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promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/lkg_sources/summarize_text_content.jinja2 | Please summarize the following text in one paragraph. 100 words.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/flow.env_files/setup.sh | #!/bin/bash
# Install your packages here.
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_exception/show_answer.py | from promptflow import tool
@tool
def show_answer(chat_answer: str):
raise Exception("mock exception")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_exception/chat.jinja2 | system:
You are a helpful assistant.
{% for item in chat_history %}
user:
{{item.inputs.question}}
assistant:
{{item.outputs.answer}}
{% endfor %}
user:
{{question}} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_exception/flow.dag.yaml | inputs:
chat_history:
type: list
question:
type: string
is_chat_input: true
default: What is ChatGPT?
outputs:
answer:
type: string
reference: ${show_answer.output}
is_chat_output: true
nodes:
- inputs:
deployment_name: gpt-35-turbo
max_tokens: "256"
temperature: "0.7"
chat_history: ${inputs.chat_history}
question: ${inputs.question}
name: chat
type: llm
source:
type: code
path: chat.jinja2
api: chat
provider: AzureOpenAI
connection: azure_open_ai_connection
- name: show_answer
type: python
source:
type: code
path: show_answer.py
inputs:
chat_answer: ${chat.output} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/experiments | promptflow_repo/promptflow/src/promptflow/tests/test_configs/experiments/basic-no-script-template/basic.exp.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Experiment.schema.json
description: Basic experiment without script node
data:
- name: my_data
path: ../../flows/web_classification/data.jsonl
inputs:
- name: my_input
type: int
default: 1
nodes:
- name: main
type: flow
path: ../../flows/web_classification/flow.dag.yaml
inputs:
url: ${data.my_data.url}
variant: ${summarize_text_content.variant_0}
environment_variables: {}
connections: {}
- name: eval
type: flow
path: ../../flows/eval-classification-accuracy
inputs:
groundtruth: ${data.my_data.answer} # No node can be named with "data"
prediction: ${main.outputs.category}
environment_variables: {}
connections: {}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/primitive_output/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow(input_val: str = "gpt"):
"""Simple flow without yaml."""
# print(f"Hello world! {input_val}")
return f"Hello world! {input_val}"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/primitive_output/flow.dag.yaml | path: ./entry.py
entry: my_flow | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/multiple_entries/entry1.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow1():
"""Simple flow without yaml."""
print("Hello world!")
def my_flow2():
"""Simple flow without yaml."""
print("Hello world!")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/multiple_entries/entry2.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow1():
"""Simple flow without yaml."""
print("Hello world!")
def my_flow2():
"""Simple flow without yaml."""
print("Hello world!")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/invalid_extra_fields_nodes/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow():
"""Simple flow without yaml."""
print("Hello world!")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/invalid_extra_fields_nodes/flow.dag.yaml | entry: my_func
path: ./entry.py
nodes: [] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/simple_without_yaml/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow(input_val: str):
"""Simple flow without yaml."""
print(f"Hello world! {input_val}")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_exception/inputs.jsonl | {"text": "text"}
{"text": "text"} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_exception/entry.py | def my_flow(text: str):
raise Exception("dummy exception")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_exception/flow.dag.yaml | path: ./entry.py
entry: my_flow | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/invalid_illegal_path/flow.dag.yaml | entry: my_func
path: ./not_exist.py | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/flow_with_environment/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow():
"""Simple flow without yaml."""
print("Hello world!")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/flow_with_environment/flow.dag.yaml | path: ./entry.py
entry: my_flow
environment:
python_requirements_txt: requirements.txt
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/simple_with_yaml/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow(input_val: str = "gpt") -> str:
"""Simple flow without yaml."""
return f"Hello world! {input_val}"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/simple_with_yaml/flow.dag.yaml | path: ./entry.py
entry: my_flow | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/invalid_no_path/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow():
"""Simple flow without yaml."""
print("Hello world!")
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/invalid_no_path/flow.dag.yaml | entry: my_func | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/flow_with_dataclass_output/inputs.jsonl | {"text": "text", "models": ["model"]}
{"text": "text", "models": ["model", "model_2", "model_3"]} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/flow_with_dataclass_output/entry.py | from dataclasses import dataclass
@dataclass
class Data:
text: str
models: list
def my_flow(text: str = "default_text", models: list = ["default_model"]):
return Data(text=text, models=models)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/flow_with_dataclass_output/flow.dag.yaml | path: ./entry.py
entry: my_flow | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_trace/inputs.jsonl | {"text": "text", "models": ["model"]}
{"text": "text", "models": ["model", "model_2", "model_3"]} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_trace/entry.py | import asyncio
from promptflow import trace
@trace
async def wait(n: int):
await asyncio.sleep(n)
@trace
async def dummy_llm(prompt: str, model: str, wait_seconds: int):
await wait(wait_seconds)
return prompt
async def my_flow(text: str, models: list = []):
tasks = []
for i, model in enumerate(models):
tasks.append(asyncio.create_task(dummy_llm(text, model, i + 1)))
await asyncio.wait(tasks)
return "dummy_output"
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows | promptflow_repo/promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_trace/flow.dag.yaml | path: ./entry.py
entry: my_flow
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/simple_hello_world.jsonl | {"name": "promptflow"} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/classificationAccuracy.csv | line_number,variant_id,groundtruth,prediction
0,variant_0,App,App
0,variant_0,App,App
0,variant_0,App,App
0,variant_0,App,App
0,variant_0,App,App
0,variant_0,App,App
0,variant_0,App,App
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/numbers.jsonl | {"value": 0}
{"value": 1}
{"value": 2}
{"value": 3}
{"value": 4}
{"value": 5}
{"value": 6}
{"value": 7}
{"value": 8}
{"value": 9}
{"value": 10}
{"value": 11}
{"value": 12}
{"value": 13}
{"value": 14}
{"value": 15}
{"value": 16}
{"value": 17}
{"value": 18}
{"value": 19}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/dictInput1.jsonl | {"key": {"key": "value in data"}}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/env_var_names.jsonl | {"key": "API_BASE", "extra_key": "EXTRA_VALUE"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/simple_eager_flow_data.jsonl | {"input_val": "input1"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/webClassification1.jsonl | {"url": "https://www.youtube.com/watch?v=o5ZQyXaAv1g", "answer": "Channel", "evidence": "Url", "variant_id": "fake_variant1"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/print_input_flow.jsonl | {"text": "https://www.youtube.com/watch?v=o5ZQyXaAv1g"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/webClassification3.jsonl | {"url": "https://www.youtube.com/watch?v=o5ZQyXaAv1g", "answer": "Channel", "evidence": "Url", "variant_id": "fake_variant1"}
{"url": "https://www.youtube.com/watch?v=o5ZQyXaAv1g", "answer": "Channel", "evidence": "Url", "variant_id": "fake_variant1"}
{"url": "https://www.youtube.com/watch?v=o5ZQyXaAv1g", "answer": "Channel", "evidence": "Url", "variant_id": "fake_variant1"} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/image_inputs/inputs.jsonl | {"image": {"data:image/png;path":"logo_1.png"}}
{"image": {"data:image/png;path":"logo_2.png"}} | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/load_data_cases/colors.json | [
{
"name": "Red",
"id_text": "1.0",
"id_int": 1,
"id_float": 1.0
},
{
"name": "Blue",
"id_text": "3.0",
"id_int": 3,
"id_float": 3.0
},
{
"name": "Yellow",
"id_text": "2.0",
"id_int": 2,
"id_float": 2.0
}
] | 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/load_data_cases/colors.tsv | name id_text id_int id_float
0 Red 1.0 1 1.0
1 Blue 3.0 3 3.0
2 Yellow 2.0 2 2.0
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas | promptflow_repo/promptflow/src/promptflow/tests/test_configs/datas/load_data_cases/10k.jsonl | {"text":"data_0000"}
{"text":"data_0001"}
{"text":"data_0002"}
{"text":"data_0003"}
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| 0 |
promptflow_repo/promptflow/src/promptflow/tests/test_configs | promptflow_repo/promptflow/src/promptflow/tests/test_configs/notebooks/dummy.ipynb | from promptflow import PFClient
# client can help manage your runs and connections.
pf = PFClient()
flow = "../flows/simple_hello_world" # path to the flow directory# Test flow
flow_inputs = {
"name": "dummy",
}
flow_result = pf.test(flow=flow, inputs=flow_inputs)# Test single node in the flow
node_name = "hello_world"
node_inputs = {
"name": "dummy"
}
flow_result = pf.test(flow=flow, inputs=node_inputs, node=node_name)
print(f"Node result: {flow_result}")data = "../datas/simple_hello_world.jsonl" # path to the data file
# create run with default variant
base_run = pf.run(flow=flow, data=data, stream=True) | 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/_azure_utils.py | import signal
from azure.identity import AzureCliCredential, DefaultAzureCredential
def get_cred():
"""get credential for azure storage"""
# resolve requests
try:
credential = AzureCliCredential()
token = credential.get_token("https://management.azure.com/.default")
except Exception:
credential = DefaultAzureCredential()
# ensure we can get token
token = credential.get_token("https://management.azure.com/.default")
assert token is not None
return credential
PYTEST_TIMEOUT_METHOD = "signal" if hasattr(signal, "SIGALRM") else "thread" # use signal when os support SIGALRM
DEFAULT_TEST_TIMEOUT = 10 * 60 # 10mins
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/conftest.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
import os
import uuid
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Callable, Optional
from unittest.mock import patch
import jwt
import pytest
from azure.core.exceptions import ResourceNotFoundError
from mock import mock
from pytest_mock import MockerFixture
from promptflow._sdk._constants import FlowType, RunStatus
from promptflow._sdk._utils import ClientUserAgentUtil
from promptflow._sdk.entities import Run
from promptflow.azure import PFClient
from promptflow.azure._entities._flow import Flow
from ._azure_utils import get_cred
from .recording_utilities import (
PFAzureIntegrationTestRecording,
SanitizedValues,
VariableRecorder,
get_created_flow_name_from_flow_path,
get_pf_client_for_replay,
is_live,
is_record,
is_replay,
)
FLOWS_DIR = "./tests/test_configs/flows"
DATAS_DIR = "./tests/test_configs/datas"
AZUREML_RESOURCE_PROVIDER = "Microsoft.MachineLearningServices"
RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}"
def package_scope_in_live_mode() -> str:
"""Determine the scope of some expected sharing fixtures.
We have many tests against flows and runs, and it's very time consuming to create a new flow/run
for each test. So we expect to leverage pytest fixture concept to share flows/runs across tests.
However, we also have replay tests, which require function scope fixture as it will locate the
recording YAML based on the test function info.
Use this function to determine the scope of the fixtures dynamically. For those fixtures that
will request dynamic scope fixture(s), they also need to be dynamic scope.
"""
# package-scope should be enough for Azure tests
return "package" if is_live() else "function"
@pytest.fixture(scope=package_scope_in_live_mode())
def user_object_id() -> str:
if is_replay():
return SanitizedValues.USER_OBJECT_ID
credential = get_cred()
access_token = credential.get_token("https://management.azure.com/.default")
decoded_token = jwt.decode(access_token.token, options={"verify_signature": False})
return decoded_token["oid"]
@pytest.fixture(scope=package_scope_in_live_mode())
def tenant_id() -> str:
if is_replay():
return SanitizedValues.TENANT_ID
credential = get_cred()
access_token = credential.get_token("https://management.azure.com/.default")
decoded_token = jwt.decode(access_token.token, options={"verify_signature": False})
return decoded_token["tid"]
@pytest.fixture(scope=package_scope_in_live_mode())
def ml_client(
subscription_id: str,
resource_group_name: str,
workspace_name: str,
):
"""return a machine learning client using default e2e testing workspace"""
from azure.ai.ml import MLClient
return MLClient(
credential=get_cred(),
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
cloud="AzureCloud",
)
@pytest.fixture(scope=package_scope_in_live_mode())
def remote_client(subscription_id: str, resource_group_name: str, workspace_name: str):
from promptflow.azure import PFClient
if is_replay():
client = get_pf_client_for_replay()
else:
client = PFClient(
credential=get_cred(),
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
)
assert "promptflow-sdk" in ClientUserAgentUtil.get_user_agent()
assert "promptflow/" not in ClientUserAgentUtil.get_user_agent()
yield client
@pytest.fixture
def remote_workspace_resource_id(subscription_id: str, resource_group_name: str, workspace_name: str) -> str:
return "azureml:" + RESOURCE_ID_FORMAT.format(
subscription_id, resource_group_name, AZUREML_RESOURCE_PROVIDER, workspace_name
)
@pytest.fixture(scope=package_scope_in_live_mode())
def pf(remote_client):
# do not add annotation here, because PFClient will trigger promptflow.azure imports and break the isolation
# between azure and non-azure tests
yield remote_client
@pytest.fixture
def remote_web_classification_data(remote_client):
from azure.ai.ml.entities import Data
data_name, data_version = "webClassification1", "1"
try:
return remote_client.ml_client.data.get(name=data_name, version=data_version)
except ResourceNotFoundError:
return remote_client.ml_client.data.create_or_update(
Data(name=data_name, version=data_version, path=f"{DATAS_DIR}/webClassification1.jsonl", type="uri_file")
)
@pytest.fixture(scope="session")
def runtime(runtime_name: str) -> str:
return runtime_name
PROMPTFLOW_ROOT = Path(__file__) / "../../.."
MODEL_ROOT = Path(PROMPTFLOW_ROOT / "tests/test_configs/flows")
@pytest.fixture
def flow_serving_client_remote_connection(mocker: MockerFixture, remote_workspace_resource_id):
from promptflow._sdk._serving.app import create_app as create_serving_app
model_path = (Path(MODEL_ROOT) / "basic-with-connection").resolve().absolute().as_posix()
mocker.patch.dict(os.environ, {"PROMPTFLOW_PROJECT_PATH": model_path})
mocker.patch.dict(os.environ, {"USER_AGENT": "test-user-agent"})
app = create_serving_app(
config={"connection.provider": remote_workspace_resource_id},
environment_variables={"API_TYPE": "${azure_open_ai_connection.api_type}"},
)
app.config.update(
{
"TESTING": True,
}
)
return app.test_client()
@pytest.fixture
def flow_serving_client_with_prt_config_env(
mocker: MockerFixture, subscription_id, resource_group_name, workspace_name
): # noqa: E501
connections = {
"PRT_CONFIG_OVERRIDE": f"deployment.subscription_id={subscription_id},"
f"deployment.resource_group={resource_group_name},"
f"deployment.workspace_name={workspace_name},"
"app.port=8088",
}
return create_serving_client_with_connections("basic-with-connection", mocker, connections)
@pytest.fixture
def flow_serving_client_with_connection_provider_env(mocker: MockerFixture, remote_workspace_resource_id):
connections = {"PROMPTFLOW_CONNECTION_PROVIDER": remote_workspace_resource_id}
return create_serving_client_with_connections("basic-with-connection", mocker, connections)
@pytest.fixture
def flow_serving_client_with_aml_resource_id_env(mocker: MockerFixture, remote_workspace_resource_id):
aml_resource_id = "{}/onlineEndpoints/{}/deployments/{}".format(remote_workspace_resource_id, "myendpoint", "blue")
connections = {"AML_DEPLOYMENT_RESOURCE_ID": aml_resource_id}
return create_serving_client_with_connections("basic-with-connection", mocker, connections)
@pytest.fixture
def serving_client_with_connection_name_override(mocker: MockerFixture, remote_workspace_resource_id):
connections = {
"aoai_connection": "azure_open_ai_connection",
"PROMPTFLOW_CONNECTION_PROVIDER": remote_workspace_resource_id,
}
return create_serving_client_with_connections("llm_connection_override", mocker, connections)
@pytest.fixture
def serving_client_with_connection_data_override(mocker: MockerFixture, remote_workspace_resource_id):
model_name = "llm_connection_override"
model_path = (Path(MODEL_ROOT) / model_name).resolve().absolute()
# load arm connection template
connection_arm_template = model_path.joinpath("connection_arm_template.json").read_text()
connections = {
"aoai_connection": connection_arm_template,
"PROMPTFLOW_CONNECTION_PROVIDER": remote_workspace_resource_id,
}
return create_serving_client_with_connections(model_name, mocker, connections)
def create_serving_client_with_connections(model_name, mocker: MockerFixture, connections: dict = {}):
from promptflow._sdk._serving.app import create_app as create_serving_app
model_path = (Path(MODEL_ROOT) / model_name).resolve().absolute().as_posix()
mocker.patch.dict(os.environ, {"PROMPTFLOW_PROJECT_PATH": model_path})
mocker.patch.dict(
os.environ,
{
**connections,
},
)
# Set credential to None for azureml extension type
# As we mock app in github workflow, which do not have managed identity credential
func = "promptflow._sdk._serving.extension.azureml_extension._get_managed_identity_credential_with_retry"
with mock.patch(func) as mock_cred_func:
mock_cred_func.return_value = None
app = create_serving_app(
environment_variables={"API_TYPE": "${azure_open_ai_connection.api_type}"},
extension_type="azureml",
)
app.config.update(
{
"TESTING": True,
}
)
return app.test_client()
@pytest.fixture(scope=package_scope_in_live_mode())
def variable_recorder() -> VariableRecorder:
yield VariableRecorder()
@pytest.fixture(scope=package_scope_in_live_mode())
def randstr(variable_recorder: VariableRecorder) -> Callable[[str], str]:
"""Return a "random" UUID."""
def generate_random_string(variable_name: str) -> str:
random_string = str(uuid.uuid4())
if is_live():
return random_string
elif is_replay():
return variable_name
else:
return variable_recorder.get_or_record_variable(variable_name, random_string)
return generate_random_string
@pytest.fixture(scope=package_scope_in_live_mode())
def vcr_recording(
request: pytest.FixtureRequest, user_object_id: str, tenant_id: str, variable_recorder: VariableRecorder
) -> Optional[PFAzureIntegrationTestRecording]:
"""Fixture to record or replay network traffic.
If the test mode is "live", nothing will happen.
If the test mode is "record" or "replay", this fixture will locate a YAML (recording) file
based on the test file, class and function name, write to (record) or read from (replay) the file.
"""
if is_live():
yield None
else:
recording = PFAzureIntegrationTestRecording.from_test_case(
test_class=request.cls,
test_func_name=request.node.name,
user_object_id=user_object_id,
tenant_id=tenant_id,
variable_recorder=variable_recorder,
)
recording.enter_vcr()
request.addfinalizer(recording.exit_vcr)
yield recording
# we expect this fixture only work when running live test without recording
# when recording, we don't want to record any application insights secrets
# when replaying, we also don't need this
@pytest.fixture(autouse=not is_live())
def mock_appinsights_log_handler(mocker: MockerFixture) -> None:
dummy_logger = logging.getLogger("dummy")
mocker.patch("promptflow._sdk._telemetry.telemetry.get_telemetry_logger", return_value=dummy_logger)
return
@pytest.fixture
def single_worker_thread_pool() -> None:
"""Mock to use one thread for thread pool executor.
VCR.py cannot record network traffic in other threads, and we have multi-thread operations
during resolving the flow. Mock it using one thread to make VCR.py work.
"""
def single_worker_thread_pool_executor(*args, **kwargs):
return ThreadPoolExecutor(max_workers=1)
if is_live():
yield
else:
with patch(
"promptflow.azure.operations._run_operations.ThreadPoolExecutor",
new=single_worker_thread_pool_executor,
):
yield
@pytest.fixture
def mock_set_headers_with_user_aml_token(mocker: MockerFixture) -> None:
"""Mock set aml-user-token operation.
There will be requests fetching cloud metadata during retrieving AML token, which will break during replay.
As the logic comes from azure-ai-ml, changes in Prompt Flow can hardly affect it, mock it here.
"""
if not is_live():
mocker.patch(
"promptflow.azure._restclient.flow_service_caller.FlowServiceCaller._set_headers_with_user_aml_token"
)
yield
@pytest.fixture
def mock_get_azure_pf_client(mocker: MockerFixture, remote_client) -> None:
"""Mock PF Azure client to avoid network traffic during replay test."""
if not is_live():
mocker.patch(
"promptflow._cli._pf_azure._run._get_azure_pf_client",
return_value=remote_client,
)
mocker.patch(
"promptflow._cli._pf_azure._flow._get_azure_pf_client",
return_value=remote_client,
)
yield
@pytest.fixture(scope=package_scope_in_live_mode())
def mock_get_user_identity_info(user_object_id: str, tenant_id: str) -> None:
"""Mock get user object id and tenant id, currently used in flow list operation."""
if not is_live():
with patch(
"promptflow.azure._restclient.flow_service_caller.FlowServiceCaller._get_user_identity_info",
return_value=(user_object_id, tenant_id),
):
yield
else:
yield
@pytest.fixture(scope=package_scope_in_live_mode())
def created_flow(pf: PFClient, randstr: Callable[[str], str], variable_recorder: VariableRecorder) -> Flow:
"""Create a flow for test."""
flow_display_name = randstr("flow_display_name")
flow_source = FLOWS_DIR + "/simple_hello_world/"
description = "test flow description"
tags = {"owner": "sdk-test"}
result = pf.flows.create_or_update(
flow=flow_source, display_name=flow_display_name, type=FlowType.STANDARD, description=description, tags=tags
)
remote_flow_dag_path = result.path
# make sure the flow is created successfully
assert pf.flows._storage_client._check_file_share_file_exist(remote_flow_dag_path) is True
assert result.display_name == flow_display_name
assert result.type == FlowType.STANDARD
assert result.tags == tags
assert result.path.endswith("flow.dag.yaml")
# flow in Azure will have different file share name with timestamp
# and this is a client-side behavior, so we need to sanitize this in recording
# so extract this during record test
if is_record():
flow_name_const = "flow_name"
flow_name = get_created_flow_name_from_flow_path(result.path)
variable_recorder.get_or_record_variable(flow_name_const, flow_name)
yield result
@pytest.fixture(scope=package_scope_in_live_mode())
def created_batch_run_without_llm(pf: PFClient, randstr: Callable[[str], str], runtime: str) -> Run:
"""Create a batch run that does not require LLM."""
name = randstr("batch_run_name")
run = pf.run(
# copy test_configs/flows/simple_hello_world to a separate folder
# as pf.run will generate .promptflow/flow.tools.json
# it will affect Azure file share upload logic and replay test
flow=f"{FLOWS_DIR}/hello-world",
data=f"{DATAS_DIR}/webClassification3.jsonl",
column_mapping={"name": "${data.url}"},
name=name,
display_name="sdk-cli-test-fixture-batch-run-without-llm",
)
run = pf.runs.stream(run=name)
assert run.status == RunStatus.COMPLETED
yield run
@pytest.fixture(scope=package_scope_in_live_mode())
def created_eval_run_without_llm(
pf: PFClient, randstr: Callable[[str], str], runtime: str, created_batch_run_without_llm: Run
) -> Run:
"""Create a evaluation run against batch run without LLM dependency."""
name = randstr("eval_run_name")
run = pf.run(
flow=f"{FLOWS_DIR}/eval-classification-accuracy",
data=f"{DATAS_DIR}/webClassification3.jsonl",
run=created_batch_run_without_llm,
column_mapping={"groundtruth": "${data.answer}", "prediction": "${run.outputs.result}"},
runtime=runtime,
name=name,
display_name="sdk-cli-test-fixture-eval-run-without-llm",
)
run = pf.runs.stream(run=name)
assert run.status == RunStatus.COMPLETED
yield run
@pytest.fixture(scope=package_scope_in_live_mode())
def created_failed_run(pf: PFClient, randstr: Callable[[str], str], runtime: str) -> Run:
"""Create a failed run."""
name = randstr("failed_run_name")
run = pf.run(
flow=f"{FLOWS_DIR}/partial_fail",
data=f"{DATAS_DIR}/webClassification3.jsonl",
runtime=runtime,
name=name,
display_name="sdk-cli-test-fixture-failed-run",
)
# set raise_on_error to False to promise returning something
run = pf.runs.stream(run=name, raise_on_error=False)
assert run.status == RunStatus.FAILED
yield run
@pytest.fixture(autouse=not is_live())
def mock_vcrpy_for_httpx() -> None:
# there is a known issue in vcrpy handling httpx response: https://github.com/kevin1024/vcrpy/pull/591
# the related code change has not been merged, so we need such a fixture for patch
def _transform_headers(httpx_response):
out = {}
for key, var in httpx_response.headers.raw:
decoded_key = key.decode("utf-8")
decoded_var = var.decode("utf-8")
if decoded_key.lower() == "content-encoding" and decoded_var in ("gzip", "deflate"):
continue
out.setdefault(decoded_key, [])
out[decoded_key].append(decoded_var)
return out
with patch("vcr.stubs.httpx_stubs._transform_headers", new=_transform_headers):
yield
@pytest.fixture(autouse=not is_live())
def mock_to_thread() -> None:
# https://docs.python.org/3/library/asyncio-task.html#asyncio.to_thread
# to_thread actually uses a separate thread, which will break mocks
# so we need to mock it to avoid using a separate thread
# this is only for AsyncRunDownloader.to_thread
async def to_thread(func, /, *args, **kwargs):
func(*args, **kwargs)
with patch(
"promptflow.azure.operations._async_run_downloader.to_thread",
new=to_thread,
):
yield
@pytest.fixture
def mock_isinstance_for_mock_datastore() -> None:
"""Mock built-in function isinstance.
We have an isinstance check during run download for datastore type for better error message;
while our mock datastore in replay mode is not a valid type, so mock it with strict condition.
"""
if not is_replay():
yield
else:
from azure.ai.ml.entities._datastore.azure_storage import AzureBlobDatastore
from .recording_utilities.utils import MockDatastore
original_isinstance = isinstance
def mock_isinstance(*args):
if original_isinstance(args[0], MockDatastore) and args[1] == AzureBlobDatastore:
return True
return original_isinstance(*args)
with patch("builtins.isinstance", new=mock_isinstance):
yield
@pytest.fixture(autouse=True)
def mock_check_latest_version() -> None:
"""Mock check latest version.
As CI uses docker, it will always trigger this check behavior, and we don't have recording for this;
and this will hit many unknown issue with vcrpy.
"""
with patch("promptflow._utils.version_hint_utils.check_latest_version", new=lambda: None):
yield
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/utils.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
import re
from dataclasses import dataclass
from typing import Dict
import jwt
from azure.core.credentials import AccessToken
from vcr.request import Request
from .constants import ENVIRON_TEST_MODE, SanitizedValues, TestMode
def get_test_mode_from_environ() -> str:
return os.getenv(ENVIRON_TEST_MODE, TestMode.LIVE)
def is_live() -> bool:
return get_test_mode_from_environ() == TestMode.LIVE
def is_record() -> bool:
return get_test_mode_from_environ() == TestMode.RECORD
def is_replay() -> bool:
return get_test_mode_from_environ() == TestMode.REPLAY
class FakeTokenCredential:
"""Refer from Azure SDK for Python repository.
https://github.com/Azure/azure-sdk-for-python/blob/main/tools/azure-sdk-tools/devtools_testutils/fake_credentials.py
"""
def __init__(self):
token = jwt.encode(
payload={
"aud": "https://management.azure.com",
},
key="",
)
self.token = AccessToken(token, 0)
self.get_token_count = 0
def get_token(self, *args, **kwargs) -> AccessToken:
self.get_token_count += 1
return self.token
@dataclass
class MockDatastore:
"""Mock Datastore class for `DatastoreOperations.get_default().name`."""
name: str
account_name: str
container_name: str
endpoint: str
def mock_datastore_get_default(*args, **kwargs) -> MockDatastore:
return MockDatastore(
name="workspaceblobstore",
account_name=SanitizedValues.FAKE_ACCOUNT_NAME,
container_name=SanitizedValues.FAKE_CONTAINER_NAME,
endpoint="core.windows.net",
)
def mock_workspace_get(*args, **kwargs):
from azure.ai.ml.entities import Workspace
return Workspace(
name=SanitizedValues.WORKSPACE_NAME,
resource_group=SanitizedValues.RESOURCE_GROUP_NAME,
discovery_url=SanitizedValues.DISCOVERY_URL,
workspace_id=SanitizedValues.WORKSPACE_ID,
)
def get_pf_client_for_replay():
from azure.ai.ml import MLClient
from promptflow.azure import PFClient
ml_client = MLClient(
credential=FakeTokenCredential(),
subscription_id=SanitizedValues.SUBSCRIPTION_ID,
resource_group_name=SanitizedValues.RESOURCE_GROUP_NAME,
workspace_name=SanitizedValues.WORKSPACE_NAME,
)
ml_client.datastores.get_default = mock_datastore_get_default
ml_client.workspaces.get = mock_workspace_get
return PFClient(ml_client=ml_client)
def sanitize_azure_workspace_triad(value: str) -> str:
sanitized_sub = re.sub(
"/(subscriptions)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format("00000000-0000-0000-0000-000000000000"),
value,
flags=re.IGNORECASE,
)
# for regex pattern for resource group name and workspace name, refer from:
# https://learn.microsoft.com/en-us/rest/api/resources/resource-groups/create-or-update?tabs=HTTP
sanitized_rg = re.sub(
r"/(resourceGroups)/[-\w\._\(\)]+",
r"/\1/{}".format("00000"),
sanitized_sub,
flags=re.IGNORECASE,
)
sanitized_ws = re.sub(
r"/(workspaces)/[-\w\._\(\)]+[/?]",
r"/\1/{}/".format("00000"),
sanitized_rg,
flags=re.IGNORECASE,
)
# workspace name can be the last part of the string
# e.g. xxx/Microsoft.MachineLearningServices/workspaces/<workspace-name>
# apply a special handle here to sanitize
if sanitized_ws.startswith("https://"):
split1, split2 = sanitized_ws.split("/")[-2:]
if split1 == "workspaces":
sanitized_ws = sanitized_ws.replace(split2, SanitizedValues.WORKSPACE_NAME)
return sanitized_ws
def sanitize_experiment_id(value: str) -> str:
value = re.sub(
r"(experimentId)=[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"\1={}".format(SanitizedValues.WORKSPACE_ID),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_upload_hash(value: str) -> str:
value = re.sub(
r"(az-ml-artifacts)/([0-9a-f]{32})",
r"\1/{}".format(SanitizedValues.UPLOAD_HASH),
value,
flags=re.IGNORECASE,
)
value = re.sub(
r"(LocalUpload)/([0-9a-f]{32})",
r"\1/{}".format(SanitizedValues.UPLOAD_HASH),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_username(value: str) -> str:
value = re.sub(
r"/(Users%2F)([^%?]+)(%2F|\?)",
r"/\1{}\3".format(SanitizedValues.USERNAME),
value,
flags=re.IGNORECASE,
)
value = re.sub(
r"(Users/)([^/]+)(/)",
r"\1{}\3".format(SanitizedValues.USERNAME),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_flow_asset_id(value: str) -> str:
# input: azureml://locations/<region>/workspaces/<workspace-id>/flows/<flow-id>
# sanitize those with angle brackets
sanitized_region = re.sub(
r"/(locations)/[^/]+",
r"/\1/{}".format(SanitizedValues.REGION),
value,
flags=re.IGNORECASE,
)
sanitized_workspace_id = re.sub(
r"/(workspaces)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format(SanitizedValues.WORKSPACE_ID),
sanitized_region,
flags=re.IGNORECASE,
)
sanitized_flow_id = re.sub(
r"/(flows)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}/".format(SanitizedValues.FLOW_ID),
sanitized_workspace_id,
flags=re.IGNORECASE,
)
return sanitized_flow_id
def sanitize_pfs_request_body(body: str) -> str:
# sanitize workspace triad for longhand syntax asset, e.g. "batchDataInput.dataUri"
body = sanitize_azure_workspace_triad(body)
body_dict = json.loads(body)
# /BulkRuns/submit
if "runtimeName" in body_dict:
body_dict["runtimeName"] = SanitizedValues.RUNTIME_NAME
if "sessionId" in body_dict:
body_dict["sessionId"] = SanitizedValues.SESSION_ID
if "flowLineageId" in body:
body_dict["flowLineageId"] = SanitizedValues.FLOW_LINEAGE_ID
if "flowDefinitionResourceId" in body_dict:
body_dict["flowDefinitionResourceId"] = sanitize_flow_asset_id(body_dict["flowDefinitionResourceId"])
# PFS will help handle this field, so client does not need to pass this value
if "runExperimentName" in body:
body_dict["runExperimentName"] = ""
return json.dumps(body_dict)
def sanitize_pfs_response_body(body: str) -> str:
body_dict = json.loads(body)
# BulkRuns/{flowRunId}
if "studioPortalEndpoint" in body:
body_dict["studioPortalEndpoint"] = sanitize_azure_workspace_triad(body_dict["studioPortalEndpoint"])
return json.dumps(body_dict)
def sanitize_email(value: str) -> str:
return re.sub(r"([\w\.-]+)@(microsoft.com)", r"{}@\2".format(SanitizedValues.EMAIL_USERNAME), value)
def sanitize_file_share_flow_path(value: str) -> str:
flow_folder_name = "simple_hello_world"
if flow_folder_name not in value:
return value
start_index = value.index(flow_folder_name)
flow_name_length = 38 # len("simple_hello_world-01-01-2024-00-00-00")
flow_name = value[start_index : start_index + flow_name_length]
return value.replace(flow_name, "flow_name")
def _sanitize_session_id_creating_automatic_runtime(value: str) -> str:
value = re.sub(
"/(FlowSessions)/[0-9a-f]{48}",
r"/\1/{}".format(SanitizedValues.SESSION_ID),
value,
flags=re.IGNORECASE,
)
return value
def _sanitize_operation_id_polling_automatic_runtime(value: str) -> str:
value = re.sub(
"/(operations)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format(SanitizedValues.UUID),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_automatic_runtime_request_path(value: str) -> str:
return _sanitize_operation_id_polling_automatic_runtime(_sanitize_session_id_creating_automatic_runtime(value))
def _is_json_payload(headers: Dict, key: str) -> bool:
if not headers:
return False
content_type = headers.get(key)
if not content_type:
return False
# content-type can be an array, e.g. ["application/json; charset=utf-8"]
content_type = content_type[0] if isinstance(content_type, list) else content_type
content_type = content_type.split(";")[0].lower()
return "application/json" in content_type
def is_json_payload_request(request: Request) -> bool:
headers = request.headers
return _is_json_payload(headers, key="Content-Type")
def is_json_payload_response(response: Dict) -> bool:
headers = response.get("headers")
# PFAzureIntegrationTestRecording will lower keys in response headers
return _is_json_payload(headers, key="content-type")
def is_httpx_response(response: Dict) -> bool:
# different from other stubs in vcrpy, httpx response uses "content" instead of "body"
# this leads to different handle logic to response
# so we need a utility to check if a response is from httpx
return "content" in response
def get_created_flow_name_from_flow_path(flow_path: str) -> str:
# pytest fixture "created_flow" will create flow on file share with timestamp as suffix
# we need to extract the flow name from the path
# flow name is expected to start with "simple_hello_world" and follow with "/flow.dag.yaml"
return flow_path[flow_path.index("simple_hello_world") : flow_path.index("/flow.dag.yaml")]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/bases.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import inspect
import json
from pathlib import Path
from typing import Dict, List
import vcr
from vcr import matchers
from vcr.request import Request
from .constants import FILTER_HEADERS, TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING, SanitizedValues
from .processors import (
AzureMLExperimentIDProcessor,
AzureOpenAIConnectionProcessor,
AzureResourceProcessor,
AzureWorkspaceTriadProcessor,
DropProcessor,
EmailProcessor,
IndexServiceProcessor,
PFSProcessor,
RecordingProcessor,
StorageProcessor,
UserInfoProcessor,
)
from .utils import (
is_httpx_response,
is_json_payload_request,
is_live,
is_record,
is_replay,
sanitize_automatic_runtime_request_path,
sanitize_azure_workspace_triad,
sanitize_file_share_flow_path,
sanitize_pfs_request_body,
sanitize_upload_hash,
)
from .variable_recorder import VariableRecorder
class PFAzureIntegrationTestRecording:
def __init__(
self,
test_class,
test_func_name: str,
user_object_id: str,
tenant_id: str,
variable_recorder: VariableRecorder,
):
self.test_class = test_class
self.test_func_name = test_func_name
self.user_object_id = user_object_id
self.tenant_id = tenant_id
self.recording_file = self._get_recording_file()
self.recording_processors = self._get_recording_processors()
self.vcr = self._init_vcr()
self._cm = None # context manager from VCR
self.cassette = None
self.variable_recorder = variable_recorder
@staticmethod
def from_test_case(test_class, test_func_name: str, **kwargs) -> "PFAzureIntegrationTestRecording":
test_class_name = test_class.__name__
if test_class_name in TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING:
return PFAzureRunIntegrationTestRecording(
test_class=test_class,
test_func_name=test_func_name,
user_object_id=kwargs["user_object_id"],
tenant_id=kwargs["tenant_id"],
variable_recorder=kwargs["variable_recorder"],
)
else:
return PFAzureIntegrationTestRecording(
test_class=test_class,
test_func_name=test_func_name,
user_object_id=kwargs["user_object_id"],
tenant_id=kwargs["tenant_id"],
variable_recorder=kwargs["variable_recorder"],
)
def _get_recording_file(self) -> Path:
# recording files are expected to be located at "tests/test_configs/recordings"
# test file path should locate at "tests/sdk_cli_azure_test/e2etests"
test_file_path = Path(inspect.getfile(self.test_class)).resolve()
recording_dir = (test_file_path.parent.parent.parent / "test_configs" / "recordings").resolve()
recording_dir.mkdir(exist_ok=True)
test_file_name = test_file_path.stem
test_class_name = self.test_class.__name__
if "[" in self.test_func_name:
# for tests that use pytest.mark.parametrize, there will be "[]" in test function name
# recording filename pattern:
# {test_file_name}_{test_class_name}_{test_func_name}/{parameter_id}.yaml
test_func_name, parameter_id = self.test_func_name.split("[")
parameter_id = parameter_id.rstrip("]")
test_func_dir = (recording_dir / f"{test_file_name}_{test_class_name}_{test_func_name}").resolve()
test_func_dir.mkdir(exist_ok=True)
recording_file = (test_func_dir / f"{parameter_id}.yaml").resolve()
else:
# for most remaining tests
# recording filename pattern: {test_file_name}_{test_class_name}_{test_func_name}.yaml
recording_filename = f"{test_file_name}_{test_class_name}_{self.test_func_name}.yaml"
recording_file = (recording_dir / recording_filename).resolve()
if is_record() and recording_file.is_file():
recording_file.unlink()
return recording_file
def _init_vcr(self) -> vcr.VCR:
_vcr = vcr.VCR(
cassette_library_dir=self.recording_file.parent.as_posix(),
before_record_request=self._process_request_recording,
before_record_response=self._process_response_recording,
decode_compressed_response=True,
record_mode="none" if is_replay() else "all",
filter_headers=FILTER_HEADERS,
)
_vcr.match_on += ("body",)
return _vcr
def enter_vcr(self):
self._cm = self.vcr.use_cassette(self.recording_file.as_posix())
self.cassette = self._cm.__enter__()
def exit_vcr(self):
if is_record():
self._postprocess_recording()
self._cm.__exit__()
def _process_request_recording(self, request: Request) -> Request:
if is_live():
return request
if is_record():
for processor in self.recording_processors:
request = processor.process_request(request)
return request
def _process_response_recording(self, response: Dict) -> Dict:
if is_live():
return response
# httpx and non-httpx responses have different structure
# non-httpx has .body.string, while httpx has .content
# in our sanitizers (processors) logic, we only handle .body.string
# so make httpx align non-httpx for less code change
is_httpx = is_httpx_response(response)
if is_httpx:
body_string = response.pop("content")
response["body"] = {"string": body_string}
else:
response["body"]["string"] = response["body"]["string"].decode("utf-8")
if is_record():
# lower and filter some headers
headers = {}
for k in response["headers"]:
if k.lower() not in FILTER_HEADERS:
headers[k.lower()] = response["headers"][k]
response["headers"] = headers
for processor in self.recording_processors:
response = processor.process_response(response)
if is_httpx:
response["content"] = response["body"]["string"]
if not is_replay():
response.pop("body")
if isinstance(response["content"], bytes):
response["content"] = response["content"].decode("utf-8")
else:
# vcrpy does not handle well with httpx, so we need some transformations
# otherwise, replay tests will break during init VCR response instance
response["status"] = {"code": response["status_code"], "message": ""}
if isinstance(response["body"]["string"], str):
response["body"]["string"] = response["body"]["string"].encode("utf-8")
else:
response["body"]["string"] = response["body"]["string"].encode("utf-8")
return response
def _get_recording_processors(self) -> List[RecordingProcessor]:
return [
AzureMLExperimentIDProcessor(),
AzureOpenAIConnectionProcessor(),
AzureResourceProcessor(),
AzureWorkspaceTriadProcessor(),
DropProcessor(),
EmailProcessor(),
IndexServiceProcessor(),
PFSProcessor(),
StorageProcessor(),
UserInfoProcessor(user_object_id=self.user_object_id, tenant_id=self.tenant_id),
]
def _postprocess_recording(self) -> None:
self._apply_replacement_for_recordings()
return
def _apply_replacement_for_recordings(self) -> None:
for i in range(len(self.cassette.data)):
req, resp = self.cassette.data[i]
req = self.variable_recorder.sanitize_request(req)
resp = self.variable_recorder.sanitize_response(resp)
self.cassette.data[i] = (req, resp)
return
class PFAzureRunIntegrationTestRecording(PFAzureIntegrationTestRecording):
"""Test class for run operations in Prompt Flow Azure.
Different from other operations, run operations have:
- duplicate network requests for stream run
- blob storage requests contain upload hash
- Submit and get run data API requests are indistinguishable without run name in body
Use a separate class with more pre/post recording processing method or
request matchers to handle above cases.
"""
def _init_vcr(self) -> vcr.VCR:
_vcr = super(PFAzureRunIntegrationTestRecording, self)._init_vcr()
_vcr.register_matcher("path", self._custom_request_path_matcher)
_vcr.register_matcher("body", self._custom_request_body_matcher)
return _vcr
def enter_vcr(self):
self._cm = self.vcr.use_cassette(
self.recording_file.as_posix(),
allow_playback_repeats=True,
filter_query_parameters=["api-version"],
)
self.cassette = self._cm.__enter__()
def _postprocess_recording(self) -> None:
self._drop_duplicate_recordings()
super(PFAzureRunIntegrationTestRecording, self)._postprocess_recording()
def _drop_duplicate_recordings(self) -> None:
# stream run operation contains two requests:
# 1. get status; 2. get logs
# before the run is terminated, there will be many duplicate requests
# getting status/logs, which leads to infinite loop during replay
# therefore apply such post process to drop those duplicate recordings
dropped_recordings = []
run_data_requests = dict()
log_content_requests = dict()
for req, resp in self.cassette.data:
# run hisotry's rundata API
if str(req.path).endswith("/rundata"):
body = req.body.decode("utf-8")
body_dict = json.loads(body)
name = body_dict["runId"]
run_data_requests[name] = (req, resp)
continue
if str(req.path).endswith("/logContent"):
log_content_requests[req.uri] = (req, resp)
continue
dropped_recordings.append((req, resp))
# append rundata recording(s)
for req, resp in run_data_requests.values():
dropped_recordings.append((req, resp))
for req, resp in log_content_requests.values():
dropped_recordings.append((req, resp))
self.cassette.data = dropped_recordings
return
def _custom_request_path_matcher(self, r1: Request, r2: Request) -> bool:
# NOTE: orders of below conditions matter, please modify with caution
# in run download scenario, observed below wired path: https://<xxx>/https://<yyy>/<remaining>
# as we don't have append/replace logic, it might result from Azure blob client,
# which is hard to patch; therefore, hack this in matcher (here)
# https:// should appear in path, so it's safe to use this as a condition
if "https://" in r1.path:
_path = str(r1.path)
endpoint = ".blob.core.windows.net/"
duplicate_path = _path[_path.index(endpoint) + len(endpoint) :]
path_for_compare = _path[: _path.index("https://")] + duplicate_path[duplicate_path.index("/") + 1 :]
return path_for_compare == r2.path
# for blob storage request, sanitize the upload hash in path
if r1.host == r2.host and r1.host == SanitizedValues.BLOB_STORAGE_REQUEST_HOST:
return sanitize_upload_hash(r1.path) == r2.path
# for file share request, mainly target pytest fixture "created_flow"
if r1.host == r2.host and r1.host == SanitizedValues.FILE_SHARE_REQUEST_HOST:
return sanitize_file_share_flow_path(r1.path) == r2.path
# for automatic runtime, sanitize flow session id in path
if r1.host == r2.host and ("FlowSessions" in r1.path and "FlowSessions" in r2.path):
path1 = sanitize_automatic_runtime_request_path(r1.path)
path2 = sanitize_automatic_runtime_request_path(r2.path)
return sanitize_azure_workspace_triad(path1) == path2
return r1.path == r2.path
def _custom_request_body_matcher(self, r1: Request, r2: Request) -> bool:
if is_json_payload_request(r1) and r1.body is not None:
# note that `sanitize_upload_hash` is not idempotent
# so we should not modify r1 directly
# otherwise it will be sanitized multiple times with many zeros
_r1 = copy.deepcopy(r1)
body1 = _r1.body.decode("utf-8")
body1 = sanitize_pfs_request_body(body1)
body1 = sanitize_upload_hash(body1)
_r1.body = body1.encode("utf-8")
try:
return matchers.body(_r1, r2)
except AssertionError:
# if not match, extra sanitize flow file share path (if exists)
# for potential pytest fixture "created_flow" scenario
body_dict = json.loads(body1)
if "flowDefinitionFilePath" in body_dict:
body_dict["flowDefinitionFilePath"] = "Users/unknown_user/promptflow/flow_name/flow.dag.yaml"
body1 = json.dumps(body_dict)
_r1.body = body1.encode("utf-8")
return matchers.body(_r1, r2)
else:
return False
else:
return matchers.body(r1, r2)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from .bases import PFAzureIntegrationTestRecording
from .constants import SanitizedValues
from .utils import get_created_flow_name_from_flow_path, get_pf_client_for_replay, is_live, is_record, is_replay
from .variable_recorder import VariableRecorder
__all__ = [
"PFAzureIntegrationTestRecording",
"SanitizedValues",
"VariableRecorder",
"get_created_flow_name_from_flow_path",
"get_pf_client_for_replay",
"is_live",
"is_record",
"is_replay",
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/variable_recorder.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from typing import Dict
from vcr.request import Request
from .utils import is_httpx_response, is_json_payload_request
class VariableRecorder:
def __init__(self):
self.variables = dict()
def get_or_record_variable(self, variable: str, default: str) -> str:
return self.variables.setdefault(variable, default)
def sanitize_request(self, request: Request) -> Request:
request.uri = self._sanitize(request.uri)
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = self._sanitize(body)
request.body = body.encode("utf-8")
return request
def sanitize_response(self, response: Dict) -> Dict:
# httpx response: .content, string; no action needed
# non-httpx response: .body.string, bytes, need decode/encode
if is_httpx_response(response):
response["content"] = self._sanitize(response["content"])
else:
response["body"]["string"] = response["body"]["string"].decode("utf-8")
response["body"]["string"] = self._sanitize(response["body"]["string"])
response["body"]["string"] = response["body"]["string"].encode("utf-8")
return response
def _sanitize(self, value: str) -> str:
for k, v in self.variables.items():
value = value.replace(v, k)
return value
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/constants.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
ENVIRON_TEST_MODE = "PROMPT_FLOW_TEST_MODE"
class TestMode:
LIVE = "live"
RECORD = "record"
REPLAY = "replay"
FILTER_HEADERS = [
"aml-user-token",
"authorization",
"date",
"etag",
"request-context",
"x-aml-cluster",
"x-ms-access-tier",
"x-ms-access-tier-inferred",
"x-ms-client-request-id",
"x-ms-client-session-id",
"x-ms-client-user-type",
"x-ms-correlation-request-id",
"x-ms-file-permission-key",
"x-ms-lease-state",
"x-ms-lease-status",
"x-ms-server-encrypted",
"x-ms-ratelimit-remaining-subscription-reads",
"x-ms-ratelimit-remaining-subscription-writes",
"x-ms-response-type",
"x-ms-request-id",
"x-ms-routing-request-id",
"x-msedge-ref",
]
class SanitizedValues:
UUID = "00000000-0000-0000-0000-000000000000"
SUBSCRIPTION_ID = "00000000-0000-0000-0000-000000000000"
RESOURCE_GROUP_NAME = "00000"
WORKSPACE_NAME = "00000"
WORKSPACE_ID = "00000000-0000-0000-0000-000000000000"
TENANT_ID = "00000000-0000-0000-0000-000000000000"
USER_OBJECT_ID = "00000000-0000-0000-0000-000000000000"
# workspace
DISCOVERY_URL = "https://eastus.api.azureml.ms/discovery"
# datastore
FAKE_KEY = "this is fake key"
FAKE_ACCOUNT_NAME = "fake_account_name"
FAKE_CONTAINER_NAME = "fake-container-name"
FAKE_FILE_SHARE_NAME = "fake-file-share-name"
# aoai connection
FAKE_API_BASE = "https://fake.openai.azure.com"
# storage
UPLOAD_HASH = "000000000000000000000000000000000000"
BLOB_STORAGE_REQUEST_HOST = "fake_account_name.blob.core.windows.net"
FILE_SHARE_REQUEST_HOST = "fake_account_name.file.core.windows.net"
# PFS
RUNTIME_NAME = "fake-runtime-name"
SESSION_ID = "000000000000000000000000000000000000000000000000"
FLOW_LINEAGE_ID = "0000000000000000000000000000000000000000000000000000000000000000"
REGION = "fake-region"
FLOW_ID = "00000000-0000-0000-0000-000000000000"
# trick: "unknown_user" is the value when client fails to get username
# use this value so that we don't do extra logic when replay
USERNAME = "unknown_user"
# MISC
EMAIL_USERNAME = "username"
class AzureMLResourceTypes:
CONNECTION = "Microsoft.MachineLearningServices/workspaces/connections"
DATASTORE = "Microsoft.MachineLearningServices/workspaces/datastores"
WORKSPACE = "Microsoft.MachineLearningServices/workspaces"
TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING = [
"TestCliWithAzure",
"TestFlowRun",
"TestFlow",
"TestTelemetry",
"TestAzureCliPerf",
]
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/processors.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import base64
import json
from typing import Dict
from vcr.request import Request
from .constants import AzureMLResourceTypes, SanitizedValues
from .utils import (
is_json_payload_request,
is_json_payload_response,
sanitize_azure_workspace_triad,
sanitize_email,
sanitize_experiment_id,
sanitize_pfs_request_body,
sanitize_pfs_response_body,
sanitize_upload_hash,
sanitize_username,
)
class RecordingProcessor:
def process_request(self, request: Request) -> Request:
return request
def process_response(self, response: Dict) -> Dict:
return response
class AzureWorkspaceTriadProcessor(RecordingProcessor):
"""Sanitize subscription id, resource group name and workspace name."""
def process_request(self, request: Request) -> Request:
request.uri = sanitize_azure_workspace_triad(request.uri)
return request
def process_response(self, response: Dict) -> Dict:
response["body"]["string"] = sanitize_azure_workspace_triad(response["body"]["string"])
return response
class AzureMLExperimentIDProcessor(RecordingProcessor):
"""Sanitize Azure ML experiment id, currently we use workspace id as the value."""
def process_request(self, request: Request) -> Request:
request.uri = sanitize_experiment_id(request.uri)
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
if "experimentId" in response["body"]["string"]:
body = json.loads(response["body"]["string"])
if "experimentId" in body:
body["experimentId"] = SanitizedValues.WORKSPACE_ID
response["body"]["string"] = json.dumps(body)
return response
class AzureResourceProcessor(RecordingProcessor):
"""Sanitize sensitive data in Azure resource GET response."""
def __init__(self):
# datastore related
self.storage_account_names = set()
self.storage_container_names = set()
self.file_share_names = set()
def _sanitize_request_url_for_storage(self, uri: str) -> str:
# this instance will store storage account names and container names
# so we can apply the sanitization here with simple string replace rather than regex
for account_name in self.storage_account_names:
uri = uri.replace(account_name, SanitizedValues.FAKE_ACCOUNT_NAME)
for container_name in self.storage_container_names:
uri = uri.replace(container_name, SanitizedValues.FAKE_CONTAINER_NAME)
for file_share_name in self.file_share_names:
uri = uri.replace(file_share_name, SanitizedValues.FAKE_FILE_SHARE_NAME)
return uri
def process_request(self, request: Request) -> Request:
request.uri = self._sanitize_request_url_for_storage(request.uri)
return request
def _sanitize_response_body(self, body: Dict) -> Dict:
resource_type = body.get("type")
if resource_type == AzureMLResourceTypes.WORKSPACE:
body = self._sanitize_response_for_workspace(body)
elif resource_type == AzureMLResourceTypes.CONNECTION:
body = self._sanitize_response_for_arm_connection(body)
elif resource_type == AzureMLResourceTypes.DATASTORE:
body = self._sanitize_response_for_datastore(body)
return body
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
body = json.loads(response["body"]["string"])
if isinstance(body, dict):
# response can be a list sometimes (e.g. get workspace datastores)
# need to sanitize each with a for loop
if "value" in body:
resources = body["value"]
for i in range(len(resources)):
resources[i] = self._sanitize_response_body(resources[i])
body["value"] = resources
else:
body = self._sanitize_response_body(body)
response["body"]["string"] = json.dumps(body)
return response
def _sanitize_response_for_workspace(self, body: Dict) -> Dict:
filter_keys = ["identity", "properties", "systemData"]
for k in filter_keys:
if k in body:
body.pop(k)
# need during the constructor of FlowServiceCaller (for vNet case)
body["properties"] = {"discoveryUrl": SanitizedValues.DISCOVERY_URL}
name = body["name"]
body["name"] = SanitizedValues.WORKSPACE_NAME
body["id"] = body["id"].replace(name, SanitizedValues.WORKSPACE_NAME)
return body
def _sanitize_response_for_arm_connection(self, body: Dict) -> Dict:
if body["properties"]["authType"] == "CustomKeys":
# custom connection, sanitize "properties.credentials.keys"
body["properties"]["credentials"]["keys"] = {}
else:
# others, sanitize "properties.credentials.key"
body["properties"]["credentials"]["key"] = "_"
body["properties"]["target"] = "_"
return body
def _sanitize_response_for_datastore(self, body: Dict) -> Dict:
body["properties"]["subscriptionId"] = SanitizedValues.SUBSCRIPTION_ID
body["properties"]["resourceGroup"] = SanitizedValues.RESOURCE_GROUP_NAME
self.storage_account_names.add(body["properties"]["accountName"])
body["properties"]["accountName"] = SanitizedValues.FAKE_ACCOUNT_NAME
# blob storage
if "containerName" in body["properties"]:
self.storage_container_names.add(body["properties"]["containerName"])
body["properties"]["containerName"] = SanitizedValues.FAKE_CONTAINER_NAME
# file share
elif "fileShareName" in body["properties"]:
self.file_share_names.add(body["properties"]["fileShareName"])
body["properties"]["fileShareName"] = SanitizedValues.FAKE_FILE_SHARE_NAME
return body
class AzureOpenAIConnectionProcessor(RecordingProcessor):
"""Sanitize api_base in AOAI connection GET response."""
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
body = json.loads(response["body"]["string"])
if isinstance(body, dict) and body.get("connectionType") == "AzureOpenAI":
body["configs"]["api_base"] = SanitizedValues.FAKE_API_BASE
response["body"]["string"] = json.dumps(body)
return response
class StorageProcessor(RecordingProcessor):
"""Sanitize sensitive data during storage operations when submit run."""
def process_request(self, request: Request) -> Request:
request.uri = sanitize_upload_hash(request.uri)
request.uri = sanitize_username(request.uri)
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = sanitize_upload_hash(body)
body = sanitize_username(body)
request.body = body.encode("utf-8")
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
response["body"]["string"] = sanitize_username(response["body"]["string"])
body = json.loads(response["body"]["string"])
if isinstance(body, dict):
self._sanitize_list_secrets_response(body)
response["body"]["string"] = json.dumps(body)
return response
def _sanitize_list_secrets_response(self, body: Dict) -> Dict:
if "key" in body:
b64_key = base64.b64encode(SanitizedValues.FAKE_KEY.encode("ascii"))
body["key"] = str(b64_key, "ascii")
return body
class DropProcessor(RecordingProcessor):
"""Ignore some requests that won't be used during playback."""
def process_request(self, request: Request) -> Request:
if "/metadata/identity/oauth2/token" in request.path:
return None
return request
class PFSProcessor(RecordingProcessor):
"""Sanitize request/response for PFS operations."""
def process_request(self, request: Request) -> Request:
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = sanitize_pfs_request_body(body)
request.body = body.encode("utf-8")
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
response["body"]["string"] = sanitize_pfs_response_body(response["body"]["string"])
return response
class UserInfoProcessor(RecordingProcessor):
"""Sanitize user object id and tenant id in responses."""
def __init__(self, user_object_id: str, tenant_id: str):
self.user_object_id = user_object_id
self.tenant_id = tenant_id
def process_request(self, request: Request) -> Request:
if is_json_payload_request(request) and request.body is not None:
body = request.body.decode("utf-8")
body = str(body).replace(self.user_object_id, SanitizedValues.USER_OBJECT_ID)
body = body.replace(self.tenant_id, SanitizedValues.TENANT_ID)
request.body = body.encode("utf-8")
return request
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
response["body"]["string"] = str(response["body"]["string"]).replace(
self.user_object_id, SanitizedValues.USER_OBJECT_ID
)
response["body"]["string"] = str(response["body"]["string"]).replace(
self.tenant_id, SanitizedValues.TENANT_ID
)
return response
class IndexServiceProcessor(RecordingProcessor):
"""Sanitize index service responses."""
def process_response(self, response: Dict) -> Dict:
if is_json_payload_response(response):
if "continuationToken" in response["body"]["string"]:
body = json.loads(response["body"]["string"])
body.pop("continuationToken", None)
response["body"]["string"] = json.dumps(body)
return response
class EmailProcessor(RecordingProcessor):
"""Sanitize email address in responses."""
def process_response(self, response: Dict) -> Dict:
response["body"]["string"] = sanitize_email(response["body"]["string"])
return response
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_run_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import json
import shutil
from logging import Logger
from pathlib import Path
from tempfile import TemporaryDirectory
from time import sleep
from typing import Callable
from unittest.mock import MagicMock, patch
import pandas as pd
import pydash
import pytest
from promptflow._sdk._constants import DownloadedRun, RunStatus
from promptflow._sdk._errors import InvalidRunError, InvalidRunStatusError, RunNotFoundError
from promptflow._sdk._load_functions import load_run
from promptflow._sdk.entities import Run
from promptflow._utils.flow_utils import get_flow_lineage_id
from promptflow._utils.yaml_utils import load_yaml
from promptflow.azure import PFClient
from promptflow.azure._constants._flow import ENVIRONMENT, PYTHON_REQUIREMENTS_TXT
from promptflow.azure._entities._flow import Flow
from promptflow.exceptions import UserErrorException
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
PROMOTFLOW_ROOT = Path(__file__) / "../../../.."
TEST_ROOT = Path(__file__).parent.parent.parent
MODEL_ROOT = TEST_ROOT / "test_configs/e2e_samples"
CONNECTION_FILE = (PROMOTFLOW_ROOT / "connections.json").resolve().absolute().as_posix()
FLOWS_DIR = "./tests/test_configs/flows"
RUNS_DIR = "./tests/test_configs/runs"
DATAS_DIR = "./tests/test_configs/datas"
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures(
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestFlowRun:
def test_run_bulk(self, pf, runtime: str, randstr: Callable[[str], str]):
name = randstr("name")
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=name,
)
assert isinstance(run, Run)
assert run.name == name
def test_run_bulk_from_yaml(self, pf, runtime: str, randstr: Callable[[str], str]):
run_id = randstr("run_id")
run = load_run(
source=f"{RUNS_DIR}/sample_bulk_run_cloud.yaml",
params_override=[{"name": run_id, "runtime": runtime}],
)
run = pf.runs.create_or_update(run=run)
assert isinstance(run, Run)
def test_basic_evaluation(self, pf, runtime: str, randstr: Callable[[str], str]):
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=data_path,
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("batch_run_name"),
)
assert isinstance(run, Run)
run = pf.runs.stream(run=run.name)
assert run.status == RunStatus.COMPLETED
eval_run = pf.run(
flow=f"{FLOWS_DIR}/eval-classification-accuracy",
data=data_path,
run=run,
column_mapping={"groundtruth": "${data.answer}", "prediction": "${run.outputs.category}"},
runtime=runtime,
name=randstr("eval_run_name"),
)
assert isinstance(eval_run, Run)
eval_run = pf.runs.stream(run=eval_run.name)
assert eval_run.status == RunStatus.COMPLETED
def test_basic_evaluation_without_data(self, pf, runtime: str, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification3.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("batch_run_name"),
)
assert isinstance(run, Run)
run = pf.runs.stream(run=run.name)
assert run.status == RunStatus.COMPLETED
eval_run = pf.run(
flow=f"{FLOWS_DIR}/eval-classification-accuracy",
run=run,
column_mapping={
# evaluation reference run.inputs
"groundtruth": "${run.inputs.url}",
"prediction": "${run.outputs.category}",
},
runtime=runtime,
name=randstr("eval_run_name"),
)
assert isinstance(eval_run, Run)
eval_run = pf.runs.stream(run=eval_run.name)
assert eval_run.status == RunStatus.COMPLETED
def test_run_bulk_with_remote_flow(
self, pf: PFClient, runtime: str, randstr: Callable[[str], str], created_flow: Flow
):
"""Test run bulk with remote workspace flow."""
name = randstr("name")
run = pf.run(
flow=f"azureml:{created_flow.name}",
data=f"{DATAS_DIR}/simple_hello_world.jsonl",
column_mapping={"name": "${data.name}"},
runtime=runtime,
name=name,
)
assert isinstance(run, Run)
assert run.name == name
def test_run_bulk_with_registry_flow(
self, pf: PFClient, runtime: str, randstr: Callable[[str], str], registry_name: str
):
"""Test run bulk with remote registry flow."""
name = randstr("name")
run = pf.run(
flow=f"azureml://registries/{registry_name}/models/simple_hello_world/versions/202311241",
data=f"{DATAS_DIR}/simple_hello_world.jsonl",
column_mapping={"name": "${data.name}"},
runtime=runtime,
name=name,
)
assert isinstance(run, Run)
assert run.name == name
# test invalid registry flow
with pytest.raises(UserErrorException, match="Invalid remote flow pattern, got"):
pf.run(
flow="azureml://registries/no-flow",
data=f"{DATAS_DIR}/simple_hello_world.jsonl",
column_mapping={"name": "${data.name}"},
runtime=runtime,
name=name,
)
def test_run_with_connection_overwrite(self, pf, runtime: str, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
connections={"classify_with_llm": {"connection": "azure_open_ai", "model": "gpt-3.5-turbo"}},
runtime=runtime,
name=randstr("name"),
)
assert isinstance(run, Run)
def test_run_with_env_overwrite(self, pf, runtime: str, randstr: Callable[[str], str]):
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
run = pf.runs.create_or_update(run=run)
assert isinstance(run, Run)
def test_run_display_name_with_macro(self, pf, runtime: str, randstr: Callable[[str], str]):
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
run.display_name = "my_display_name_${variant_id}_${timestamp}"
run = pf.runs.create_or_update(run=run)
assert run.display_name.startswith("my_display_name_variant_0_")
assert "${timestamp}" not in run.display_name
assert isinstance(run, Run)
def test_default_run_display_name(self, pf, runtime: str, randstr: Callable[[str], str]):
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
run = pf.runs.create_or_update(run=run)
assert run.display_name == run.name
assert isinstance(run, Run)
def test_run_with_remote_data(
self, pf, runtime: str, remote_web_classification_data, randstr: Callable[[str], str]
):
# run with arm id
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"azureml:{remote_web_classification_data.id}",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name1"),
)
assert isinstance(run, Run)
# run with name version
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"azureml:{remote_web_classification_data.name}:{remote_web_classification_data.version}",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name2"),
)
assert isinstance(run, Run)
# TODO: confirm whether this test is a end-to-end test
def test_run_bulk_not_exist(self, pf, runtime: str, randstr: Callable[[str], str]):
test_data = f"{DATAS_DIR}/webClassification1.jsonl"
with pytest.raises(UserErrorException) as e:
pf.run(
flow=f"{FLOWS_DIR}/web_classification",
# data with file:/// prefix is not supported, should raise not exist error
data=f"file:///{Path(test_data).resolve().absolute()}",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name"),
)
assert "does not exist" in str(e.value)
def test_list_runs(self, pf):
runs = pf.runs.list(max_results=10)
for run in runs:
print(json.dumps(run._to_dict(), indent=4))
assert len(runs) == 10
def test_show_run(self, pf: PFClient, created_eval_run_without_llm: Run):
run = pf.runs.get(run=created_eval_run_without_llm.name)
run_dict = run._to_dict()
print(json.dumps(run_dict, indent=4))
# it's hard to assert with precise value, so just assert existence, type and length
expected_keys = [
"name",
"created_on",
"status",
"display_name",
"description",
"tags",
"properties",
"creation_context",
"start_time",
"end_time",
"duration",
"portal_url",
"data",
"output",
"run",
]
for expected_key in expected_keys:
assert expected_key in run_dict
if expected_key == "description":
assert run_dict[expected_key] is None
elif expected_key in {"tags", "properties", "creation_context"}:
assert isinstance(run_dict[expected_key], dict)
else:
assert isinstance(run_dict[expected_key], str)
assert len(run_dict[expected_key]) > 0
def test_show_run_details(self, pf: PFClient, created_batch_run_without_llm: Run):
# get first 2 results
details = pf.get_details(run=created_batch_run_without_llm.name, max_results=2)
assert details.shape[0] == 2
# get first 10 results while it only has 3
details = pf.get_details(run=created_batch_run_without_llm.name, max_results=10)
assert details.shape[0] == 3
# get all results
details = pf.get_details(run=created_batch_run_without_llm.name, all_results=True)
assert details.shape[0] == 3
# get all results even if max_results is set to 2
details = pf.get_details(
run=created_batch_run_without_llm.name,
max_results=2,
all_results=True,
)
assert details.shape[0] == 3
def test_show_metrics(self, pf: PFClient, created_eval_run_without_llm: Run):
metrics = pf.runs.get_metrics(run=created_eval_run_without_llm.name)
print(json.dumps(metrics, indent=4))
# as we use unmatched data, we can assert the accuracy is 0
assert metrics == {"accuracy": 0.0}
def test_stream_invalid_run_logs(self, pf, randstr: Callable[[str], str]):
# test get invalid run name
non_exist_run = randstr("non_exist_run")
with pytest.raises(RunNotFoundError, match=f"Run {non_exist_run!r} not found"):
pf.runs.stream(run=non_exist_run)
def test_stream_run_logs(self, pf: PFClient, created_batch_run_without_llm: Run):
run = pf.runs.stream(run=created_batch_run_without_llm.name)
assert run.status == RunStatus.COMPLETED
def test_stream_failed_run_logs(self, pf: PFClient, created_failed_run: Run, capfd: pytest.CaptureFixture):
# (default) raise_on_error=True
with pytest.raises(InvalidRunStatusError):
pf.stream(run=created_failed_run.name)
# raise_on_error=False
pf.stream(run=created_failed_run.name, raise_on_error=False)
out, _ = capfd.readouterr()
assert "The input for batch run is incorrect. Couldn't find these mapping relations: ${data.key}" in out
def test_failed_run_to_dict_exclude(self, pf: PFClient, created_failed_run: Run):
failed_run = pf.runs.get(run=created_failed_run.name)
# Azure run object reference a dict, use deepcopy to avoid unexpected modification
default = copy.deepcopy(failed_run._to_dict())
exclude = failed_run._to_dict(exclude_additional_info=True, exclude_debug_info=True)
assert "additionalInfo" in default["error"]["error"] and "additionalInfo" not in exclude["error"]["error"]
assert "debugInfo" in default["error"]["error"] and "debugInfo" not in exclude["error"]["error"]
@pytest.mark.skipif(
condition=not is_live(),
reason="cannot differ the two requests to run history in replay mode.",
)
def test_archive_and_restore_run(self, pf: PFClient, created_batch_run_without_llm: Run):
from promptflow._sdk._constants import RunHistoryKeys
run_meta_data = RunHistoryKeys.RunMetaData
hidden = RunHistoryKeys.HIDDEN
run_id = created_batch_run_without_llm.name
# test archive
pf.runs.archive(run=run_id)
run_data = pf.runs._get_run_from_run_history(run_id, original_form=True)[run_meta_data]
assert run_data[hidden] is True
# test restore
pf.runs.restore(run=run_id)
run_data = pf.runs._get_run_from_run_history(run_id, original_form=True)[run_meta_data]
assert run_data[hidden] is False
def test_update_run(self, pf: PFClient, created_batch_run_without_llm: Run, randstr: Callable[[str], str]):
run_id = created_batch_run_without_llm.name
test_mark = randstr("test_mark")
new_display_name = f"test_display_name_{test_mark}"
new_description = f"test_description_{test_mark}"
new_tags = {"test_tag": test_mark}
run = pf.runs.update(
run=run_id,
display_name=new_display_name,
description=new_description,
tags=new_tags,
)
# sleep to wait for update to take effect
sleep(3)
assert run.display_name == new_display_name
assert run.description == new_description
assert run.tags["test_tag"] == test_mark
# test wrong type of parameters won't raise error, just log warnings and got ignored
run = pf.runs.update(
run=run_id,
tags={"test_tag": {"a": 1}},
)
# sleep to wait for update to take effect
sleep(3)
assert run.display_name == new_display_name
assert run.description == new_description
assert run.tags["test_tag"] == test_mark
def test_cancel_run(self, pf, runtime: str, randstr: Callable[[str], str]):
# create a run
run_name = randstr("name")
pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=run_name,
)
pf.runs.cancel(run=run_name)
sleep(3)
run = pf.runs.get(run=run_name)
# the run status might still be cancel requested, but it should be canceled eventually
assert run.status in [RunStatus.CANCELED, RunStatus.CANCEL_REQUESTED]
@pytest.mark.skipif(
condition=not is_live(), reason="request uri contains temp folder name, need some time to sanitize."
)
def test_run_with_additional_includes(self, pf, runtime: str, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification_with_additional_include",
data=f"{DATAS_DIR}/webClassification1.jsonl",
inputs_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name"),
)
run = pf.runs.stream(run=run.name)
assert run.status == RunStatus.COMPLETED
# Test additional includes don't exist
with pytest.raises(ValueError) as e:
pf.run(
flow=f"{FLOWS_DIR}/web_classification_with_invalid_additional_include",
data=f"{DATAS_DIR}/webClassification1.jsonl",
inputs_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name_invalid"),
)
assert "Unable to find additional include ../invalid/file/path" in str(e.value)
@pytest.mark.skip(reason="Cannot find tools of the flow with symbolic.")
def test_run_with_symbolic(self, remote_client, pf, runtime, prepare_symbolic_flow):
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification_with_symbolic",
data=f"{DATAS_DIR}/webClassification1.jsonl",
inputs_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
)
remote_client.runs.stream(run=run.name)
def test_run_bulk_without_retry(self, remote_client):
from azure.core.exceptions import ServiceResponseError
from azure.core.pipeline.transport._requests_basic import RequestsTransport
from azure.core.rest._requests_basic import RestRequestsTransportResponse
from requests import Response
from promptflow.azure._restclient.flow.models import SubmitBulkRunRequest
from promptflow.azure._restclient.flow_service_caller import FlowRequestException, FlowServiceCaller
from promptflow.azure.operations import RunOperations
mock_run = MagicMock()
mock_run._runtime = "fake_runtime"
mock_run._to_rest_object.return_value = SubmitBulkRunRequest()
mock_run._use_remote_flow = False
with patch.object(RunOperations, "_resolve_data_to_asset_id"), patch.object(RunOperations, "_resolve_flow"):
with patch.object(RequestsTransport, "send") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
mock_request.side_effect = ServiceResponseError(
"Connection aborted.",
error=ConnectionResetError(10054, "An existing connection was forcibly closed", None, 10054, None),
)
with pytest.raises(ServiceResponseError):
remote_client.runs.create_or_update(run=mock_run)
# won't retry connection error since POST without response code is not retryable according to
# retry policy
assert mock_request.call_count == 1
with patch.object(RunOperations, "_resolve_data_to_asset_id"), patch.object(RunOperations, "_resolve_flow"):
with patch.object(RequestsTransport, "send") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
fake_response = Response()
# won't retry 500
fake_response.status_code = 500
fake_response._content = b'{"error": "error"}'
fake_response._content_consumed = True
mock_request.return_value = RestRequestsTransportResponse(
request=None,
internal_response=fake_response,
)
with pytest.raises(FlowRequestException):
remote_client.runs.create_or_update(run=mock_run)
assert mock_request.call_count == 1
with patch.object(RunOperations, "_resolve_data_to_asset_id"), patch.object(RunOperations, "_resolve_flow"):
with patch.object(RequestsTransport, "send") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
fake_response = Response()
# will retry 503
fake_response.status_code = 503
fake_response._content = b'{"error": "error"}'
fake_response._content_consumed = True
mock_request.return_value = RestRequestsTransportResponse(
request=None,
internal_response=fake_response,
)
with pytest.raises(FlowRequestException):
remote_client.runs.create_or_update(run=mock_run)
assert mock_request.call_count == 4
def test_pf_run_with_env_var(self, pf, randstr: Callable[[str], str]):
from promptflow.azure.operations import RunOperations
def create_or_update(run, **kwargs):
# make run.flow a datastore path uri, so that it can be parsed by AzureMLDatastorePathUri
run.flow = "azureml://datastores/workspaceblobstore/paths/LocalUpload/not/important/path"
return run
with patch.object(RunOperations, "create_or_update") as mock_create_or_update:
mock_create_or_update.side_effect = create_or_update
env_var = {"API_BASE": "${azure_open_ai_connection.api_base}"}
run = pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
environment_variables=env_var,
name=randstr("name"),
)
assert run._to_rest_object().environment_variables == env_var
def test_automatic_runtime(self, pf, randstr: Callable[[str], str]):
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
from promptflow.azure.operations import RunOperations
def submit(*args, **kwargs):
body = kwargs.get("body", None)
assert body.runtime_name == "automatic"
assert body.vm_size is None
assert body.max_idle_time_seconds is None
return body
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(RunOperations, "get"):
mock_submit.side_effect = submit
# no runtime provided, will use automatic runtime
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name1"),
)
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(RunOperations, "get"):
mock_submit.side_effect = submit
# automatic is a reserved runtime name, will use automatic runtime if specified.
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime="automatic",
name=randstr("name2"),
)
def test_automatic_runtime_with_resources(self, pf, randstr: Callable[[str], str]):
from promptflow.azure._restclient.flow.models import SessionSetupModeEnum
source = f"{RUNS_DIR}/sample_bulk_run_with_resources.yaml"
run_id = randstr("run_id")
run = load_run(
source=source,
params_override=[{"name": run_id}],
)
rest_run = run._to_rest_object()
assert rest_run.vm_size == "Standard_D2"
assert rest_run.max_idle_time_seconds == 3600
assert rest_run.session_setup_mode == SessionSetupModeEnum.SYSTEM_WAIT
run = pf.runs.create_or_update(run=run)
assert isinstance(run, Run)
def test_run_data_not_provided(self, pf, randstr: Callable[[str], str]):
with pytest.raises(UserErrorException) as e:
pf.run(
flow=f"{FLOWS_DIR}/web_classification",
name=randstr("name"),
)
assert "at least one of data or run must be provided" in str(e)
def test_run_without_dump(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
from promptflow._sdk._errors import RunNotFoundError
from promptflow._sdk._orm.run_info import RunInfo
run = pf.run(
flow=f"{FLOWS_DIR}/web_classification",
data=f"{DATAS_DIR}/webClassification1.jsonl",
column_mapping={"url": "${data.url}"},
variant="${summarize_text_content.variant_0}",
runtime=runtime,
name=randstr("name"),
)
# cloud run should not dump to database
with pytest.raises(RunNotFoundError):
RunInfo.get(run.name)
def test_input_mapping_with_dict(self, pf, runtime: str, randstr: Callable[[str], str]):
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
run = pf.run(
flow=f"{FLOWS_DIR}/flow_with_dict_input",
data=data_path,
column_mapping=dict(key={"a": 1}, extra="${data.url}"),
runtime=runtime,
name=randstr("name"),
)
assert '"{\\"a\\": 1}"' in run.properties["azureml.promptflow.inputs_mapping"]
run = pf.runs.stream(run=run)
assert run.status == "Completed"
def test_get_invalid_run_cases(self, pf, randstr: Callable[[str], str]):
# test get invalid run type
with pytest.raises(InvalidRunError, match="expected 'str' or 'Run' object"):
pf.runs.get(run=object())
# test get invalid run name
non_exist_run = randstr("non_exist_run")
with pytest.raises(RunNotFoundError, match=f"Run {non_exist_run!r} not found"):
pf.runs.get(run=non_exist_run)
# TODO: need to confirm whether this is an end-to-end test
def test_exp_id(self):
with TemporaryDirectory() as tmp_dir:
shutil.copytree(f"{FLOWS_DIR}/flow_with_dict_input", f"{tmp_dir}/flow dir with space")
run = Run(
flow=Path(f"{tmp_dir}/flow dir with space"),
data=f"{DATAS_DIR}/webClassification3.jsonl",
)
rest_run = run._to_rest_object()
assert rest_run.run_experiment_name == "flow_dir_with_space"
shutil.copytree(f"{FLOWS_DIR}/flow_with_dict_input", f"{tmp_dir}/flow-dir-with-dash")
run = Run(
flow=Path(f"{tmp_dir}/flow-dir-with-dash"),
data=f"{DATAS_DIR}/webClassification3.jsonl",
)
rest_run = run._to_rest_object()
assert rest_run.run_experiment_name == "flow_dir_with_dash"
def test_tools_json_ignored(self, pf, randstr: Callable[[str], str]):
from azure.ai.ml._artifacts._blob_storage_helper import BlobStorageClient
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
from promptflow.azure.operations import RunOperations
files_to_upload = []
def fake_upload_file(storage_client, source: str, dest, *args, **kwargs):
files_to_upload.append(source)
storage_client.uploaded_file_count += 1
with patch("azure.ai.ml._utils._asset_utils.upload_file") as mock_upload_file, patch.object(
FlowServiceCaller, "submit_bulk_run"
), patch.object(BlobStorageClient, "_set_confirmation_metadata"), patch.object(RunOperations, "get"):
mock_upload_file.side_effect = fake_upload_file
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
pf.run(
flow=f"{FLOWS_DIR}/flow_with_dict_input",
data=data_path,
column_mapping={"key": {"value": "1"}, "url": "${data.url}"},
runtime="fake_runtime",
name=randstr("name"),
)
# make sure .promptflow/flow.tools.json not uploaded
for f in files_to_upload:
if ".promptflow/flow.tools.json" in f:
raise Exception(f"flow.tools.json should not be uploaded, got {f}")
def test_flow_id_in_submission(self, pf, runtime: str, randstr: Callable[[str], str]):
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
from promptflow.azure.operations import RunOperations
flow_path = f"{FLOWS_DIR}/print_env_var"
flow_lineage_id = get_flow_lineage_id(flow_path)
flow_session_id = pf._runs._get_session_id(flow_path)
def submit(*args, **kwargs):
body = kwargs.get("body", None)
assert flow_session_id == body.session_id
assert flow_lineage_id == body.flow_lineage_id
return body
# flow session id is same with or without session creation
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(
RunOperations, "get"
), patch.object(FlowServiceCaller, "create_flow_session"):
mock_submit.side_effect = submit
pf.run(
flow=flow_path,
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime=runtime,
name=randstr("name1"),
)
with patch.object(FlowServiceCaller, "submit_bulk_run") as mock_submit, patch.object(
RunOperations, "get"
), patch.object(FlowServiceCaller, "create_flow_session"):
mock_submit.side_effect = submit
# no runtime provided, will use automatic runtime
pf.run(
flow=flow_path,
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name2"),
)
def test_run_submission_exception(self, pf):
from azure.core.exceptions import HttpResponseError
from promptflow.azure._restclient.flow.operations import BulkRunsOperations
from promptflow.azure._restclient.flow_service_caller import FlowRequestException, FlowServiceCaller
def fake_submit(*args, **kwargs):
headers = kwargs.get("headers", None)
request_id_in_headers = headers["x-ms-client-request-id"]
# request id in headers should be same with request id in service caller
assert request_id_in_headers == pf.runs._service_caller._request_id
raise HttpResponseError("customized error message.")
with patch.object(BulkRunsOperations, "submit_bulk_run") as mock_request, patch.object(
FlowServiceCaller, "_set_headers_with_user_aml_token"
):
mock_request.side_effect = fake_submit
with pytest.raises(FlowRequestException) as e:
original_request_id = pf.runs._service_caller._request_id
pf.runs._service_caller.submit_bulk_run(
subscription_id="fake_subscription_id",
resource_group_name="fake_resource_group",
workspace_name="fake_workspace_name",
)
# request id has been updated
assert original_request_id != pf.runs._service_caller._request_id
# original error message should be included in FlowRequestException
assert "customized error message" in str(e.value)
# request id should be included in FlowRequestException
assert f"request id: {pf.runs._service_caller._request_id}" in str(e.value)
def test_get_detail_against_partial_fail_run(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
run = pf.run(
flow=f"{FLOWS_DIR}/partial_fail",
data=f"{FLOWS_DIR}/partial_fail/data.jsonl",
runtime=runtime,
name=randstr("name"),
)
pf.runs.stream(run=run.name)
detail = pf.get_details(run=run.name)
assert len(detail) == 3
# TODO: seems another unit test...
def test_vnext_workspace_base_url(self):
from promptflow.azure._restclient.service_caller_factory import _FlowServiceCallerFactory
mock_workspace = MagicMock()
mock_workspace.discovery_url = "https://promptflow.azure-api.net/discovery/workspaces/fake_workspace_id"
service_caller = _FlowServiceCallerFactory.get_instance(
workspace=mock_workspace, credential=MagicMock(), operation_scope=MagicMock()
)
assert service_caller.caller._client._base_url == "https://promptflow.azure-api.net/"
@pytest.mark.usefixtures("mock_isinstance_for_mock_datastore")
def test_download_run(self, pf: PFClient, created_batch_run_without_llm: Run):
expected_files = [
DownloadedRun.RUN_METADATA_FILE_NAME,
DownloadedRun.LOGS_FILE_NAME,
DownloadedRun.METRICS_FILE_NAME,
f"{DownloadedRun.SNAPSHOT_FOLDER}/flow.dag.yaml",
]
with TemporaryDirectory() as tmp_dir:
pf.runs.download(run=created_batch_run_without_llm.name, output=tmp_dir)
for file in expected_files:
assert Path(tmp_dir, created_batch_run_without_llm.name, file).exists()
def test_request_id_when_making_http_requests(self, pf, runtime: str, randstr: Callable[[str], str]):
from azure.core.exceptions import HttpResponseError
from promptflow.azure._restclient.flow.operations import BulkRunsOperations
from promptflow.azure._restclient.flow_service_caller import FlowRequestException
request_ids = set()
def fake_submit(*args, **kwargs):
headers = kwargs.get("headers", None)
request_id_in_headers = headers["x-ms-client-request-id"]
# request id in headers should be same with request id in service caller
assert request_id_in_headers == pf.runs._service_caller._request_id
# request id in request is same request id in collected logs
assert request_id_in_headers in request_ids
raise HttpResponseError("customized error message.")
def check_inner_call(*args, **kwargs):
if "extra" in kwargs:
request_id = pydash.get(kwargs, "extra.custom_dimensions.request_id")
request_ids.add(request_id)
with patch.object(BulkRunsOperations, "submit_bulk_run") as mock_request, patch.object(
Logger, "info"
) as mock_logger:
mock_logger.side_effect = check_inner_call
mock_request.side_effect = fake_submit
with pytest.raises(FlowRequestException) as e:
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime=runtime,
name=randstr("name1"),
)
# request id in service caller is same request id in collected logs
assert pf.runs._service_caller._request_id in request_ids
# only 1 request id generated in logs
assert len(request_ids) == 1
# request id should be included in FlowRequestException
assert f"request id: {pf.runs._service_caller._request_id}" in str(e.value)
old_request_id = request_ids.pop()
with pytest.raises(FlowRequestException) as e:
pf.run(
flow=f"{FLOWS_DIR}/print_env_var",
data=f"{DATAS_DIR}/env_var_names.jsonl",
runtime=runtime,
name=randstr("name1"),
)
# request id in service caller is same request id in collected logs
assert pf.runs._service_caller._request_id in request_ids
# request id is not same with before
assert old_request_id not in request_ids
# only 1 request id generated in logs
assert len(request_ids) == 1
# request id should be included in FlowRequestException
assert f"request id: {pf.runs._service_caller._request_id}" in str(e.value)
def test_get_details_against_partial_completed_run(
self, pf: PFClient, runtime: str, randstr: Callable[[str], str]
) -> None:
flow_mod2 = f"{FLOWS_DIR}/mod-n/two"
flow_mod3 = f"{FLOWS_DIR}/mod-n/three"
data_path = f"{DATAS_DIR}/numbers.jsonl"
# batch run against data
run1 = pf.run(
flow=flow_mod2,
data=data_path,
column_mapping={"number": "${data.value}"},
runtime=runtime,
name=randstr("run1"),
)
pf.runs.stream(run1)
details1 = pf.get_details(run1)
assert len(details1) == 20
assert len(details1[details1["outputs.output"].notnull()]) == 10
# assert to ensure inputs and outputs are aligned
for _, row in details1.iterrows():
if pd.notnull(row["outputs.output"]):
assert int(row["inputs.number"]) == int(row["outputs.output"])
# batch run against previous run
run2 = pf.run(
flow=flow_mod3,
run=run1,
column_mapping={"number": "${run.outputs.output}"},
runtime=runtime,
name=randstr("run2"),
)
pf.runs.stream(run2)
details2 = pf.get_details(run2)
assert len(details2) == 10
assert len(details2[details2["outputs.output"].notnull()]) == 4
# assert to ensure inputs and outputs are aligned
for _, row in details2.iterrows():
if pd.notnull(row["outputs.output"]):
assert int(row["inputs.number"]) == int(row["outputs.output"])
@pytest.mark.usefixtures("mock_isinstance_for_mock_datastore")
def test_auto_resolve_requirements(self, pf: PFClient, randstr: Callable[[str], str]):
# will add requirements.txt to flow.dag.yaml if exists when submitting run.
with TemporaryDirectory() as temp:
temp = Path(temp)
shutil.copytree(f"{FLOWS_DIR}/flow_with_requirements_txt", temp / "flow_with_requirements_txt")
run = pf.run(
flow=temp / "flow_with_requirements_txt",
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name"),
)
pf.runs.stream(run)
pf.runs.download(run=run.name, output=temp)
flow_dag = load_yaml(Path(temp, run.name, "snapshot/flow.dag.yaml"))
assert "requirements.txt" in flow_dag[ENVIRONMENT][PYTHON_REQUIREMENTS_TXT]
local_flow_dag = load_yaml(f"{FLOWS_DIR}/flow_with_requirements_txt/flow.dag.yaml")
assert "environment" not in local_flow_dag
@pytest.mark.usefixtures("mock_isinstance_for_mock_datastore")
def test_requirements_in_additional_includes(self, pf: PFClient, randstr: Callable[[str], str]):
run = pf.run(
flow=f"{FLOWS_DIR}/flow_with_additional_include_req",
data=f"{DATAS_DIR}/env_var_names.jsonl",
name=randstr("name"),
)
run = pf.runs.stream(run)
assert run._error is None
with TemporaryDirectory() as temp:
pf.runs.download(run=run.name, output=temp)
assert Path(temp, run.name, "snapshot/requirements").exists()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_azure_cli_perf.py | import os
import sys
import timeit
from typing import Callable
from unittest import mock
import pytest
from promptflow._cli._user_agent import USER_AGENT as CLI_USER_AGENT # noqa: E402
from promptflow._sdk._telemetry import log_activity
from promptflow._sdk._utils import ClientUserAgentUtil
from sdk_cli_azure_test.recording_utilities import is_replay
FLOWS_DIR = "./tests/test_configs/flows"
DATAS_DIR = "./tests/test_configs/datas"
def mock_log_activity(*args, **kwargs):
custom_message = "github run: https://github.com/microsoft/promptflow/actions/runs/{0}".format(
os.environ.get("GITHUB_RUN_ID")
)
if len(args) == 4:
if args[3] is not None:
args[3]["custom_message"] = custom_message
else:
args = list(args)
args[3] = {"custom_message": custom_message}
elif "custom_dimensions" in kwargs and kwargs["custom_dimensions"] is not None:
kwargs["custom_dimensions"]["custom_message"] = custom_message
else:
kwargs["custom_dimensions"] = {"custom_message": custom_message}
return log_activity(*args, **kwargs)
def run_cli_command(cmd, time_limit=3600):
from promptflow._cli._pf_azure.entry import main
sys.argv = list(cmd)
st = timeit.default_timer()
with mock.patch.object(ClientUserAgentUtil, "get_user_agent") as get_user_agent_fun, mock.patch(
"promptflow._sdk._telemetry.activity.log_activity", side_effect=mock_log_activity
), mock.patch("promptflow._cli._pf_azure.entry.log_activity", side_effect=mock_log_activity):
# Client side will modify user agent only through ClientUserAgentUtil to avoid impact executor/runtime.
get_user_agent_fun.return_value = f"{CLI_USER_AGENT} perf_monitor/1.0"
user_agent = ClientUserAgentUtil.get_user_agent()
assert user_agent == f"{CLI_USER_AGENT} perf_monitor/1.0"
main()
ed = timeit.default_timer()
print(f"{cmd}, \nTotal time: {ed - st}s")
if is_replay():
assert ed - st < time_limit, f"The time limit is {time_limit}s, but it took {ed - st}s."
@pytest.fixture
def operation_scope_args(subscription_id: str, resource_group_name: str, workspace_name: str):
return [
"--subscription",
subscription_id,
"--resource-group",
resource_group_name,
"--workspace-name",
workspace_name,
]
@pytest.mark.perf_monitor_test
@pytest.mark.usefixtures(
"mock_get_azure_pf_client",
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestAzureCliPerf:
def test_pfazure_run_create(self, operation_scope_args, randstr: Callable[[str], str], time_limit=15):
name = randstr("name")
run_cli_command(
cmd=(
"pfazure",
"run",
"create",
"--flow",
f"{FLOWS_DIR}/print_input_flow",
"--data",
f"{DATAS_DIR}/print_input_flow.jsonl",
"--name",
name,
*operation_scope_args,
),
time_limit=time_limit,
)
def test_pfazure_run_update(self, operation_scope_args, time_limit=15):
run_cli_command(
cmd=(
"pfazure",
"run",
"update",
"--name",
"test_run",
"--set",
"display_name=test_run",
"description='test_description'",
"tags.key1=value1",
*operation_scope_args,
),
time_limit=time_limit,
)
def test_run_restore(self, operation_scope_args, time_limit=15):
run_cli_command(
cmd=(
"pfazure",
"run",
"restore",
"--name",
"test_run",
*operation_scope_args,
),
time_limit=time_limit,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_arm_connection_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pytest
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
@pytest.fixture
def connection_ops(pf):
return pf._arm_connections
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures("vcr_recording")
class TestArmConnectionOperations:
def test_get_connection(self, connection_ops):
# Note: Secrets will be returned by arm api
result = connection_ops.get(name="azure_open_ai_connection")
assert (
result._to_dict().items()
> {
"api_type": "azure",
"module": "promptflow.connections",
"name": "azure_open_ai_connection",
}.items()
)
result = connection_ops.get(name="custom_connection")
assert (
result._to_dict().items()
> {
"name": "custom_connection",
"module": "promptflow.connections",
"configs": {},
}.items()
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py | import re
from pathlib import Path
import pydash
import pytest
from promptflow._utils.yaml_utils import dump_yaml, load_yaml_string
from promptflow.connections import AzureOpenAIConnection
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
PROMOTFLOW_ROOT = Path(__file__) / "../../../.."
TEST_ROOT = Path(__file__).parent.parent.parent
MODEL_ROOT = TEST_ROOT / "test_configs/e2e_samples"
CONNECTION_FILE = (PROMOTFLOW_ROOT / "connections.json").resolve().absolute().as_posix()
def assert_dict_equals_with_skip_fields(item1, item2, skip_fields):
for fot_key in skip_fields:
pydash.set_(item1, fot_key, None)
pydash.set_(item2, fot_key, None)
assert item1 == item2
def normalize_arm_id(origin_value: str):
if origin_value:
m = re.match(
r"(.*)/subscriptions/[a-z0-9\-]+/resourceGroups/[a-z0-9\-]+/providers/"
r"Microsoft.MachineLearningServices/workspaces/[a-z0-9\-]+/([a-z]+)/[^/]+/versions/([a-z0-9\-]+)",
origin_value,
)
if m:
prefix, asset_type, _ = m.groups()
return (
f"{prefix}/subscriptions/xxx/resourceGroups/xxx/providers/"
f"Microsoft.MachineLearningServices/workspaces/xxx/{asset_type}/xxx/versions/xxx"
)
return None
def update_saved_spec(component, saved_spec_path: str):
yaml_text = component._to_yaml()
saved_spec_path = Path(saved_spec_path)
yaml_content = load_yaml_string(yaml_text)
if yaml_content.get("creation_context"):
for key in yaml_content.get("creation_context"):
yaml_content["creation_context"][key] = "xxx"
for key in ["task.code", "task.environment", "id"]:
target_value = normalize_arm_id(pydash.get(yaml_content, key))
if target_value:
pydash.set_(yaml_content, key, target_value)
yaml_text = dump_yaml(yaml_content)
if saved_spec_path.is_file():
current_spec_text = saved_spec_path.read_text()
if current_spec_text == yaml_text:
return
saved_spec_path.parent.mkdir(parents=True, exist_ok=True)
saved_spec_path.write_text(yaml_text)
@pytest.mark.skipif(
condition=not is_live(),
reason="flow in pipeline tests require secrets config file, only run in live mode.",
)
@pytest.mark.usefixtures("use_secrets_config_file")
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
class TestFlowInAzureML:
@pytest.mark.parametrize(
"load_params, expected_spec_attrs",
[
pytest.param(
{
"name": "web_classification_4",
"version": "1.0.0",
"description": "Create flows that use large language models to "
"classify URLs into multiple categories.",
"environment_variables": {
"verbose": "true",
},
},
{
"name": "web_classification_4",
"version": "1.0.0",
"description": "Create flows that use large language models to "
"classify URLs into multiple categories.",
"type": "parallel",
},
id="parallel",
),
],
)
def test_flow_as_component(
self,
azure_open_ai_connection: AzureOpenAIConnection,
temp_output_dir,
ml_client,
load_params: dict,
expected_spec_attrs: dict,
request,
) -> None:
# keep the simplest test here, more tests are in azure-ai-ml
from azure.ai.ml import load_component
flows_dir = "./tests/test_configs/flows"
flow_func: Component = load_component(
f"{flows_dir}/web_classification/flow.dag.yaml", params_override=[load_params]
)
# TODO: snapshot of flow component changed every time?
created_component = ml_client.components.create_or_update(flow_func, is_anonymous=True)
update_saved_spec(
created_component, f"./tests/test_configs/flows/saved_component_spec/{request.node.callspec.id}.yaml"
)
component_dict = created_component._to_dict()
slimmed_created_component_attrs = {key: pydash.get(component_dict, key) for key in expected_spec_attrs.keys()}
assert slimmed_created_component_attrs == expected_spec_attrs
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_connection_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pydash
import pytest
from promptflow._sdk.entities._connection import _Connection
from promptflow.connections import AzureOpenAIConnection, CustomConnection
from promptflow.contracts.types import Secret
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
@pytest.fixture
def connection_ops(pf):
return pf._connections
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures("vcr_recording")
class TestConnectionOperations:
@pytest.mark.skip(reason="Skip to avoid flooded connections in workspace.")
def test_connection_get_create_delete(self, connection_ops):
from promptflow.azure._restclient.flow_service_caller import FlowRequestException
connection = _Connection(
name="test_connection_1",
type="AzureOpenAI",
configs=AzureOpenAIConnection(
api_key=Secret("test_key"),
api_base="test_base",
api_type="azure",
api_version="2023-07-01-preview",
),
)
try:
result = connection_ops.get(name=connection.name)
except FlowRequestException:
result = connection_ops.create_or_update(connection)
config_dict = pydash.omit(result._to_dict(), "configs.api_key")
assert config_dict == {
"name": "test_connection_1",
"connection_type": "AzureOpenAI",
"connection_scope": "User",
"configs": {"api_base": "test_base", "api_type": "azure", "api_version": "2023-07-01-preview"},
}
# soft delete
connection_ops.delete(name=connection.name)
@pytest.mark.skip(reason="Skip to avoid flooded connections in workspace.")
def test_custom_connection_create(self, connection_ops):
from promptflow.azure._restclient.flow_service_caller import FlowRequestException
connection = _Connection(
name="test_connection_2", type="Custom", custom_configs=CustomConnection(a="1", b=Secret("2"))
)
try:
result = connection_ops.get(name=connection.name)
except FlowRequestException:
result = connection_ops.create_or_update(connection)
config_dict = pydash.omit(result._to_dict(), "custom_configs")
assert config_dict == {"connection_scope": "User", "connection_type": "Custom", "name": "test_connection_2"}
# soft delete
connection_ops.delete(name=connection.name)
def test_list_connection_spec(self, connection_ops):
result = {v.connection_type: v._to_dict() for v in connection_ops.list_connection_specs()}
# Assert custom keys type
assert "Custom" in result
assert result["Custom"] == {
"module": "promptflow.connections",
"connection_type": "Custom",
"flow_value_type": "CustomConnection",
"config_specs": [],
}
# assert strong type
assert "AzureOpenAI" in result
aoai_config_specs = result["AzureOpenAI"]["config_specs"]
for config_dict in aoai_config_specs:
if config_dict["name"] == "api_version":
del config_dict["default_value"]
expected_config_specs = [
{"name": "api_key", "display_name": "API key", "config_value_type": "Secret", "is_optional": False},
{"name": "api_base", "display_name": "API base", "config_value_type": "String", "is_optional": False},
{
"name": "api_type",
"display_name": "API type",
"config_value_type": "String",
"default_value": "azure",
"is_optional": False,
},
{
"name": "api_version",
"display_name": "API version",
"config_value_type": "String",
"is_optional": False,
},
]
for spec in expected_config_specs:
assert spec in result["AzureOpenAI"]["config_specs"]
def test_get_connection(self, connection_ops):
# Note: No secrets will be returned by MT api
result = connection_ops.get(name="azure_open_ai_connection")
assert (
result._to_dict().items()
> {
"api_type": "azure",
"module": "promptflow.connections",
"name": "azure_open_ai_connection",
}.items()
)
result = connection_ops.get(name="custom_connection")
assert (
result._to_dict().items()
> {
"name": "custom_connection",
"module": "promptflow.connections",
"configs": {},
"secrets": {},
}.items()
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/classificationAccuracy.csv | groundtruth,prediction
App,App
App,App
App,App
App,App
App,App
App,App
App,App
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_cli.py | import os
import os.path
import sys
from pathlib import Path
import pytest
from promptflow._cli._pf.entry import main
from ..recording_utilities import is_live
FLOWS_DIR = "./tests/test_configs/flows"
RUNS_DIR = "./tests/test_configs/runs"
CONNECTIONS_DIR = "./tests/test_configs/connections"
DATAS_DIR = "./tests/test_configs/datas"
# TODO: move this to a shared utility module
def run_pf_command(*args, cwd=None):
"""Run a pf command with the given arguments and working directory.
There have been some unknown issues in using subprocess on CI, so we use this function instead, which will also
provide better debugging experience.
"""
origin_argv, origin_cwd = sys.argv, os.path.abspath(os.curdir)
try:
sys.argv = ["pf"] + list(args)
if cwd:
os.chdir(cwd)
main()
finally:
sys.argv = origin_argv
os.chdir(origin_cwd)
@pytest.mark.skipif(condition=not is_live(), reason="CLI tests, only run in live mode.")
@pytest.mark.cli_test
@pytest.mark.e2etest
class TestCli:
# PF cli test is here because when designate connection provider to remote, we need azure dependencies.
def test_pf_flow_test(self, remote_workspace_resource_id):
# Test with connection provider
run_pf_command(
"flow",
"test",
"--flow",
f"{FLOWS_DIR}/web_classification",
"--config",
f"connection.provider={remote_workspace_resource_id}",
)
output_path = Path(FLOWS_DIR) / "web_classification" / ".promptflow" / "flow.output.json"
assert output_path.exists()
def test_flow_chat(self, monkeypatch, capsys, remote_workspace_resource_id):
chat_list = ["hi", "what is chat gpt?"]
def mock_input(*args, **kwargs):
if chat_list:
return chat_list.pop()
else:
raise KeyboardInterrupt()
monkeypatch.setattr("builtins.input", mock_input)
run_pf_command(
"flow",
"test",
"--flow",
f"{FLOWS_DIR}/chat_flow",
"--interactive",
"--verbose",
"--config",
f"connection.provider={remote_workspace_resource_id}",
)
output_path = Path(FLOWS_DIR) / "chat_flow" / ".promptflow" / "chat.output.json"
assert output_path.exists()
detail_path = Path(FLOWS_DIR) / "chat_flow" / ".promptflow" / "chat.detail.json"
assert detail_path.exists()
outerr = capsys.readouterr()
# Check node output
assert "chat_node:" in outerr.out
assert "show_answer:" in outerr.out
assert "[show_answer]: print:" in outerr.out
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_cli_with_azure.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import os
import sys
import uuid
from typing import Callable
import pytest
from mock.mock import patch
from promptflow._constants import PF_USER_AGENT
from promptflow._core.operation_context import OperationContext
from promptflow._sdk._utils import ClientUserAgentUtil
from promptflow._sdk.entities import Run
from promptflow._utils.utils import environment_variable_overwrite, parse_ua_to_dict
from promptflow.azure import PFClient
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
FLOWS_DIR = "./tests/test_configs/flows"
DATAS_DIR = "./tests/test_configs/datas"
RUNS_DIR = "./tests/test_configs/runs"
# TODO: move this to a shared utility module
def run_pf_command(*args, pf, runtime=None, cwd=None):
from promptflow._cli._pf_azure.entry import main
origin_argv, origin_cwd = sys.argv, os.path.abspath(os.curdir)
try:
sys.argv = (
["pfazure"]
+ list(args)
+ [
"--subscription",
pf._ml_client.subscription_id,
"--resource-group",
pf._ml_client.resource_group_name,
"--workspace-name",
pf._ml_client.workspace_name,
]
)
if runtime:
sys.argv += ["--runtime", runtime]
if cwd:
os.chdir(cwd)
main()
finally:
sys.argv = origin_argv
os.chdir(origin_cwd)
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures(
"mock_get_azure_pf_client",
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestCliWithAzure:
def test_basic_flow_run_bulk_without_env(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
name = randstr("name")
run_pf_command(
"run",
"create",
"--flow",
f"{FLOWS_DIR}/web_classification",
"--data",
f"{DATAS_DIR}/webClassification3.jsonl",
"--name",
name,
pf=pf,
runtime=runtime,
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
@pytest.mark.skip("Custom tool pkg and promptprompt pkg with CustomStrongTypeConnection not installed on runtime.")
def test_basic_flow_with_package_tool_with_custom_strong_type_connection(self, pf, runtime) -> None:
name = str(uuid.uuid4())
run_pf_command(
"run",
"create",
"--flow",
f"{FLOWS_DIR}/flow_with_package_tool_with_custom_strong_type_connection",
"--data",
f"{FLOWS_DIR}/flow_with_package_tool_with_custom_strong_type_connection/data.jsonl",
"--name",
name,
pf=pf,
runtime=runtime,
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
def test_run_with_remote_data(
self, pf, runtime: str, remote_web_classification_data, randstr: Callable[[str], str]
) -> None:
# run with arm id
name = randstr("name1")
run_pf_command(
"run",
"create",
"--flow",
"web_classification",
"--data",
f"azureml:{remote_web_classification_data.id}",
"--name",
name,
pf=pf,
runtime=runtime,
cwd=f"{FLOWS_DIR}",
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
# run with name version
name = randstr("name2")
run_pf_command(
"run",
"create",
"--flow",
"web_classification",
"--data",
f"azureml:{remote_web_classification_data.name}:{remote_web_classification_data.version}",
"--name",
name,
pf=pf,
runtime=runtime,
cwd=f"{FLOWS_DIR}",
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
def test_run_file_with_set(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
name = randstr("name")
run_pf_command(
"run",
"create",
"--file",
f"{RUNS_DIR}/run_with_env.yaml",
"--set",
f"runtime={runtime}",
"--name",
name,
pf=pf,
)
run = pf.runs.get(run=name)
assert isinstance(run, Run)
assert run.properties["azureml.promptflow.runtime_name"] == runtime
@pytest.mark.skipif(condition=not is_live(), reason="This test requires an actual PFClient")
def test_azure_cli_ua(self, pf: PFClient):
# clear user agent before test
context = OperationContext().get_instance()
context.user_agent = ""
with environment_variable_overwrite(PF_USER_AGENT, ""):
with pytest.raises(SystemExit):
run_pf_command(
"run",
"show",
"--name",
"not_exist",
pf=pf,
)
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk", "promptflow-cli"}
def test_cli_telemetry(self, pf, runtime: str, randstr: Callable[[str], str]) -> None:
name = randstr("name")
@contextlib.contextmanager
def check_workspace_info(*args, **kwargs):
if "custom_dimensions" in kwargs:
assert kwargs["custom_dimensions"]["workspace_name"] == pf._ml_client.workspace_name
assert kwargs["custom_dimensions"]["resource_group_name"] == pf._ml_client.resource_group_name
assert kwargs["custom_dimensions"]["subscription_id"] == pf._ml_client.subscription_id
yield None
with patch("promptflow._sdk._telemetry.activity.log_activity") as mock_log_activity:
mock_log_activity.side_effect = check_workspace_info
run_pf_command(
"run",
"create",
"--file",
f"{RUNS_DIR}/run_with_env.yaml",
"--set",
f"runtime={runtime}",
"--name",
name,
pf=pf,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_serve.py | import json
import pytest
from ..recording_utilities import is_live
testdata = """The event sourcing pattern involves using an append-only store to record the full series
of actions on that data. The Azure Cosmos DB change feed is a great choice as a central data store in
event sourcing architectures in which all data ingestion is modeled as writes (no updates or deletes).
In this case, each write to Azure Cosmos DB is an \"event,\" so there's a full record of past events
in the change feed. Typical uses of the events published by the central event store are to maintain materialized
views or to integrate with external systems. Because there's no time limit for retention in the change feed latest
version mode, you can replay all past events by reading from the beginning of your Azure Cosmos DB container's
change feed. You can even have multiple change feed consumers subscribe to the same container's change feed."""
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_remote_connection")
@pytest.mark.e2etest
def test_local_serving_api_with_remote_connection(flow_serving_client_remote_connection):
response = flow_serving_client_remote_connection.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_remote_connection.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_with_prt_config_env")
@pytest.mark.e2etest
def test_azureml_serving_api_with_prt_config_env(flow_serving_client_with_prt_config_env):
response = flow_serving_client_with_prt_config_env.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_with_prt_config_env.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
response = flow_serving_client_with_prt_config_env.get("/")
assert b"Welcome to promptflow app" in response.data
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_with_connection_provider_env")
@pytest.mark.e2etest
def test_azureml_serving_api_with_conn_provider_env(flow_serving_client_with_connection_provider_env):
response = flow_serving_client_with_connection_provider_env.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_with_connection_provider_env.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
response = flow_serving_client_with_connection_provider_env.get("/")
assert b"Welcome to promptflow app" in response.data
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("flow_serving_client_with_connection_provider_env")
@pytest.mark.e2etest
def test_azureml_serving_api_with_aml_resource_id_env(flow_serving_client_with_aml_resource_id_env):
response = flow_serving_client_with_aml_resource_id_env.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_with_aml_resource_id_env.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
@pytest.mark.skipif(condition=not is_live(), reason="serving tests, only run in live mode.")
@pytest.mark.usefixtures("serving_client_with_connection_name_override")
@pytest.mark.e2etest
def test_azureml_serving_api_with_connection_name_override(serving_client_with_connection_name_override):
response = serving_client_with_connection_name_override.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = serving_client_with_connection_name_override.post("/score", data=json.dumps({"text": testdata}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "api_base" not in json.loads(response.data.decode()).values()
@pytest.mark.usefixtures("serving_client_with_connection_data_override")
@pytest.mark.e2etest
def test_azureml_serving_api_with_connection_data_override(serving_client_with_connection_data_override):
response = serving_client_with_connection_data_override.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = serving_client_with_connection_data_override.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "api_base" in json.loads(response.data.decode()).values()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
from pathlib import Path
import pytest
from promptflow.azure._entities._flow import Flow
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
tests_root_dir = Path(__file__).parent.parent.parent
flow_test_dir = tests_root_dir / "test_configs/flows"
data_dir = tests_root_dir / "test_configs/datas"
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
@pytest.mark.usefixtures(
"mock_set_headers_with_user_aml_token",
"single_worker_thread_pool",
"vcr_recording",
)
class TestFlow:
def test_create_flow(self, created_flow: Flow):
# most of the assertions are in the fixture itself
assert isinstance(created_flow, Flow)
def test_get_flow(self, pf, created_flow: Flow):
result = pf.flows.get(name=created_flow.name)
# assert created flow is the same as the one retrieved
attributes = vars(result)
for attr in attributes:
assert getattr(result, attr) == getattr(created_flow, attr), f"Assertion failed for attribute: {attr!r}"
@pytest.mark.skipif(
condition=not is_live(),
reason="Complicated test combining `pf flow test` and global config",
)
def test_flow_test_with_config(self, remote_workspace_resource_id):
from promptflow import PFClient
client = PFClient(config={"connection.provider": remote_workspace_resource_id})
output = client.test(flow=flow_test_dir / "web_classification")
assert output.keys() == {"category", "evidence"}
@pytest.mark.usefixtures("mock_get_user_identity_info")
def test_list_flows(self, pf):
flows = pf.flows.list(max_results=3)
for flow in flows:
print(json.dumps(flow._to_dict(), indent=4))
assert len(flows) == 3
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_telemetry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import os
import shutil
import sys
import tempfile
import uuid
from logging import Logger
from pathlib import Path
from typing import Callable
from unittest.mock import MagicMock, patch
import pydash
import pytest
from promptflow import load_run
from promptflow._constants import PF_USER_AGENT
from promptflow._core.operation_context import OperationContext
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._errors import RunNotFoundError
from promptflow._sdk._telemetry import (
ActivityType,
PromptFlowSDKLogHandler,
get_appinsights_log_handler,
get_telemetry_logger,
is_telemetry_enabled,
log_activity,
)
from promptflow._sdk._utils import ClientUserAgentUtil, call_from_extension
from promptflow._utils.utils import environment_variable_overwrite, parse_ua_to_dict
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
@contextlib.contextmanager
def cli_consent_config_overwrite(val):
config = Configuration.get_instance()
original_consent = config.get_telemetry_consent()
config.set_telemetry_consent(val)
try:
yield
finally:
if original_consent:
config.set_telemetry_consent(original_consent)
else:
config.set_telemetry_consent(True)
@contextlib.contextmanager
def extension_consent_config_overwrite(val):
config = Configuration.get_instance()
original_consent = config.get_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY)
config.set_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY, value=val)
try:
yield
finally:
if original_consent:
config.set_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY, value=original_consent)
else:
config.set_config(key=Configuration.EXTENSION_COLLECT_TELEMETRY, value=True)
RUNS_DIR = "./tests/test_configs/runs"
FLOWS_DIR = "./tests/test_configs/flows"
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.usefixtures("mock_set_headers_with_user_aml_token", "single_worker_thread_pool", "vcr_recording")
@pytest.mark.e2etest
class TestTelemetry:
def test_logging_handler(self):
# override environment variable
with cli_consent_config_overwrite(True):
handler = get_appinsights_log_handler()
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is True
with cli_consent_config_overwrite(False):
handler = get_appinsights_log_handler()
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is False
def test_call_from_extension(self):
from promptflow._core.operation_context import OperationContext
assert call_from_extension() is False
with environment_variable_overwrite(PF_USER_AGENT, "prompt-flow-extension/1.0.0"):
assert call_from_extension() is True
# remove extension ua in context
context = OperationContext().get_instance()
context.user_agent = context.user_agent.replace("prompt-flow-extension/1.0.0", "")
def test_custom_event(self, pf):
from promptflow._sdk._telemetry.logging_handler import PromptFlowSDKLogHandler
def log_event(*args, **kwargs):
record = args[0]
assert record.custom_dimensions is not None
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
envelope = handler.log_record_to_envelope(record)
custom_dimensions = pydash.get(envelope, "data.baseData.properties")
assert isinstance(custom_dimensions, dict)
# Note: need privacy review if we add new fields.
if "start" in record.message:
assert custom_dimensions.keys() == {
"request_id",
"activity_name",
"activity_type",
"subscription_id",
"resource_group_name",
"workspace_name",
"level",
"python_version",
"user_agent",
"installation_id",
"first_call",
"from_ci",
}
elif "complete" in record.message:
assert custom_dimensions.keys() == {
"request_id",
"activity_name",
"activity_type",
"subscription_id",
"resource_group_name",
"workspace_name",
"completion_status",
"duration_ms",
"level",
"python_version",
"user_agent",
"installation_id",
"first_call",
"from_ci",
}
else:
raise ValueError("Invalid message: {}".format(record.message))
assert record.message.startswith("pfazure.runs.get")
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = log_event
# mock_error_logger.side_effect = log_event
try:
pf.runs.get("not_exist")
except RunNotFoundError:
pass
def test_default_logging_behavior(self):
assert is_telemetry_enabled() is True
# default enable telemetry
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is True
def test_close_logging_handler(self):
with cli_consent_config_overwrite(False):
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is False
with extension_consent_config_overwrite(False):
with environment_variable_overwrite(PF_USER_AGENT, "prompt-flow-extension/1.0.0"):
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is False
# default enable telemetry
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
assert handler._is_telemetry_enabled is True
def test_cached_logging_handler(self):
# should get same logger & handler instance if called multiple times
logger = get_telemetry_logger()
handler = next((h for h in logger.handlers if isinstance(h, PromptFlowSDKLogHandler)), None)
another_logger = get_telemetry_logger()
another_handler = next((h for h in another_logger.handlers if isinstance(h, PromptFlowSDKLogHandler)), None)
assert logger is another_logger
assert handler is another_handler
def test_sdk_telemetry_ua(self, pf):
from promptflow import PFClient
from promptflow.azure import PFClient as PFAzureClient
# log activity will pick correct ua
def assert_ua(*args, **kwargs):
ua = pydash.get(kwargs, "extra.custom_dimensions.user_agent", None)
ua_dict = parse_ua_to_dict(ua)
assert ua_dict.keys() == {"promptflow-sdk"}
logger = MagicMock()
logger.info = MagicMock()
logger.info.side_effect = assert_ua
# clear user agent before test
context = OperationContext().get_instance()
context.user_agent = ""
# get telemetry logger from SDK should not have extension ua
# start a clean local SDK client
with environment_variable_overwrite(PF_USER_AGENT, ""):
PFClient()
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk"}
# Call log_activity
with log_activity(logger, "test_activity", activity_type=ActivityType.PUBLICAPI):
# Perform some activity
pass
# start a clean Azure SDK client
with environment_variable_overwrite(PF_USER_AGENT, ""):
PFAzureClient(
ml_client=pf._ml_client,
subscription_id=pf._ml_client.subscription_id,
resource_group_name=pf._ml_client.resource_group_name,
workspace_name=pf._ml_client.workspace_name,
)
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk"}
# Call log_activity
with log_activity(logger, "test_activity", activity_type=ActivityType.PUBLICAPI):
# Perform some activity
pass
PFAzureClient(
ml_client=pf._ml_client,
subscription_id=pf._ml_client.subscription_id,
resource_group_name=pf._ml_client.resource_group_name,
workspace_name=pf._ml_client.workspace_name,
user_agent="a/1.0.0",
)
user_agent = ClientUserAgentUtil.get_user_agent()
ua_dict = parse_ua_to_dict(user_agent)
assert ua_dict.keys() == {"promptflow-sdk", "a"}
context = OperationContext().get_instance()
context.user_agent = ""
def test_inner_function_call(self, pf, runtime: str, randstr: Callable[[str], str]):
request_ids = set()
first_sdk_calls = []
def check_inner_call(*args, **kwargs):
if "extra" in kwargs:
request_id = pydash.get(kwargs, "extra.custom_dimensions.request_id")
first_sdk_call = pydash.get(kwargs, "extra.custom_dimensions.first_call")
request_ids.add(request_id)
first_sdk_calls.append(first_sdk_call)
with patch.object(Logger, "info") as mock_logger:
mock_logger.side_effect = check_inner_call
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
params_override=[{"runtime": runtime}],
)
run.name = randstr("name")
pf.runs.create_or_update(run=run)
# only 1 request id
assert len(request_ids) == 1
# only 1 and last call is public call
assert first_sdk_calls[0] is True
assert first_sdk_calls[-1] is True
assert set(first_sdk_calls[1:-1]) == {False}
def test_different_request_id(self):
from promptflow import PFClient
pf = PFClient()
request_ids = set()
first_sdk_calls = []
def check_inner_call(*args, **kwargs):
if "extra" in kwargs:
request_id = pydash.get(kwargs, "extra.custom_dimensions.request_id")
first_sdk_call = pydash.get(kwargs, "extra.custom_dimensions.first_call")
request_ids.add(request_id)
first_sdk_calls.append(first_sdk_call)
with patch.object(Logger, "info") as mock_logger:
mock_logger.side_effect = check_inner_call
run = load_run(
source=f"{RUNS_DIR}/run_with_env.yaml",
)
# create 2 times will get 2 request ids
run.name = str(uuid.uuid4())
pf.runs.create_or_update(run=run)
run.name = str(uuid.uuid4())
pf.runs.create_or_update(run=run)
# only 1 request id
assert len(request_ids) == 2
# 1 and last call is public call
assert first_sdk_calls[0] is True
assert first_sdk_calls[-1] is True
def test_scrub_fields(self):
from promptflow import PFClient
pf = PFClient()
from promptflow._sdk._telemetry.logging_handler import PromptFlowSDKLogHandler
def log_event(*args, **kwargs):
record = args[0]
assert record.custom_dimensions is not None
logger = get_telemetry_logger()
handler = logger.handlers[0]
assert isinstance(handler, PromptFlowSDKLogHandler)
envelope = handler.log_record_to_envelope(record)
# device name removed
assert "ai.cloud.roleInstance" not in envelope.tags
assert "ai.device.id" not in envelope.tags
# role name should be scrubbed or kept in whitelist
assert envelope.tags["ai.cloud.role"] in [os.path.basename(sys.argv[0]), "***"]
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = log_event
# mock_error_logger.side_effect = log_event
try:
pf.runs.get("not_exist")
except RunNotFoundError:
pass
def test_different_event_for_node_run(self):
from promptflow import PFClient
pf = PFClient()
from promptflow._sdk._telemetry.logging_handler import PromptFlowSDKLogHandler
def assert_node_run(*args, **kwargs):
record = args[0]
assert record.msg.startswith("pf.flows.node_test")
assert record.custom_dimensions["activity_name"] == "pf.flows.node_test"
def assert_flow_test(*args, **kwargs):
record = args[0]
assert record.msg.startswith("pf.flows.test")
assert record.custom_dimensions["activity_name"] == "pf.flows.test"
with tempfile.TemporaryDirectory() as temp_dir:
shutil.copytree((Path(FLOWS_DIR) / "print_env_var").resolve().as_posix(), temp_dir, dirs_exist_ok=True)
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = assert_node_run
pf.flows.test(temp_dir, node="print_env", inputs={"key": "API_BASE"})
with patch.object(PromptFlowSDKLogHandler, "emit") as mock_logger:
mock_logger.side_effect = assert_flow_test
pf.flows.test(temp_dir, inputs={"key": "API_BASE"})
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_flow_entity.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import shutil
import tempfile
import uuid
from pathlib import Path
import pytest
from mock.mock import Mock
from promptflow._sdk._load_functions import load_run
from promptflow._sdk._vendor import get_upload_files_from_folder
from promptflow._utils.flow_utils import load_flow_dag
from promptflow.azure._constants._flow import ENVIRONMENT, PYTHON_REQUIREMENTS_TXT
from promptflow.azure._entities._flow import Flow
tests_root_dir = Path(__file__).parent.parent.parent
FLOWS_DIR = (tests_root_dir / "test_configs/flows").resolve()
RUNS_DIR = (tests_root_dir / "test_configs/runs").resolve()
def load_flow(source):
from promptflow.azure._load_functions import load_flow
return load_flow(source=source)
@pytest.mark.unittest
class TestFlow:
@pytest.mark.skip(reason="TODO: add back when we bring back meta.yaml")
def test_load_flow(self):
local_file = tests_root_dir / "test_configs/flows/meta_files/flow.meta.yaml"
flow = load_flow(source=local_file)
assert flow._to_dict() == {
"name": "web_classificiation_flow_3",
"description": "Create flows that use large language models to classify URLs into multiple categories.",
"display_name": "Web Classification",
"type": "default",
"path": "./flow.dag.yaml",
}
rest_dict = flow._to_rest_object().as_dict()
assert rest_dict == {
"description": "Create flows that use large language models to classify URLs into multiple categories.",
"flow_name": "Web Classification",
"flow_run_settings": {},
"flow_type": "default",
"is_archived": True,
"flow_definition_file_path": "./flow.dag.yaml",
}
@pytest.mark.skip(reason="TODO: add back when we bring back meta.yaml")
def test_load_flow_from_remote_storage(self):
from promptflow.azure.operations._flow_operations import FlowOperations
local_file = tests_root_dir / "test_configs/flows/meta_files/remote_fs.meta.yaml"
flow = load_flow(source=local_file)
assert flow._to_dict() == {
"name": "classification_accuracy_eval",
"path": "azureml://datastores/workspaceworkingdirectory/paths/Users/wanhan/my_flow_snapshot/flow.dag.yaml",
"type": "evaluation",
}
FlowOperations._try_resolve_code_for_flow(flow, Mock())
rest_dict = flow._to_rest_object().as_dict()
assert rest_dict == {
"flow_definition_file_path": "Users/wanhan/my_flow_snapshot/flow.dag.yaml",
"flow_run_settings": {},
"flow_type": "evaluation",
"is_archived": True,
}
def test_ignore_files_in_flow(self):
local_file = tests_root_dir / "test_configs/flows/web_classification"
with tempfile.TemporaryDirectory() as temp:
flow_path = Path(temp) / "flow"
shutil.copytree(local_file, flow_path)
assert (Path(temp) / "flow/.promptflow/flow.tools.json").exists()
(Path(flow_path) / ".runs").mkdir(parents=True)
(Path(flow_path) / ".runs" / "mock.file").touch()
flow = load_flow(source=flow_path)
with flow._build_code() as code:
assert code is not None
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
# assert that .runs/mock.file are ignored
assert ".runs/mock.file" not in flow_files
# Web classification may be executed and include flow.detail.json, flow.logs, flow.outputs.json
assert all(
file in flow_files
for file in [
".promptflow/flow.tools.json",
"classify_with_llm.jinja2",
"convert_to_dict.py",
"fetch_text_content_from_url.py",
"fetch_text_content_from_url_input.jsonl",
"flow.dag.yaml",
"prepare_examples.py",
"samples.json",
"summarize_text_content.jinja2",
"summarize_text_content__variant_1.jinja2",
"webClassification20.csv",
]
)
def test_load_yaml_run_with_resources(self):
source = f"{RUNS_DIR}/sample_bulk_run_with_resources.yaml"
run = load_run(source=source, params_override=[{"name": str(uuid.uuid4())}])
assert run._resources["instance_type"] == "Standard_D2"
assert run._resources["idle_time_before_shutdown_minutes"] == 60
def test_flow_with_additional_includes(self):
flow_folder = FLOWS_DIR / "web_classification_with_additional_include"
flow = load_flow(source=flow_folder)
with flow._build_code() as code:
assert code is not None
_, temp_flow = load_flow_dag(code.path)
assert "additional_includes" not in temp_flow
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
target_additional_includes = [
"convert_to_dict.py",
"fetch_text_content_from_url.py",
"summarize_text_content.jinja2",
"external_files/convert_to_dict.py",
"external_files/fetch_text_content_from_url.py",
"external_files/summarize_text_content.jinja2",
]
# assert all additional includes are included
for file in target_additional_includes:
assert file in flow_files
def test_flow_with_ignore_file(self):
flow_folder = FLOWS_DIR / "flow_with_ignore_file"
flow = load_flow(source=flow_folder)
with flow._build_code() as code:
assert code is not None
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
assert len(flow_files) > 0
target_ignored_files = ["ignored_folder/1.txt", "random.ignored"]
# assert all ignored files are ignored
for file in target_ignored_files:
assert file not in flow_files
def test_resolve_requirements(self):
flow_dag = {}
# Test when requirements.txt does not exist
assert not Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_ignore_file", flow_dag=flow_dag)
# Test when requirements.txt exists but already added to flow_dag
flow_dag[ENVIRONMENT] = {PYTHON_REQUIREMENTS_TXT: "another_requirements.txt"}
assert not Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_requirements_txt", flow_dag=flow_dag)
# Test when requirements.txt exists and not added to flow_dag
flow_dag = {}
assert Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_requirements_txt", flow_dag=flow_dag)
def test_resolve_requirements_for_flow(self):
with tempfile.TemporaryDirectory() as temp:
temp = Path(temp)
# flow without environment section
flow_folder = FLOWS_DIR / "flow_with_requirements_txt"
shutil.copytree(flow_folder, temp / "flow_with_requirements_txt")
flow_folder = temp / "flow_with_requirements_txt"
flow = load_flow(source=flow_folder)
with flow._build_code():
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {"python_requirements_txt": "requirements.txt"}
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert ENVIRONMENT not in flow_dag
# flow with environment section
flow_folder = FLOWS_DIR / "flow_with_requirements_txt_and_env"
shutil.copytree(flow_folder, temp / "flow_with_requirements_txt_and_env")
flow_folder = temp / "flow_with_requirements_txt_and_env"
flow = load_flow(source=flow_folder)
with flow._build_code():
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {
"image": "python:3.8-slim",
"python_requirements_txt": "requirements.txt",
}
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {"image": "python:3.8-slim"}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_run_operations.py | import tempfile
import pytest
from pytest_mock import MockerFixture
from promptflow.azure import PFClient
from promptflow.exceptions import UserErrorException
@pytest.mark.unittest
class TestRunOperations:
def test_download_run_with_invalid_workspace_datastore(self, pf: PFClient, mocker: MockerFixture):
# test download with invalid workspace datastore
mocker.patch.object(pf.runs, "_validate_for_run_download")
mocker.patch.object(pf.runs, "_workspace_default_datastore", "test")
with tempfile.TemporaryDirectory() as tmp_dir:
with pytest.raises(UserErrorException, match="workspace default datastore is not supported"):
pf.runs.download(run="fake_run_name", output=tmp_dir)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_config.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from pathlib import Path
import pytest
from promptflow._sdk._configuration import ConfigFileNotFound, Configuration, InvalidConfigFile
from promptflow._utils.context_utils import _change_working_dir
AZUREML_RESOURCE_PROVIDER = "Microsoft.MachineLearningServices"
RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}"
CONFIG_DATA_ROOT = Path(__file__).parent.parent.parent / "test_configs" / "configs"
@pytest.fixture
def config():
return Configuration.get_instance()
@pytest.mark.unittest
class TestConfig:
def test_get_workspace_from_config(self):
# New instance instead of get_instance() to avoid side effect
conf = Configuration(overrides={"connection.provider": "azureml"})
# Test config within flow folder
target_folder = CONFIG_DATA_ROOT / "mock_flow1"
with _change_working_dir(target_folder):
config1 = conf.get_connection_provider()
assert config1 == "azureml:" + RESOURCE_ID_FORMAT.format("sub1", "rg1", AZUREML_RESOURCE_PROVIDER, "ws1")
# Test config using flow parent folder
target_folder = CONFIG_DATA_ROOT / "mock_flow2"
with _change_working_dir(target_folder):
config2 = conf.get_connection_provider()
assert config2 == "azureml:" + RESOURCE_ID_FORMAT.format(
"sub_default", "rg_default", AZUREML_RESOURCE_PROVIDER, "ws_default"
)
# Test config not found
with pytest.raises(ConfigFileNotFound):
Configuration._get_workspace_from_config(path=CONFIG_DATA_ROOT.parent)
# Test empty config
target_folder = CONFIG_DATA_ROOT / "mock_flow_empty_config"
with pytest.raises(InvalidConfigFile):
with _change_working_dir(target_folder):
conf.get_connection_provider()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_utils.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from unittest.mock import MagicMock
import pytest
from promptflow.exceptions import UserErrorException
@pytest.mark.unittest
class TestUtils:
def test_url_parse(self):
from promptflow.azure._utils._url_utils import BulkRunId, BulkRunURL
flow_id = (
"azureml://experiment/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/flow/"
"0ab9d2dd-3bac-4b68-bb28-12af959b1165/bulktest/715efeaf-b0b4-4778-b94a-2538152b8766/"
"run/f88faee6-e510-45b7-9e63-08671b30b3a2"
)
flow_id = BulkRunId(flow_id)
assert flow_id.experiment_id == "3e123da1-f9a5-4c91-9234-8d9ffbb39ff5"
assert flow_id.flow_id == "0ab9d2dd-3bac-4b68-bb28-12af959b1165"
assert flow_id.bulk_test_id == "715efeaf-b0b4-4778-b94a-2538152b8766"
flow_run_url = (
"https://ml.azure.com/prompts/flow/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/"
"0ab9d2dd-3bac-4b68-bb28-12af959b1165/bulktest/715efeaf-b0b4-4778-b94a-2538152b8766/"
"details?wsid=/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourcegroups/promptflow/"
"providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus"
)
flow_url = BulkRunURL(flow_run_url)
assert flow_url.experiment_id == "3e123da1-f9a5-4c91-9234-8d9ffbb39ff5"
assert flow_url.flow_id == "0ab9d2dd-3bac-4b68-bb28-12af959b1165"
assert flow_url.bulk_test_id == "715efeaf-b0b4-4778-b94a-2538152b8766"
def test_forbidden_new_caller(self):
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
with pytest.raises(UserErrorException) as e:
FlowServiceCaller(MagicMock(), MagicMock(), MagicMock())
assert "_FlowServiceCallerFactory" in str(e.value)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_arm_connection_build.py | import copy
import pytest
def build_from_data_and_assert(data, expected):
from azure.ai.ml._restclient.v2023_06_01_preview.models import WorkspaceConnectionPropertiesV2BasicResource
from promptflow.azure.operations._arm_connection_operations import ArmConnectionOperations
data = copy.deepcopy(data)
obj = WorkspaceConnectionPropertiesV2BasicResource.deserialize(data)
assert ArmConnectionOperations.build_connection_dict_from_rest_object("mock", obj) == expected
@pytest.mark.unittest
def test_build_azure_openai_connection_from_rest_object():
# Test on ApiKey type with AzureOpenAI category
data = {
"id": "mock_id",
"name": "azure_open_ai_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "AzureOpenAI",
"target": "<api-base>",
"metadata": {
"azureml.flow.connection_type": "AzureOpenAI",
"azureml.flow.module": "promptflow.connections",
"apiType": "azure",
"ApiVersion": "2023-07-01-preview",
"ResourceId": "mock_id",
},
},
}
expected = {
"type": "AzureOpenAIConnection",
"module": "promptflow.connections",
"value": {
"api_base": "<api-base>",
"api_key": "***",
"api_type": "azure",
"api_version": "2023-07-01-preview",
"resource_id": "mock_id",
},
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_default_azure_openai_connection_missing_metadata():
# Test on ApiKey type with AzureOpenAI category
data = {
"id": "mock_id",
"name": "azure_open_ai_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "AzureOpenAI",
"target": "<api-base>",
"metadata": {
# Missing ApiType and ApiVersion
# "ApiType": "azure",
# "ApiVersion": "2023-07-01-preview",
},
},
}
expected = {
"type": "AzureOpenAIConnection",
"module": "promptflow.connections",
"value": {
"api_base": "<api-base>",
"api_key": "***",
# Assert below keys are filtered out
# "api_type": None,
# "api_version": None,
},
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_custom_keys_connection_from_rest_object():
# Test on CustomKeys type with CustomConnection category
data = {
"id": "mock_id",
"name": "custom_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "CustomKeys",
"credentials": {"keys": {"my_key1": "***", "my_key2": "***"}},
"category": "CustomKeys",
"target": "<api-base>",
"metadata": {
"azureml.flow.connection_type": "Custom",
"azureml.flow.module": "promptflow.connections",
"general_key": "general_value",
},
},
}
expected = {
"type": "CustomConnection",
"module": "promptflow.connections",
"value": {"my_key1": "***", "my_key2": "***", "general_key": "general_value"},
"secret_keys": ["my_key1", "my_key2"],
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_cognitive_search_connection_from_rest_object():
# Test on ApiKey type with CognitiveSearch category
data = {
"tags": None,
"location": None,
"id": "mock_id",
"name": "test",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "CognitiveSearch",
"expiryTime": None,
"target": "mock_target",
"metadata": {
"azureml.flow.connection_type": "CognitiveSearch",
"azureml.flow.module": "promptflow.connections",
"ApiVersion": "2023-07-01-Preview",
},
},
}
expected = {
"type": "CognitiveSearchConnection",
"module": "promptflow.connections",
"value": {"api_key": "***", "api_base": "mock_target", "api_version": "2023-07-01-Preview"},
}
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_cognitive_service_category_connection_from_rest_object():
# Test on Api type with CognitiveService category
data = {
"id": "mock_id",
"name": "ACS_Connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "CognitiveService",
"target": "mock_target",
"metadata": {
"azureml.flow.connection_type": "AzureContentSafety",
"azureml.flow.module": "promptflow.connections",
"Kind": "Content Safety",
"ApiVersion": "2023-04-30-preview",
},
},
}
expected = {
"type": "AzureContentSafetyConnection",
"module": "promptflow.connections",
"value": {"api_key": "***", "endpoint": "mock_target", "api_version": "2023-04-30-preview"},
}
build_from_data_and_assert(data, expected)
# Test category + kind as connection type
del data["properties"]["metadata"]["azureml.flow.connection_type"]
build_from_data_and_assert(data, expected)
@pytest.mark.unittest
def test_build_connection_missing_metadata():
data = {
"id": "mock_id",
"name": "ACS_Connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "CognitiveService",
"target": "mock_target",
"metadata": {
"ApiVersion": "2023-04-30-preview",
},
},
}
with pytest.raises(Exception) as e:
build_from_data_and_assert(data, {})
assert "is not recognized in PromptFlow" in str(e.value)
@pytest.mark.unittest
def test_build_connection_unknown_category():
data = {
"id": "mock_id",
"name": "ACS_Connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {"key": "***"},
"category": "Unknown",
"target": "mock_target",
"metadata": {
"azureml.flow.connection_type": "AzureContentSafety",
"azureml.flow.module": "promptflow.connections",
"Kind": "Content Safety",
"ApiVersion": "2023-04-30-preview",
},
},
}
with pytest.raises(Exception) as e:
build_from_data_and_assert(data, {})
assert "Unknown connection mock category Unknown" in str(e.value)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_azure_cli_activity_name.py | import pytest
from promptflow._cli._pf_azure.entry import get_parser_args
from promptflow._cli._utils import _get_cli_activity_name
def get_cli_activity_name(cmd):
prog, args = get_parser_args(list(cmd)[1:])
return _get_cli_activity_name(cli=prog, args=args)
@pytest.mark.unittest
class TestAzureCliTimeConsume:
def test_pfazure_run_create(self, activity_name="pfazure.run.create"):
assert (
get_cli_activity_name(
cmd=("pfazure", "run", "create", "--flow", "print_input_flow", "--data", "print_input_flow.jsonl")
)
== activity_name
)
def test_pfazure_run_update(self, activity_name="pfazure.run.update"):
assert (
get_cli_activity_name(
cmd=(
"pfazure",
"run",
"update",
"--name",
"test_run",
"--set",
"display_name=test_run",
"description='test_description'",
"tags.key1=value1",
)
)
== activity_name
)
def test_run_restore(self, activity_name="pfazure.run.restore"):
assert get_cli_activity_name(cmd=("pfazure", "run", "restore", "--name", "test_run")) == activity_name
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_cli.py | import contextlib
import os
import sys
from pathlib import Path
from typing import List
from unittest.mock import MagicMock, patch
import pandas as pd
import pytest
from pytest_mock import MockFixture
from promptflow._sdk._constants import VIS_PORTAL_URL_TMPL
tests_root_dir = Path(__file__).parent.parent.parent
flow_test_dir = tests_root_dir / "test_configs/flows"
data_dir = tests_root_dir / "test_configs/datas"
def run_pf_command(*args, cwd=None):
from promptflow._cli._pf_azure.entry import main
origin_argv, origin_cwd = sys.argv, os.path.abspath(os.curdir)
try:
sys.argv = ["pfazure"] + list(args)
if cwd:
os.chdir(cwd)
main()
finally:
sys.argv = origin_argv
os.chdir(origin_cwd)
@pytest.fixture
def operation_scope_args(subscription_id: str, resource_group_name: str, workspace_name: str):
return [
"--subscription",
subscription_id,
"--resource-group",
resource_group_name,
"--workspace-name",
workspace_name,
]
@pytest.mark.usefixtures("mock_get_azure_pf_client")
@pytest.mark.unittest
class TestAzureCli:
def test_pf_azure_version(self, capfd):
run_pf_command("--version")
out, err = capfd.readouterr()
assert "0.0.1\n" in out
def test_run_show(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "get")
# show_run will print the run object, so we need to mock the return value
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"show",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_show_details(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "get_details")
# show_run_details will print details, so we need to mock the return value
mocked.return_value = pd.DataFrame([{"input": "input_value", "output": "output_value"}])
run_pf_command(
"run",
"show-details",
"--name",
"test_run",
"--max-results",
"10",
"--all-results",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_show_metrics(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "get_metrics")
# show_metrics will print the metrics, so we need to mock the return value
mocked.return_value = {"accuracy": 0.9}
run_pf_command(
"run",
"show-metrics",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_list_runs(
self,
mocker: MockFixture,
operation_scope_args,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked_run = MagicMock()
mocked_run._to_dict.return_value = {"name": "test_run"}
mocked = mocker.patch.object(RunOperations, "list")
# list_runs will print the run list, so we need to mock the return value
mocked.return_value = [mocked_run]
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
*operation_scope_args,
)
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
"--output",
"table",
*operation_scope_args,
)
mocker.patch.dict(
os.environ,
{
"AZUREML_ARM_WORKSPACE_NAME": workspace_name,
"AZUREML_ARM_SUBSCRIPTION": subscription_id,
"AZUREML_ARM_RESOURCEGROUP": resource_group_name,
},
)
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
)
assert mocked.call_count == 3
def test_run_visualize(
self,
operation_scope_args: List[str],
capfd: pytest.CaptureFixture,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
) -> None:
# cloud version visualize is actually a string concatenation
names = "name1,name2,name3"
run_pf_command(
"run",
"visualize",
"--names",
names,
*operation_scope_args,
)
captured = capfd.readouterr()
expected_portal_url = VIS_PORTAL_URL_TMPL.format(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
names=names,
)
assert expected_portal_url in captured.out
def test_run_archive(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "archive")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"archive",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_restore(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "restore")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"restore",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_update(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "update")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
run_pf_command(
"run",
"update",
"--name",
"test_run",
"--set",
"display_name=test_run",
"description='test_description'",
"tags.key1=value1",
*operation_scope_args,
)
mocked.assert_called_once()
def test_flow_create(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._flow_operations import FlowOperations
mocked = mocker.patch.object(FlowOperations, "create_or_update")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
flow_dir = Path(flow_test_dir, "web_classification").resolve().as_posix()
run_pf_command(
"flow",
"create",
"--flow",
flow_dir,
"--set",
"display_name=test_flow",
"type=standard",
"description='test_description'",
"tags.key1=value1",
*operation_scope_args,
)
mocked.assert_called_with(
flow=flow_dir,
display_name="test_flow",
type="standard",
description="test_description",
tags={"key1": "value1"},
)
def test_flow_create_with_unknown_field(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._flow_operations import FlowOperations
mocked = mocker.patch.object(FlowOperations, "create_or_update")
mocked.return_value._to_dict.return_value = {"name": "test_run"}
flow_dir = Path(flow_test_dir, "web_classification").resolve().as_posix()
run_pf_command(
"flow",
"create",
"--flow",
flow_dir,
"--set",
"random_key=random_value",
*operation_scope_args,
)
mocked.assert_called_with(flow=flow_dir, random_key="random_value")
def test_flow_list(
self,
mocker: MockFixture,
operation_scope_args,
):
from promptflow.azure.operations._flow_operations import FlowOperations
mocked_flow = MagicMock()
mocked_flow._to_dict.return_value = {"name": "test_flow"}
mocked = mocker.patch.object(FlowOperations, "list")
mocked.return_value = [mocked_flow]
run_pf_command(
"flow",
"list",
"--max-results",
"10",
"--include-archived",
"--type",
"standard",
"--include-others",
"--output",
"table",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_telemetry(
self,
mocker: MockFixture,
operation_scope_args,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
):
from promptflow.azure.operations._run_operations import RunOperations
mocked_run = MagicMock()
mocked_run._to_dict.return_value = {"name": "test_run"}
mocked = mocker.patch.object(RunOperations, "list")
# list_runs will print the run list, so we need to mock the return value
mocked.return_value = [mocked_run]
mocker.patch.dict(
os.environ,
{
"AZUREML_ARM_WORKSPACE_NAME": workspace_name,
"AZUREML_ARM_SUBSCRIPTION": subscription_id,
"AZUREML_ARM_RESOURCEGROUP": resource_group_name,
},
)
@contextlib.contextmanager
def check_workspace_info(*args, **kwargs):
if "custom_dimensions" in kwargs:
assert kwargs["custom_dimensions"]["workspace_name"] == workspace_name
assert kwargs["custom_dimensions"]["resource_group_name"] == resource_group_name
assert kwargs["custom_dimensions"]["subscription_id"] == subscription_id
yield None
with patch("promptflow._sdk._telemetry.activity.log_activity") as mock_log_activity:
mock_log_activity.side_effect = check_workspace_info
run_pf_command(
"run",
"list",
"--max-results",
"10",
"--include-archived",
*operation_scope_args,
)
def test_run_download(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "download")
mocked.return_value = "fake_output_run_dir"
run_pf_command(
"run",
"download",
"--name",
"test_run",
"--output",
"fake_output_dir",
"--overwrite",
*operation_scope_args,
)
mocked.assert_called_once()
def test_run_cancel(self, mocker: MockFixture, operation_scope_args):
from promptflow.azure.operations._run_operations import RunOperations
mocked = mocker.patch.object(RunOperations, "cancel")
run_pf_command(
"run",
"cancel",
"--name",
"test_run",
*operation_scope_args,
)
mocked.assert_called_once()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_run_entity.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import shutil
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock
import pytest
from promptflow._sdk.entities import Run
from promptflow._utils.flow_utils import get_flow_lineage_id
from promptflow.exceptions import UserErrorException
PROMOTFLOW_ROOT = Path(__file__) / "../../../.."
