelineJobConstants._TEST_PIPELINE_JOB_ID,
outputs={
"evaluation_metrics": gca_pipeline_job.PipelineTaskDetail.ArtifactList(
artifacts=[
gca_artifact.Artifact(
display_name="evaluation_metrics",
uri="gs://test-bucket/evaluation_metrics",
),
]
)
},
),
],
),
)
@pytest.fixture
def mock_pipeline_service_create():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_pipeline_job"
) as mock_create_pipeline_job:
mock_create_pipeline_job.return_value = make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
)
yield mock_create_pipeline_job
@pytest.fixture
def mock_pipeline_service_create_eval():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_pipeline_job"
) as mock_create_pipeline_job:
mock_create_pipeline_job.return_value = make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
)
yield mock_create_pipeline_job
@pytest.fixture
def mock_pipeline_service_create_eval_classification():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_pipeline_job"
) as mock_create_pipeline_job:
mock_create_pipeline_job.return_value = make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
)
yield mock_create_pipeline_job
@pytest.fixture
def mock_pipeline_job_get():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "get_pipeline_job"
) as mock_get_pipeline_job:
mock_get_pipeline_job.side_effect = [
make_pipeline_job(gca_pipeline_state.PipelineState.PIPELINE_STATE_RUNNING),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
]
yield mock_get_pipeline_job
@pytest.fixture
def mock_pipeline_job_get_eval():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "get_pipeline_job"
) as mock_get_pipeline_job:
mock_get_pipeline_job.side_effect = [
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_RUNNING
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
]
yield mock_get_pipeline_job
@pytest.fixture
def mock_pipeline_job_get_eval_classification():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "get_pipeline_job"
) as mock_get_pipeline_job:
mock_get_pipeline_job.side_effect = [
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_RUNNING
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
make_eval_classification_pipeline_job(
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
),
]
yield mock_get_pipeline_job
@pytest.fixture
def mock_load_yaml_and_json(job_spec):
with mock.patch.object(
storage.Blob, "download_as_bytes"
) as mock_load_yaml_and_json:
mock_load_yaml_and_json.return_value = job_spec.encode()
yield mock_load_yaml_and_json
@pytest.fixture
def mock_gcs_from_string():
with mock.patch.object(storage.Blob, "from_string") as mock_from_string:
yield mock_from_string
@pytest.fixture
def mock_gcs_upload():
with mock.patch.object(storage.Blob, "upload_from_filename") as mock_from_filename:
yield mock_from_filename
@pytest.fixture
def mock_request_urlopen(request: str) -> Tuple[str, mock.MagicMock]:
data = _TEST_PIPELINE_SPEC
with mock.patch.object(urllib_request, "urlopen") as mock_urlopen:
mock_read_response = mock.MagicMock()
mock_decode_response = mock.MagicMock()
mock_decode_response.return_value = json.dumps(data)
mock_read_response.return_value.decode = mock_decode_response
mock_urlopen.return_value.read = mock_read_response
yield request.param, mock_urlopen
@pytest.fixture
def mock_request_urlopen_eval(request: str) -> Tuple[str, mock.MagicMock]: