diff --git a/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/_cache_utils.py b/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/_cache_utils.py index 11aed2f18d71..c8e4396045af 100644 --- a/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/_cache_utils.py +++ b/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/_cache_utils.py @@ -6,14 +6,18 @@ import os.path import tempfile import threading +import time from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass +from functools import partial from pathlib import Path -from typing import List, Dict, Optional +from typing import List, Dict, Optional, Union, Callable from azure.ai.ml._utils._asset_utils import get_object_hash -from azure.ai.ml._utils.utils import is_on_disk_cache_enabled -from azure.ai.ml.constants._common import AzureMLResourceType +from azure.ai.ml._utils.utils import is_on_disk_cache_enabled, is_concurrent_component_registration_enabled, \ + is_private_preview_enabled, open_file_with_int_mode +from azure.ai.ml.constants._common import AzureMLResourceType, AZUREML_COMPONENT_REGISTRATION_MAX_WORKERS from azure.ai.ml.entities import Component from azure.ai.ml.entities._builders import BaseNode @@ -23,8 +27,9 @@ _ANONYMOUS_HASH_PREFIX = "anonymous-component-" _YAML_SOURCE_PREFIX = "yaml-source-" _CODE_INVOLVED_PREFIX = "code-involved-" +EXPIRE_TIME_IN_SECONDS = 60 * 60 * 24 * 7 # 7 days -_node_resolution_lock = threading.Lock() +_node_resolution_lock = defaultdict(threading.Lock) @dataclass @@ -50,11 +55,11 @@ class CachedNodeResolver(object): state of nodes, e.g., hash of its inner component. 2) self._resolve_component is only called concurrently on independent components a) we have used an in-memory component hash to deduplicate components to resolve first; - b) dependent components have been resolved before as nodes are registered & resolved + b) dependent components have been resolved before registered as nodes are registered & resolved layer by layer; - c) dependent code will never be an instance, so it won't cause cache hit issue. - d) resolution of potential shared dependencies other than code and components are thread-safe - as they do not involve further dependency resolution. However, it's still a good practice to + c) dependent code will never be an instance, so it won't cause cache hit issue described in d; + d) resolution of potential shared dependencies (1 instance used in 2 components) other than components + are thread-safe as they do not involve further dependency resolution. However, it's still a good practice to resolve them before calling self.register_node_for_lazy_resolution as it will impact cache hit rate. For example, if: node1.component, node2.component = Component(environment=env1, ...), Component(environment=env1, ...) @@ -83,20 +88,63 @@ class CachedNodeResolver(object): def __init__( self, - resolver, - subscription_id: str, - resource_group_name: str, - workspace_name: str, - registry_name: str, + resolver: Callable[[Union[Component, str]], str], + subscription_id: Optional[str], + resource_group_name: Optional[str], + workspace_name: Optional[str], + registry_name: Optional[str], ): self._resolver = resolver self._cache: Dict[str, _CacheContent] = {} self._nodes_to_resolve: List[BaseNode] = [] + self._client_hash = self._get_client_hash( + subscription_id, resource_group_name, workspace_name, registry_name + ) + # the same client share 1 lock + self._lock = _node_resolution_lock[self._client_hash] + + @staticmethod + def _get_client_hash( + subscription_id: Optional[str], + resource_group_name: Optional[str], + workspace_name: Optional[str], + registry_name: Optional[str], + ) -> str: + """Get a hash for used client. + Works for both workspace client and registry client. + """ object_hash = hashlib.sha256() for s in [subscription_id, resource_group_name, workspace_name, registry_name]: object_hash.update(str(s).encode("utf-8")) - self._workspace_hash = object_hash.hexdigest() + return object_hash.hexdigest() + + @staticmethod + def _get_component_registration_max_workers(): + """Get the max workers for component registration. + + Before Python 3.8, the default max_worker is the number of processors multiplied by 5. + It may send a large number of the uploading snapshot requests that will occur remote refuses requests. + In order to avoid retrying the upload requests, max_worker will use the default value in Python 3.8, + min(32, os.cpu_count + 4). + + 1 risk is that, asset_utils will create a new thread pool to upload files in subprocesses, which may cause + the number of threads exceed the max_worker. + """ + default_max_workers = min(32, (os.cpu_count() or 1) + 4) + try: + max_workers = int(os.environ.get(AZUREML_COMPONENT_REGISTRATION_MAX_WORKERS, default_max_workers)) + except ValueError: + logger.info( + "Environment variable %s with value %s set but failed to parse. " + "Use the default max_worker %s as registration thread pool max_worker." + "Please reset the value to an integer.", + AZUREML_COMPONENT_REGISTRATION_MAX_WORKERS, + os.environ.get(AZUREML_COMPONENT_REGISTRATION_MAX_WORKERS), + default_max_workers + ) + max_workers = default_max_workers + return max_workers @staticmethod def _get_in_memory_hash_for_component(component: Component) -> str: @@ -156,22 +204,37 @@ def _get_on_disk_hash_for_component(component: Component, in_memory_hash: str) - object_hash.update(content_hash.encode("utf-8")) return _CODE_INVOLVED_PREFIX + object_hash.hexdigest() - @staticmethod - def get_on_disk_cache_base_dir() -> Path: + @property + def _on_disk_cache_dir(self) -> Path: """Get the base path for on disk cache.""" from azure.ai.ml._version import VERSION - return Path(tempfile.gettempdir()).joinpath(".azureml", "azure-ai-ml", VERSION, "cache", "components") + return Path(tempfile.gettempdir()).joinpath( + ".azureml", + "azure-ai-ml", + VERSION, + "cache", + self._client_hash, + "components", + ) def _get_on_disk_cache_path(self, on_disk_hash: str) -> Path: """Get the on disk cache path for a component.""" - return self.get_on_disk_cache_base_dir().joinpath(self._workspace_hash, on_disk_hash) + return self._on_disk_cache_dir.joinpath(on_disk_hash) def _load_from_on_disk_cache(self, on_disk_hash: str) -> Optional[str]: """Load component arm id from on disk cache.""" # on-disk cache will expire in a new SDK version on_disk_cache_path = self._get_on_disk_cache_path(on_disk_hash) - if on_disk_cache_path.is_file(): - return on_disk_cache_path.read_text().strip() + if on_disk_cache_path.is_file() and time.time() - on_disk_cache_path.stat().st_ctime < EXPIRE_TIME_IN_SECONDS: + try: + return on_disk_cache_path.read_text().strip() + except (OSError, PermissionError) as e: + logger.warning( + "Failed to read on-disk cache for component due to %s. " + "Please check if the file %s is in use or current user doesn't have the permission.", + type(e).__name__, + on_disk_cache_path.as_posix(), + ) return None def _save_to_on_disk_cache(self, on_disk_hash: str, arm_id: str) -> None: @@ -181,24 +244,33 @@ def _save_to_on_disk_cache(self, on_disk_hash: str, arm_id: str) -> None: return on_disk_cache_path = self._get_on_disk_cache_path(on_disk_hash) on_disk_cache_path.parent.mkdir(parents=True, exist_ok=True) - on_disk_cache_path.write_text(arm_id) + try: + with open_file_with_int_mode(on_disk_cache_path, "w") as f: + f.write(arm_id) + except PermissionError: + logger.warning( + "Failed to save on-disk cache for component due to permission error. " + "Please check if the file %s is in use or current user doesn't have the permission.", + on_disk_cache_path.as_posix(), + ) def _resolve_cache_contents(self, cache_contents_to_resolve: List[_CacheContent], resolver): """Resolve all components to resolve and save the results in cache. """ _components = list(map(lambda x: x.component_ref, cache_contents_to_resolve)) - - # TODO: do concurrent resolution controlled by an environment variable here - # given deduplication has already been done, we can safely assume that there is no - # conflict in concurrent local cache access - # multiprocessing need to dump input objects before starting a new process, which will fail - # on _AttrDict for now, so put off the concurrent resolution - for cache_content in cache_contents_to_resolve: - cache_content.arm_id = resolver( - cache_content.component_ref, - azureml_type=AzureMLResourceType.COMPONENT - ) - if is_on_disk_cache_enabled(): + _map_func = partial(resolver, azureml_type=AzureMLResourceType.COMPONENT) + + if len(_components) > 1 