TEST_ROOT = Path(__file__).parent.parent.parent
MODEL_ROOT = TEST_ROOT / "test_configs/e2e_samples"
CONNECTION_FILE = (PROMOTFLOW_ROOT / "connections.json").resolve().absolute().as_posix()
FLOWS_DIR = "./tests/test_configs/flows"
RUNS_DIR = "./tests/test_configs/runs"
DATAS_DIR = "./tests/test_configs/datas"
@pytest.mark.unittest
class TestRun:
def test_input_mapping_types(self, pf):
data_path = f"{DATAS_DIR}/webClassification3.jsonl"
flow_path = Path(f"{FLOWS_DIR}/flow_with_dict_input")
# run with dict inputs
run = Run(
flow=flow_path,
data=data_path,
column_mapping=dict(key={"a": 1}),
)
rest_run = run._to_rest_object()
assert rest_run.inputs_mapping == {"key": '{"a": 1}'}
# run with list inputs
run = Run(
flow=flow_path,
data=data_path,
column_mapping=dict(key=["a", "b"]),
)
rest_run = run._to_rest_object()
assert rest_run.inputs_mapping == {"key": '["a", "b"]'}
# unsupported inputs
run = Run(
flow=flow_path,
data=data_path,
column_mapping=dict(key=Mock()),
)
with pytest.raises(UserErrorException):
run._to_rest_object()
run = Run(flow=flow_path, data=data_path, column_mapping="str")
with pytest.raises(UserErrorException):
run._to_rest_object()
def test_flow_id(self):
# same flow id for same flow in same GIT repo
flow_path = Path(f"{FLOWS_DIR}/flow_with_dict_input")
# run with dict inputs
session_id1 = get_flow_lineage_id(flow_path)
session_id2 = get_flow_lineage_id(flow_path)
assert session_id1 == session_id2
# same flow id for same flow in same device
with TemporaryDirectory() as tmp_dir:
shutil.copytree(f"{FLOWS_DIR}/flow_with_dict_input", f"{tmp_dir}/flow_with_dict_input")
session_id3 = get_flow_lineage_id(f"{tmp_dir}/flow_with_dict_input")
session_id4 = get_flow_lineage_id(f"{tmp_dir}/flow_with_dict_input")
assert session_id3 == session_id4
assert session_id3 != session_id1
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_pf_client.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import mock
import pytest
from promptflow import PFClient
from promptflow._sdk.operations._connection_operations import ConnectionOperations
from promptflow._sdk.operations._local_azure_connection_operations import LocalAzureConnectionOperations
from promptflow.exceptions import UserErrorException
from ..recording_utilities import is_live
AZUREML_RESOURCE_PROVIDER = "Microsoft.MachineLearningServices"
RESOURCE_ID_FORMAT = "/subscriptions/{}/resourceGroups/{}/providers/{}/workspaces/{}"
@pytest.mark.sdk_test
@pytest.mark.e2etest
class TestPFClient:
# Test pf client when connection provider is azureml.
# This tests suites need azure dependencies.
@pytest.mark.skipif(condition=not is_live(), reason="This test requires an actual PFClient")
def test_connection_provider(self, subscription_id: str, resource_group_name: str, workspace_name: str):
target = "promptflow._sdk._pf_client.Configuration"
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "abc"
with pytest.raises(UserErrorException) as e:
client = PFClient()
assert client.connections
assert "Unsupported connection provider" in str(e.value)
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "azureml:xx"
with pytest.raises(ValueError) as e:
client = PFClient()
assert client.connections
assert "Malformed connection provider string" in str(e.value)
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "local"
client = PFClient()
assert isinstance(client.connections, ConnectionOperations)
with mock.patch(target) as mocked:
mocked.return_value.get_connection_provider.return_value = "azureml:" + RESOURCE_ID_FORMAT.format(
subscription_id, resource_group_name, AZUREML_RESOURCE_PROVIDER, workspace_name
)
client = PFClient()
assert isinstance(client.connections, LocalAzureConnectionOperations)
client = PFClient(
config={
"connection.provider": "azureml:"
+ RESOURCE_ID_FORMAT.format(
subscription_id, resource_group_name, AZUREML_RESOURCE_PROVIDER, workspace_name
)
}
)
assert isinstance(client.connections, LocalAzureConnectionOperations)
def test_local_azure_connection_extract_workspace(self):
res = LocalAzureConnectionOperations._extract_workspace(
"azureml://subscriptions/123/resourceGroups/456/providers/Microsoft.MachineLearningServices/workspaces/789"
)
assert res == ("123", "456", "789")
res = LocalAzureConnectionOperations._extract_workspace(
"azureml://subscriptions/123/resourcegroups/456/workspaces/789"
)
assert res == ("123", "456", "789")
with pytest.raises(ValueError) as e:
LocalAzureConnectionOperations._extract_workspace("azureml:xx")
assert "Malformed connection provider string" in str(e.value)
with pytest.raises(ValueError) as e:
LocalAzureConnectionOperations._extract_workspace(
"azureml://subscriptions/123/resourceGroups/456/providers/Microsoft.MachineLearningServices/workspaces/"
)
assert "Malformed connection provider string" in str(e.value)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_pf_client_azure.py | import pytest
from promptflow._sdk._errors import RunOperationParameterError
@pytest.mark.unittest
class TestPFClientAzure:
def test_wrong_client_parameters(self):
from promptflow.azure import PFClient
# test wrong client parameters
with pytest.raises(RunOperationParameterError, match="You have passed in the wrong parameter name"):
PFClient(
subscription_id="fake_subscription_id",
resource_group="fake_resource_group",
workspace_name="fake_workspace_name",
)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test | promptflow_repo/promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_flow_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from pathlib import Path
from unittest.mock import patch
import pytest
from promptflow._sdk._errors import FlowOperationError
from promptflow.exceptions import UserErrorException
tests_root_dir = Path(__file__).parent.parent.parent
flow_test_dir = tests_root_dir / "test_configs/flows"
data_dir = tests_root_dir / "test_configs/datas"
@pytest.mark.unittest
class TestFlowOperations:
def test_create_flow_with_invalid_parameters(self, pf):
with pytest.raises(UserErrorException, match=r"Flow source must be a directory with"):
pf.flows.create_or_update(flow="fake_source")
flow_source = flow_test_dir / "web_classification/"
with pytest.raises(UserErrorException, match="Not a valid string"):
pf.flows.create_or_update(flow=flow_source, display_name=False)
with pytest.raises(UserErrorException, match="Must be one of: standard, evaluation, chat"):
pf.flows.create_or_update(flow=flow_source, type="unknown")
with pytest.raises(UserErrorException, match="Not a valid string"):
pf.flows.create_or_update(flow=flow_source, description=False)
with pytest.raises(UserErrorException, match="Not a valid string"):
pf.flows.create_or_update(flow=flow_source, tags={"key": False})
@pytest.mark.usefixtures("enable_logger_propagate")
def test_create_flow_with_warnings(self, pf, caplog):
flow_source = flow_test_dir / "web_classification/"
pf.flows._validate_flow_creation_parameters(source=flow_source, random="random")
assert "random: Unknown field" in caplog.text
def test_list_flows_invalid_cases(self, pf):
with pytest.raises(FlowOperationError, match="'max_results' must be a positive integer"):
pf.flows.list(max_results=0)
with pytest.raises(FlowOperationError, match="'flow_type' must be one of"):
pf.flows.list(flow_type="unknown")
with pytest.raises(FlowOperationError, match="Invalid list view type"):
pf.flows.list(list_view_type="invalid")
def test_get_user_identity_info(self):
# we have a fixture "mock_get_user_identity_info" to mock this function during record and replay
# as we don't want to deal with token in these modes; meanwhile, considering coverage, add this
# unit test to try to cover this code path.
import jwt
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
mock_oid, mock_tid = "mock_oid", "mock_tid"
def mock_init(*args, **kwargs) -> str:
self = args[0]
self._credential = None
def mock_get_arm_token(*args, **kwargs) -> str:
return jwt.encode(
payload={
"oid": mock_oid,
"tid": mock_tid,
},
key="",
)
with patch(
"promptflow.azure._restclient.flow_service_caller.get_arm_token",
new=mock_get_arm_token,
):
with patch.object(FlowServiceCaller, "__init__", new=mock_init):
service_caller = FlowServiceCaller(workspace=None, credential=None, operation_scope=None)
user_object_id, user_tenant_id = service_caller._get_user_identity_info()
assert user_object_id == mock_oid
assert user_tenant_id == mock_tid
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/executor/utils.py | import json
from pathlib import Path
from typing import Union, Dict
from promptflow._utils.yaml_utils import load_yaml
from promptflow.contracts.flow import Flow
from promptflow.contracts.run_info import FlowRunInfo
from promptflow.contracts.run_info import RunInfo as NodeRunInfo
from promptflow.storage import AbstractRunStorage
TEST_ROOT = Path(__file__).parent.parent
DATA_ROOT = TEST_ROOT / "test_configs/datas"
FLOW_ROOT = TEST_ROOT / "test_configs/flows"
EAGER_FLOW_ROOT = TEST_ROOT / "test_configs/eager_flows"
WRONG_FLOW_ROOT = TEST_ROOT / "test_configs/wrong_flows"
EAGER_FLOWS_ROOT = TEST_ROOT / "test_configs/eager_flows"
def get_flow_folder(folder_name, root: str = FLOW_ROOT):
flow_folder_path = Path(root) / folder_name
return flow_folder_path
def get_yaml_file(folder_name, root: str = FLOW_ROOT, file_name: str = "flow.dag.yaml"):
yaml_file = get_flow_folder(folder_name, root) / file_name
return yaml_file
def get_entry_file(folder_name, root: str = EAGER_FLOW_ROOT, file_name: str = "entry.py"):
entry_file = get_flow_folder(folder_name, root) / file_name
return entry_file
def get_flow_from_folder(folder_name, root: str = FLOW_ROOT, file_name: str = "flow.dag.yaml"):
flow_yaml = get_yaml_file(folder_name, root, file_name)
with open(flow_yaml, "r") as fin:
return Flow.deserialize(load_yaml(fin))
def get_flow_inputs_file(folder_name, root: str = FLOW_ROOT, file_name: str = "inputs.jsonl"):
inputs_file = get_flow_folder(folder_name, root) / file_name
return inputs_file
def get_flow_inputs(folder_name, root: str = FLOW_ROOT, file_name: str = "inputs.json"):
inputs = load_json(get_flow_inputs_file(folder_name, root, file_name))
return inputs[0] if isinstance(inputs, list) else inputs
def get_bulk_inputs_from_jsonl(folder_name, root: str = FLOW_ROOT, file_name: str = "inputs.jsonl"):
inputs = load_jsonl(get_flow_inputs_file(folder_name, root, file_name))
return inputs
def get_bulk_inputs(folder_name, root: str = FLOW_ROOT, file_name: str = "inputs.json"):
inputs = load_json(get_flow_inputs_file(folder_name, root=root, file_name=file_name))
return [inputs] if isinstance(inputs, dict) else inputs
def get_flow_sample_inputs(folder_name, root: str = FLOW_ROOT, sample_inputs_file="samples.json"):
samples_inputs = load_json(get_flow_folder(folder_name, root) / sample_inputs_file)
return samples_inputs
def get_flow_expected_metrics(folder_name):
samples_inputs = load_json(get_flow_folder(folder_name) / "expected_metrics.json")
return samples_inputs
def get_flow_expected_status_summary(folder_name):
samples_inputs = load_json(get_flow_folder(folder_name) / "expected_status_summary.json")
return samples_inputs
def get_flow_expected_result(folder_name):
samples_inputs = load_json(get_flow_folder(folder_name) / "expected_result.json")
return samples_inputs
def get_flow_package_tool_definition(folder_name):
return load_json(get_flow_folder(folder_name) / "package_tool_definition.json")
def load_json(source: Union[str, Path]) -> dict:
"""Load json file to dict"""
with open(source, "r") as f:
loaded_data = json.load(f)
return loaded_data
def load_jsonl(source: Union[str, Path]) -> list:
"""Load jsonl file to list"""
with open(source, "r") as f:
loaded_data = [json.loads(line.strip()) for line in f]
return loaded_data
def load_content(source: Union[str, Path]) -> str:
"""Load file content to string"""
return Path(source).read_text()
def is_jsonl_file(file_path: Path):
return file_path.suffix.lower() == ".jsonl"
def is_image_file(file_path: Path):
image_extensions = [".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff"]
file_extension = file_path.suffix.lower()
return file_extension in image_extensions
class MemoryRunStorage(AbstractRunStorage):
def __init__(self):
self._node_runs: Dict[str, NodeRunInfo] = {}
self._flow_runs: Dict[str, FlowRunInfo] = {}
def persist_flow_run(self, run_info: FlowRunInfo):
self._flow_runs[run_info.run_id] = run_info
def persist_node_run(self, run_info: NodeRunInfo):
self._node_runs[run_info.run_id] = run_info
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/executor/.coveragerc | [run]
omit =
*/promptflow/_cli/*
*/promptflow/_sdk/*
*/promptflow/_telemetry/*
*/promptflow/azure/*
*/promptflow/entities/*
*/promptflow/operations/*
*__init__.py*
| 0 |
promptflow_repo/promptflow/src/promptflow/tests | promptflow_repo/promptflow/src/promptflow/tests/executor/mock_execution_server.py | import json
from functools import partial
from aiohttp import web
from promptflow.exceptions import ValidationException
def run_executor_server(port, has_error=False, init_error_file=None):
app = web.Application()
app.router.add_get("/health", _handle_health)
handle_execution_with_customization = partial(_handle_execution, has_error=has_error)
app.router.add_post("/execution", handle_execution_with_customization)
print(f"Starting server on port {port}")
if init_error_file is None:
web.run_app(app, host="localhost", port=port)
else:
raise ValidationException("Error for tests")
async def _handle_health(request: web.Request):
return web.Response(text="Healthy")
async def _handle_execution(request: web.Request, has_error=False):
try:
request = await request.json()
return _get_execution_result(request, has_error=has_error)
except json.JSONDecodeError:
return web.Response(status=400, text="Bad Request: Invalid JSON")
def _get_execution_result(request: dict, has_error=False):
run_id = request.get("run_id", "dummy_run_id")
index = request.get("line_number", 1)
inputs = request.get("inputs", {"question": "Hello world!"})
if has_error and index == 1:
# simulate error
line_result_dict = _get_line_result_dict(run_id, index, inputs, has_error=True)
return web.json_response(line_result_dict, status=500)
line_result_dict = _get_line_result_dict(run_id, index, inputs)
return web.json_response(line_result_dict)
def _get_line_result_dict(run_id, index, inputs, has_error=False):
if has_error:
return {
"error": {
"message": "error for tests",
"messageFormat": "",
"messageParameters": {},
"referenceCode": None,
"debugInfo": {
"type": "Exception",
"message": "error for tests",
"stackTrace": "...",
"innerException": None,
},
"additionalInfo": None,
"code": "UserError",
"innerError": {"code": "Exception", "innerError": None},
},
}
return {
"output": {"answer": "Hello world!"},
"aggregation_inputs": {},
"run_info": {
"run_id": run_id,
"status": "Completed",
"inputs": inputs,
"output": {"answer": "Hello world!"},
"parent_run_id": run_id,
"root_run_id": run_id,
"start_time": "2023-11-24T06:03:20.2685529Z",
"end_time": "2023-11-24T06:03:20.2688869Z",
"index": index,
"system_metrics": {"duration": "00:00:00.0003340", "total_tokens": 0},
"result": {"answer": "Hello world!"},
},
"node_run_infos": {
"get_answer": {
"node": "get_answer",
"flow_run_id": run_id,
"parent_run_id": run_id,
"run_id": "dummy_node_run_id",
"status": "Completed",
"inputs": inputs,
"output": "Hello world!",
"start_time": "2023-11-24T06:03:20.2688262Z",
"end_time": "2023-11-24T06:03:20.268858Z",
"index": index,
"system_metrics": {"duration": "00:00:00.0000318", "total_tokens": 0},
"result": "Hello world!",
}
},
}
def _get_aggr_result_dict(run_id, inputs, has_error=False):
if has_error:
return {
"error": {
"message": "error for tests",
"messageFormat": "",
"messageParameters": {},
"referenceCode": None,
"debugInfo": {
"type": "Exception",
"message": "error for tests",
"stackTrace": "...",
"innerException": None,
},
"additionalInfo": None,
"code": "UserError",
"innerError": {"code": "Exception", "innerError": None},
},
}
return {
"output": None,
"metrics": {"accuracy": 0.5},
"node_run_infos": {
"aggregation": {
"node": "aggregation",
"flow_run_id": run_id,
"parent_run_id": run_id,
"run_id": "dummy_node_run_id",
"status": "Completed",
"inputs": inputs,
"output": "Hello world!",
"start_time": "2023-11-24T06:03:20.2688262Z",
"end_time": "2023-11-24T06:03:20.268858Z",
"system_metrics": {"duration": "00:00:00.0000318", "total_tokens": 0},
"result": "Hello world!",
}
},
}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_batch_engine.py | import asyncio
import multiprocessing
import os
import uuid
from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow._utils.utils import dump_list_to_jsonl
from promptflow.batch._batch_engine import OUTPUT_FILE_NAME, BatchEngine
from promptflow.batch._errors import EmptyInputsData
from promptflow.batch._result import BatchResult
from promptflow.contracts.run_info import Status
from promptflow.executor._errors import InputNotFound
from ..utils import (
MemoryRunStorage,
get_flow_expected_metrics,
get_flow_expected_status_summary,
get_flow_folder,
get_flow_inputs_file,
get_flow_sample_inputs,
get_yaml_file,
load_jsonl,
)
SAMPLE_FLOW = "web_classification_no_variants"
SAMPLE_EVAL_FLOW = "classification_accuracy_evaluation"
SAMPLE_FLOW_WITH_PARTIAL_FAILURE = "python_tool_partial_failure"
async def async_submit_batch_run(flow_folder, inputs_mapping, connections):
batch_result = submit_batch_run(flow_folder, inputs_mapping, connections=connections)
await asyncio.sleep(1)
return batch_result
def run_batch_with_start_method(multiprocessing_start_method, flow_folder, inputs_mapping, dev_connections):
os.environ["PF_BATCH_METHOD"] = multiprocessing_start_method
batch_result, output_dir = submit_batch_run(
flow_folder, inputs_mapping, connections=dev_connections, return_output_dir=True
)
assert isinstance(batch_result, BatchResult)
nlines = get_batch_inputs_line(flow_folder)
assert batch_result.total_lines == nlines
assert batch_result.completed_lines == nlines
assert batch_result.start_time < batch_result.end_time
assert batch_result.system_metrics.duration > 0
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == nlines
for i, output in enumerate(outputs):
assert isinstance(output, dict)
assert "line_number" in output, f"line_number is not in {i}th output {output}"
assert output["line_number"] == i, f"line_number is not correct in {i}th output {output}"
def submit_batch_run(
flow_folder,
inputs_mapping,
*,
input_dirs={},
input_file_name="samples.json",
run_id=None,
connections={},
storage=None,
return_output_dir=False,
):
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=connections, storage=storage
)
if not input_dirs and inputs_mapping:
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name=input_file_name)}
output_dir = Path(mkdtemp())
if return_output_dir:
return batch_engine.run(input_dirs, inputs_mapping, output_dir, run_id=run_id), output_dir
return batch_engine.run(input_dirs, inputs_mapping, output_dir, run_id=run_id)
def get_batch_inputs_line(flow_folder, sample_inputs_file="samples.json"):
inputs = get_flow_sample_inputs(flow_folder, sample_inputs_file=sample_inputs_file)
return len(inputs)
@pytest.mark.usefixtures("use_secrets_config_file", "dev_connections")
@pytest.mark.e2etest
class TestBatch:
def test_batch_storage(self, dev_connections):
mem_run_storage = MemoryRunStorage()
run_id = str(uuid.uuid4())
inputs_mapping = {"url": "${data.url}"}
batch_result = submit_batch_run(
SAMPLE_FLOW, inputs_mapping, run_id=run_id, connections=dev_connections, storage=mem_run_storage
)
nlines = get_batch_inputs_line(SAMPLE_FLOW)
assert batch_result.total_lines == nlines
assert batch_result.completed_lines == nlines
assert len(mem_run_storage._flow_runs) == nlines
assert all(flow_run_info.status == Status.Completed for flow_run_info in mem_run_storage._flow_runs.values())
assert all(node_run_info.status == Status.Completed for node_run_info in mem_run_storage._node_runs.values())
@pytest.mark.parametrize(
"flow_folder, inputs_mapping",
[
(
SAMPLE_FLOW,
{"url": "${data.url}"},
),
(
"prompt_tools",
{"text": "${data.text}"},
),
(
"script_with___file__",
{"text": "${data.text}"},
),
(
"sample_flow_with_functions",
{"question": "${data.question}"},
),
],
)
def test_batch_run(self, flow_folder, inputs_mapping, dev_connections):
batch_result, output_dir = submit_batch_run(
flow_folder, inputs_mapping, connections=dev_connections, return_output_dir=True
)
assert isinstance(batch_result, BatchResult)
nlines = get_batch_inputs_line(flow_folder)
assert batch_result.total_lines == nlines
assert batch_result.completed_lines == nlines
assert batch_result.start_time < batch_result.end_time
assert batch_result.system_metrics.duration > 0
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == nlines
for i, output in enumerate(outputs):
assert isinstance(output, dict)
assert "line_number" in output, f"line_number is not in {i}th output {output}"
assert output["line_number"] == i, f"line_number is not correct in {i}th output {output}"
@pytest.mark.parametrize(
"flow_folder, inputs_mapping",
[
(
SAMPLE_FLOW,
{"url": "${data.url}"},
),
(
"prompt_tools",
{"text": "${data.text}"},
),
(
"script_with___file__",
{"text": "${data.text}"},
),
(
"sample_flow_with_functions",
{"question": "${data.question}"},
),
],
)
def test_spawn_mode_batch_run(self, flow_folder, inputs_mapping, dev_connections):
if "spawn" not in multiprocessing.get_all_start_methods():
pytest.skip("Unsupported start method: spawn")
p = multiprocessing.Process(
target=run_batch_with_start_method, args=("spawn", flow_folder, inputs_mapping, dev_connections)
)
p.start()
p.join()
assert p.exitcode == 0
@pytest.mark.parametrize(
"flow_folder, inputs_mapping",
[
(
SAMPLE_FLOW,
{"url": "${data.url}"},
),
(
"prompt_tools",
{"text": "${data.text}"},
),
(
"script_with___file__",
{"text": "${data.text}"},
),
(
"sample_flow_with_functions",
{"question": "${data.question}"},
),
],
)
def test_forkserver_mode_batch_run(self, flow_folder, inputs_mapping, dev_connections):
if "forkserver" not in multiprocessing.get_all_start_methods():
pytest.skip("Unsupported start method: forkserver")
p = multiprocessing.Process(
target=run_batch_with_start_method, args=("forkserver", flow_folder, inputs_mapping, dev_connections)
)
p.start()
p.join()
assert p.exitcode == 0
def test_batch_run_then_eval(self, dev_connections):
batch_resutls, output_dir = submit_batch_run(
SAMPLE_FLOW, {"url": "${data.url}"}, connections=dev_connections, return_output_dir=True
)
nlines = get_batch_inputs_line(SAMPLE_FLOW)
assert batch_resutls.completed_lines == nlines
input_dirs = {"data": get_flow_inputs_file(SAMPLE_FLOW, file_name="samples.json"), "run.outputs": output_dir}
inputs_mapping = {
"variant_id": "baseline",
"groundtruth": "${data.url}",
"prediction": "${run.outputs.category}",
}
eval_result = submit_batch_run(SAMPLE_EVAL_FLOW, inputs_mapping, input_dirs=input_dirs)
assert eval_result.completed_lines == nlines, f"Only returned {eval_result.completed_lines}/{nlines} outputs."
assert len(eval_result.metrics) > 0, "No metrics are returned."
assert eval_result.metrics["accuracy"] == 0, f"Accuracy should be 0, got {eval_result.metrics}."
def test_batch_with_metrics(self, dev_connections):
flow_folder = SAMPLE_EVAL_FLOW
inputs_mapping = {
"variant_id": "${data.variant_id}",
"groundtruth": "${data.groundtruth}",
"prediction": "${data.prediction}",
}
batch_results = submit_batch_run(flow_folder, inputs_mapping, connections=dev_connections)
assert isinstance(batch_results, BatchResult)
assert isinstance(batch_results.metrics, dict)
assert batch_results.metrics == get_flow_expected_metrics(flow_folder)
assert batch_results.total_lines == batch_results.completed_lines
assert batch_results.node_status == get_flow_expected_status_summary(flow_folder)
def test_batch_with_partial_failure(self, dev_connections):
flow_folder = SAMPLE_FLOW_WITH_PARTIAL_FAILURE
inputs_mapping = {"idx": "${data.idx}", "mod": "${data.mod}", "mod_2": "${data.mod_2}"}
batch_results = submit_batch_run(flow_folder, inputs_mapping, connections=dev_connections)
assert isinstance(batch_results, BatchResult)
assert batch_results.total_lines == 10
assert batch_results.completed_lines == 5
assert batch_results.failed_lines == 5
assert batch_results.node_status == get_flow_expected_status_summary(flow_folder)
def test_batch_with_line_number(self, dev_connections):
flow_folder = SAMPLE_FLOW_WITH_PARTIAL_FAILURE
input_dirs = {"data": "inputs/data.jsonl", "output": "inputs/output.jsonl"}
inputs_mapping = {"idx": "${output.idx}", "mod": "${data.mod}", "mod_2": "${data.mod_2}"}
batch_results, output_dir = submit_batch_run(
flow_folder, inputs_mapping, input_dirs=input_dirs, connections=dev_connections, return_output_dir=True
)
assert isinstance(batch_results, BatchResult)
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == 2
assert outputs == [
{"line_number": 0, "output": 1},
{"line_number": 6, "output": 7},
]
def test_batch_with_openai_metrics(self, dev_connections):
inputs_mapping = {"url": "${data.url}"}
batch_result, output_dir = submit_batch_run(
SAMPLE_FLOW, inputs_mapping, connections=dev_connections, return_output_dir=True
)
nlines = get_batch_inputs_line(SAMPLE_FLOW)
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == nlines
assert batch_result.system_metrics.total_tokens > 0
assert batch_result.system_metrics.prompt_tokens > 0
assert batch_result.system_metrics.completion_tokens > 0
def test_batch_with_default_input(self):
mem_run_storage = MemoryRunStorage()
default_input_value = "input value from default"
inputs_mapping = {"text": "${data.text}"}
batch_result, output_dir = submit_batch_run(
"default_input", inputs_mapping, storage=mem_run_storage, return_output_dir=True
)
assert batch_result.total_lines == batch_result.completed_lines
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == 1
assert outputs[0]["output"] == default_input_value
assert all(
node_run_info.status == Status.Completed and node_run_info.output == [default_input_value]
for node_run_info in mem_run_storage._node_runs.values()
if node_run_info.node == "aggregate_node"
)
@pytest.mark.parametrize(
"flow_folder, batch_input, expected_type",
[
("simple_aggregation", [{"text": 4}], str),
("simple_aggregation", [{"text": 4.5}], str),
("simple_aggregation", [{"text": "3.0"}], str),
],
)
def test_batch_run_line_result(self, flow_folder, batch_input, expected_type):
mem_run_storage = MemoryRunStorage()
input_file = Path(mkdtemp()) / "inputs.jsonl"
dump_list_to_jsonl(input_file, batch_input)
input_dirs = {"data": input_file}
inputs_mapping = {"text": "${data.text}"}
batch_results = submit_batch_run(flow_folder, inputs_mapping, input_dirs=input_dirs, storage=mem_run_storage)
assert isinstance(batch_results, BatchResult)
assert all(
type(flow_run_info.inputs["text"]) is expected_type for flow_run_info in mem_run_storage._flow_runs.values()
)
@pytest.mark.parametrize(
"flow_folder, input_mapping, error_class, error_message",
[
(
"connection_as_input",
{},
InputNotFound,
"The input for flow cannot be empty in batch mode. Please review your flow and provide valid inputs.",
),
(
"script_with___file__",
{"text": "${data.text}"},
EmptyInputsData,
"Couldn't find any inputs data at the given input paths. Please review the provided path "
"and consider resubmitting.",
),
],
)
def test_batch_run_failure(self, flow_folder, input_mapping, error_class, error_message):
with pytest.raises(error_class) as e:
submit_batch_run(flow_folder, input_mapping, input_file_name="empty_inputs.jsonl")
assert error_message in e.value.message
def test_batch_run_in_existing_loop(self, dev_connections):
flow_folder = "prompt_tools"
inputs_mapping = {"text": "${data.text}"}
batch_result = asyncio.run(async_submit_batch_run(flow_folder, inputs_mapping, dev_connections))
assert isinstance(batch_result, BatchResult)
assert batch_result.total_lines == batch_result.completed_lines
def test_batch_run_with_aggregation_failure(self, dev_connections):
flow_folder = "aggregation_node_failed"
inputs_mapping = {"groundtruth": "${data.groundtruth}", "prediction": "${data.prediction}"}
batch_result = submit_batch_run(flow_folder, inputs_mapping, connections=dev_connections)
assert isinstance(batch_result, BatchResult)
assert batch_result.total_lines == batch_result.completed_lines
assert batch_result.node_status == get_flow_expected_status_summary(flow_folder)
# assert aggregation node error summary
assert batch_result.failed_lines == 0
aggre_node_error = batch_result.error_summary.aggr_error_dict["aggregate"]
assert aggre_node_error["message"] == "Execution failure in 'aggregate': (ZeroDivisionError) division by zero"
assert aggre_node_error["code"] == "UserError"
assert aggre_node_error["innerError"] == {"code": "ToolExecutionError", "innerError": None}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_batch_timeout.py | from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow.batch import BatchEngine
from promptflow.batch._result import BatchResult, LineError
from promptflow.contracts.run_info import Status
from ..utils import MemoryRunStorage, get_flow_folder, get_flow_inputs_file, get_yaml_file
SAMPLE_FLOW = "web_classification_no_variants"
ONE_LINE_OF_BULK_TEST_TIMEOUT = "one_line_of_bulktest_timeout"
@pytest.mark.usefixtures("use_secrets_config_file", "dev_connections")
@pytest.mark.e2etest
class TestBatchTimeout:
@pytest.mark.parametrize(
"flow_folder",
[
ONE_LINE_OF_BULK_TEST_TIMEOUT,
],
)
def test_batch_with_line_timeout(self, flow_folder, dev_connections):
# set line timeout to 1 second for testing
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
connections=dev_connections,
storage=mem_run_storage,
)
batch_engine._line_timeout_sec = 5
# prepare input file and output dir
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="samples_all_timeout.json")}
output_dir = Path(mkdtemp())
inputs_mapping = {"idx": "${data.idx}"}
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert isinstance(batch_results, BatchResult)
assert batch_results.completed_lines == 0
assert batch_results.failed_lines == 2
assert batch_results.total_lines == 2
assert batch_results.node_status == {
"my_python_tool_with_failed_line.canceled": 2,
"my_python_tool.completed": 2,
}
# assert mem_run_storage persists run infos correctly
assert len(mem_run_storage._flow_runs) == 2, "Flow runs are persisted in memory storage."
assert len(mem_run_storage._node_runs) == 4, "Node runs are persisted in memory storage."
msg = "Tool execution is canceled because of the error: Line execution timeout after 5 seconds."
for run in mem_run_storage._node_runs.values():
if run.node == "my_python_tool_with_failed_line":
assert run.status == Status.Canceled
assert run.error["message"] == msg
else:
assert run.status == Status.Completed
assert batch_results.status == Status.Completed
assert batch_results.total_lines == 2
assert batch_results.completed_lines == 0
assert batch_results.failed_lines == 2
assert batch_results.error_summary.failed_user_error_lines == 2
assert batch_results.error_summary.failed_system_error_lines == 0
for i, line_error in enumerate(batch_results.error_summary.error_list):
assert isinstance(line_error, LineError)
assert line_error.error["message"] == f"Line {i} execution timeout for exceeding 5 seconds"
assert line_error.error["code"] == "UserError"
@pytest.mark.parametrize(
"flow_folder",
[
ONE_LINE_OF_BULK_TEST_TIMEOUT,
],
)
def test_batch_with_one_line_timeout(self, flow_folder, dev_connections):
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
connections=dev_connections,
storage=mem_run_storage,
)
batch_engine._line_timeout_sec = 5
# set line timeout to 5 seconds for testing
# prepare input file and output dir
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="samples.json")}
output_dir = Path(mkdtemp())
inputs_mapping = {"idx": "${data.idx}"}
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert isinstance(batch_results, BatchResult)
# assert the line status in batch result
assert batch_results.status == Status.Completed
assert batch_results.total_lines == 3
assert batch_results.completed_lines == 2
assert batch_results.failed_lines == 1
assert batch_results.node_status == {
"my_python_tool_with_failed_line.completed": 2,
"my_python_tool_with_failed_line.canceled": 1,
"my_python_tool.completed": 3,
}
# assert the error summary in batch result
assert batch_results.error_summary.failed_user_error_lines == 1
assert batch_results.error_summary.failed_system_error_lines == 0
assert isinstance(batch_results.error_summary.error_list[0], LineError)
assert batch_results.error_summary.error_list[0].line_number == 2
assert batch_results.error_summary.error_list[0].error["code"] == "UserError"
assert batch_results.error_summary.error_list[0].error["referenceCode"] == "Executor"
assert batch_results.error_summary.error_list[0].error["innerError"]["code"] == "LineExecutionTimeoutError"
assert (
batch_results.error_summary.error_list[0].error["message"]
== "Line 2 execution timeout for exceeding 5 seconds"
)
# assert mem_run_storage persists run infos correctly
assert len(mem_run_storage._flow_runs) == 3, "Flow runs are persisted in memory storage."
assert len(mem_run_storage._node_runs) == 6, "Node runs are persisted in memory storage."