and is_concurrent_component_registration_enabled() and is_private_preview_enabled(): + # given deduplication has already been done, we can safely assume that there is no + # conflict in concurrent local cache access + with ThreadPoolExecutor(max_workers=self._get_component_registration_max_workers()) as executor: + resolution_results = executor.map(_map_func, _components) + else: + resolution_results = map(_map_func, _components) + + for cache_content, resolution_results in zip(cache_contents_to_resolve, resolution_results): + cache_content.arm_id = resolution_results + if is_on_disk_cache_enabled() and is_private_preview_enabled(): self._save_to_on_disk_cache(cache_content.on_disk_hash, cache_content.arm_id) def _prepare_items_to_resolve(self): @@ -263,7 +335,7 @@ def _resolve_nodes(self): """ dict_of_nodes_to_resolve, cache_contents_to_resolve = self._prepare_items_to_resolve() - if is_on_disk_cache_enabled(): + if is_on_disk_cache_enabled() and is_private_preview_enabled(): cache_contents_to_resolve = self._resolve_cache_contents_from_disk(cache_contents_to_resolve) self._resolve_cache_contents(cache_contents_to_resolve, resolver=self._resolver) @@ -300,9 +372,9 @@ def resolve_nodes(self): # state of nodes, e.g. hash of its inner component. # This will happen only on concurrent external calls; In 1 external call, all nodes in # subgraph will be skipped on register_node_for_lazy_resolution when resolving subgraph - _node_resolution_lock.acquire() + self._lock.acquire() try: self._resolve_nodes() finally: # release lock even if exception happens - _node_resolution_lock.release() + self._lock.release() diff --git a/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/utils.py b/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/utils.py index d2d758baf4fe..9b5e2626d546 100644 --- a/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/utils.py +++ b/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/utils.py @@ -36,6 +36,7 @@ AZUREML_INTERNAL_COMPONENTS_ENV_VAR, AZUREML_PRIVATE_FEATURES_ENV_VAR, AZUREML_DISABLE_ON_DISK_CACHE_ENV_VAR, + AZUREML_DISABLE_CONCURRENT_COMPONENT_REGISTRATION, ) from azure.core.pipeline.policies import RetryPolicy @@ -772,8 +773,11 @@ def is_private_preview_enabled(): def is_on_disk_cache_enabled(): - return os.getenv(AZUREML_DISABLE_ON_DISK_CACHE_ENV_VAR) not in ["True", "true", True] \ - and is_private_preview_enabled() + return os.getenv(AZUREML_DISABLE_ON_DISK_CACHE_ENV_VAR) not in ["True", "true", True] + + +def is_concurrent_component_registration_enabled(): + return os.getenv(AZUREML_DISABLE_CONCURRENT_COMPONENT_REGISTRATION) not in ["True", "true", True] def is_internal_components_enabled(): @@ -928,3 +932,26 @@ def _validate_missing_sub_or_rg_and_raise(subscription_id: Optional[str], resour target=ErrorTarget.GENERAL, error_category=ErrorCategory.USER_ERROR, ) + + +@contextmanager +def open_file_with_int_mode(file: Union[str, PathLike], mode: str = 'r', int_mode: int = 0o666, **kwargs) -> IO: + """Open file with specific mode and return the file object. + + :param file: Path to the file. + :param mode: Mode to open the file. + :param int_mode: Mode for opener in integer. Default value is 0o666, which means + w+r for owner, group and others. + :param int_mode: Mode for the opener. + :return: The file object. + """ + origin_mask = os.umask(0) + try: + def opener(path, flags): + # w+r for owner, group and others + return os.open(path, flags, int_mode) + + with open(file=file, mode=mode, **kwargs, opener=opener) as f: + yield f + finally: + os.umask(origin_mask) diff --git a/sdk/ml/azure-ai-ml/azure/ai/ml/constants/_common.py b/sdk/ml/azure-ai-ml/azure/ai/ml/constants/_common.py index 3e28dc526f0c..1832f8641c2e 100644 --- a/sdk/ml/azure-ai-ml/azure/ai/ml/constants/_common.py +++ b/sdk/ml/azure-ai-ml/azure/ai/ml/constants/_common.py @@ -49,6 +49,8 @@ AZUREML_PRIVATE_FEATURES_ENV_VAR = "AZURE_ML_CLI_PRIVATE_FEATURES_ENABLED" AZUREML_INTERNAL_COMPONENTS_ENV_VAR = "AZURE_ML_INTERNAL_COMPONENTS_ENABLED" AZUREML_DISABLE_ON_DISK_CACHE_ENV_VAR = "AZURE_ML_DISABLE_ON_DISK_CACHE" +AZUREML_COMPONENT_REGISTRATION_MAX_WORKERS = "AZURE_ML_COMPONENT_REGISTRATION_MAX_WORKERS" +AZUREML_DISABLE_CONCURRENT_COMPONENT_REGISTRATION = "AZURE_ML_DISABLE_CONCURRENT_COMPONENT_REGISTRATION" AZUREML_INTERNAL_COMPONENTS_SCHEMA_PREFIX = "https://componentsdk.azureedge.net/jsonschema/" COMMON_RUNTIME_ENV_VAR = "AZUREML_COMPUTE_USE_COMMON_RUNTIME" ENDPOINT_DEPLOYMENT_START_MSG = ( diff --git a/sdk/ml/azure-ai-ml/azure/ai/ml/operations/_job_operations.py b/sdk/ml/azure-ai-ml/azure/ai/ml/operations/_job_operations.py index 8425d306caf9..21befe139b3f 100644 --- a/sdk/ml/azure-ai-ml/azure/ai/ml/operations/_job_operations.py +++ b/sdk/ml/azure-ai-ml/azure/ai/ml/operations/_job_operations.py @@ -996,7 +996,12 @@ def _resolve_job_input(self, entry: Union[Input, str, bool, int, float], base_pa entry.path = self._orchestrators.get_asset_arm_id(entry.path, asset_type) else: # relative local path, upload, transform to remote url - local_path = Path(base_path, entry.path).resolve() + # Base path will be None for dsl pipeline component for now. We have 2 choices if the dsl pipeline + # function is imported from another file: + # 1) Use cwd as default base path; + # 2) Use the file path of the dsl pipeline function as default base path. + # Pick solution 1 for now as defining input path in the script to submit is a more common scenario. + local_path = Path(base_path or Path.cwd(), entry.path).resolve() entry.path = _upload_and_generate_remote_uri( self._operation_scope, self._datastore_operations, diff --git a/sdk/ml/azure-ai-ml/tests/conftest.py b/sdk/ml/azure-ai-ml/tests/conftest.py index a0196cd7d289..0c3791cf62cb 100644 --- a/sdk/ml/azure-ai-ml/tests/conftest.py +++ b/sdk/ml/azure-ai-ml/tests/conftest.py @@ -1,4 +1,5 @@ import base64 +import hashlib import json import os import random @@ -7,13 +8,15 @@ import time import uuid from datetime import datetime +from functools import partial from importlib import reload from os import getenv from pathlib import Path -from typing import Callable, Tuple, Union +from typing import Callable, Tuple, Union, Optional from unittest.mock import Mock, patch import pytest +from _pytest.fixtures import FixtureRequest from devtools_testutils import ( add_body_key_sanitizer, add_general_regex_sanitizer, @@ -524,8 +527,35 @@ def generate_component_hash(*args, **kwargs): return dict_hash +def get_client_hash_with_request_node_name( + subscription_id: Optional[str], + resource_group_name: Optional[str], + workspace_name: Optional[str], + registry_name: Optional[str], + random_seed: str +): + """Generate a hash for the client.""" + object_hash = hashlib.sha256() + for s in [ + subscription_id, + resource_group_name, + workspace_name, + registry_name, + random_seed, + ]: + object_hash.update(str(s).encode("utf-8")) + return object_hash.hexdigest() + + +def clear_on_disk_cache(cached_resolver): + """Clear on disk cache for current client.""" + cached_resolver._lock.acquire() + shutil.rmtree(cached_resolver._on_disk_cache_dir, ignore_errors=True) + cached_resolver._lock.release() + + @pytest.fixture -def mock_component_hash(mocker: MockFixture): +def mock_component_hash(mocker: MockFixture, request: FixtureRequest): """Mock the component hash function. In playback mode, workspace information in returned arm_id will be normalized like this: @@ -538,6 +568,9 @@ def mock_component_hash(mocker: MockFixture): Note that component hash value in playback mode can be different from the one in live mode, so tests that check component hash directly should be skipped if not is_live. """ + # do nothing if in live mode and not recording + if is_live_and_not_recording(): + return if is_live(): mocker.patch("azure.ai.ml.entities._component.component.hash_dict", side_effect=generate_component_hash) @@ -546,29 +579,44 @@ def mock_component_hash(mocker: MockFixture): side_effect=generate_component_hash ) - if is_live_and_not_recording(): - return + # On-disk cache can't be shared among different tests in playback mode or when recording. + # When doing recording: + # 1) Recorded requests may be impacted by the order to run tests. Tests run later will reuse + # the cached result from tests run earlier, so we won't found enough recordings when + # running tests in reversed order. + # In playback mode: + # 1) We can't guarantee that server-side will return the same version for 2 anonymous component + # with the same on-disk hash. + # 2) Server-side may return