@pytest.mark.parametrize(
"flow_folder",
[
ONE_LINE_OF_BULK_TEST_TIMEOUT,
],
)
def test_batch_timeout(self, flow_folder):
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
connections={},
storage=mem_run_storage,
)
batch_engine._batch_timeout_sec = 5
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="samples.json")}
output_dir = Path(mkdtemp())
inputs_mapping = {"idx": "${data.idx}"}
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert isinstance(batch_results, BatchResult)
# assert the line status in batch result
assert batch_results.status == Status.Completed
assert batch_results.total_lines == 3
assert batch_results.completed_lines == 2
assert batch_results.failed_lines == 1
# TODO: Currently, the node status is incomplete.
# We will assert the correct result after refining the implementation of batch timeout.
assert batch_results.node_status == {
"my_python_tool_with_failed_line.completed": 2,
"my_python_tool.completed": 3,
}
# assert the error summary in batch result
assert batch_results.error_summary.failed_user_error_lines == 1
assert batch_results.error_summary.failed_system_error_lines == 0
assert isinstance(batch_results.error_summary.error_list[0], LineError)
assert batch_results.error_summary.error_list[0].line_number == 2
assert batch_results.error_summary.error_list[0].error["code"] == "UserError"
assert batch_results.error_summary.error_list[0].error["referenceCode"] == "Batch"
assert batch_results.error_summary.error_list[0].error["innerError"]["code"] == "BatchExecutionTimeoutError"
assert (
batch_results.error_summary.error_list[0].error["message"]
== "Line 2 execution terminated due to the total batch run exceeding the batch timeout (5s)."
)
# assert mem_run_storage persists run infos correctly
assert len(mem_run_storage._flow_runs) == 3, "Flow runs are persisted in memory storage."
# TODO: Currently, the node status is incomplete.
# We will assert the correct result after refining the implementation of batch timeout.
assert len(mem_run_storage._node_runs) == 5, "Node runs are persisted in memory storage."
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_activate.py | from pathlib import Path
from tempfile import mkdtemp
from typing import Dict
import pytest
from promptflow._utils.logger_utils import LogContext
from promptflow.batch._batch_engine import OUTPUT_FILE_NAME, BatchEngine
from promptflow.batch._result import BatchResult
from promptflow.contracts._errors import FlowDefinitionError
from promptflow.contracts.run_info import FlowRunInfo
from promptflow.contracts.run_info import RunInfo as NodeRunInfo
from promptflow.contracts.run_info import Status
from promptflow.executor import FlowExecutor
from ..utils import (
WRONG_FLOW_ROOT,
MemoryRunStorage,
get_flow_expected_result,
get_flow_expected_status_summary,
get_flow_folder,
get_flow_inputs,
get_flow_inputs_file,
get_yaml_file,
load_jsonl,
)
ACTIVATE_FLOW_TEST_CASES = [
"conditional_flow_with_activate",
"activate_with_no_inputs",
"all_depedencies_bypassed_with_activate_met",
"activate_condition_always_met",
]
@pytest.mark.usefixtures("dev_connections")
@pytest.mark.e2etest
class TestExecutorActivate:
@pytest.mark.parametrize("flow_folder", ACTIVATE_FLOW_TEST_CASES)
def test_flow_run_activate(self, dev_connections, flow_folder):
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
results = executor.exec_line(get_flow_inputs(flow_folder))
# Assert the flow result
expected_result = get_flow_expected_result(flow_folder)
expected_result = expected_result[0] if isinstance(expected_result, list) else get_flow_expected_result
self.assert_activate_flow_run_result(results.run_info, expected_result)
self.assert_activate_node_run_result(results.node_run_infos, expected_result)
def test_batch_run_activate(self, dev_connections):
flow_folder = "conditional_flow_with_activate"
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
connections=dev_connections,
storage=mem_run_storage,
)
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="inputs.json")}
inputs_mapping = {"incident_id": "${data.incident_id}", "incident_content": "${data.incident_content}"}
output_dir = Path(mkdtemp())
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
expected_result = get_flow_expected_result(flow_folder)
expected_status_summary = get_flow_expected_status_summary(flow_folder)
self.assert_activate_bulk_run_result(
output_dir, mem_run_storage, batch_results, expected_result, expected_status_summary
)
def test_all_nodes_bypassed(self, dev_connections):
flow_folder = "all_nodes_bypassed"
file_path = Path(mkdtemp()) / "flow.log"
with LogContext(file_path):
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
result = executor.exec_line(get_flow_inputs(flow_folder))
assert result.output["result"] is None
with open(file_path) as fin:
content = fin.read()
assert "The node referenced by output:'third_node' is bypassed, which is not recommended." in content
def test_invalid_activate_config(self):
flow_folder = "invalid_activate_config"
with pytest.raises(FlowDefinitionError) as ex:
FlowExecutor.create(get_yaml_file(flow_folder, root=WRONG_FLOW_ROOT), {})
assert ex.value.message == (
"The definition of activate config for node divide_num is incorrect. "
"Please check your flow yaml and resubmit."
)
def test_aggregate_bypassed_nodes(self):
flow_folder = "conditional_flow_with_aggregate_bypassed"
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections={}, storage=mem_run_storage
)
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="inputs.json")}
inputs_mapping = {"case": "${data.case}", "value": "${data.value}"}
output_dir = Path(mkdtemp())
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
expected_result = get_flow_expected_result(flow_folder)
expected_status_summary = get_flow_expected_status_summary(flow_folder)
self.assert_activate_bulk_run_result(
output_dir, mem_run_storage, batch_results, expected_result, expected_status_summary
)
# Validate the aggregate result
for node_run_info in mem_run_storage._node_runs.values():
if node_run_info.node == "aggregation_double":
assert node_run_info.status == Status.Completed
assert node_run_info.output == 3
elif node_run_info.node == "aggregation_square":
assert node_run_info.status == Status.Completed
assert node_run_info.output == 12.5
def assert_activate_bulk_run_result(
self,
output_dir: Path,
mem_run_storage: MemoryRunStorage,
batch_result: BatchResult,
expected_result,
expected_status_summary,
):
# Validate the flow outputs
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
for i, output in enumerate(outputs):
expected_outputs = expected_result[i]["expected_outputs"].copy()
expected_outputs.update({"line_number": i})
assert output == expected_outputs
# Validate the flow line results
flow_runs = {k: v for k, v in sorted(mem_run_storage._flow_runs.items(), key=lambda item: item[1].index)}
for i, (flow_run_id, flow_run_info) in enumerate(flow_runs.items()):
self.assert_activate_flow_run_result(flow_run_info, expected_result[i])
node_run_infos = {
node_run_info.node: node_run_info
for node_run_info in mem_run_storage._node_runs.values()
if node_run_info.parent_run_id == flow_run_id
}
self.assert_activate_node_run_result(node_run_infos, expected_result[i])
# Validate the flow status summary
assert batch_result.total_lines == batch_result.completed_lines
assert batch_result.node_status == expected_status_summary
def assert_activate_flow_run_result(self, flow_run_info: FlowRunInfo, expected_result):
# Validate the flow status
assert flow_run_info.status == Status.Completed
# Validate the flow output
assert isinstance(flow_run_info.output, dict)
assert flow_run_info.output == expected_result["expected_outputs"]
def assert_activate_node_run_result(self, node_run_infos: Dict[str, NodeRunInfo], expected_result):
# Validate the flow node run infos for the completed nodes
assert len(node_run_infos) == expected_result["expected_node_count"]
expected_bypassed_nodes = expected_result["expected_bypassed_nodes"]
completed_nodes_run_infos = [
run_info for i, run_info in node_run_infos.items() if i not in expected_bypassed_nodes
]
assert all([node.status == Status.Completed for node in completed_nodes_run_infos])
# Validate the flow node run infos for the bypassed nodes
bypassed_nodes_run_infos = [node_run_infos[i] for i in expected_bypassed_nodes]
assert all([node.status == Status.Bypassed for node in bypassed_nodes_run_infos])
assert all([node.output is None for node in bypassed_nodes_run_infos])
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_csharp_executor_proxy.py | import json
import multiprocessing
import threading
from pathlib import Path
from tempfile import mkdtemp
from typing import Optional, Tuple, Union
import pytest
from promptflow._constants import FlowLanguage
from promptflow._utils.exception_utils import ExceptionPresenter
from promptflow.batch._batch_engine import BatchEngine
from promptflow.batch._csharp_executor_proxy import CSharpExecutorProxy
from promptflow.batch._result import BatchResult
from promptflow.contracts.run_info import Status
from promptflow.exceptions import ErrorTarget, ValidationException
from promptflow.executor._errors import ConnectionNotFound
from promptflow.storage._run_storage import AbstractRunStorage
from ..mock_execution_server import run_executor_server
from ..utils import MemoryRunStorage, get_flow_folder, get_flow_inputs_file, get_yaml_file
@pytest.mark.unittest
class TestCSharpExecutorProxy:
def setup_method(self):
BatchEngine.register_executor(FlowLanguage.CSharp, MockCSharpExecutorProxy)
def test_batch(self):
# submit a batch run
_, batch_result = self._submit_batch_run()
assert batch_result.status == Status.Completed
assert batch_result.completed_lines == batch_result.total_lines
assert batch_result.system_metrics.duration > 0
assert batch_result.completed_lines > 0
def test_batch_execution_error(self):
# submit a batch run
_, batch_result = self._submit_batch_run(has_error=True)
assert batch_result.status == Status.Completed
assert batch_result.total_lines == 3
assert batch_result.failed_lines == 1
assert batch_result.system_metrics.duration > 0
def test_batch_validation_error(self):
# prepare the init error file to mock the validation error
error_message = "'test_connection' not found."
test_exception = ConnectionNotFound(message=error_message)
error_dict = ExceptionPresenter.create(test_exception).to_dict()
init_error_file = Path(mkdtemp()) / "init_error.json"
with open(init_error_file, "w") as file:
json.dump(error_dict, file)
# submit a batch run
with pytest.raises(ValidationException) as e:
self._submit_batch_run(init_error_file=init_error_file)
assert error_message in e.value.message
assert e.value.error_codes == ["UserError", "ValidationError"]
assert e.value.target == ErrorTarget.BATCH
def test_batch_cancel(self):
# use a thread to submit a batch run
batch_engine, batch_run_thread = self._submit_batch_run(run_in_thread=True)
assert batch_engine._is_canceled is False
batch_run_thread.start()
# cancel the batch run
batch_engine.cancel()
batch_run_thread.join()
assert batch_engine._is_canceled is True
assert batch_result_global.status == Status.Canceled
assert batch_result_global.system_metrics.duration > 0
def _submit_batch_run(
self, run_in_thread=False, has_error=False, init_error_file=None
) -> Union[Tuple[BatchEngine, threading.Thread], Tuple[BatchEngine, BatchResult]]:
flow_folder = "csharp_flow"
mem_run_storage = MemoryRunStorage()
# init the batch engine
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
storage=mem_run_storage,
has_error=has_error,
init_error_file=init_error_file,
)
# prepare the inputs
input_dirs = {"data": get_flow_inputs_file(flow_folder)}
inputs_mapping = {"question": "${data.question}"}
output_dir = Path(mkdtemp())
if run_in_thread:
return batch_engine, threading.Thread(
target=self._batch_run_in_thread, args=(batch_engine, input_dirs, inputs_mapping, output_dir)
)
else:
return batch_engine, batch_engine.run(input_dirs, inputs_mapping, output_dir)
def _batch_run_in_thread(self, batch_engine: BatchEngine, input_dirs, inputs_mapping, output_dir):
global batch_result_global
batch_result_global = batch_engine.run(input_dirs, inputs_mapping, output_dir)
class MockCSharpExecutorProxy(CSharpExecutorProxy):
def __init__(self, process: multiprocessing.Process, port: str):
self._process = process
self._port = port
@classmethod
async def create(
cls,
flow_file: Path,
working_dir: Optional[Path] = None,
*,
connections: Optional[dict] = None,
storage: Optional[AbstractRunStorage] = None,
**kwargs,
) -> "MockCSharpExecutorProxy":
"""Create a new executor"""
has_error = kwargs.get("has_error", False)
init_error_file = kwargs.get("init_error_file", None)
port = cls.find_available_port()
process = multiprocessing.Process(
target=run_executor_server,
args=(
int(port),
has_error,
init_error_file,
),
)
process.start()
executor_proxy = cls(process, port)
await executor_proxy.ensure_executor_startup(init_error_file)
return executor_proxy
async def destroy(self):
"""Destroy the executor"""
if self._process and self._process.is_alive():
self._process.terminate()
try:
self._process.join(timeout=5)
except TimeoutError:
self._process.kill()
def _is_executor_active(self):
return self._process and self._process.is_alive()
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_traces.py | from types import GeneratorType
import pytest
from promptflow._utils.dataclass_serializer import serialize
from promptflow.contracts.run_info import Status
from promptflow.executor import FlowExecutor
from ..utils import get_yaml_file
@pytest.mark.usefixtures("dev_connections")
@pytest.mark.e2etest
class TestExecutorTraces:
def validate_openai_apicall(self, apicall: dict):
"""Validates an apicall dict.
Ensure that the trace output of openai api is a list of dicts.
Args:
apicall (dict): A dictionary representing apicall.
Raises:
AssertionError: If the API call is invalid.
"""
get_trace = False
if apicall.get("name", "") in (
"openai.api_resources.chat_completion.ChatCompletion.create",
"openai.api_resources.completion.Completion.create",
"openai.api_resources.embedding.Embedding.create",
"openai.resources.completions.Completions.create", # openai>=1.0.0
"openai.resources.chat.completions.Completions.create", # openai>=1.0.0
):
get_trace = True
output = apicall.get("output")
assert not isinstance(output, str)
assert isinstance(output, (list, dict))
if isinstance(output, list):
assert all(isinstance(item, dict) for item in output)
children = apicall.get("children", [])
if children is not None:
for child in children:
get_trace = get_trace or self.validate_openai_apicall(child)
return get_trace
def get_chat_input(stream):
return {
"question": "What is the capital of the United States of America?",
"chat_history": [],
"stream": stream,
}
def get_comletion_input(stream):
return {"prompt": "What is the capital of the United States of America?", "stream": stream}
@pytest.mark.parametrize(
"flow_folder, inputs",
[
("openai_chat_api_flow", get_chat_input(False)),
("openai_chat_api_flow", get_chat_input(True)),
("openai_completion_api_flow", get_comletion_input(False)),
("openai_completion_api_flow", get_comletion_input(True)),
("llm_tool", {"topic": "Hello", "stream": False}),
("llm_tool", {"topic": "Hello", "stream": True}),
],
)
def test_executor_openai_api_flow(self, flow_folder, inputs, dev_connections):
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
flow_result = executor.exec_line(inputs)
assert isinstance(flow_result.output, dict)
assert flow_result.run_info.status == Status.Completed
assert flow_result.run_info.api_calls is not None
assert "total_tokens" in flow_result.run_info.system_metrics
assert flow_result.run_info.system_metrics["total_tokens"] > 0
get_traced = False
for api_call in flow_result.run_info.api_calls:
get_traced = get_traced or self.validate_openai_apicall(serialize(api_call))
assert get_traced is True
def test_executor_generator_tools(self, dev_connections):
executor = FlowExecutor.create(get_yaml_file("generator_tools"), dev_connections)
inputs = {"text": "This is a test"}
flow_result = executor.exec_line(inputs)
assert isinstance(flow_result.output, dict)
assert flow_result.run_info.status == Status.Completed
assert flow_result.run_info.api_calls is not None
tool_trace = flow_result.run_info.api_calls[0]["children"][0]
generator_trace = tool_trace.get("children")[0]
assert generator_trace is not None
output = generator_trace.get("output")
assert isinstance(output, list)
@pytest.mark.parametrize("allow_generator_output", [False, True])
def test_trace_behavior_with_generator_node(self, dev_connections, allow_generator_output):
"""Test to verify the trace output list behavior for a flow with a generator node.
This test checks the trace output list in two scenarios based on the 'allow_generator_output' flag:
- When 'allow_generator_output' is True, the output list should initially be empty until the generator is
consumed.
- When 'allow_generator_output' is False, the output list should contain items produced by the generator node.
The test ensures that the trace accurately reflects the generator's consumption status and helps in monitoring
and debugging flow execution.
"""
# Set up executor with a flow that contains a generator node
executor = FlowExecutor.create(get_yaml_file("generator_nodes"), dev_connections)
inputs = {"text": "This is a test"}
# Execute the flow with the given inputs and 'allow_generator_output' setting
flow_result = executor.exec_line(inputs, allow_generator_output=allow_generator_output)
# Verify that the flow execution result is a dictionary and the flow has completed successfully
assert isinstance(flow_result.output, dict)
assert flow_result.run_info.status == Status.Completed
assert flow_result.run_info.api_calls is not None
# Extract the trace for the generator node
tool_trace = flow_result.run_info.api_calls[0]["children"][0]
generator_output_trace = tool_trace.get("output")
# Verify that the trace output is a list
assert isinstance(generator_output_trace, list)
if allow_generator_output:
# If generator output is allowed, the trace list should be empty before consumption
assert not generator_output_trace
# Obtain the generator from the flow result
answer_gen = flow_result.output.get("answer")
assert isinstance(answer_gen, GeneratorType)
# Consume the generator and check that it yields text
try:
generated_text = next(answer_gen)
assert isinstance(generated_text, str)
# Verify the trace list contains the most recently generated item
assert generator_output_trace[-1] == generated_text
except StopIteration:
assert False, "Generator did not generate any text"
else:
# If generator output is not allowed, the trace list should contain generated items
assert generator_output_trace
assert all(isinstance(item, str) for item in generator_output_trace)
@pytest.mark.parametrize("flow_file", ["flow_with_trace", "flow_with_trace_async"])
def test_flow_with_trace(self, flow_file, dev_connections):
"""Tests to verify the flows that contains @trace marks.
They should generate traces with "Function" type and nested in the "Tool" traces.
This test case is to verify a flow like following structure, both sync and async mode:
.. code-block::
flow (Flow, 1.5s)
greetings (Tool, 1.5s)
get_user_name (Function, 1.0s)
is_valid_name (Function, 0.5s)
format_greeting (Function, 0.5s)
"""
executor = FlowExecutor.create(get_yaml_file(flow_file), dev_connections)
inputs = {"user_id": 1}
flow_result = executor.exec_line(inputs)
# Assert the run status is completed
assert flow_result.output == {"output": "Hello, User 1!"}
assert flow_result.run_info.status == Status.Completed
assert flow_result.run_info.api_calls is not None
# Verify the traces are as expected
api_calls = flow_result.run_info.api_calls
assert len(api_calls) == 1
# Assert the "flow" root level trace
flow_trace = api_calls[0]
assert flow_trace["name"] == "flow"
assert flow_trace["type"] == "Flow"
assert flow_trace["end_time"] - flow_trace["start_time"] == pytest.approx(1.5, abs=0.3)
assert len(flow_trace["children"]) == 1
assert flow_trace["system_metrics"]["duration"] == pytest.approx(1.5, abs=0.3)
assert flow_trace["system_metrics"]["prompt_tokens"] == 0
assert flow_trace["system_metrics"]["completion_tokens"] == 0
assert flow_trace["system_metrics"]["total_tokens"] == 0
# TODO: These assertions should be fixed after added these fields to the top level trace
assert "inputs" not in flow_trace
assert "output" not in flow_trace
assert "error" not in flow_trace
# Assert the "greetings" tool
greetings_trace = flow_trace["children"][0]
assert greetings_trace["name"] == "greetings"
assert greetings_trace["type"] == "Tool"
assert greetings_trace["inputs"] == inputs
assert greetings_trace["output"] == {"greeting": "Hello, User 1!"}
assert greetings_trace["error"] is None
assert greetings_trace["children"] is not None
assert greetings_trace["end_time"] - greetings_trace["start_time"] == pytest.approx(1.5, abs=0.3)
assert len(greetings_trace["children"]) == 2
# TODO: to verfiy the system metrics. This might need to be fixed.
assert greetings_trace["system_metrics"] == {}
# Assert the "get_user_name" function
get_user_name_trace = greetings_trace["children"][0]
assert get_user_name_trace["name"] == "get_user_name"
assert get_user_name_trace["type"] == "Function"
assert get_user_name_trace["inputs"] == {"user_id": 1}
assert get_user_name_trace["output"] == "User 1"
assert get_user_name_trace["error"] is None
assert get_user_name_trace["end_time"] - get_user_name_trace["start_time"] == pytest.approx(1.0, abs=0.2)
assert len(get_user_name_trace["children"]) == 1
# TODO: to verfiy the system metrics. This might need to be fixed.
assert get_user_name_trace["system_metrics"] == {}
# Assert the "get_user_name/is_valid_name" function
is_valid_name_trace = get_user_name_trace["children"][0]
assert is_valid_name_trace["name"] == "is_valid_name"
assert is_valid_name_trace["type"] == "Function"
assert is_valid_name_trace["inputs"] == {"name": "User 1"}
assert is_valid_name_trace["output"] is True
assert is_valid_name_trace["error"] is None
# When running tests in MacOS, it will take longer. So we adjust abs to 0.15 and see if it needs to be extended.
assert is_valid_name_trace["end_time"] - is_valid_name_trace["start_time"] == pytest.approx(0.5, abs=0.15)
assert is_valid_name_trace["children"] == []
# TODO: to verfiy the system metrics. This might need to be fixed.
assert is_valid_name_trace["system_metrics"] == {}
# Assert the "format_greeting" function
format_greeting_trace = greetings_trace["children"][1]
assert format_greeting_trace["name"] == "format_greeting"
assert format_greeting_trace["type"] == "Function"
assert format_greeting_trace["inputs"] == {"user_name": "User 1"}
assert format_greeting_trace["output"] == "Hello, User 1!"
assert format_greeting_trace["error"] is None
# When running tests in MacOS, it will take longer. So we adjust abs to 0.15 and see if it needs to be extended.
assert format_greeting_trace["end_time"] - format_greeting_trace["start_time"] == pytest.approx(0.5, abs=0.15)
assert format_greeting_trace["children"] == []
# TODO: to verfiy the system metrics. This might need to be fixed.
assert format_greeting_trace["system_metrics"] == {}
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_executor_happypath.py | import logging
import multiprocessing
import os
import re
import shutil
import sys
from pathlib import Path
from types import GeneratorType
import pytest
from promptflow.contracts.run_info import Status
from promptflow.exceptions import UserErrorException
from promptflow.executor import FlowExecutor
from promptflow.executor._errors import ConnectionNotFound, InputTypeError, ResolveToolError
from promptflow.executor.flow_executor import execute_flow
from promptflow.storage._run_storage import DefaultRunStorage
from ..utils import FLOW_ROOT, get_flow_folder, get_flow_sample_inputs, get_yaml_file, is_image_file
SAMPLE_FLOW = "web_classification_no_variants"
@pytest.mark.usefixtures("use_secrets_config_file", "dev_connections")
@pytest.mark.e2etest
class TestExecutor:
def get_line_inputs(self, flow_folder=""):
if flow_folder:
inputs = self.get_bulk_inputs(flow_folder)
return inputs[0]
return {
"url": "https://www.microsoft.com/en-us/windows/",
"text": "some_text",
}
def get_bulk_inputs(self, nlinee=4, flow_folder="", sample_inputs_file="", return_dict=False):
if flow_folder:
if not sample_inputs_file:
sample_inputs_file = "samples.json"
inputs = get_flow_sample_inputs(flow_folder, sample_inputs_file=sample_inputs_file)
if isinstance(inputs, list) and len(inputs) > 0:
return inputs
elif isinstance(inputs, dict):
if return_dict:
return inputs
return [inputs]
else:
raise Exception(f"Invalid type of bulk input: {inputs}")
return [self.get_line_inputs() for _ in range(nlinee)]
def skip_serp(self, flow_folder, dev_connections):
serp_required_flows = ["package_tools"]
# Real key is usually more than 32 chars
serp_key = dev_connections.get("serp_connection", {"value": {"api_key": ""}})["value"]["api_key"]
if flow_folder in serp_required_flows and len(serp_key) < 32:
pytest.skip("serp_connection is not prepared")
@pytest.mark.parametrize(
"flow_folder",
[
SAMPLE_FLOW,
"prompt_tools",
"script_with___file__",
"script_with_import",
"package_tools",
"connection_as_input",
"async_tools",
"async_tools_with_sync_tools",
"tool_with_assistant_definition",
],
)
def test_executor_exec_line(self, flow_folder, dev_connections):
self.skip_serp(flow_folder, dev_connections)
os.chdir(get_flow_folder(flow_folder))
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
flow_result = executor.exec_line(self.get_line_inputs())
assert not executor._run_tracker._flow_runs, "Flow runs in run tracker should be empty."
assert not executor._run_tracker._node_runs, "Node runs in run tracker should be empty."
assert isinstance(flow_result.output, dict)
assert flow_result.run_info.status == Status.Completed
node_count = len(executor._flow.nodes)
assert isinstance(flow_result.run_info.api_calls, list) and len(flow_result.run_info.api_calls) == 1
assert (
isinstance(flow_result.run_info.api_calls[0]["children"], list)
and len(flow_result.run_info.api_calls[0]["children"]) == node_count
)
assert len(flow_result.node_run_infos) == node_count
for node, node_run_info in flow_result.node_run_infos.items():
assert node_run_info.status == Status.Completed
assert node_run_info.node == node
assert isinstance(node_run_info.api_calls, list) # api calls is set
def test_long_running_log(self, dev_connections, capsys):
# TODO: investigate why flow_logger does not output to stdout in test case
from promptflow._utils.logger_utils import flow_logger
flow_logger.addHandler(logging.StreamHandler(sys.stdout))
os.environ["PF_TASK_PEEKING_INTERVAL"] = "1"
executor = FlowExecutor.create(get_yaml_file("async_tools"), dev_connections)
executor.exec_line(self.get_line_inputs())
captured = capsys.readouterr()
expected_long_running_str_1 = r".*.*Task async_passthrough has been running for 1 seconds, stacktrace:\n.*async_passthrough\.py.*in passthrough_str_and_wait\n.*await asyncio.sleep\(1\).*tasks\.py.*" # noqa E501
assert re.match(
expected_long_running_str_1, captured.out, re.DOTALL
), "flow_logger should contain long running async tool log"
expected_long_running_str_2 = r".*.*Task async_passthrough has been running for 2 seconds, stacktrace:\n.*async_passthrough\.py.*in passthrough_str_and_wait\n.*await asyncio.sleep\(1\).*tasks\.py.*" # noqa E501
assert re.match(
expected_long_running_str_2, captured.out, re.DOTALL
), "flow_logger should contain long running async tool log"
flow_logger.handlers.pop()
os.environ.pop("PF_TASK_PEEKING_INTERVAL")
@pytest.mark.parametrize(
"flow_folder, node_name, flow_inputs, dependency_nodes_outputs",
[
("web_classification_no_variants", "summarize_text_content", {}, {"fetch_text_content_from_url": "Hello"}),
("prompt_tools", "summarize_text_content_prompt", {"text": "text"}, {}),
("script_with___file__", "node1", {"text": "text"}, None),
("script_with___file__", "node2", None, {"node1": "text"}),
("script_with___file__", "node3", None, None),
("package_tools", "search_by_text", {"text": "elon mask"}, None), # Skip since no api key in CI
("connection_as_input", "conn_node", None, None),
("simple_aggregation", "accuracy", {"text": "A"}, {"passthrough": "B"}),
("script_with_import", "node1", {"text": "text"}, None),
],
)
def test_executor_exec_node(self, flow_folder, node_name, flow_inputs, dependency_nodes_outputs, dev_connections):
self.skip_serp(flow_folder, dev_connections)
yaml_file = get_yaml_file(flow_folder)
run_info = FlowExecutor.load_and_exec_node(
yaml_file,
node_name,
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
connections=dev_connections,
raise_ex=True,
)
assert run_info.output is not None
assert run_info.status == Status.Completed
assert isinstance(run_info.api_calls, list)
assert run_info.node == node_name
assert run_info.system_metrics["duration"] >= 0
def test_executor_exec_node_with_llm_node(self, dev_connections):
# Run the test in a new process to ensure the openai api is injected correctly for the single node run
context = multiprocessing.get_context("spawn")
queue = context.Queue()
process = context.Process(
target=exec_node_within_process,
args=(queue, "llm_tool", "joke", {"topic": "fruit"}, {}, dev_connections, True),
)
process.start()
process.join()
if not queue.empty():
raise queue.get()
def test_executor_node_overrides(self, dev_connections):
inputs = self.get_line_inputs()
executor = FlowExecutor.create(
get_yaml_file(SAMPLE_FLOW),
dev_connections,
node_override={"classify_with_llm.deployment_name": "dummy_deployment"},
raise_ex=True,
)
with pytest.raises(UserErrorException) as e:
executor.exec_line(inputs)
assert type(e.value).__name__ == "WrappedOpenAIError"
assert "The API deployment for this resource does not exist." in str(e.value)
with pytest.raises(ResolveToolError) as e:
executor = FlowExecutor.create(
get_yaml_file(SAMPLE_FLOW),
dev_connections,
node_override={"classify_with_llm.connection": "dummy_connection"},
raise_ex=True,
)
assert isinstance(e.value.inner_exception, ConnectionNotFound)
assert "Connection 'dummy_connection' not found" in str(e.value)
@pytest.mark.parametrize(
"flow_folder",
[
"no_inputs_outputs",
],
)
def test_flow_with_no_inputs_and_output(self, flow_folder, dev_connections):
executor = FlowExecutor.create(get_yaml_file(flow_folder, FLOW_ROOT), dev_connections)
flow_result = executor.exec_line({})
assert flow_result.output == {}
assert flow_result.run_info.status == Status.Completed
node_count = len(executor._flow.nodes)
assert isinstance(flow_result.run_info.api_calls, list) and len(flow_result.run_info.api_calls) == node_count
assert len(flow_result.node_run_infos) == node_count
for node, node_run_info in flow_result.node_run_infos.items():
assert node_run_info.status == Status.Completed
assert node_run_info.node == node
assert isinstance(node_run_info.api_calls, list) # api calls is set
@pytest.mark.parametrize(
"flow_folder",
[
"simple_flow_with_python_tool",
],
)
def test_convert_flow_input_types(self, flow_folder, dev_connections) -> None:
executor = FlowExecutor.create(get_yaml_file(flow_folder, FLOW_ROOT), dev_connections)
ret = executor.convert_flow_input_types(inputs={"num": "11"})
assert ret == {"num": 11}
ret = executor.convert_flow_input_types(inputs={"text": "12", "num": "11"})
assert ret == {"text": "12", "num": 11}
with pytest.raises(InputTypeError):
ret = executor.convert_flow_input_types(inputs={"num": "hello"})
executor.convert_flow_input_types(inputs={"num": "hello"})
def test_chat_flow_stream_mode(self, dev_connections) -> None:
executor = FlowExecutor.create(get_yaml_file("python_stream_tools", FLOW_ROOT), dev_connections)
# To run a flow with stream output, we need to set this flag to run tracker.
# TODO: refine the interface
inputs = {"text": "hello", "chat_history": []}
line_result = executor.exec_line(inputs, allow_generator_output=True)
# Assert there's only one output
assert len(line_result.output) == 1
assert set(line_result.output.keys()) == {"output_echo"}
# Assert the only output is a generator
output_echo = line_result.output["output_echo"]
assert isinstance(output_echo, GeneratorType)
assert list(output_echo) == ["Echo: ", "hello "]
# Assert the flow is completed and no errors are raised
flow_run_info = line_result.run_info
assert flow_run_info.status == Status.Completed
assert flow_run_info.error is None
@pytest.mark.parametrize(
"flow_folder",
[
"web_classification",
],
)
def test_executor_creation_with_default_variants(self, flow_folder, dev_connections):
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
flow_result = executor.exec_line(self.get_line_inputs())
assert flow_result.run_info.status == Status.Completed
def test_executor_creation_with_default_input(self):
# Assert for single node run.
default_input_value = "input value from default"
yaml_file = get_yaml_file("default_input")
executor = FlowExecutor.create(yaml_file, {})
node_result = executor.load_and_exec_node(yaml_file, "test_print_input")
assert node_result.status == Status.Completed
assert node_result.output == default_input_value
# Assert for flow run.
flow_result = executor.exec_line({})
assert flow_result.run_info.status == Status.Completed
assert flow_result.output["output"] == default_input_value
aggr_results = executor.exec_aggregation({}, aggregation_inputs={})
flow_aggregate_node = aggr_results.node_run_infos["aggregate_node"]
assert flow_aggregate_node.status == Status.Completed
assert flow_aggregate_node.output == [default_input_value]
# Assert for exec
exec_result = executor.exec({})
assert exec_result["output"] == default_input_value
def test_executor_for_script_tool_with_init(self, dev_connections):
executor = FlowExecutor.create(get_yaml_file("script_tool_with_init"), dev_connections)
flow_result = executor.exec_line({"input": "World"})
assert flow_result.run_info.status == Status.Completed
assert flow_result.output["output"] == "Hello World"
@pytest.mark.parametrize(
"output_dir_name, intermediate_dir_name, run_aggregation, expected_node_counts",
[
("output", "intermediate", True, 2),
("output_1", "intermediate_1", False, 1),
],
)
def test_execute_flow(
self, output_dir_name: str, intermediate_dir_name: str, run_aggregation: bool, expected_node_counts: int
):
flow_folder = get_flow_folder("eval_flow_with_simple_image")
# prepare output folder
output_dir = flow_folder / output_dir_name
intermediate_dir = flow_folder / intermediate_dir_name
output_dir.mkdir(exist_ok=True)
intermediate_dir.mkdir(exist_ok=True)
storage = DefaultRunStorage(base_dir=flow_folder, sub_dir=Path(intermediate_dir_name))
line_result = execute_flow(
flow_file=get_yaml_file(flow_folder),
working_dir=flow_folder,
output_dir=Path(output_dir_name),
inputs={},
connections={},
run_aggregation=run_aggregation,
storage=storage,
)
assert line_result.run_info.status == Status.Completed
assert len(line_result.node_run_infos) == expected_node_counts
assert all(is_image_file(output_file) for output_file in output_dir.iterdir())
assert all(is_image_file(output_file) for output_file in intermediate_dir.iterdir())
# clean up output folder
shutil.rmtree(output_dir)
shutil.rmtree(intermediate_dir)
def exec_node_within_process(queue, flow_file, node_name, flow_inputs, dependency_nodes_outputs, connections, raise_ex):
try:
result = FlowExecutor.load_and_exec_node(
flow_file=get_yaml_file(flow_file),
node_name=node_name,
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
connections=connections,
raise_ex=raise_ex,
)
# Assert llm single node run contains openai traces
# And the traces contains system metrics
OPENAI_AGGREGATE_METRICS = ["prompt_tokens", "completion_tokens", "total_tokens"]
assert len(result.api_calls) == 1
assert len(result.api_calls[0]["children"]) == 1
assert isinstance(result.api_calls[0]["children"][0]["system_metrics"], dict)
for key in OPENAI_AGGREGATE_METRICS:
assert key in result.api_calls[0]["children"][0]["system_metrics"]
for key in OPENAI_AGGREGATE_METRICS:
assert (
result.api_calls[0]["system_metrics"][key] == result.api_calls[0]["children"][0]["system_metrics"][key]
)
except Exception as ex:
queue.put(ex)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_concurent_execution.py | import re
from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow._utils.exception_utils import ErrorResponse
from promptflow._utils.logger_utils import LogContext
from promptflow.contracts.run_info import Status
from promptflow.contracts.run_mode import RunMode
from promptflow.executor._flow_nodes_scheduler import RUN_FLOW_NODES_LINEARLY
from promptflow.executor._result import LineResult
from promptflow.executor.flow_executor import FlowExecutor
from ..utils import get_flow_inputs, get_yaml_file, load_content
TEST_ROOT = Path(__file__).parent.parent.parent
FLOWS_ROOT = TEST_ROOT / "test_configs/flows"
FLOW_FOLDER = "concurrent_execution_flow"
@pytest.mark.e2etest
class TestConcurrentExecution:
def test_concurrent_run(self):
logs_directory = Path(mkdtemp())
executor = FlowExecutor.create(get_yaml_file(FLOW_FOLDER), {})
flow_run_log_path = str(logs_directory / "test_flow_run.log")
# flow run: test exec_line
with LogContext(flow_run_log_path, run_mode=RunMode.Test):
results = executor.exec_line(get_flow_inputs(FLOW_FOLDER))
log_content = load_content(flow_run_log_path)
pattern = r"\[wait_(\d+) in line None.*Thread (\d+)"
matches = re.findall(pattern, log_content)
wait_thread_mapping = {}
for wait, thread in matches:
if wait in wait_thread_mapping:
if wait_thread_mapping[wait] != thread:
raise Exception(f"wait_{wait} corresponds to more than one thread number")
else:
wait_thread_mapping[wait] = thread
self.assert_run_result(results)
assert (
results.run_info.system_metrics["duration"] < 10
), "run nodes concurrently should decrease the total run time."
def test_concurrent_run_with_exception(self):
executor = FlowExecutor.create(get_yaml_file(FLOW_FOLDER), {}, raise_ex=False)
flow_result = executor.exec_line({"input1": "True", "input2": "False", "input3": "False", "input4": "False"})
assert 2 < flow_result.run_info.system_metrics["duration"] < 4, "Should at least finish the running job."
error_response = ErrorResponse.from_error_dict(flow_result.run_info.error)
assert error_response.error_code_hierarchy == "UserError/ToolExecutionError"
def test_linear_run(self):
executor = FlowExecutor.create(get_yaml_file(FLOW_FOLDER), {})
# flow run: test exec_line run linearly
results = executor.exec_line(get_flow_inputs(FLOW_FOLDER), node_concurrency=RUN_FLOW_NODES_LINEARLY)
self.assert_run_result(results)
assert 15 > results.run_info.system_metrics["duration"] > 10, "run nodes linearly will consume more time."
def assert_run_result(self, result: LineResult):
# Validate the flow status
assert result.run_info.status == Status.Completed
# Validate the flow output
assert isinstance(result.output, dict)
# Validate the flow node run infos
assert len(result.node_run_infos) == 5
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_logs.py | from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow._utils.logger_utils import LogContext
from promptflow.batch import BatchEngine
from promptflow.batch._result import BatchResult
from promptflow.contracts.run_info import Status
from promptflow.contracts.run_mode import RunMode
from promptflow.executor import FlowExecutor
from ..utils import (
get_flow_folder,
get_flow_inputs_file,
get_flow_sample_inputs,
get_yaml_file,
load_content,
load_jsonl,
)
TEST_LOGS_FLOW = ["print_input_flow"]
SAMPLE_FLOW_WITH_TEN_INPUTS = "simple_flow_with_ten_inputs"
OUTPUT_FILE_NAME = "output.jsonl"
def submit_batch_run(
flow_folder,
inputs_mapping,
*,
input_dirs={},
input_file_name="samples.json",
run_id=None,
connections={},
storage=None,
return_output_dir=False,
):
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=connections, storage=storage
)
if not input_dirs and inputs_mapping:
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name=input_file_name)}
output_dir = Path(mkdtemp())
if return_output_dir:
return batch_engine.run(input_dirs, inputs_mapping, output_dir, run_id=run_id), output_dir
return batch_engine.run(input_dirs, inputs_mapping, output_dir, run_id=run_id)
def get_batch_inputs_line(flow_folder, sample_inputs_file="samples.json"):
inputs = get_flow_sample_inputs(flow_folder, sample_inputs_file=sample_inputs_file)
return len(inputs)
@pytest.mark.usefixtures("dev_connections")
@pytest.mark.e2etest
class TestExecutorLogs:
def assert_node_run_info(self, node_run_info, content):
assert node_run_info.status == Status.Completed
assert content in node_run_info.logs["stdout"]
assert "STDOUT:" in node_run_info.logs["stdout"]
assert content in node_run_info.logs["stderr"]
assert "STDERR:" in node_run_info.logs["stderr"]
def assert_flow_result(self, flow_result, content):
assert isinstance(flow_result.output, dict)
assert flow_result.run_info.status == Status.Completed
for node_run_info in flow_result.node_run_infos.values():
self.assert_node_run_info(node_run_info, content)
def submit_bulk_run(self, folder_name):
batch_engine = BatchEngine(get_yaml_file(folder_name), get_flow_folder(folder_name), connections={})
input_dirs = {"data": get_flow_inputs_file(folder_name)}
inputs_mapping = {"text": "${data.text}"}
output_dir = Path(mkdtemp())
return batch_engine.run(input_dirs, inputs_mapping, output_dir)
@pytest.mark.parametrize(
"folder_name",
TEST_LOGS_FLOW,
)
def test_node_logs(self, folder_name):
# Test node logs in flow run
executor = FlowExecutor.create(get_yaml_file(folder_name), {})
content = "line_text"
flow_result = executor.exec_line({"text": content})
node_run_ids = [node_run_info.run_id for node_run_info in flow_result.node_run_infos.values()]
for node_run_id in node_run_ids:
logs = executor._run_tracker.node_log_manager.get_logs(node_run_id)
assert logs["stderr"] is None and logs["stdout"] is None, f"Logs for node {node_run_id} is cleared."
self.assert_flow_result(flow_result, content)
# Test node logs in single node run
content = "single_node_text"
node_run_info = FlowExecutor.load_and_exec_node(
get_yaml_file(folder_name),
"print_input",
flow_inputs={"text": content},
)
self.assert_node_run_info(node_run_info, content)
@pytest.mark.parametrize(
"folder_name",
TEST_LOGS_FLOW,
)
def test_executor_logs(self, folder_name):
logs_directory = Path(mkdtemp())
flow_run_log_path = str(logs_directory / "test_flow_run.log")
bulk_run_log_path = str(logs_directory / "test_bulk_run.log")
# flow run: test exec_line
with LogContext(flow_run_log_path):
executor = FlowExecutor.create(get_yaml_file(folder_name), {})
executor.exec_line({"text": "line_text"})
log_content = load_content(flow_run_log_path)
loggers_name_list = ["execution", "execution.flow"]
assert all(logger in log_content for logger in loggers_name_list)
# bulk run: test batch_engine.run
# setting run_mode to BulkTest is a requirement to use bulk_logger
with LogContext(bulk_run_log_path, run_mode=RunMode.Batch):
self.submit_bulk_run(folder_name)
log_content = load_content(bulk_run_log_path)
loggers_name_list = ["execution", "execution.bulk"]
# bulk logger will print the average execution time and estimated time
bulk_logs_keywords = ["Average execution time for completed lines", "Estimated time for incomplete lines"]
assert all(logger in log_content for logger in loggers_name_list)
assert all(keyword in log_content for keyword in bulk_logs_keywords)
@pytest.mark.parametrize(
"folder_name",
TEST_LOGS_FLOW,
)
def test_node_logs_in_executor_logs(self, folder_name):
logs_directory = Path(mkdtemp())
flow_run_log_path = str(logs_directory / "test_flow_run.log")
bulk_run_log_path = str(logs_directory / "test_bulk_run.log")
# flow run: test exec_line
with LogContext(flow_run_log_path, run_mode=RunMode.Test):
executor = FlowExecutor.create(get_yaml_file(folder_name), {})
executor.exec_line({"text": "line_text"})
log_content = load_content(flow_run_log_path)
node_logs_list = ["print_input in line", "stdout> STDOUT:", "stderr> STDERR:"]
assert all(node_log in log_content for node_log in node_logs_list)
# bulk run: test batch_engine.run
# setting run_mode to BulkTest is a requirement to use bulk_logger
with LogContext(bulk_run_log_path, run_mode=RunMode.Batch):
self.submit_bulk_run(folder_name)
log_content = load_content(bulk_run_log_path)
node_logs_list = ["print_input in line", "stderr> STDERR:"]
assert all(node_log in log_content for node_log in node_logs_list)
def test_long_run_log(self):
executor = FlowExecutor.create(get_yaml_file("long_run"), {})
file_path = Path(mkdtemp()) / "flow.log"
with LogContext(file_path):
flow_result = executor.exec_line({}, index=0)
node_run = flow_result.node_run_infos["long_run_node"]
assert node_run.status == Status.Completed
with open(file_path) as fin:
lines = fin.readlines()
lines = [line for line in lines if line.strip()]
target_texts = [
"INFO Start executing nodes in thread pool mode.",
"INFO Start to run 1 nodes with concurrency level 16.",
"INFO Executing node long_run_node.",
"WARNING long_run_node in line 0 has been running for 60 seconds, stacktrace of thread",
", line 16, in long_run_func",
"return f2()",
", line 11, in f2",
"return f1()",
", line 6, in f1",
"time.sleep(61)",
"INFO Node long_run_node completes.",
]
msg = f"Got {len(lines)} lines in {file_path}, expected {len(target_texts)}."
assert len(lines) == len(target_texts), msg
for actual, expected in zip(lines, target_texts):
assert expected in actual, f"Expected {expected} in {actual}"
@pytest.mark.parametrize(
"flow_folder, inputs_mapping",
[
(
SAMPLE_FLOW_WITH_TEN_INPUTS,
{"input": "${data.input}", "index": "${data.index}"},
)
],
)
def test_log_progress(self, flow_folder, inputs_mapping, dev_connections):
logs_directory = Path(mkdtemp())
bulk_run_log_path = str(logs_directory / "test_bulk_run.log")
with LogContext(bulk_run_log_path, run_mode=RunMode.Batch):
batch_result, output_dir = submit_batch_run(
flow_folder, inputs_mapping, connections=dev_connections, return_output_dir=True
)
nlines = get_batch_inputs_line(flow_folder)
log_content = load_content(bulk_run_log_path)
for i in range(1, nlines + 1):
assert f"Finished {i} / {nlines} lines." in log_content
assert isinstance(batch_result, BatchResult)
assert batch_result.total_lines == nlines
assert batch_result.completed_lines == nlines
assert batch_result.start_time < batch_result.end_time
assert batch_result.system_metrics.duration > 0
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == nlines
for i, output in enumerate(outputs):
assert isinstance(output, dict)
assert "line_number" in output, f"line_number is not in {i}th output {output}"
assert output["line_number"] == i, f"line_number is not correct in {i}th output {output}"
def test_activate_config_log(self):
logs_directory = Path(mkdtemp())
log_path = str(logs_directory / "flow.log")
# flow run: test exec_line
with LogContext(log_path, run_mode=RunMode.Test):
executor = FlowExecutor.create(get_yaml_file("activate_flow"), {})
# use default inputs
executor.exec_line({})
log_content = load_content(log_path)
logs_list = [
"execution.flow",
"The node 'nodeA' will be bypassed because the activate condition is not met, "
"i.e. '${flow.text}' is not equal to 'hello'.",
"The node 'nodeB' will be bypassed because it depends on the node 'nodeA' "
"which has already been bypassed in the activate config.",
"The node 'nodeC' will be bypassed because all nodes ['nodeB'] it depends on are bypassed.",
"The node 'nodeD' will be executed because the activate condition is met, "
"i.e. '${flow.text}' is equal to 'world'.",
]
assert all(log in log_content for log in logs_list)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_eager_flow.py | import os
from dataclasses import is_dataclass
from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow.batch._batch_engine import OUTPUT_FILE_NAME, BatchEngine
from promptflow.batch._result import BatchResult, LineResult
from promptflow.contracts.run_info import Status
from promptflow.executor._script_executor import ScriptExecutor
from promptflow.executor.flow_executor import FlowExecutor
from ..utils import (
EAGER_FLOW_ROOT,
get_bulk_inputs_from_jsonl,
get_entry_file,
get_flow_folder,
get_flow_inputs_file,
get_yaml_file,
load_jsonl,
)
SAMPLE_FLOW = "web_classification_no_variants"
SAMPLE_EVAL_FLOW = "classification_accuracy_evaluation"
SAMPLE_FLOW_WITH_PARTIAL_FAILURE = "python_tool_partial_failure"
def validate_batch_result(batch_result: BatchResult, flow_folder, output_dir, ensure_output):
assert isinstance(batch_result, BatchResult)
nlines = len(get_bulk_inputs_from_jsonl(flow_folder, root=EAGER_FLOW_ROOT))
assert batch_result.total_lines == nlines
assert batch_result.completed_lines == nlines
assert batch_result.start_time < batch_result.end_time
assert batch_result.system_metrics.duration > 0
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == nlines
for i, output in enumerate(outputs):
assert isinstance(output, dict)
assert "line_number" in output, f"line_number is not in {i}th output {output}"
assert output["line_number"] == i, f"line_number is not correct in {i}th output {output}"
assert ensure_output(output)
@pytest.mark.usefixtures("dev_connections")
@pytest.mark.e2etest
class TestEagerFlow:
@pytest.mark.parametrize(
"flow_folder, entry, inputs, ensure_output",
[
(
"dummy_flow_with_trace",
"my_flow",
{"text": "text", "models": ["model"]},
lambda x: x == "dummy_output"
),
(
"flow_with_dataclass_output",
"my_flow",
{"text": "text", "models": ["model"]},
lambda x: is_dataclass(x) and x.text == "text" and x.models == ["model"]
),
]
)
def test_flow_run(self, flow_folder, entry, inputs, ensure_output):
# Test submitting eager flow to script executor
flow_file = get_entry_file(flow_folder, root=EAGER_FLOW_ROOT)
executor = ScriptExecutor(flow_file=flow_file, entry=entry)
line_result = executor.exec_line(inputs=inputs, index=0)
assert isinstance(line_result, LineResult)
assert ensure_output(line_result.output)
# Test submitting eager flow to flow executor
working_dir = get_flow_folder(flow_folder, root=EAGER_FLOW_ROOT)
os.chdir(working_dir)
flow_file = get_yaml_file(flow_folder, root=EAGER_FLOW_ROOT)
executor = FlowExecutor.create(flow_file=flow_file, connections={})
line_result = executor.exec_line(inputs=inputs, index=0)
assert isinstance(line_result, LineResult)
assert ensure_output(line_result.output)
@pytest.mark.parametrize(
"flow_folder, inputs, ensure_output",
[
(
"dummy_flow_with_trace",
{"text": "text", "models": ["model"]},
lambda x: x == "dummy_output"
),
(
"flow_with_dataclass_output",
{"text": "text", "models": ["model"]},
lambda x: is_dataclass(x) and x.text == "text" and x.models == ["model"]
),
]
)
def test_flow_run_with_flow_yaml(self, flow_folder, inputs, ensure_output):
working_dir = get_flow_folder(flow_folder, root=EAGER_FLOW_ROOT)
os.chdir(working_dir)
flow_file = get_yaml_file(flow_folder, root=EAGER_FLOW_ROOT)
executor = FlowExecutor.create(flow_file=flow_file, connections={})
line_result = executor.exec_line(inputs=inputs, index=0)
assert isinstance(line_result, LineResult)
assert ensure_output(line_result.output)
def test_exec_line_with_invalid_case(self):
flow_file = get_entry_file("dummy_flow_with_exception", root=EAGER_FLOW_ROOT)
executor = ScriptExecutor(flow_file=flow_file, entry="my_flow")
line_result = executor.exec_line(inputs={"text": "text"}, index=0)
assert isinstance(line_result, LineResult)
assert line_result.output is None
assert line_result.run_info.status == Status.Failed
assert "dummy exception" in line_result.run_info.error["message"]
@pytest.mark.parametrize(
"flow_folder, inputs_mapping, entry, ensure_output",
[
(
"dummy_flow_with_trace",
{"text": "${data.text}", "models": "${data.models}"},
"my_flow",
lambda x: "output" in x and x["output"] == "dummy_output",
),
(
"flow_with_dataclass_output",
{"text": "${data.text}", "models": "${data.models}"},
"my_flow",
lambda x: x["text"] == "text" and isinstance(x["models"], list),
),
(
"flow_with_dataclass_output",
{}, # if inputs_mapping is empty, then the inputs will be the default value
"my_flow",
lambda x: x["text"] == "default_text" and x["models"] == ["default_model"],
)
]
)
def test_batch_run(self, flow_folder, entry, inputs_mapping, ensure_output):
batch_engine = BatchEngine(
get_entry_file(flow_folder, root=EAGER_FLOW_ROOT),
get_flow_folder(flow_folder, root=EAGER_FLOW_ROOT),
entry=entry,
)
input_dirs = {"data": get_flow_inputs_file(flow_folder, root=EAGER_FLOW_ROOT)}
output_dir = Path(mkdtemp())
batch_result = batch_engine.run(input_dirs, inputs_mapping, output_dir)
validate_batch_result(batch_result, flow_folder, output_dir, ensure_output)
@pytest.mark.parametrize(
"flow_folder, inputs_mapping, ensure_output",
[
(
"dummy_flow_with_trace",
{"text": "${data.text}", "models": "${data.models}"},
lambda x: "output" in x and x["output"] == "dummy_output",
),
(
"flow_with_dataclass_output",
{"text": "${data.text}", "models": "${data.models}"},
lambda x: x["text"] == "text" and isinstance(x["models"], list),
),
]
)
def test_batch_run_with_flow_yaml(self, flow_folder, inputs_mapping, ensure_output):
batch_engine = BatchEngine(
get_yaml_file(flow_folder, root=EAGER_FLOW_ROOT),
get_flow_folder(flow_folder, root=EAGER_FLOW_ROOT),
)
input_dirs = {"data": get_flow_inputs_file(flow_folder, root=EAGER_FLOW_ROOT)}
output_dir = Path(mkdtemp())
batch_result = batch_engine.run(input_dirs, inputs_mapping, output_dir)
validate_batch_result(batch_result, flow_folder, output_dir, ensure_output)
def test_batch_run_with_invalid_case(self):
flow_folder = "dummy_flow_with_exception"
batch_engine = BatchEngine(
get_entry_file(flow_folder, root=EAGER_FLOW_ROOT),
get_flow_folder(flow_folder, root=EAGER_FLOW_ROOT),
entry="my_flow",
)
input_dirs = {"data": get_flow_inputs_file(flow_folder, root=EAGER_FLOW_ROOT)}
output_dir = Path(mkdtemp())
batch_result = batch_engine.run(input_dirs, {"text": "${data.text}"}, output_dir)
assert isinstance(batch_result, BatchResult)
nlines = len(get_bulk_inputs_from_jsonl(flow_folder, root=EAGER_FLOW_ROOT))
assert batch_result.total_lines == nlines
assert batch_result.failed_lines == nlines
assert batch_result.start_time < batch_result.end_time
assert batch_result.system_metrics.duration > 0
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_package_tool.py | import sys
from pathlib import Path
from unittest.mock import patch
import pytest
from promptflow._core._errors import PackageToolNotFoundError, ToolLoadError
from promptflow.contracts.run_info import Status
from promptflow.executor import FlowExecutor
from promptflow.executor._errors import NodeInputValidationError, ResolveToolError
from promptflow.executor._result import LineResult
from ..utils import WRONG_FLOW_ROOT, get_flow_package_tool_definition, get_flow_sample_inputs, get_yaml_file
PACKAGE_TOOL_BASE = Path(__file__).parent.parent / "package_tools"
PACKAGE_TOOL_ENTRY = "promptflow._core.tools_manager.collect_package_tools"
sys.path.insert(0, str(PACKAGE_TOOL_BASE.resolve()))
@pytest.mark.e2etest
class TestPackageTool:
def get_line_inputs(self, flow_folder=""):
if flow_folder:
inputs = self.get_bulk_inputs(flow_folder)
return inputs[0]
return {
"url": "https://www.microsoft.com/en-us/windows/",
"text": "some_text",
}
def get_bulk_inputs(self, nlinee=4, flow_folder=""):
if flow_folder:
inputs = get_flow_sample_inputs(flow_folder)
if isinstance(inputs, list) and len(inputs) > 0:
return inputs
elif isinstance(inputs, dict):
return [inputs]
else:
raise Exception(f"Invalid type of bulk input: {inputs}")
return [self.get_line_inputs() for _ in range(nlinee)]
def test_executor_package_tool_with_conn(self, mocker):
flow_folder = PACKAGE_TOOL_BASE / "tool_with_connection"
package_tool_definition = get_flow_package_tool_definition(flow_folder)
mocker.patch(
"promptflow.tools.list.list_package_tools",
return_value=package_tool_definition,
)
name, secret = "dummy_name", "dummy_secret"
connections = {
"test_conn": {
"type": "TestConnection",
"value": {"name": name, "secret": secret},
}
}
executor = FlowExecutor.create(get_yaml_file(flow_folder), connections, raise_ex=True)
flow_result = executor.exec_line({})
assert flow_result.run_info.status == Status.Completed
assert len(flow_result.node_run_infos) == 1
for _, v in flow_result.node_run_infos.items():
assert v.status == Status.Completed
assert v.output == name + secret
@pytest.mark.skipif(sys.platform == "darwin", reason="Skip on Mac")
def test_executor_package_with_prompt_tool(self, dev_connections, mocker):
flow_folder = PACKAGE_TOOL_BASE / "custom_llm_tool"
package_tool_definition = get_flow_package_tool_definition(flow_folder)
with mocker.patch(PACKAGE_TOOL_ENTRY, return_value=package_tool_definition):
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections, raise_ex=True)
bulk_inputs = self.get_bulk_inputs(flow_folder=flow_folder)
for i in bulk_inputs:
line_result = executor.exec_line(i)
assert isinstance(line_result, LineResult)
msg = f"Got {line_result.run_info.status} for input {i}"
assert line_result.run_info.status == Status.Completed, msg
def test_custom_llm_tool_with_duplicated_inputs(self, dev_connections, mocker):
flow_folder = PACKAGE_TOOL_BASE / "custom_llm_tool_with_duplicated_inputs"
package_tool_definition = get_flow_package_tool_definition(flow_folder)
with mocker.patch(PACKAGE_TOOL_ENTRY, return_value=package_tool_definition):
msg = (
"Invalid inputs {'api'} in prompt template of node custom_llm_tool_with_duplicated_inputs. "
"These inputs are duplicated with the inputs of custom llm tool."
)
with pytest.raises(ResolveToolError, match=msg) as e:
FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
assert isinstance(e.value.inner_exception, NodeInputValidationError)
@pytest.mark.parametrize(
"flow_folder, error_class, inner_class, error_message",
[
(
"wrong_tool_in_package_tools",
ResolveToolError,
PackageToolNotFoundError,
"Tool load failed in 'search_by_text': (PackageToolNotFoundError) "
"Package tool 'promptflow.tools.serpapi.SerpAPI.search_11' is not found in the current environment. "
"All available package tools are: "
"['promptflow.tools.azure_content_safety.AzureContentSafety.analyze_text', "
"'promptflow.tools.azure_detect.AzureDetect.get_language'].",
),
(
"wrong_package_in_package_tools",
ResolveToolError,
PackageToolNotFoundError,
"Tool load failed in 'search_by_text': (PackageToolNotFoundError) "
"Package tool 'promptflow.tools.serpapi11.SerpAPI.search' is not found in the current environment. "
"All available package tools are: "
"['promptflow.tools.azure_content_safety.AzureContentSafety.analyze_text', "
"'promptflow.tools.azure_detect.AzureDetect.get_language'].",
),
],
)
def test_package_tool_execution(self, flow_folder, error_class, inner_class, error_message, dev_connections):
def mock_collect_package_tools(keys=None):
return {
"promptflow.tools.azure_content_safety.AzureContentSafety.analyze_text": None,
"promptflow.tools.azure_detect.AzureDetect.get_language": None,
}
with patch(PACKAGE_TOOL_ENTRY, side_effect=mock_collect_package_tools):
with pytest.raises(error_class) as exce_info:
FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT), dev_connections)
if isinstance(exce_info.value, ResolveToolError):
assert isinstance(exce_info.value.inner_exception, inner_class)
assert error_message == exce_info.value.message
@pytest.mark.parametrize(
"flow_folder, error_message",
[
(
"tool_with_init_error",
"Tool load failed in 'tool_with_init_error': "
"(ToolLoadError) Failed to load package tool 'Tool with init error': (Exception) Tool load error.",
)
],
)
def test_package_tool_load_error(self, flow_folder, error_message, dev_connections, mocker):
flow_folder = PACKAGE_TOOL_BASE / flow_folder
package_tool_definition = get_flow_package_tool_definition(flow_folder)
with mocker.patch(PACKAGE_TOOL_ENTRY, return_value=package_tool_definition):
with pytest.raises(ResolveToolError) as exce_info:
FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
assert isinstance(exce_info.value.inner_exception, ToolLoadError)
assert exce_info.value.message == error_message
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_executor_validation.py | import json
from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow._core._errors import FlowOutputUnserializable, InvalidSource
from promptflow._core.tools_manager import APINotFound
from promptflow._sdk._constants import DAG_FILE_NAME
from promptflow._utils.utils import dump_list_to_jsonl
from promptflow.batch import BatchEngine
from promptflow.contracts._errors import FailedToImportModule
from promptflow.executor import FlowExecutor
from promptflow.executor._errors import (
ConnectionNotFound,
DuplicateNodeName,
EmptyOutputReference,
InputNotFound,
InputReferenceNotFound,
InputTypeError,
InvalidConnectionType,
NodeCircularDependency,
NodeInputValidationError,
NodeReferenceNotFound,
OutputReferenceNotFound,
ResolveToolError,
SingleNodeValidationError,
)
from ..utils import FLOW_ROOT, WRONG_FLOW_ROOT, get_flow_folder, get_flow_inputs_file, get_yaml_file
@pytest.mark.usefixtures("use_secrets_config_file", "dev_connections")
@pytest.mark.e2etest
class TestValidation:
@pytest.mark.parametrize(
"flow_folder, yml_file, error_class, inner_class, error_msg",
[
(
"flow_llm_with_wrong_conn",
"flow.dag.yaml",
ResolveToolError,
InvalidConnectionType,
(
"Tool load failed in 'wrong_llm': "
"(InvalidConnectionType) Connection type CustomConnection is not supported for LLM."
),
),
(
"nodes_names_duplicated",
"flow.dag.yaml",
DuplicateNodeName,
None,
(
"Invalid node definitions found in the flow graph. Node with name 'stringify_num' appears more "
"than once in the node definitions in your flow, which is not allowed. To "
"address this issue, please review your flow and either rename or remove "
"nodes with identical names."
),
),
(
"source_file_missing",
"flow.dag.jinja.yaml",
ResolveToolError,
InvalidSource,
(
"Tool load failed in 'summarize_text_content': (InvalidSource) "
"Node source path 'summarize_text_content__variant_1.jinja2' is invalid on node "
"'summarize_text_content'."
),
),
(
"node_reference_not_found",
"flow.dag.yaml",
NodeReferenceNotFound,
None,
(
"Invalid node definitions found in the flow graph. Node 'divide_num_2' references a non-existent "
"node 'divide_num_3' in your flow. Please review your flow to ensure that the "
"node name is accurately specified."
),
),
(
"node_circular_dependency",
"flow.dag.yaml",
NodeCircularDependency,
None,
(
"Invalid node definitions found in the flow graph. Node circular dependency has been detected "
"among the nodes in your flow. Kindly review the reference relationships for "
"the nodes ['divide_num', 'divide_num_1', 'divide_num_2'] and resolve the "
"circular reference issue in the flow."
),
),
(
"flow_input_reference_invalid",
"flow.dag.yaml",
InputReferenceNotFound,
None,
(
"Invalid node definitions found in the flow graph. Node 'divide_num' references flow input 'num_1' "
"which is not defined in your flow. To resolve this issue, please review your "
"flow, ensuring that you either add the missing flow inputs or adjust node "
"reference to the correct flow input."
),
),
(
"flow_output_reference_invalid",
"flow.dag.yaml",
EmptyOutputReference,
None,
(
"The output 'content' for flow is incorrect. The reference is not specified for the output "
"'content' in the flow. To rectify this, ensure that you accurately specify "
"the reference in the flow."
),
),
(
"outputs_reference_not_valid",
"flow.dag.yaml",
OutputReferenceNotFound,
None,
(
"The output 'content' for flow is incorrect. The output 'content' references non-existent "
"node 'another_stringify_num' in your flow. To resolve this issue, please "
"carefully review your flow and correct the reference definition for the "
"output in question."
),
),
(
"outputs_with_invalid_flow_inputs_ref",
"flow.dag.yaml",
OutputReferenceNotFound,
None,
(
"The output 'num' for flow is incorrect. The output 'num' references non-existent flow "
"input 'num11' in your flow. Please carefully review your flow and correct "
"the reference definition for the output in question."
),
),
],
)
def test_executor_create_failure_type_and_message(
self, flow_folder, yml_file, error_class, inner_class, error_msg, dev_connections
):
with pytest.raises(error_class) as exc_info:
FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT, yml_file), dev_connections)
if isinstance(exc_info.value, ResolveToolError):
assert isinstance(exc_info.value.inner_exception, inner_class)
assert error_msg == exc_info.value.message
@pytest.mark.parametrize(
"flow_folder, yml_file, error_class, inner_class",
[
("source_file_missing", "flow.dag.python.yaml", ResolveToolError, InvalidSource),
],
)
def test_executor_create_failure_type(self, flow_folder, yml_file, error_class, inner_class, dev_connections):
with pytest.raises(error_class) as e:
FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT, yml_file), dev_connections)
if isinstance(e.value, ResolveToolError):
assert isinstance(e.value.inner_exception, inner_class)
@pytest.mark.parametrize(
"ordered_flow_folder, unordered_flow_folder",
[
("web_classification_no_variants", "web_classification_no_variants_unordered"),
],
)
def test_node_topology_in_order(self, ordered_flow_folder, unordered_flow_folder, dev_connections):
ordered_executor = FlowExecutor.create(get_yaml_file(ordered_flow_folder), dev_connections)
unordered_executor = FlowExecutor.create(get_yaml_file(unordered_flow_folder), dev_connections)
for node1, node2 in zip(ordered_executor._flow.nodes, unordered_executor._flow.nodes):
assert node1.name == node2.name
@pytest.mark.parametrize(
"flow_folder, error_class, inner_class",
[
("invalid_connection", ResolveToolError, ConnectionNotFound),
("tool_type_missing", ResolveToolError, NotImplementedError),
("wrong_module", FailedToImportModule, None),
("wrong_api", ResolveToolError, APINotFound),
("wrong_provider", ResolveToolError, APINotFound),
],
)
def test_invalid_flow_dag(self, flow_folder, error_class, inner_class, dev_connections):
with pytest.raises(error_class) as e:
FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT), dev_connections)
if isinstance(e.value, ResolveToolError):
assert isinstance(e.value.inner_exception, inner_class)
@pytest.mark.parametrize(
"flow_folder, line_input, error_class",
[
("simple_flow_with_python_tool", {"num11": "22"}, InputNotFound),
("simple_flow_with_python_tool", {"num": "hello"}, InputTypeError),
("python_tool_with_simple_image_without_default", {}, InputNotFound),
],
)
def test_flow_run_input_type_invalid(self, flow_folder, line_input, error_class, dev_connections):
# Flow run - the input is from get_partial_line_inputs()
executor = FlowExecutor.create(get_yaml_file(flow_folder, FLOW_ROOT), dev_connections)
with pytest.raises(error_class):
executor.exec_line(line_input)
@pytest.mark.parametrize(
"flow_folder, line_input, error_class, error_msg",
[
(
"flow_output_unserializable",
{"num": "22"},
FlowOutputUnserializable,
(
"The output 'content' for flow is incorrect. The output value is not JSON serializable. "
"JSON dump failed: (TypeError) Object of type UnserializableClass is not JSON serializable. "
"Please verify your flow output and make sure the value serializable."
),
),
],
)
def test_flow_run_execution_errors(self, flow_folder, line_input, error_class, error_msg, dev_connections):
executor = FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT), dev_connections)
# For now, there exception is designed to be swallowed in executor. But Run Info would have the error details
res = executor.exec_line(line_input)
assert error_msg == res.run_info.error["message"]
@pytest.mark.parametrize(
"flow_folder, inputs_mapping, error_message, error_class",
[
(
"simple_flow_with_python_tool",
{"num": "${data.num}"},
(
"The input for flow is incorrect. The value for flow input 'num' in line 0 of input data does not "
"match the expected type 'int'. Please change flow input type or adjust the input value in "
"your input data."
),
"InputTypeError",
),
],
)
def test_batch_run_input_type_invalid(
self, flow_folder, inputs_mapping, error_message, error_class, dev_connections
):
# Bulk run - the input is from sample.json
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=dev_connections
)
input_dirs = {"data": get_flow_inputs_file(flow_folder)}
output_dir = Path(mkdtemp())
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert error_message in str(
batch_results.error_summary.error_list[0].error
), f"Expected message {error_message} but got {str(batch_results.error_summary.error_list[0].error)}"
assert error_class in str(
batch_results.error_summary.error_list[0].error
), f"Expected message {error_class} but got {str(batch_results.error_summary.error_list[0].error)}"
@pytest.mark.parametrize(
"path_root, flow_folder, node_name, line_input, error_class, error_msg",
[
(
FLOW_ROOT,
"simple_flow_with_python_tool",
"divide_num",
{"num11": "22"},
InputNotFound,
(
"The input for node is incorrect. Node input 'num' is not found in input data "
"for node 'divide_num'. Please verify the inputs data for the node."
),
),
(
FLOW_ROOT,
"simple_flow_with_python_tool",
"divide_num",
{"num": "hello"},
InputTypeError,
(
"The input for node is incorrect. Value for input 'num' of node 'divide_num' "
"is not type 'int'. Please review and rectify the input data."
),
),
(
WRONG_FLOW_ROOT,
"flow_input_reference_invalid",
"divide_num",
{"num": "22"},
InputNotFound,
(
"The input for node is incorrect. Node input 'num_1' is not found from flow "
"inputs of node 'divide_num'. Please review the node definition in your flow."