different version for the same anonymous component in different workspace, + # while workspace information will be normalized in recordings. If we record test1 in workspace A + # and test2 in workspace B, the version in recordings can be different. + # So we use a random (probably unique) on-disk cache base directory for each test, and on-disk cache operations + # will be thread-safe when concurrently running different tests. + mocker.patch( + "azure.ai.ml._utils._cache_utils.CachedNodeResolver._get_client_hash", + side_effect=partial(get_client_hash_with_request_node_name, random_seed=uuid.uuid4().hex) + ) + + # Collect involved resolvers before yield, as fixtures may be destroyed after yield. + from azure.ai.ml._utils._cache_utils import CachedNodeResolver + involved_resolvers = [] + for client_fixture_name in ["client", "registry_client"]: + if client_fixture_name not in request.fixturenames: + continue + client: MLClient = request.getfixturevalue(client_fixture_name) + involved_resolvers.append(CachedNodeResolver( + resolver=None, + subscription_id=client.subscription_id, + resource_group_name=client.resource_group_name, + workspace_name=client.workspace_name, + registry_name=client._operation_scope.registry_name, + )) + + yield - if not os.getenv("ENABLE_ON_DISK_CACHE_ACROSS_TESTS", False) in ["True", "true", True]: - # On-disk cache can't be shared among different tests in playback mode or when recording. - # When doing recording: - # 1) Recorded requests may be impacted by the order to run tests. Tests run later will reuse - # the cached result from tests run earlier, so we won't found enough recordings when - # running tests in reversed order. - # In playback mode: - # 1) We can't guarantee that server-side will return the same version for 2 anonymous component - # with the same on-disk hash. - # 2) Server-side may return different version for the same anonymous component in different workspace, - # while workspace information will be normalized in recordings. If we record test1 in workspace A - # and test2 in workspace B, the version in recordings can be different. - # So we need to clear on-disk cache for each test. - - # If you want to run tests concurrently, you can either: - # 1) Disable on-disk cache or - # 2) Set ENABLE_ON_DISK_CACHE_ACROSS_TESTS to True and acknowledge that you may meet - # the issues mentioned above. - from azure.ai.ml._utils._cache_utils import CachedNodeResolver - shutil.rmtree(CachedNodeResolver.get_on_disk_cache_base_dir(), ignore_errors=True) + # clear on-disk cache after each test + for resolver in involved_resolvers: + clear_on_disk_cache(resolver) @pytest.fixture @@ -682,6 +730,7 @@ def enable_pipeline_private_preview_features(mocker: MockFixture): mocker.patch("azure.ai.ml._schema.pipeline.pipeline_component.is_private_preview_enabled", return_value=True) mocker.patch("azure.ai.ml.entities._schedule.schedule.is_private_preview_enabled", return_value=True) mocker.patch("azure.ai.ml.dsl._pipeline_decorator.is_private_preview_enabled", return_value=True) + mocker.patch("azure.ai.ml._utils._cache_utils.is_private_preview_enabled", return_value=True) @pytest.fixture() diff --git a/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_with_specific_nodes.py b/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_with_specific_nodes.py index a3b155acd44b..925847f56631 100644 --- a/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_with_specific_nodes.py +++ b/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_with_specific_nodes.py @@ -1,9 +1,17 @@ +import multiprocessing +import uuid +from functools import partial from pathlib import Path -from typing import Callable +from typing import Callable, Union import pytest -from devtools_testutils import AzureRecordedTestCase, is_live -from test_utilities.utils import _PYTEST_TIMEOUT_METHOD, assert_job_cancel +from devtools_testutils import AzureRecordedTestCase +from mock import mock +from pytest_mock import MockFixture + +from azure.ai.ml.operations._operation_orchestrator import OperationOrchestrator +from test_utilities.utils import _PYTEST_TIMEOUT_METHOD, assert_job_cancel, submit_and_cancel_new_dsl_pipeline, \ + omit_with_wildcard from azure.ai.ml import ( Input, @@ -12,7 +20,7 @@ load_component, ) from azure.ai.ml.constants._common import AssetTypes -from azure.ai.ml.entities import CommandComponent, Command, Choice, Sweep +from azure.ai.ml.entities import CommandComponent, Command, Choice, Sweep, Component, Environment, PipelineComponent from azure.ai.ml.entities import PipelineJob from .._util import _DSL_TIMEOUT_SECOND @@ -38,6 +46,19 @@ ] +def _get_component_in_first_child(_with_jobs: Union[PipelineJob, PipelineComponent], client: MLClient) -> Component: + if not _with_jobs.jobs: + raise ValueError("No jobs found in the pipeline") + _result = next(iter(_with_jobs.jobs.values())).component.split(":") + if len(_result) == 2: + _name, _version = _result + elif len(_result) == 3: + _, _name, _version = _result + else: + raise ValueError("Invalid component arm string: {}".format(_result)) + return client.components.get(_name, _version) + + @pytest.mark.usefixtures( "enable_environment_id_arm_expansion", "enable_pipeline_private_preview_features", @@ -49,6 +70,89 @@ @pytest.mark.e2etest @pytest.mark.pipeline_test class TestDSLPipelineWithSpecificNodes(AzureRecordedTestCase): + @staticmethod + def _generate_multi_layer_pipeline_func(): + path = "./tests/test_configs/components/helloworld_component.yml" + + @dsl.pipeline + def pipeline_leaf(component_in_path: Input): + component_func1 = load_component(source=path) + component_func1(component_in_path=component_in_path, component_in_number=1) + + component_func2 = load_component(source=path, params_override=[{ + "name": "another_component_name", + "version": "another_component_version", + }]) + component_func2(component_in_path=component_in_path, component_in_number=1) + + component_func3 = load_component(source=path, params_override=[{ + "environment": "azureml:AzureML-sklearn-0.24-ubuntu18.04-py37-cpu:2" + }]) + component_func3(component_in_path=component_in_path, component_in_number=1) + + component_func4 = load_component(source=path) + component_func4.command += " & echo updated1" + component_func4(component_in_path=component_in_path, component_in_number=1) + + @dsl.pipeline + def pipeline_mid(job_in_path: Input): + pipeline_leaf(job_in_path) + pipeline_leaf(job_in_path) + + @dsl.pipeline + def pipeline_root(job_in_path: Input): + pipeline_mid(job_in_path) + pipeline_mid(job_in_path) + return pipeline_root + + @staticmethod + def _generate_pipeline_func_for_concurrent_component_registration_test(shared_input): + path = "./tests/test_configs/components/helloworld_component.yml" + conda_file_path = "./tests/test_configs/environment/environment_files/environment.yml" + + environment = Environment( + name="test-environment", + conda_file=conda_file_path, + image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04", + version="2", # TODO: anonymous environment has potential version conflict? + description="This is an anonymous environment", + ) + + @dsl.pipeline + def pipeline_leaf(): + component_func1a = load_component(source=path) + component_func1a.environment = environment + component_func1a.command += " & echo updated1" + component_func1a(component_in_path=shared_input, component_in_number=1) + + component_func1b = load_component(source=path) + component_func1b.environment = environment + component_func1b.command += " & echo updated1" + component_func1b(component_in_path=shared_input, component_in_number=1) + + component_func2 = load_component(source=path) + component_func2.command += " & echo updated2" + component_func2.environment = environment + component_func2(component_in_path=shared_input, component_in_number=1) + + component_func3 = load_component(source=path) + component_func3.command += " & echo updated3" + component_func3.environment = environment + component_func3(component_in_path=shared_input, component_in_number=1) + + # TODO: test with multiple pipelines after server-side return jobs for pipeline component + # @dsl.pipeline + # def pipeline_mid(): + # pipeline_leaf() + # pipeline_leaf() + # + # @dsl.pipeline + # def pipeline_root(): + # pipeline_mid() + # pipeline_mid() + + return pipeline_leaf + def test_dsl_pipeline_sweep_node(self, client: MLClient, randstr: Callable[[str], str]) -> None: yaml_file = "./tests/test_configs/components/helloworld_component.yml" @@ -110,3 +214,89 @@ def train_with_sweep_in_pipeline(raw_data, primary_metric: str = "AUC", max_tota name, version = created_pipeline.jobs["sweep_job1"].trial.split(":") created_component = client.components.get(name, version) assert created_component.display_name == "sweep_job1" + + def test_dsl_pipeline_component_cache_in_resolver(self, client: MLClient) -> None: + input_data_path = "./tests/test_configs/data/" + pipeline_root = self._generate_multi_layer_pipeline_func() + + _submit_and_cancel = partial( + submit_and_cancel_new_dsl_pipeline, + client=client, + job_in_path=Input(path=input_data_path) + ) + + def _mock_get_component_arm_id(_component: Component) -> str: + # the logic has no diff comparing to original function other than always using show_progress=False + # just to mock the function and check call information + if not _component.id: + _component._id = client.components.create_or_update( + _component, is_anonymous=True, show_progress=False + ).id + return _component.id + + with mock.patch.object( + OperationOrchestrator, + "_get_component_arm_id", + side_effect=_mock_get_component_arm_id + ) as mock_resolve: + _submit_and_cancel(pipeline_root) + # pipeline_leaf, pipeline_mid and 3 command components will be resolved + assert mock_resolve.call_count == 5 + + with mock.patch.object( + OperationOrchestrator, + "_get_component_arm_id", + side_effect=_mock_get_component_arm_id + ) as mock_resolve: + _submit_and_cancel(pipeline_root) + # no more requests to resolve components as local cache is hit + assert mock_resolve.call_count == 0 + + pipeline_job = pipeline_root(job_in_path=Input(path=input_data_path)) + pipeline_job.settings.default_compute = "cpu-cluster" + leaf_subgraph = pipeline_job.jobs["pipeline_mid"].component.jobs["pipeline_leaf"].component + leaf_subgraph.jobs["another_component_name"].component.command += " & echo updated2" + with mock.patch.object( + OperationOrchestrator, + "_get_component_arm_id", + side_effect=_mock_get_component_arm_id + ) as mock_resolve: + assert_job_cancel(pipeline_job, client) + # updated command component and its parents (pipeline_leaf and pipeline_mid) will be resolved + assert mock_resolve.call_count == 3 + + def test_dsl_pipeline_concurrent_component_registration(self, client: MLClient, mocker: MockFixture) -> None: + # disable on-disk cache to test concurrent component registration + mocker.patch("azure.ai.ml._utils.utils.is_on_disk_cache_enabled", return_value=False) + + input_data_path = "./tests/test_configs/data/" + pipeline_root = self._generate_pipeline_func_for_concurrent_component_registration_test( + shared_input=Input(path=input_data_path) + ) + + _submit_and_cancel = partial( + submit_and_cancel_new_dsl_pipeline, + client=client, + ) + + treatment_pipeline_job = _submit_and_cancel(pipeline_root) + + with mock.patch("azure.ai.ml._utils.utils.is_concurrent_component_registration_enabled", return_value=False): + base_pipeline_job = _submit_and_cancel(pipeline_root) + + # Server-side does not guarantee the same anonymous pipeline component share the same version + # So omit name and version and do comparison layer by layer + omit_fields = ["id", "name", "version", "creation_context", "services", "jobs.*.component"] + + base, treat = base_pipeline_job, treatment_pipeline_job + # TODO: test with multiple pipelines after server-side return jobs for pipeline component + for _ in range(0, 0): + assert omit_with_wildcard(base._to_dict(), *omit_fields) == omit_with_wildcard( + treat._to_dict(), *omit_fields) + base = _get_component_in_first_child(base, client) + treat = _get_component_in_first_child(treat, client) + + # The last layer contains the command components + omit_fields.pop() + assert omit_with_wildcard(base._to_dict(), *omit_fields) == omit_with_wildcard( + treat._to_dict(), *omit_fields) diff --git a/sdk/ml/azure-ai-ml/tests/internal_utils/unittests/test_cache_utils.py b/sdk/ml/azure-ai-ml/tests/internal_utils/unittests/test_cache_utils.py new file mode 100644 index 000000000000..b9c1fab95938 --- /dev/null +++ b/sdk/ml/azure-ai-ml/tests/internal_utils/unittests/test_cache_utils.py @@ -0,0 +1,79 @@ +import os +import stat +from pathlib import Path +from typing import Union + +import mock +import pytest + +from azure.ai.ml import MLClient, load_job +from