),
),
(
FLOW_ROOT,
"simple_flow_with_python_tool",
"bad_node_name",
{"num": "22"},
SingleNodeValidationError,
(
"Validation failed when attempting to execute the node. Node 'bad_node_name' is not found in flow "
"'flow.dag.yaml'. Please change node name or correct the flow file."
),
),
(
WRONG_FLOW_ROOT,
"node_missing_type_or_source",
"divide_num",
{"num": "22"},
SingleNodeValidationError,
(
"Validation failed when attempting to execute the node. Properties 'source' or 'type' are not "
"specified for Node 'divide_num' in flow 'flow.dag.yaml'. Please make sure "
"these properties are in place and try again."
),
),
],
)
def test_single_node_input_type_invalid(
self, path_root: str, flow_folder, node_name, line_input, error_class, error_msg, dev_connections
):
# Single Node run - the inputs are from flow_inputs + dependency_nodes_outputs
with pytest.raises(error_class) as exe_info:
FlowExecutor.load_and_exec_node(
flow_file=DAG_FILE_NAME,
node_name=node_name,
flow_inputs=line_input,
dependency_nodes_outputs={},
connections=dev_connections,
working_dir=Path(path_root) / flow_folder,
raise_ex=True,
)
assert error_msg == exe_info.value.message
@pytest.mark.parametrize(
"flow_folder, msg",
[
(
"prompt_tool_with_duplicated_inputs",
"Invalid inputs {'template'} in prompt template of node prompt_tool_with_duplicated_inputs. "
"These inputs are duplicated with the reserved parameters of prompt tool.",
),
(
"llm_tool_with_duplicated_inputs",
"Invalid inputs {'prompt'} in prompt template of node llm_tool_with_duplicated_inputs. "
"These inputs are duplicated with the parameters of AzureOpenAI.completion.",
),
],
)
def test_flow_run_with_duplicated_inputs(self, flow_folder, msg, dev_connections):
with pytest.raises(ResolveToolError, match=msg) as e:
FlowExecutor.create(get_yaml_file(flow_folder, FLOW_ROOT), dev_connections)
assert isinstance(e.value.inner_exception, NodeInputValidationError)
@pytest.mark.parametrize(
"flow_folder, batch_input, raise_on_line_failure, error_class",
[
("simple_flow_with_python_tool", [{"num": "hello"}], True, Exception),
("simple_flow_with_python_tool", [{"num": "hello"}], False, InputTypeError),
("simple_flow_with_python_tool", [{"num": "22"}], True, None),
("simple_flow_with_python_tool", [{"num": "22"}], False, None),
],
)
def test_batch_run_raise_on_line_failure(
self, flow_folder, batch_input, raise_on_line_failure, error_class, dev_connections
):
# Bulk run - the input is from sample.json
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=dev_connections
)
# prepare input file and output dir
input_file = Path(mkdtemp()) / "inputs.jsonl"
dump_list_to_jsonl(input_file, batch_input)
input_dirs = {"data": input_file}
output_dir = Path(mkdtemp())
inputs_mapping = {"num": "${data.num}"}
if error_class is None:
batch_result = batch_engine.run(
input_dirs, inputs_mapping, output_dir, raise_on_line_failure=raise_on_line_failure
)
assert batch_result.total_lines == 1
assert batch_result.completed_lines == 1
assert batch_result.error_summary.error_list == []
else:
if raise_on_line_failure:
with pytest.raises(error_class):
batch_engine.run(
input_dirs, inputs_mapping, output_dir, raise_on_line_failure=raise_on_line_failure
)
else:
batch_result = batch_engine.run(
input_dirs, inputs_mapping, output_dir, raise_on_line_failure=raise_on_line_failure
)
assert batch_result.total_lines == 1
assert batch_result.failed_lines == 1
assert error_class.__name__ in json.dumps(batch_result.error_summary.error_list[0].error)
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_async.py | import os
import pytest
from promptflow.executor import FlowExecutor
from ..utils import get_flow_folder, get_yaml_file
@pytest.mark.e2etest
class TestAsync:
@pytest.mark.parametrize(
"folder_name, concurrency_levels, expected_concurrency",
[
("async_tools", [1, 2, 3], [1, 2, 2]),
("async_tools_with_sync_tools", [1, 2, 3], [1, 2, 2]),
],
)
def test_executor_node_concurrency(self, folder_name, concurrency_levels, expected_concurrency):
os.chdir(get_flow_folder(folder_name))
executor = FlowExecutor.create(get_yaml_file(folder_name), {})
def calculate_max_concurrency(flow_result):
timeline = []
api_calls = flow_result.run_info.api_calls[0]["children"]
for api_call in api_calls:
timeline.append(("start", api_call["start_time"]))
timeline.append(("end", api_call["end_time"]))
timeline.sort(key=lambda x: x[1])
current_concurrency = 0
max_concurrency = 0
for event, _ in timeline:
if event == "start":
current_concurrency += 1
max_concurrency = max(max_concurrency, current_concurrency)
elif event == "end":
current_concurrency -= 1
return max_concurrency
for i in range(len(concurrency_levels)):
concurrency = concurrency_levels[i]
flow_result = executor.exec_line({"input_str": "Hello"}, node_concurrency=concurrency)
max_concurrency = calculate_max_concurrency(flow_result)
assert max_concurrency == expected_concurrency[i]
assert max_concurrency <= concurrency
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_langchain.py | from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow.batch import BatchEngine
from promptflow.batch._result import BatchResult
from ..utils import get_flow_folder, get_flow_inputs_file, get_yaml_file
@pytest.mark.usefixtures("use_secrets_config_file", "dev_connections")
@pytest.mark.e2etest
class TestLangchain:
@pytest.mark.parametrize(
"flow_folder, inputs_mapping",
[
("flow_with_langchain_traces", {"question": "${data.question}"}),
("openai_chat_api_flow", {"question": "${data.question}", "chat_history": "${data.chat_history}"}),
("openai_completion_api_flow", {"prompt": "${data.prompt}"}),
],
)
def test_batch_with_langchain(self, flow_folder, inputs_mapping, dev_connections):
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=dev_connections
)
input_dirs = {"data": get_flow_inputs_file(flow_folder)}
output_dir = Path(mkdtemp())
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert isinstance(batch_results, BatchResult)
assert batch_results.total_lines == batch_results.completed_lines
assert batch_results.system_metrics.total_tokens > 0
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_executor_execution_failures.py | import pytest
from promptflow.contracts.run_info import Status
from promptflow.executor import FlowExecutor
from ..utils import (
get_yaml_file,
)
SAMPLE_FLOW = "web_classification_no_variants"
SAMPLE_EVAL_FLOW = "classification_accuracy_evaluation"
SAMPLE_FLOW_WITH_PARTIAL_FAILURE = "python_tool_partial_failure"
SAMPLE_FLOW_WITH_LANGCHAIN_TRACES = "flow_with_langchain_traces"
expected_stack_traces = {
"sync_tools_failures": """Traceback (most recent call last):
sync_fail.py", line 11, in raise_an_exception
raise_exception(s)
sync_fail.py", line 5, in raise_exception
raise Exception(msg)
Exception: In raise_exception: dummy_input
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
sync_fail.py", line 13, in raise_an_exception
raise Exception(f"In tool raise_an_exception: {s}") from e
Exception: In tool raise_an_exception: dummy_input
""".split("\n"),
"async_tools_failures": """Traceback (most recent call last):
async_fail.py", line 11, in raise_an_exception_async
await raise_exception_async(s)
async_fail.py", line 5, in raise_exception_async
raise Exception(msg)
Exception: In raise_exception_async: dummy_input
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
in raise_an_exception_async
raise Exception(f"In tool raise_an_exception_async: {s}") from e
Exception: In tool raise_an_exception_async: dummy_input
""".split("\n"),
}
@pytest.mark.e2etest
class TestExecutorFailures:
@pytest.mark.parametrize(
"flow_folder, node_name, message",
[
("sync_tools_failures", "sync_fail", "In tool raise_an_exception: dummy_input"),
("async_tools_failures", "async_fail", "In tool raise_an_exception_async: dummy_input"),
],
)
def test_executor_exec_node_fail(self, flow_folder, node_name, message):
yaml_file = get_yaml_file(flow_folder)
run_info = FlowExecutor.load_and_exec_node(yaml_file, node_name)
assert run_info.output is None
assert run_info.status == Status.Failed
assert isinstance(run_info.api_calls, list)
assert len(run_info.api_calls) == 1
assert run_info.node == node_name
assert run_info.system_metrics["duration"] >= 0
assert run_info.error is not None
assert f"Execution failure in '{node_name}'" in run_info.error["message"]
assert len(run_info.error["additionalInfo"]) == 1
user_error_info_dict = run_info.error["additionalInfo"][0]
assert "ToolExecutionErrorDetails" == user_error_info_dict["type"]
user_error_info = user_error_info_dict["info"]
assert message == user_error_info["message"]
# Make sure the stack trace is as expected
stacktrace = user_error_info["traceback"].split("\n")
expected_stack_trace = expected_stack_traces[flow_folder]
assert len(stacktrace) == len(expected_stack_trace)
for expected_item, actual_item in zip(expected_stack_trace, stacktrace):
assert expected_item in actual_item
@pytest.mark.parametrize(
"flow_folder, failed_node_name, message",
[
("sync_tools_failures", "sync_fail", "In tool raise_an_exception: dummy_input"),
("async_tools_failures", "async_fail", "In tool raise_an_exception_async: dummy_input"),
],
)
def test_executor_exec_line_fail(self, flow_folder, failed_node_name, message):
yaml_file = get_yaml_file(flow_folder)
executor = FlowExecutor.create(yaml_file, {}, raise_ex=False)
line_result = executor.exec_line({})
run_info = line_result.run_info
assert run_info.output is None
assert run_info.status == Status.Failed
assert isinstance(run_info.api_calls, list)
assert len(run_info.api_calls) == 1
assert run_info.system_metrics["duration"] >= 0
assert run_info.error is not None
assert f"Execution failure in '{failed_node_name}'" in run_info.error["message"]
assert len(run_info.error["additionalInfo"]) == 1
user_error_info_dict = run_info.error["additionalInfo"][0]
assert "ToolExecutionErrorDetails" == user_error_info_dict["type"]
user_error_info = user_error_info_dict["info"]
assert message == user_error_info["message"]
# Make sure the stack trace is as expected
stacktrace = user_error_info["traceback"].split("\n")
expected_stack_trace = expected_stack_traces[flow_folder]
assert len(stacktrace) == len(expected_stack_trace)
for expected_item, actual_item in zip(expected_stack_trace, stacktrace):
assert expected_item in actual_item
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_image.py | import os
from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow._utils.multimedia_utils import MIME_PATTERN, _create_image_from_file, _is_url, is_multimedia_dict
from promptflow.batch._batch_engine import OUTPUT_FILE_NAME, BatchEngine
from promptflow.batch._result import BatchResult
from promptflow.contracts.multimedia import Image
from promptflow.contracts.run_info import FlowRunInfo, RunInfo, Status
from promptflow.executor import FlowExecutor
from promptflow.storage._run_storage import DefaultRunStorage
from ..utils import get_flow_folder, get_yaml_file, is_image_file, is_jsonl_file, load_jsonl
SIMPLE_IMAGE_FLOW = "python_tool_with_simple_image"
SAMPLE_IMAGE_FLOW_WITH_DEFAULT = "python_tool_with_simple_image_with_default"
SIMPLE_IMAGE_WITH_INVALID_DEFAULT_VALUE_FLOW = "python_tool_with_invalid_default_value"
COMPOSITE_IMAGE_FLOW = "python_tool_with_composite_image"
CHAT_FLOW_WITH_IMAGE = "chat_flow_with_image"
EVAL_FLOW_WITH_SIMPLE_IMAGE = "eval_flow_with_simple_image"
EVAL_FLOW_WITH_COMPOSITE_IMAGE = "eval_flow_with_composite_image"
NESTED_API_CALLS_FLOW = "python_tool_with_image_nested_api_calls"
IMAGE_URL = (
"https://raw.githubusercontent.com/microsoft/promptflow/main/src/promptflow/tests/test_configs/datas/logo.jpg"
)
def get_test_cases_for_simple_input(flow_folder):
working_dir = get_flow_folder(flow_folder)
image = _create_image_from_file(working_dir / "logo.jpg")
inputs = [
{"data:image/jpg;path": str(working_dir / "logo.jpg")},
{"data:image/jpg;base64": image.to_base64()},
{"data:image/jpg;url": IMAGE_URL},
str(working_dir / "logo.jpg"),
image.to_base64(),
IMAGE_URL,
]
return [(flow_folder, {"image": input}) for input in inputs]
def get_test_cases_for_composite_input(flow_folder):
working_dir = get_flow_folder(flow_folder)
image_1 = _create_image_from_file(working_dir / "logo.jpg")
image_2 = _create_image_from_file(working_dir / "logo_2.png")
inputs = [
[
{"data:image/jpg;path": str(working_dir / "logo.jpg")},
{"data:image/png;path": str(working_dir / "logo_2.png")},
],
[{"data:image/jpg;base64": image_1.to_base64()}, {"data:image/png;base64": image_2.to_base64()}],
[{"data:image/jpg;url": IMAGE_URL}, {"data:image/png;url": IMAGE_URL}],
]
return [
(flow_folder, {"image_list": input, "image_dict": {"image_1": input[0], "image_2": input[1]}})
for input in inputs
]
def get_test_cases_for_node_run():
image = {"data:image/jpg;path": str(get_flow_folder(SIMPLE_IMAGE_FLOW) / "logo.jpg")}
simple_image_input = {"image": image}
image_list = [{"data:image/jpg;path": "logo.jpg"}, {"data:image/png;path": "logo_2.png"}]
image_dict = {
"image_dict": {
"image_1": {"data:image/jpg;path": "logo.jpg"},
"image_2": {"data:image/png;path": "logo_2.png"},
}
}
composite_image_input = {"image_list": image_list, "image_dcit": image_dict}
return [
(SIMPLE_IMAGE_FLOW, "python_node", simple_image_input, None),
(SIMPLE_IMAGE_FLOW, "python_node_2", simple_image_input, {"python_node": image}),
(COMPOSITE_IMAGE_FLOW, "python_node", composite_image_input, None),
(COMPOSITE_IMAGE_FLOW, "python_node_2", composite_image_input, None),
(
COMPOSITE_IMAGE_FLOW,
"python_node_3",
composite_image_input,
{"python_node": image_list, "python_node_2": image_dict},
),
]
def contain_image_reference(value, parent_path="temp"):
if isinstance(value, (FlowRunInfo, RunInfo)):
assert contain_image_reference(value.api_calls, parent_path)
assert contain_image_reference(value.inputs, parent_path)
assert contain_image_reference(value.output, parent_path)
return True
assert not isinstance(value, Image)
if isinstance(value, list):
return any(contain_image_reference(item, parent_path) for item in value)
if isinstance(value, dict):
if is_multimedia_dict(value):
v = list(value.values())[0]
assert isinstance(v, str)
assert _is_url(v) or str(Path(v).parent) == parent_path
return True
return any(contain_image_reference(v, parent_path) for v in value.values())
return False
def contain_image_object(value):
if isinstance(value, list):
return any(contain_image_object(item) for item in value)
elif isinstance(value, dict):
assert not is_multimedia_dict(value)
return any(contain_image_object(v) for v in value.values())
else:
return isinstance(value, Image)
@pytest.mark.usefixtures("dev_connections")
@pytest.mark.e2etest
class TestExecutorWithImage:
@pytest.mark.parametrize(
"flow_folder, inputs",
get_test_cases_for_simple_input(SIMPLE_IMAGE_FLOW)
+ get_test_cases_for_composite_input(COMPOSITE_IMAGE_FLOW)
+ [(CHAT_FLOW_WITH_IMAGE, {}), (NESTED_API_CALLS_FLOW, {})],
)
def test_executor_exec_line_with_image(self, flow_folder, inputs, dev_connections):
working_dir = get_flow_folder(flow_folder)
os.chdir(working_dir)
storage = DefaultRunStorage(base_dir=working_dir, sub_dir=Path("./temp"))
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections, storage=storage)
flow_result = executor.exec_line(inputs)
assert isinstance(flow_result.output, dict)
assert contain_image_object(flow_result.output)
# Assert output also contains plain text.
assert any(isinstance(v, str) for v in flow_result.output)
assert flow_result.run_info.status == Status.Completed
assert contain_image_reference(flow_result.run_info)
for _, node_run_info in flow_result.node_run_infos.items():
assert node_run_info.status == Status.Completed
assert contain_image_reference(node_run_info)
@pytest.mark.parametrize(
"flow_folder, node_name, flow_inputs, dependency_nodes_outputs", get_test_cases_for_node_run()
)
def test_executor_exec_node_with_image(
self, flow_folder, node_name, flow_inputs, dependency_nodes_outputs, dev_connections
):
working_dir = get_flow_folder(flow_folder)
os.chdir(working_dir)
storage = DefaultRunStorage(base_dir=working_dir, sub_dir=Path("./temp"))
run_info = FlowExecutor.load_and_exec_node(
get_yaml_file(flow_folder),
node_name,
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
connections=dev_connections,
storage=storage,
raise_ex=True,
)
assert run_info.status == Status.Completed
assert contain_image_reference(run_info)
# Assert image could be persisted to the specified path.
@pytest.mark.parametrize(
"output_sub_dir, assign_storage, expected_path",
[
("test_path", True, "test_storage"),
("test_path", False, "test_path"),
(None, True, "test_storage"),
(None, False, "."),
],
)
def test_executor_exec_node_with_image_storage_and_path(self, output_sub_dir, assign_storage, expected_path):
flow_folder = SIMPLE_IMAGE_FLOW
node_name = "python_node"
image = {"data:image/jpg;path": str(get_flow_folder(SIMPLE_IMAGE_FLOW) / "logo.jpg")}
flow_inputs = {"image": image}
working_dir = get_flow_folder(flow_folder)
os.chdir(working_dir)
storage = DefaultRunStorage(base_dir=working_dir, sub_dir=Path("./test_storage"))
run_info = FlowExecutor.load_and_exec_node(
get_yaml_file(flow_folder),
node_name,
flow_inputs=flow_inputs,
dependency_nodes_outputs=None,
connections=None,
storage=storage if assign_storage else None,
output_sub_dir=output_sub_dir,
raise_ex=True,
)
assert run_info.status == Status.Completed
assert contain_image_reference(run_info, parent_path=expected_path)
@pytest.mark.parametrize(
"flow_folder, node_name, flow_inputs, dependency_nodes_outputs",
[
(
SIMPLE_IMAGE_WITH_INVALID_DEFAULT_VALUE_FLOW,
"python_node_2",
{},
{
"python_node": {
"data:image/jpg;path": str(
get_flow_folder(SIMPLE_IMAGE_WITH_INVALID_DEFAULT_VALUE_FLOW) / "logo.jpg"
)
}
},
)
],
)
def test_executor_exec_node_with_invalid_default_value(
self, flow_folder, node_name, flow_inputs, dependency_nodes_outputs, dev_connections
):
working_dir = get_flow_folder(flow_folder)
os.chdir(working_dir)
storage = DefaultRunStorage(base_dir=working_dir, sub_dir=Path("./temp"))
run_info = FlowExecutor.load_and_exec_node(
get_yaml_file(flow_folder),
node_name,
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
connections=dev_connections,
storage=storage,
raise_ex=True,
)
assert run_info.status == Status.Completed
assert contain_image_reference(run_info)
@pytest.mark.parametrize(
"flow_folder, input_dirs, inputs_mapping, output_key, expected_outputs_number, has_aggregation_node",
[
(
SIMPLE_IMAGE_FLOW,
{"data": "."},
{"image": "${data.image}"},
"output",
4,
False,
),
(
SAMPLE_IMAGE_FLOW_WITH_DEFAULT,
{"data": "."},
{"image_2": "${data.image_2}"},
"output",
4,
False,
),
(
COMPOSITE_IMAGE_FLOW,
{"data": "inputs.jsonl"},
{"image_list": "${data.image_list}", "image_dict": "${data.image_dict}"},
"output",
2,
False,
),
(
CHAT_FLOW_WITH_IMAGE,
{"data": "inputs.jsonl"},
{"question": "${data.question}", "chat_history": "${data.chat_history}"},
"answer",
2,
False,
),
(
EVAL_FLOW_WITH_SIMPLE_IMAGE,
{"data": "inputs.jsonl"},
{"image": "${data.image}"},
"output",
2,
True,
),
(
EVAL_FLOW_WITH_COMPOSITE_IMAGE,
{"data": "inputs.jsonl"},
{"image_list": "${data.image_list}", "image_dict": "${data.image_dict}"},
"output",
2,
True,
),
],
)
def test_batch_engine_with_image(
self, flow_folder, input_dirs, inputs_mapping, output_key, expected_outputs_number, has_aggregation_node
):
flow_file = get_yaml_file(flow_folder)
working_dir = get_flow_folder(flow_folder)
output_dir = Path(mkdtemp())
batch_result = BatchEngine(flow_file, working_dir).run(
input_dirs, inputs_mapping, output_dir, max_lines_count=4
)
assert isinstance(batch_result, BatchResult)
assert batch_result.completed_lines == expected_outputs_number
assert all(is_jsonl_file(output_file) or is_image_file(output_file) for output_file in output_dir.iterdir())
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == expected_outputs_number
for i, output in enumerate(outputs):
assert isinstance(output, dict)
assert "line_number" in output, f"line_number is not in {i}th output {output}"
assert output["line_number"] == i, f"line_number is not correct in {i}th output {output}"
result = output[output_key][0] if isinstance(output[output_key], list) else output[output_key]
assert all(MIME_PATTERN.search(key) for key in result), f"image is not in {i}th output {output}"
@pytest.mark.parametrize(
"flow_folder, inputs",
get_test_cases_for_simple_input(EVAL_FLOW_WITH_SIMPLE_IMAGE)
+ get_test_cases_for_composite_input(EVAL_FLOW_WITH_COMPOSITE_IMAGE),
)
def test_executor_exec_aggregation_with_image(self, flow_folder, inputs, dev_connections):
working_dir = get_flow_folder(flow_folder)
os.chdir(working_dir)
storage = DefaultRunStorage(base_dir=working_dir, sub_dir=Path("./temp"))
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections, storage=storage)
flow_result = executor.exec_line(inputs, index=0)
flow_inputs = {k: [v] for k, v in inputs.items()}
aggregation_inputs = {k: [v] for k, v in flow_result.aggregation_inputs.items()}
aggregation_results = executor.exec_aggregation(flow_inputs, aggregation_inputs=aggregation_inputs)
for _, node_run_info in aggregation_results.node_run_infos.items():
assert node_run_info.status == Status.Completed
assert contain_image_reference(node_run_info)
def test_batch_run_then_eval_with_image(self):
# submit a flow in batch mode fisrt
batch_flow_folder = get_flow_folder(COMPOSITE_IMAGE_FLOW)
batch_flow_file = get_yaml_file(batch_flow_folder)
batch_working_dir = get_flow_folder(batch_flow_folder)
batch_output_dir = Path(mkdtemp())
batch_input_dirs = {"data": "inputs.jsonl"}
batch_inputs_mapping = {"image_list": "${data.image_list}", "image_dict": "${data.image_dict}"}
batch_result = BatchEngine(batch_flow_file, batch_working_dir).run(
batch_input_dirs, batch_inputs_mapping, batch_output_dir
)
assert batch_result.completed_lines == batch_result.total_lines
# use the output of batch run as input of eval flow
eval_flow_folder = get_flow_folder(EVAL_FLOW_WITH_COMPOSITE_IMAGE)
eval_flow_file = get_yaml_file(eval_flow_folder)
eval_working_dir = get_flow_folder(eval_flow_folder)
eval_output_dir = Path(mkdtemp())
eval_input_dirs = {
"data": batch_flow_folder / "inputs.jsonl",
"run.outputs": batch_output_dir / OUTPUT_FILE_NAME,
}
eval_inputs_mapping = {"image_list": "${run.outputs.output}", "image_dict": "${data.image_dict}"}
eval_result = BatchEngine(eval_flow_file, eval_working_dir).run(
eval_input_dirs, eval_inputs_mapping, eval_output_dir
)
assert eval_result.completed_lines == eval_result.total_lines
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor | promptflow_repo/promptflow/src/promptflow/tests/executor/e2etests/test_telemetry.py | import json
import uuid
from collections import namedtuple
from importlib.metadata import version
from pathlib import Path
from tempfile import mkdtemp
from unittest.mock import patch
import pytest
from promptflow._core.operation_context import OperationContext
from promptflow.batch._batch_engine import OUTPUT_FILE_NAME, BatchEngine
from promptflow.contracts.run_mode import RunMode
from promptflow.executor import FlowExecutor
from ..utils import get_flow_folder, get_flow_inputs_file, get_yaml_file, load_jsonl
IS_LEGACY_OPENAI = version("openai").startswith("0.")
Completion = namedtuple("Completion", ["choices"])
Choice = namedtuple("Choice", ["delta"])
Delta = namedtuple("Delta", ["content"])
def stream_response(kwargs):
if IS_LEGACY_OPENAI:
delta = Delta(content=json.dumps(kwargs.get("headers", {})))
yield Completion(choices=[{"delta": delta}])
else:
delta = Delta(content=json.dumps(kwargs.get("extra_headers", {})))
yield Completion(choices=[Choice(delta=delta)])
def mock_stream_chat(*args, **kwargs):
return stream_response(kwargs)
@pytest.mark.skip(reason="Skip on Mac and Windows and Linux, patch does not work in the spawn process")
@pytest.mark.usefixtures("dev_connections")
@pytest.mark.e2etest
class TestExecutorTelemetry:
def test_executor_openai_telemetry(self, dev_connections):
"""This test validates telemetry info header is correctly injected to OpenAI API
by mocking chat api method. The mock method will return a generator that yields a
namedtuple with a json string of the headers passed to the method.
"""
if IS_LEGACY_OPENAI:
api = "openai.ChatCompletion.create"
else:
api = "openai.resources.chat.Completions.create"
with patch(api, new=mock_stream_chat):
operation_context = OperationContext.get_instance()
operation_context.clear()
flow_folder = "openai_chat_api_flow"
# Set user-defined properties `scenario` in context
operation_context.scenario = "test"
executor = FlowExecutor.create(get_yaml_file(flow_folder), dev_connections)
# flow run case
inputs = {"question": "What's your name?", "chat_history": [], "stream": True}
flow_result = executor.exec_line(inputs)
assert isinstance(flow_result.output, dict)
headers = json.loads(flow_result.output.get("answer", ""))
assert "promptflow/" in headers.get("x-ms-useragent")
assert headers.get("ms-azure-ai-promptflow-scenario") == "test"
assert headers.get("ms-azure-ai-promptflow-run-mode") == RunMode.Test.name
# batch run case
run_id = str(uuid.uuid4())
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=dev_connections
)
input_dirs = {"data": get_flow_inputs_file(flow_folder)}
inputs_mapping = {"question": "${data.question}", "chat_history": "${data.chat_history}"}
output_dir = Path(mkdtemp())
batch_engine.run(input_dirs, inputs_mapping, output_dir, run_id=run_id)
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
for line in outputs:
headers = json.loads(line.get("answer", ""))
assert "promptflow/" in headers.get("x-ms-useragent")
assert headers.get("ms-azure-ai-promptflow-scenario") == "test"
assert headers.get("ms-azure-ai-promptflow-run-mode") == RunMode.Batch.name
# single_node case
run_info = FlowExecutor.load_and_exec_node(
get_yaml_file("openai_chat_api_flow"),
"chat",
flow_inputs=inputs,
connections=dev_connections,
raise_ex=True,
)
assert run_info.output is not None
headers = json.loads(run_info.output)
assert "promptflow/" in headers.get("x-ms-useragent")
assert headers.get("ms-azure-ai-promptflow-scenario") == "test"
assert headers.get("ms-azure-ai-promptflow-run-mode") == RunMode.SingleNode.name
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor/unittests | promptflow_repo/promptflow/src/promptflow/tests/executor/unittests/integrations/test_langchain.py | import pytest
from langchain.schema import AgentAction, AgentFinish
from promptflow.integrations.langchain import LangChainEventType, PromptFlowCallbackHandler
@pytest.mark.unittest
class TestLangchain:
def get_handler(self):
class MockTracer():
def __init__(self):
self._trace_stack = []
def _push(self, trace):
self._trace_stack.append(trace)
def _pop(self, output=None, error=None):
self._trace_stack.pop()
handler = PromptFlowCallbackHandler()
handler._tracer = MockTracer()
return handler
def test_langchain_traces(self):
handler = self.get_handler()
handler.on_agent_action(action=AgentAction("test_agent_name", "test", "test"))
handler.on_tool_start(serialized={"name": "test_tool_name"}, input_str="test")
handler.on_chain_start(serialized={"id": ["test_chain_name"]}, inputs={"test": "test"})
handler.on_llm_start(serialized={"test": "test"}, prompts=["test"])
assert handler._events_stack == [
LangChainEventType.AGENT,
LangChainEventType.TOOL,
LangChainEventType.CHAIN,
LangChainEventType.LLM
]
assert len(handler._tracer._trace_stack) == 4
assert handler._tracer._trace_stack[0].name == "test_agent_name"
assert handler._tracer._trace_stack[1].name == "test_tool_name"
assert handler._tracer._trace_stack[2].name == "test_chain_name"
assert handler._tracer._trace_stack[3].name == "LLM" # The default name
handler.on_llm_error(error=None)
handler.on_chain_error(error=None)
handler.on_tool_error(error=None)
handler.on_agent_finish(finish=AgentFinish({"test": "test"}, "test"))
assert len(handler._events_stack) == 0
assert len(handler._tracer._trace_stack) == 0
def test_langchain_traces_with_unpaired_events(self):
handler = self.get_handler()
handler.on_tool_start(serialized={"test": "test"}, input_str="test")
# Missing on_chain_start
# Missing on_llm_start
assert len(handler._tracer._trace_stack) == 1
handler.on_llm_end(response=None)
handler.on_chain_end(outputs={"test": "test"})
assert len(handler._tracer._trace_stack) == 1
handler.on_tool_end(output="test")
assert len(handler._events_stack) == 0
assert len(handler._tracer._trace_stack) == 0
handler = self.get_handler()
handler.on_tool_start(serialized={"test": "test"}, input_str="test")
handler.on_chain_start(serialized={"test": "test"}, inputs={"test": "test"})
handler.on_llm_start(serialized={"test": "test"}, prompts=["test"])
assert len(handler._tracer._trace_stack) == 3
# Missing on_chain_end
# Missing on_llm_end
handler.on_tool_end(output="test")
assert len(handler._events_stack) == 0
assert len(handler._tracer._trace_stack) == 0
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor/unittests | promptflow_repo/promptflow/src/promptflow/tests/executor/unittests/storage/test_queue_run_storage.py | import pytest
from multiprocessing import Queue
from promptflow.executor._line_execution_process_pool import QueueRunStorage
from promptflow.contracts.run_info import FlowRunInfo
from promptflow.contracts.run_info import RunInfo as NodeRunInfo
@pytest.mark.unittest
class TestLineExecutionProcessPool:
def test_persist_node_run(self):
queue = Queue()
run_storage = QueueRunStorage(queue)
node_run_info = NodeRunInfo(
node="node1",
flow_run_id="flow_run_id",
run_id="run_id",
status="status",
inputs="inputs",
output="output",
metrics="metrics",
error="error",
parent_run_id="parent_run_id",
start_time="start_time",
end_time="end_time",
index="index",
api_calls="api_calls",
variant_id="variant_id",
cached_run_id="cached_run_id",
cached_flow_run_id="cached_flow_run_id",
logs="logs",
system_metrics="system_metrics",
result="result",
)
run_storage.persist_node_run(node_run_info)
assert queue.get() == node_run_info
def test_persist_flow_run(self):
queue = Queue()
run_storage = QueueRunStorage(queue)
flow_run_info = FlowRunInfo(
run_id="run_id",
status="status",
inputs="inputs",
output="output",
metrics="metrics",
request="request",
root_run_id="root_run_id",
source_run_id="source_run_id",
flow_id="flow_id",
error="error",
parent_run_id="parent_run_id",
start_time="start_time",
end_time="end_time",
index="index",
api_calls="api_calls",
variant_id="variant_id",
system_metrics="system_metrics",
result="result",
)
run_storage.persist_flow_run(flow_run_info)
assert queue.get() == flow_run_info
| 0 |
promptflow_repo/promptflow/src/promptflow/tests/executor/unittests | promptflow_repo/promptflow/src/promptflow/tests/executor/unittests/storage/test_run_records.py | import json
from datetime import datetime
import pytest
from promptflow._utils.dataclass_serializer import serialize
from promptflow.contracts.run_info import FlowRunInfo, RunInfo, Status
from promptflow.storage.run_records import LineRunRecord, NodeRunRecord
@pytest.mark.unittest
def test_line_record():
start_time = datetime(2023, 7, 12)
end_time = datetime(2023, 7, 13)
flow_run_info = FlowRunInfo(
run_id=None,
status=Status.Completed,
error=None,
inputs=None,
output=None,
metrics=None,
request=None,
parent_run_id=None,
root_run_id=None,
source_run_id=None,
flow_id=None,
start_time=start_time,
end_time=end_time,
index=0,
variant_id=None,
)
line_record = LineRunRecord.from_run_info(flow_run_info)
assert line_record.line_number == 0
assert line_record.start_time == start_time.isoformat()
assert line_record.end_time == end_time.isoformat()
assert line_record.status == Status.Completed.value
assert line_record.run_info == serialize(flow_run_info)
@pytest.mark.unittest
def test_line_serialize():
start_time = datetime(2023, 7, 12)
end_time = datetime(2023, 7, 13)
flow_run_info = FlowRunInfo(
run_id=None,
status=Status.Completed,
error=None,
inputs=None,
output=None,
metrics=None,
request=None,
parent_run_id=None,
root_run_id=None,
source_run_id=None,
flow_id=None,
start_time=start_time,
end_time=end_time,
index=0,
variant_id=None,
)
line_record = LineRunRecord.from_run_info(flow_run_info)
result = line_record.serialize()
expected_result = json.dumps(line_record.__dict__)
assert result == expected_result
@pytest.mark.unittest
def test_node_record():
start_time = datetime(2023, 7, 12)
end_time = datetime(2023, 7, 13)
node_run_info = RunInfo(
node=None,
run_id=None,
flow_run_id=None,
status=Status.Completed,
inputs=None,
output=None,
metrics=None,
error=None,
parent_run_id=None,
start_time=start_time,
end_time=end_time,
index=0,
)
node_record = NodeRunRecord.from_run_info(node_run_info)
assert node_record.line_number == 0
assert node_record.start_time == start_time.isoformat()
assert node_record.end_time == end_time.isoformat()
assert node_record.status == Status.Completed.value
assert node_record.run_info == serialize(node_run_info)
@pytest.mark.unittest
def test_node_serialize():
start_time = datetime(2023, 7, 12)
end_time = datetime(2023, 7, 13)
node_run_info = RunInfo(
node=None,
run_id=None,
flow_run_id=None,
status=Status.Completed,
inputs=None,
output=None,
metrics=None,
error=None,
parent_run_id=None,
start_time=start_time,
end_time=end_time,
index=0,
)
node_record = NodeRunRecord.from_run_info(node_run_info)
result = node_record.serialize()
expected_result = json.dumps(node_record.__dict__)
assert result == expected_result
| 0 |