azure.ai.ml._utils._cache_utils import CachedNodeResolver +from azure.ai.ml.entities import Component, PipelineJob + + +@pytest.mark.unittest +@pytest.mark.pipeline_test +@pytest.mark.usefixtures("enable_pipeline_private_preview_features", "mock_component_hash") +class TestCacheUtils: + @staticmethod + def _mock_resolver(component: Union[str, Component], azureml_type: str) -> str: + if isinstance(component, str): + return azureml_type + ":" + component if not component.startswith(azureml_type + ":") else component + return component._get_anonymous_hash() + + @staticmethod + def _get_cache_path(component: Component, resolver: CachedNodeResolver) -> Path: + in_memory_hash = resolver._get_in_memory_hash_for_component(component) + on_disk_hash = resolver._get_on_disk_hash_for_component(component=component, in_memory_hash=in_memory_hash) + return resolver._get_on_disk_cache_path(on_disk_hash) + + @staticmethod + def create_resolver(client: MLClient) -> CachedNodeResolver: + return CachedNodeResolver( + resolver=TestCacheUtils._mock_resolver, + subscription_id=client.subscription_id, + resource_group_name=client.resource_group_name, + workspace_name=client.workspace_name, + registry_name=client._operation_scope.registry_name, + ) + + @staticmethod + def get_target_node(): + pipeline_job: PipelineJob = load_job( + source="./tests/test_configs/pipeline_jobs/helloworld_pipeline_job_inline_comps.yml", + ) + return pipeline_job.jobs["hello_world_component_inline"] + + @staticmethod + def register_target_node_and_resolve(resolver: CachedNodeResolver) -> None: + # clear in-memory cache + resolver._cache.clear() + target_node = TestCacheUtils.get_target_node() + # always register a newly created node + resolver.register_node_for_lazy_resolution(target_node) + resolver.resolve_nodes() + return target_node.component + + def test_on_disk_cache_occupied(self, mock_machinelearning_client: MLClient) -> None: + resolver = self.create_resolver(mock_machinelearning_client) + target_cache_path = self._get_cache_path(self.get_target_node().component, resolver) + assert not target_cache_path.exists() + self.register_target_node_and_resolve(resolver) + assert target_cache_path.exists() + os.chmod(target_cache_path, stat.S_IREAD) + + # test write to readonly file + # mock to avoid using existed on-disk cache + with mock.patch.object(resolver, "_load_from_on_disk_cache", return_value=None): + cur_time = os.stat(target_cache_path).st_mtime + self.register_target_node_and_resolve(resolver) + # no change to the file and no exception raised + assert cur_time == os.stat(target_cache_path).st_mtime + + def test_on_disk_cache_share_among_users(self, mock_machinelearning_client: MLClient) -> None: + resolver = self.create_resolver(mock_machinelearning_client) + target_cache_path = self._get_cache_path(self.get_target_node().component, resolver) + + self.register_target_node_and_resolve(resolver) + assert target_cache_path.exists() + assert stat.filemode(target_cache_path.stat().st_mode) == 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"x-aml-cluster": "vienna-test-westus2-01", + "X-Content-Type-Options": "nosniff", + "x-ms-correlation-request-id": "f0abdc6c-eecf-419a-9b17-e8231a5a51a9", + "x-ms-ratelimit-remaining-subscription-reads": "11993", + "x-ms-response-type": "standard", + "x-ms-routing-request-id": "JAPANEAST:20230106T022721Z:f0abdc6c-eecf-419a-9b17-e8231a5a51a9", + "x-request-time": "0.028" + }, + "ResponseBody": null + } + ], + "Variables": {} +} diff --git a/sdk/ml/azure-ai-ml/tests/test_utilities/utils.py b/sdk/ml/azure-ai-ml/tests/test_utilities/utils.py index 8e156ad73628..ef1352150d06 100644 --- a/sdk/ml/azure-ai-ml/tests/test_utilities/utils.py +++ b/sdk/ml/azure-ai-ml/tests/test_utilities/utils.py @@ -297,6 +297,12 @@ def assert_job_cancel( return created_job +def submit_and_cancel_new_dsl_pipeline(pipeline_func, client, default_compute="cpu-cluster", **kwargs): + pipeline_job: PipelineJob = pipeline_func(**kwargs) + pipeline_job.settings.default_compute = default_compute + return assert_job_cancel(pipeline_job, client) + + def wait_until_done(client: MLClient, job: Job, timeout: int = None) -> str: poll_start_time = time.time() while job.status not in RunHistoryConstants.TERMINAL_STATUSES: