From 793436503da6cb83ec30d9cfa0b1fa85aac1db2a Mon Sep 17 00:00:00 2001 From: Slava Kovalevskyi Date: Mon, 19 May 2025 10:38:16 -0700 Subject: [PATCH 01/10] adding good put lib code --- ml-goodput-measurement/CHANGELOG.md | 96 + ml-goodput-measurement/CONTRIBUTING.md | 33 + ml-goodput-measurement/LICENSE | 201 ++ ml-goodput-measurement/README.md | 697 ++++++ .../ml_goodput_measurement/__init__.py | 20 + .../src/checkpoint_badput_calculator.py | 676 ++++++ .../ml_goodput_measurement/src/gcp_metrics.py | 106 + .../ml_goodput_measurement/src/goodput.py | 1690 +++++++++++++ .../src/goodput_cache.py | 119 + .../src/goodput_utils.py | 258 ++ .../ml_goodput_measurement/src/monitoring.py | 638 +++++ .../checkpoint_badput_calculator_test.py | 446 ++++ .../tests/gcp_metrics_test.py | 150 ++ .../tests/goodput_cache_test.py | 141 ++ .../tests/goodput_test.py | 2102 +++++++++++++++++ .../tests/monitoring_test.py | 794 +++++++ ml-goodput-measurement/pyproject.toml | 59 + 17 files changed, 8226 insertions(+) create mode 100644 ml-goodput-measurement/CHANGELOG.md create mode 100644 ml-goodput-measurement/CONTRIBUTING.md create mode 100644 ml-goodput-measurement/LICENSE create mode 100644 ml-goodput-measurement/README.md create mode 100644 ml-goodput-measurement/ml_goodput_measurement/__init__.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/goodput.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py create mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py create mode 100644 ml-goodput-measurement/pyproject.toml diff --git a/ml-goodput-measurement/CHANGELOG.md b/ml-goodput-measurement/CHANGELOG.md new file mode 100644 index 0000000..79a8b95 --- /dev/null +++ b/ml-goodput-measurement/CHANGELOG.md @@ -0,0 +1,96 @@ +# Changelog + + +## [0.0.10] - 2025-04-28 + +* Support for custom badput events which are synchronous and training-overlapped. +* Handling of edge case caching scenario. + +## [0.0.9] - SKIPPED + +* Used for external testing. Please upgrade to 0.0.10. + +## [0.0.8] - 2025-04-03 + +* Fix computation of ideal step time when step_times is empty. + +## [0.0.7] - 2025-03-24 + +* Cache updates to Other/Unknown Badput. +* Exclude monitoring asynchronous Badput types in GCM. +* Total and last step updates with hidden events. +* Interval Query Monitoring in GCM. + +## [0.0.6] - 2025-03-17 + +* Updates to data loading Badput buckets (Separated into Async & Sync). +* Short term fix to Pathways SuspendResume anomalous step time detection. +* Updates to account for Pathways Elastic Training. +* Automatic asynchronous upload of goodput, badput and step time deviation metrics to GCM. + +## [0.0.5] - 2025-02-03 + +* Goodput Cache and library improvements. +* Query and Monitor API support for checkpoint save and restore. +* Interval Query API support. +* Query and Monitor API support for step time deviation. + +## [0.0.4] - 2024-09-13 + +* Add Badput breakdown to GoodputMonitor. +* Add Checkpoint Badput Calculator backend. +* Return last recorded step from Goodput query API. +* Bug Fixes + * Fix a potential race-condition with Tensorboard write to GCS. + * Fix zero job time issue on long running jobs + +## [0.0.3] - 2024-05-28 + +* Compute and discount Badput from first step after start or restart. +* Compute and discount Badput due to anomalous step times (Pathways only). +* Badput recording APIs +* Some Badput computation APIs (TPU initialization , training preparation, data loading, program startup) +* Goodput monitoring API to asynchronously query and upload Goodput to Tensorboard. +* Bug Fixes + * Fix Goodput calculation with disruptions + * Fix some Cloud Logging latency and batching issues. + +## [0.0.2] - 2024-02-29 + +* Bug Fixes + * Fixes a typing mismatch in total step time calculation. +* Code and documentation cleanup + +## [0.0.1] - 2024-02-26 + +* Initial release of ML Goodput Measurement PyPi package +* Feature: Contains the Goodput module which allows logging and retrieval of training job's overall productive Goodput + +[0.0.10]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.8...v0.0.10 +[0.0.8]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.7...v0.0.8 +[0.0.7]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.6...v0.0.7 +[0.0.6]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.5...v0.0.6 +[0.0.5]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.4...v0.0.5 +[0.0.4]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.3...v0.0.4 +[0.0.3]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.2...v0.0.3 +[0.0.2]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.1...v0.0.2 +[0.0.1]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/releases/tag/v0.0.1 \ No newline at end of file diff --git a/ml-goodput-measurement/CONTRIBUTING.md b/ml-goodput-measurement/CONTRIBUTING.md new file mode 100644 index 0000000..bc23aae --- /dev/null +++ b/ml-goodput-measurement/CONTRIBUTING.md @@ -0,0 +1,33 @@ +# How to contribute + +We'd love to accept your patches and contributions to this project. + +## Before you begin + +### Sign our Contributor License Agreement + +Contributions to this project must be accompanied by a +[Contributor License Agreement](https://cla.developers.google.com/about) (CLA). +You (or your employer) retain the copyright to your contribution; this simply +gives us permission to use and redistribute your contributions as part of the +project. + +If you or your current employer have already signed the Google CLA (even if it +was for a different project), you probably don't need to do it again. + +Visit to see your current agreements or to +sign a new one. + +### Review our community guidelines + +This project follows +[Google's Open Source Community Guidelines](https://opensource.google/conduct/). + +## Contribution process + +### Code reviews + +All submissions, including submissions by project members, require review. 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It + can be pip installed to import its modules, and retrieve information about a + training job's overall productive Goodput and sources of Badput. The package + exposes API interfaces to log useful information from the user application and + query Goodput for the job run, gain insight into the productivity of ML + workloads and utilization of compute resources. + + The package also exposes Goodput Monitoring APIs which allow asynchronous query + and export of the job's Goodput, Badput and Step Time Deviation to Tensorboard + with configurable upload interval. + +## Components + + + The ML Goodput Measurement library consists of the following main components: + + - `GoodputRecorder` + + - `GoodputCalculator` + - `GoodputMonitor` + - `GoodputCache` + + + The `GoodputRecorder` + exposes APIs to the client to export key timestamps while a training job makes + progress, namely APIs that allow logging of productive step time and total job + run time. The library will serialize and store this data in Google Cloud + Logging. + + The `GoodputCalculator` exposes APIs to compute Goodput based on the + recorded data. Cloud Logging handles its internal operations asynchronously. + The recommended way to compute Goodput is to run an analysis program separate + from the training application, either on a CPU instance or on the users' + development machine. + + Under the hood, the `GoodputCalculator` uses a `GoodputCache` which is an + internal component that locally caches pre-computations and useful logs such + that repeated computations can be made inexpensive. + + The `GoodputMonitor` exposes APIs to query and upload goodput and step time + deviation data to Tensorboard asynchronously. It does this by instantiating a + `GoodputCaluclator` under the hood. + +## Installation + + To install the ML Goodput Measurement package, run the following command on the + VM or machine you want to query or monitor your workload from: + + ```bash + pip install ml-goodput-measurement + ``` + +## Usage + +The usage of this package requires the setup of a Google Cloud project with +billing enabled to properly use Google Cloud Logging. If you don't have a Google +Cloud project, or if you don't have billing enabled for your Google Cloud +project, then do the following: + +1. In the Google Cloud console, on the project selector page, + [select or create a Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects). + +2. Make sure that billing is enabled for your Google Cloud project. Instructions can be found [here](https://cloud.google.com/billing/docs/how-to/verify-billing-enabled#console) + +3. [Enable](https://console.cloud.google.com/flows/enableapi?apiid=logging.googleapis.com&_ga=2.27841276.1571868865.1726250448-123998259.1726107009) the Cloud Logging API. + + To run your training on Cloud accelerator, set up the environment by following + instructions [here](https://cloud.google.com/tpu/docs/setup-gcp-account). + + To learn more about Google Cloud Logging, visit this [page](https://cloud.google.com/logging/docs). + +### Access Scopes + + You will need both read and write access scopes for cloud logging on both the + GPU or TPU and CPU node pools. Full cloud logging access is granted by the + following access scope during node pool creation: + + - `https://www.googleapis.com/auth/cloud-platform` + + XPK adds this access scope to the GPU, TPU and CPU node pools, so XPK is the + recommended method to create clusters and node-pools in you intend to run + your workloads on GKE. + + Instructions on how to create clusters using XPK can be + found [here](https://github.com/AI-Hypercomputer/xpk/blob/main/README.md#cluster-create) + and how to create workloads using XPK can be found + [here](https://github.com/AI-Hypercomputer/xpk/blob/main/README.md#workload-create). + + > **_NOTE:_** Access Scopes are immutable and workloads can only be migrated + to new node pools with required access scopes. Access scopes on already created + clusters cannot be updated. + +### Import + + To use this package, import the `goodput` module: + + ```python + from ml_goodput_measurement import goodput + from ml_goodput_measurement import monitoring + ``` + +### Define the name of the Google Cloud Logging logger. + + Create a run-specific logger name where Cloud Logging entries can be written to + and read from. + + > **IMPORTANT:** Please use a unique `run_name` for each individual experiment + or workload that you intend to monitor separately. If you unintentionally re-use + `run_name` or `goodput_logger_name` in the same storage bucket of a GCP project, + your cumulative Goodput metrics may be inaccurately taking previous runs into + account. + + For example: + + ```python + goodput_logger_name = f'goodput_{config.run_name}' # Here run_name is unique. + ``` + +### Create a `GoodputRecorder` object + + Next, create a recorder object with the following parameters: + + 1. `job_name`: The full run name of the job. + 2. `logger_name`: The name of the Cloud Logging logger object (created in the previous step). + 3. `logging_enabled`: Whether or not this process has Cloud Logging enabled. + + > **_NOTE:_** For a multi-worker setup, please ensure that only one worker + writes the logs to avoid the duplication. In JAX, for example, the check + could be `if jax.process_index() == 0` + + > **_NOTE:_** `logging_enabled` defaults to `False` and Goodput computations + cannot be completed if no logs are ever written. + + For example: + + ```python + goodput_recorder = goodput.GoodputRecorder( + job_name=config.run_name, + logger_name=goodput_logger_name, + logging_enabled=(jax.process_index() == 0) + ) + ``` + + > **_NOTE:_** JAX initialization should be complete before this call. + +### Record Data with `GoodputRecorder` + +#### Record Job Start and End Time + + Use the recorder object to record the job's overall start and end time. + + For example: + + ```python + def main(argv: Sequence[str]) -> None: + # Initialize configs… + goodput_recorder.record_job_start_time(datetime.datetime.now()) + # Device Initialization and device scanning… + # Set up other things for the main training loop… + # Main training loop + train_loop(config) + goodput_recorder.record_job_end_time(datetime.datetime.now()) + ``` + +#### Record Step Time + + Use the recorder object to record a step's start time using + `record_step_start_time(step_count)`: + +For example: + + ```python + def train_loop(config, state=None): + # Set up mesh, model, state, checkpoint manager… + + # Initialize functional train arguments and model parameters… + + # Define the compilation + + for step in np.arange(start_step, config.steps): + goodput_recorder.record_step_start_time(step) + # Training step… + + return state + ``` + +#### Record Device Initialization, Training Preparation and Data Loading Time + + - Use the recorder object to record Device Initialization time using + `record_tpu_init_start_time` and `record_tpu_init_end_time`. + - Use the recorder object to record Training Preparation time using + `record_training_preparation_start_time` and + `record_training_preparation_end_time`. + - Use the recorder object to record Data Loading time using + `record_data_loading_start_time` and `record_data_loading_end_time`. + + For example: + + ```python + def train_loop(config, state=None): + goodput_recorder.record_tpu_init_start_time() + # Set up mesh, model, state, checkpoint manager… + goodput_recorder.record_tpu_init_end_time() + goodput_recorder.record_training_preparation_start_time() + # Set up training set, initialize functional train args and model parameters… + # Define the compilation + # Set up any metrics collectors + goodput_recorder.record_training_preparation_end_time() + + for step in np.arange(start_step, config.steps): + goodput_recorder.record_data_loading_start_time() + example_batch = load_next_batch(data_iterator, example_batch, config) + goodput_recorder.record_data_loading_end_time() + goodput_recorder.record_step_start_time(step) + # Training step… + + return state + ``` + +#### Record Custom Badput Events (e.g., Evaluation, SDC Checks) + +- Use the recorder object to record the **start** of a custom badput event using + `record_custom_badput_event_start_time(custom_badput_event_type='your_event_name')`. +- Use the recorder object to record the **end** of a custom badput event using + `record_custom_badput_event_end_time(custom_badput_event_type='your_event_name')`. + +Use these APIs when you want to account for time spent on operations that +block the training loop and use accelerator resources, do not contribute to +productive training and occur while training is in progress — such as step +evaluations, SDC checks, or re-compilations. + +For example: + +```python +def train_loop(config, state=None): + goodput_recorder.record_training_preparation_start_time() + # Initialize training config, setup model, load checkpoint... + goodput_recorder.record_training_preparation_end_time() + + for step in range(config.steps): + goodput_recorder.record_data_loading_start_time() + batch = load_batch(train_data) + goodput_recorder.record_data_loading_end_time() + + goodput_recorder.record_step_start_time(step) + # Run training step... + run_train_step(step, state) + + if step % config.eval_interval == 0: + # Record a custom badput event for evaluation + goodput_recorder.record_custom_badput_event_start_time( + custom_badput_event_type="eval_step") + run_step_evaluation(model, val_data) + goodput_recorder.record_custom_badput_event_end_time( + custom_badput_event_type="eval_step") + + if step % config.sdc_check_interval == 0: + # Record a custom badput event for SDC check + goodput_recorder.record_custom_badput_event_start_time( + custom_badput_event_type="sdc_check") + run_sdc_check(state) + goodput_recorder.record_custom_badput_event_end_time( + custom_badput_event_type="sdc_check") + + return state +``` + +> **_NOTE:_** The `custom_badput_event_type` string should be descriptive and +consistent (e.g., "eval_step", "sdc_check"), to ensure accurate aggregation and +reporting in badput breakdowns. + +### Retrieve Goodput with `GoodputCalculator` + +In order to retrieve the Goodput of a job run, all you need to do is instantiate +a `GoodputCalculator` object with the job's run name and the Cloud Logging +logger name used to record data for that job run. Then call the +`get_job_goodput` API to get the computed Goodput for the job run. + +It is recommended to make the `get_job_goodput` calls for a job run from an +instance that runs elsewhere from your training machine. + +#### Create a `GoodputCalculator` object + +Create the calculator object: + +```python +goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. +goodput_calculator = goodput.GoodputCalculator(job_name=config.run_name, logger_name=goodput_logger_name) +``` + +If you want to enable Pathways, turn on the `using_pathways` flag: + +```python +goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. +goodput_calculator = goodput.GoodputCalculator(job_name=config.run_name, logger_name=goodput_logger_name, using_pathways=True) +``` + +#### Retrieve Goodput + +Finally, call the `get_job_goodput` API to retrieve Goodput for the entire job +run. This API takes an optional parameter `include_badput_breakdown`. which +defaults to `False`. + +The returned result is a tuple of the job’s Goodput at query-time, a dictionary +mapping various sources of Badput and their corresponding percentages and the +last recorded step. If `include_badput_breakdown` is not set, an empty +dictionary for Badput is returned. + +If you are only interested in Goodput: + +```python +total_goodput, _, _ = goodput_calculator.get_job_goodput() +print(f"Total job goodput: {total_goodput:.2f}%") +``` + +#### Retrieve Badput Breakdown + +Badput breakdown is dictionary representation of various sources of Badput +mapped to its corresponding value. Badput is the percentage of time spent by the +job doing work that is not training to the total lifetime of the job. This +includes time spent doing device initialization, training preparation, +program startup, checkpoint loading, compilation or re-compilation, data loading, +checkpoint saving, custom badput events, wasted progress and time lost due +to disruptions. + +Following Badput Breakdown buckets are supported by the library at this time: + +```python +# Supported Badput Types +class BadputType(enum.Enum): + """The type of Badput.""" + + TPU_INITIALIZATION = 1 + TRAINING_PREP = 2 + PROGRAM_STARTUP = 3 + DATA_LOADING_SYNC = 4 + DATA_LOADING_ASYNC = 5 # This does not affect Goodput + UNPRODUCTIVE_CHECKPOINT_SAVE_TIME = 6 + UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME = 7 + WASTED_PROGRESS_FROM_DISRUPTION = 8 + CUSTOM_BADPUT_EVENTS = 9 + OTHER = 10 +``` + +#### Badput Breakdown Details + + - Accelerator Initialization Time (TPU_INITIALIZATION) + + This is the time spent on device discovery, slice initialization, + device driver re-initialization and reset, security setup, initialization of + pre-mapped buffers and more. + + - Training Preparation Time (TRAINING_PREP) + + This is the time spent on the creation of checkpoint managers, checkpoint + loading, running mesh and model optimizers and more. + + - Program Startup Time (PROGRAM_STARTUP) + + This is the time spent on framework specific function transformations + (such as JAX tracing), compilation tasks, runtime initialization etc. + + - Data Loading Time (DATA_LOADING_SYNC) + + This is the time spent on loading each batch of data for the training at a + step to continue. This should be a small contribution to Badput if parallel + data loading is used. + + Async data loading is accumulated overlapping with training steps and is + non-blocking, therefore is not unproductive time. The time spent on overlapped + data loading is stored in BadputType.DATA_LOADING_ASYNC, but does **not** + affect overall Goodput of the workload. + + - Checkpointing Time (UNPRODUCTIVE_CHECKPOINT_SAVE_TIME, UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME) + + This is the time spent on saving a checkpoint and restoring a checkpoint. + + Depending on the type of checkpointing technology used by the program, there + could be unproductive time while saving a checkpoint. When checkpointing is + synchronous, the save operation will block training progress until it is complete. + + During asynchronous checkpointing, the model parameters or weights have to be + transferred from the device memory to the host memory which is a blocking + operation on the device. After the transfer, the device can proceed with model + training while the CPU saves the checkpoint to storage in the background. The + first blocking operation contributes to unproductive checkpoint time. + + If auto checkpointing is used, the checkpoint save operation is initiated upon + detection of a planned disruption signal. The save operation in type of + checkpointing is synchronous resulting in time lost to Badput. + + - Wasted Progress due to Disruption (WASTED_PROGRESS_FROM_DISRUPTION) + + Based on checkpointing frequency, a disruption may result in time lost in the + form of wasted progress, i.e. time that was spent on productive training but + lost after restart as well as time lost for the infrastructure to restart the + workload. + + When there is a disruption, Badput is expected to accumulate in + each of the following buckets after restart: + + - Accelerator Initialization + - Training Preparation + - Program Startup + - Wasted Progress due to Disruption + + - Custom Badput Events (CUSTOM_BADPUT_EVENTS) + + Your application can optionally use record and monitor badput from custom + synchronous (blocking training) and overlapping (between training steps) + events. These events are are generally used for useful non-training activity on + the accelerator while training is in progress such as performing SDC checks + or evaluations. + +If you are interested in retrieving Badput Breakdown along with Goodput: + +```python +goodput, badput_breakdown, last_step = goodput_calculator.get_job_goodput(include_badput_breakdown=True) +print(f"Last step recorded: {last_step}") +print(f"Goodput: {goodput:.2f}%") +print(f"Badput due to TPU initialization: {badput_breakdown[goodput.BadputType.TPU_INITIALIZATION]:.2f}%") +print(f"Badput due to training preparation: {badput_breakdown[goodput.BadputType.TRAINING_PREP]:.2f}%") +print(f"Badput due to program startup: {badput_breakdown[goodput.BadputType.PROGRAM_STARTUP]:.2f}%") +print(f"Badput due to data loading: {badput_breakdown[goodput.BadputType.DATA_LOADING_SYNC]:.2f}%") +print(f"Badput due to disruption and wasted progress: {badput_breakdown[goodput.BadputType.WASTED_PROGRESS_FROM_DISRUPTION]:.2f}%") +print(f"Badput due to checkpoint save: {badput_breakdown[goodput.BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME]:.2f}%") +print(f"Badput due to checkpoint restore: {badput_breakdown[goodput.BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME]:.2f}%") +print(f"Badput due to step evaluation: {badput_breakdown[goodput.BadputType.CUSTOM_BADPUT_EVENTS].get('EVAL_STEP', 0.0):.2f}%") +print(f"Badput due to SDC checks: {badput_breakdown[goodput.BadputType.CUSTOM_BADPUT_EVENTS].get('SDC_CHECK', 0.0):.2f}%") +print(f"Badput from unknown source: {badput_breakdown[goodput.BadputType.OTHER]:.2f}%") +``` + +#### Interval Query Goodput and Badput + +If you are interested in retrieving Goodput and Badput of the workload within a +specific window of time, the `GoodputCalculator` exposes the +`get_job_goodput_interval` API which computes metrics between the start and end +of this window. + +This API also returns the last step recorded for the job. the total job time in +this window and the number of disruptions within the interval window. + +> **_IMPORTANT:_** **Use this API if** you know the exact window of time within + the workload's total run time that you are interested in. + +> **_IMPORTANT:_** **Do NOT use this API if** your workload has been manually + disrupted. + +> **_IMPORTANT:_** **Do NOT use this API if** you have accidentally re-used a + previous `run_name`. + +```python +# Example usage +start_time_str = "2024-12-16 1:05:00" +start_time_utc = convert_pst_to_utc(start_time_str) +end_time_str = "2024-12-17 2:00:00" +end_time_utc = convert_pst_to_utc(end_time_str) +current_goodput, badput_breakdown, last_step, total_time, disruptions = goodput_calculator.get_job_goodput_interval(start_time_utc, end_time_utc) +``` + +### Monitor Goodput with `GoodputMonitor` + +In order to monitor the Goodput of a job run on Tensorboard, all you need to do +is instantiate a `GoodputMonitor` object with the job's run name, cloud logger +name and Goodput monitoring configurations (as described below). Then call the +`start_goodput_uploader` API to asynchronously query and upload measured Goodput +to the specified Tensorboard directory. + +#### Create a `GoodputMonitor` object + +Create a `GoodputMonitor` object with the following parameters: + + 1. `job_name`: The full run name of the job. + 2. `logger_name`: The name of the Cloud Logging logger object (created in the previous step). + 3. `tensorboard_dir`: The directory to write TensorBoard data to. + 4. `upload_interval`: The time interval at which to query and upload data to TensorBoard. + 5. `monitoring_enabled`: Whether or not monitoring is enabled. + If the application is interested in monitoring Goodput, it should set + this value to True. Only one worker should enable monitoring. + 6. `include_badput_breakdown`: Whether to query and upload badput breakdown + data to Tensorboard. + +> **_NOTE:_** Please ensure that only **one** worker enables monitoring of Goodput. + In JAX, for example, the check could be `if jax.process_index() == 0` + +For example: + +```python +goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. +goodput_monitoring_enabled = config.monitor_goodput and jax.process_index() == 0 # Check for configs whether or not the enable monitoring. + +goodput_monitor = monitoring.GoodputMonitor( + job_name=config.run_name, + logger_name=logger_name, + tensorboard_dir=config.tensorboard_dir, + upload_interval=config.goodput_upload_interval_seconds, + monitoring_enabled=True, + include_badput_breakdown=True, + ) +``` + +If you want to enable Pathways, turn on the `pathway_enabled` flag: + +```python +goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. +goodput_monitoring_enabled = config.monitor_goodput and jax.process_index() == 0 # Check for configs whether or not the enable monitoring. + +goodput_monitor = monitoring.GoodputMonitor( + job_name=config.run_name, + logger_name=logger_name, + tensorboard_dir=config.tensorboard_dir, + upload_interval=config.goodput_upload_interval_seconds, + monitoring_enabled=True, + include_badput_breakdown=True, + pathway_enabled=True + ) +``` + +If you want to monitor Step Time Deviation, configure the `GoodputMonitor` +as follows: + +```python +goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. +goodput_monitoring_enabled = config.monitor_goodput and jax.process_index() == 0 # Check for configs whether or not the enable monitoring. + +goodput_monitor = monitoring.GoodputMonitor( + job_name=config.run_name, + logger_name=logger_name, + tensorboard_dir=config.tensorboard_dir, + upload_interval=config.goodput_upload_interval_seconds, + monitoring_enabled=True, + include_badput_breakdown=True, + include_step_deviation=True, + configured_ideal_step_time=None # Optional, the library will compute ideal step time if it is not provided + ) +``` + +#### Start asynchronous "query and upload" of Goodput + +Call the `start_goodput_uploader` API to spin off a thread which continuously +queries and uploads Goodput. + +Note: This will upload Goodput and Badput data to Google Cloud Monitoring +by default. + +```python +goodput_monitor.start_goodput_uploader() +``` + +#### Start asynchronous "query and upload" of Step Time Deviation + +Call the `start_step_deviation_uploader` API to spin off a thread which +continuously queries and uploads step time deviation. + +Note: This will upload Step Time Deviation data to Google Cloud Monitoring +by default. + +```python +goodput_monitor.start_step_deviation_uploader() +``` + +#### Visualize on Tensorboard + +1. Make sure you have `tensorboard-plugin-profile`, `tensorflow` and `tensorboard` packages installed +2. Follow instructions [here](https://cloud.google.com/tpu/docs/profile-tpu-vm#start_profiling_the_model_training) to start the Tensorboard server + +#### Access Goodput, Badput and Step Deviation on Google Cloud Monitoring + +By default, performance data ([goodput](https://cloud.google.com/monitoring/api/metrics_gcp#:~:text=workload/goodput_time), [badput](https://cloud.google.com/monitoring/api/metrics_gcp#:~:text=workload/badput_time), and [step deviation](https://cloud.google.com/monitoring/api/metrics_gcp#:~:text=workload/performance)) is automatically sent to Google Cloud Monitoring, enabling visualization on dashboards. + +This feature leverages Google VM metadata (project ID, location, accelerator type) +and supports replica IDs for uniquely identifying workloads in multi-replica +deployments. + +```python + +gcp_options = goodput_utils.GCPOptions( + project_id=None, # If None, the library will automatically identify from GCE internal metadata + location=None, # If None, the library will automatically identify from GCE internal metadata + replica_id='0', # Default is '0' + acc_type=None, # If None, the library will automatically identify from GCE internal metadata + enable_gcp_goodput_metrics=True, + enable_gcp_step_deviation_metrics=True, + ) + +goodput_monitor = monitoring.GoodputMonitor( + job_name=config.run_name, + logger_name=logger_name, + tensorboard_dir=config.tensorboard_dir, + upload_interval=config.goodput_upload_interval_seconds, + monitoring_enabled=True, + include_badput_breakdown=True, + include_step_deviation=True, + configured_ideal_step_time=None, # Optional, the library will compute ideal step time if it is not provided + gcp_options=gcp_options + ) +``` + +If you do not wish to send metrics to Google Cloud Monitoring then please set +the flag `enable_gcp_goodput_metrics` to `False` for disabling goodput metrics +and `enable_gcp_step_deviation_metrics` to `False` for disabling step deviation +metrics while creating the GCPOptions object. + +Setting `monitoring_enabled` to `False` will disable both tensorboard and GCM +monitoring. + +```python + +gcp_options = goodput_utils.GCPOptions( + project_id=None, # If None, the library will automatically identify from GCE internal metadata + location=None, # If None, the library will automatically identify from GCE internal metadata + replica_id='0', # Default is '0' + acc_type=None, # If None, the library will automatically identify from GCE internal metadata + enable_gcp_goodput_metrics=False, + enable_gcp_step_deviation_metrics=False, + ) + + +goodput_monitor = monitoring.GoodputMonitor( + job_name=config.run_name, + logger_name=logger_name, + tensorboard_dir=config.tensorboard_dir, + upload_interval=config.goodput_upload_interval_seconds, + monitoring_enabled=True, + include_badput_breakdown=True, + include_step_deviation=True, + configured_ideal_step_time=None, + gcp_options=gcp_options, + ) +``` + +If you want to monitor Goodput and Badput metrics computed in a specific window +of time, you can use the `start_goodput_interval_uploader` monitoring API. + +#### Create the `GoodputMonitor` with `enable_gcp_goodput_metrics` set to `True` in `GCPOptions` + +```python + +gcp_options = goodput_utils.GCPOptions( + project_id=None, # If None, the library will automatically identify from GCE internal metadata + location=None, # If None, the library will automatically identify from GCE internal metadata + replica_id='0', # Default is '0' + acc_type=None, # If None, the library will automatically identify from GCE internal metadata + enable_gcp_goodput_metrics=True, + ) + +goodput_monitor = monitoring.GoodputMonitor( + job_name=config.run_name, + logger_name=logger_name, + tensorboard_dir=config.tensorboard_dir, + upload_interval=config.goodput_upload_interval_seconds, + monitoring_enabled=True, + include_badput_breakdown=True, + gcp_options=gcp_options + ) +``` + +#### Start asynchronous "query and upload" of Interval Goodput to GCM. + +Call the `start_goodput_interval_uploader` API and specify `window_size_seconds` +to compute Goodput and Badput metrics only in the sliding time window. +The interval starts `window_size_seconds` prior to time of query, ends at time +of query, and moves ahead by `upload_interval` seconds. + +This call is asynchronous and will only upload Goodput and Badput data to +Google Cloud Monitoring, and not to Tensorboard. + +```python +# Set the window size to be 12h +goodput_monitor.start_goodput_interval_uploader(window_size_seconds = 43200) +``` + +Note: Google Cloud Monitoring will allow you to view all the metrics reported +during the entire workload. GCM will also allow you to filter by any time window +(irrespective of `window_size_seconds`). Each data point that is reported by +this API will correspond to computation only within the sliding window of size +`window_size_seconds`. \ No newline at end of file diff --git a/ml-goodput-measurement/ml_goodput_measurement/__init__.py b/ml-goodput-measurement/ml_goodput_measurement/__init__.py new file mode 100644 index 0000000..88fceac --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from cloud_goodput.ml_goodput_measurement.src import checkpoint_badput_calculator +from cloud_goodput.ml_goodput_measurement.src import gcp_metrics +from cloud_goodput.ml_goodput_measurement.src import goodput +from cloud_goodput.ml_goodput_measurement.src import goodput_cache +from cloud_goodput.ml_goodput_measurement.src import goodput_utils +from cloud_goodput.ml_goodput_measurement.src import monitoring diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py b/ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py new file mode 100644 index 0000000..34a2110 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py @@ -0,0 +1,676 @@ +"""Checkpoint Badput Calculator class.""" + +import argparse +import dataclasses +import statistics +from typing import Dict, List, Optional + +import google.cloud.logging as google_cloud_logging + + +_JOB_NAME = 'checkpoint_job' +_LOGGER_NAME = 'checkpoint_logger' + +_STEP = 'step' +_EVENT_TYPE = 'event_type' +_DIRECTORY = 'directory' + +_WAIT_FOR_PREV_DURATION_SECS = 'wait_for_prev_duration_secs' + +_CHECKPOINTER_SAVE_DURATION_SECS = 'checkpointer_blocking_duration_secs' +_CHECKPOINTER_RESTORE_DURATION_SECS = 'checkpointer_duration_secs' + +_GET_OLD_STEPS_DURATION_SECS = 'get_old_steps_duration_secs' + +_CHECKPOINT_MANAGER_SAVE_DURATION_SECS = 'checkpoint_manager_blocking_duration_secs' +_CHECKPOINT_MANAGER_RESTORE_DURATION_SECS = 'checkpoint_manager_duration_secs' + +_BROADCAST_DURATION_SECS = 'broadcast_duration_secs' + +OPERATION_TYPE_SAVE = 'save' +OPERATION_TYPE_RESTORE = 'restore' +OPERATION_TYPE_EMERGENCY_RESTORE = 'emergency_restore' + +OPERATION_TYPE_LOCAL = 'local' +OPERATION_TYPE_PERSISTENT = 'persistent' +OPERATION_TYPE_PERSISTENT_AND_LOCAL = 'persistent_and_local' + +_CLOUD_LOGGING_PAGE_SIZE = 10000 + + +@dataclasses.dataclass +class SaveCheckpointManagerVerticalStepStats: + """Vertical step statistics for save operation.""" + total_checkpoint_manager_blocking_time: float = 0.0 + average_checkpoint_manager_blocking_time: float = 0.0 + minimum_checkpoint_manager_blocking_time: float = 0.0 + maximum_checkpoint_manager_blocking_time: float = 0.0 + standard_deviation_checkpoint_manager_blocking_time: float = 0.0 + + total_checkpointer_blocking_time: float = 0.0 + average_checkpointer_blocking_time: float = 0.0 + minimum_checkpointer_blocking_time: float = 0.0 + maximum_checkpointer_blocking_time: float = 0.0 + standard_deviation_checkpointer_blocking_time: float = 0.0 + + total_wait_for_prev_time: float = 0.0 + average_wait_for_prev_time: float = 0.0 + minimum_wait_for_prev_time: float = 0.0 + maximum_wait_for_prev_time: float = 0.0 + standard_deviation_wait_for_prev_time: float = 0.0 + + total_get_old_steps_time: float = 0.0 + average_get_old_steps_time: float = 0.0 + minimum_get_old_steps_time: float = 0.0 + maximum_get_old_steps_time: float = 0.0 + standard_deviation_get_old_steps_time: float = 0.0 + + +@dataclasses.dataclass +class RestoreCheckpointManagerVerticalStepStats: + """Vertical step statistics for restore operation.""" + total_checkpoint_manager_time: float = 0.0 + average_checkpoint_manager_time: float = 0.0 + minimum_checkpoint_manager_time: float = 0.0 + maximum_checkpoint_manager_time: float = 0.0 + standard_deviation_checkpoint_manager_time: float = 0.0 + + total_restore_time: float = 0.0 + average_restore_time: float = 0.0 + minimum_restore_time: float = 0.0 + maximum_restore_time: float = 0.0 + standard_deviation_restore_time: float = 0.0 + + total_broadcast_time: float = 0.0 + average_broadcast_time: float = 0.0 + minimum_broadcast_time: float = 0.0 + maximum_broadcast_time: float = 0.0 + standard_deviation_broadcast_time: float = 0.0 + + +@dataclasses.dataclass +class SaveProcessedStep: + """Horizontal save step stats for a processed step.""" + step: str = '' + total_checkpoint_manager_blocking_time: float = 0.0 + total_checkpointer_blocking_time: float = 0.0 + total_wait_for_prev_time: float = 0.0 + total_get_old_steps_time: float = 0.0 + occurrence: int = 0 + + +@dataclasses.dataclass +class RestoreProcessedStep: + """Horizontal restore step stats for a processed step.""" + step: str = '' + total_checkpoint_manager_time: float = 0.0 + total_restore_time: float = 0.0 + total_broadcast_time: float = 0.0 + broadcast_occurrence: int = 0 + occurrence: int = 0 + + +@dataclasses.dataclass +class CheckpointLoggerOptions: + """Checkpoint logger options.""" + job_name: str = _JOB_NAME + logger_name: str = _LOGGER_NAME + client: Optional[google_cloud_logging.Client] = None + use_goodput_logger: bool = False + + +class CheckpointBadputCalculator: + """Checkpoint Badput Calculator class.""" + + def __init__( + self, options: CheckpointLoggerOptions = CheckpointLoggerOptions() + ): + self._options = options + if not options.use_goodput_logger: + if options.client is None: + self.logging_client = google_cloud_logging.Client() + else: + self.logging_client = options.client + self._logger = self.logging_client.logger(options.logger_name) + self._use_goodput_logger = options.use_goodput_logger + self.entries = [] + + def read_entries(self) -> List[Dict[str, str]]: + """Queries Cloud Logging entries for the specific job. + + Returns: + Filtered entries in ascending order of timestamp. + """ + if self._use_goodput_logger: + return self.entries + + filter_entries = [ + 'severity=INFO', + f'jsonPayload.job_name="{self._options.job_name}"', + ] + + event_type_filter = ( + '(jsonPayload.event_type=save OR jsonPayload.event_type=restore OR' + ' jsonPayload.event_type=emergency_restore)' + ) + filter_entries.append(event_type_filter) + + filter_entries = ' AND '.join(filter_entries) + + entries = self._logger.list_entries( + filter_=filter_entries, + order_by=google_cloud_logging.ASCENDING, + page_size=_CLOUD_LOGGING_PAGE_SIZE, + ) + entry_payload = [entry.payload for entry in entries] + return entry_payload + + def _is_local_operation(self, step_stats: Dict[str, str]): + if (step_stats[_DIRECTORY]).startswith('gs://'): + return False + else: + return True + + def is_valid_save_stats( + self, + step_stats: Dict[str, str], + operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, + ): + """Checks if the step stats is valid. + + Args: + step_stats: The step stats to check. + operation_type: whether to check for local or persistent or both. + + Returns: + Boolean indicating whether the step stats is valid. + """ + if ( + _EVENT_TYPE not in step_stats + or step_stats[_EVENT_TYPE] != OPERATION_TYPE_SAVE + ): + return False + elif operation_type == OPERATION_TYPE_LOCAL: + return self._is_local_operation(step_stats) + elif operation_type == OPERATION_TYPE_PERSISTENT: + return not self._is_local_operation(step_stats) + else: + return True + + def is_valid_restore_stats( + self, + step_stats: Dict[str, str], + operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, + ): + """Checks if the step stats is valid. + + Args: + step_stats: The step stats to check. + operation_type: whether to check for local or persistent or both. + + Returns: + Boolean indicating whether the step stats is valid. + + """ + if _EVENT_TYPE not in step_stats: + return False + elif step_stats[_EVENT_TYPE] not in [ + OPERATION_TYPE_RESTORE, + OPERATION_TYPE_EMERGENCY_RESTORE, + ]: + return False + elif operation_type == OPERATION_TYPE_LOCAL: + return step_stats[_EVENT_TYPE] == OPERATION_TYPE_EMERGENCY_RESTORE + elif operation_type == OPERATION_TYPE_PERSISTENT: + return step_stats[_EVENT_TYPE] == OPERATION_TYPE_RESTORE + else: + return True + + def _save_statistics( + self, processed_step_stats: Dict[str, SaveProcessedStep] + ) -> SaveCheckpointManagerVerticalStepStats: + """Gets the processed step stats.""" + if not processed_step_stats: + return SaveCheckpointManagerVerticalStepStats() + + for _, stats in processed_step_stats.items(): + if stats.occurrence > 0: + stats.total_checkpoint_manager_blocking_time = ( + stats.total_checkpoint_manager_blocking_time / stats.occurrence + ) + stats.total_checkpointer_blocking_time = ( + stats.total_checkpointer_blocking_time / stats.occurrence + ) + stats.total_wait_for_prev_time = ( + stats.total_wait_for_prev_time / stats.occurrence + ) + stats.total_get_old_steps_time = ( + stats.total_get_old_steps_time / stats.occurrence + ) + + vertical_step_stats = SaveCheckpointManagerVerticalStepStats() + + # Record statistics for checkpoint_manager_blocking_time. + vertical_step_stats.total_checkpoint_manager_blocking_time = sum( + map( + lambda stats: stats.total_checkpoint_manager_blocking_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.average_checkpoint_manager_blocking_time = ( + vertical_step_stats.total_checkpoint_manager_blocking_time + / len(processed_step_stats) + ) + vertical_step_stats.minimum_checkpoint_manager_blocking_time = min( + map( + lambda stats: stats.total_checkpoint_manager_blocking_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.maximum_checkpoint_manager_blocking_time = max( + map( + lambda stats: stats.total_checkpoint_manager_blocking_time, + processed_step_stats.values(), + ) + ) + if len(processed_step_stats) > 1: + vertical_step_stats.standard_deviation_checkpoint_manager_blocking_time = ( + statistics.stdev( + map( + lambda stats: stats.total_checkpoint_manager_blocking_time, + processed_step_stats.values(), + ) + ) + ) + + # Record statistics for checkpointer_blocking_time. + vertical_step_stats.total_checkpointer_blocking_time = sum( + map( + lambda stats: stats.total_checkpointer_blocking_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.average_checkpointer_blocking_time = ( + vertical_step_stats.total_checkpointer_blocking_time + / len(processed_step_stats) + ) + vertical_step_stats.minimum_checkpointer_blocking_time = min( + map( + lambda stats: stats.total_checkpointer_blocking_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.maximum_checkpointer_blocking_time = max( + map( + lambda stats: stats.total_checkpointer_blocking_time, + processed_step_stats.values(), + ) + ) + if len(processed_step_stats) > 1: + vertical_step_stats.standard_deviation_checkpointer_blocking_time = ( + statistics.stdev( + map( + lambda stats: stats.total_checkpointer_blocking_time, + processed_step_stats.values(), + ) + ) + ) + + # Record statistics for wait_for_prev_time. + vertical_step_stats.total_wait_for_prev_time = sum( + map( + lambda stats: stats.total_wait_for_prev_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.average_wait_for_prev_time = ( + vertical_step_stats.total_wait_for_prev_time + / len(processed_step_stats) + ) + vertical_step_stats.minimum_wait_for_prev_time = min( + map( + lambda stats: stats.total_wait_for_prev_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.maximum_wait_for_prev_time = max( + map( + lambda stats: stats.total_wait_for_prev_time, + processed_step_stats.values(), + ) + ) + if len(processed_step_stats) > 1: + vertical_step_stats.standard_deviation_wait_for_prev_time = ( + statistics.stdev( + map( + lambda stats: stats.total_wait_for_prev_time, + processed_step_stats.values(), + ) + ) + ) + + # Record statistics for get_old_steps_time. + vertical_step_stats.total_get_old_steps_time = sum( + map( + lambda stats: stats.total_get_old_steps_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.average_get_old_steps_time = ( + vertical_step_stats.total_get_old_steps_time / len(processed_step_stats) + ) + vertical_step_stats.minimum_get_old_steps_time = min( + map( + lambda stats: stats.total_get_old_steps_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.maximum_get_old_steps_time = max( + map( + lambda stats: stats.total_get_old_steps_time, + processed_step_stats.values(), + ) + ) + if len(processed_step_stats) > 1: + vertical_step_stats.standard_deviation_get_old_steps_time = ( + statistics.stdev( + map( + lambda stats: stats.total_get_old_steps_time, + processed_step_stats.values(), + ) + ) + ) + return vertical_step_stats + + def calculate_save_operation_checkpoint_manager_blocking_time( + self, operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, + ) -> SaveCheckpointManagerVerticalStepStats: + """Gets checkpoint manager blocking time breakdown for save operation.""" + self.entries = self.read_entries() + + step_already_processed: dict[str, SaveProcessedStep] = dict() + for step_stats in self.entries: + if ( + not self.is_valid_save_stats(step_stats, operation_type) + ): + continue + + # Create a step info to identify the step_statistics whether local or + # persistent. + if self._is_local_operation(step_stats): + step_info = str(step_stats[_STEP]) + '-' + OPERATION_TYPE_LOCAL + else: + step_info = ( + str(step_stats[_STEP]) + '-' + OPERATION_TYPE_PERSISTENT + ) + if step_already_processed.get(step_info) is None: + step_already_processed[step_info] = SaveProcessedStep() + step_already_processed[step_info].step = step_info + step_already_processed[ + step_info + ].total_checkpoint_manager_blocking_time = float( + step_stats[_CHECKPOINT_MANAGER_SAVE_DURATION_SECS] + ) + step_already_processed[step_info].total_checkpointer_blocking_time = ( + float(step_stats[_CHECKPOINTER_SAVE_DURATION_SECS]) + ) + step_already_processed[step_info].total_wait_for_prev_time = float( + step_stats[_WAIT_FOR_PREV_DURATION_SECS] + ) + step_already_processed[step_info].total_get_old_steps_time = float( + step_stats[_GET_OLD_STEPS_DURATION_SECS] + ) + step_already_processed[step_info].occurrence = 1 + else: + step_already_processed[step_info].step = step_info + step_already_processed[ + step_info + ].total_checkpoint_manager_blocking_time += float( + step_stats[_CHECKPOINT_MANAGER_SAVE_DURATION_SECS] + ) + step_already_processed[ + step_info + ].total_checkpointer_blocking_time += float( + step_stats[_CHECKPOINTER_SAVE_DURATION_SECS] + ) + step_already_processed[step_info].total_wait_for_prev_time += float( + step_stats[_WAIT_FOR_PREV_DURATION_SECS] + ) + step_already_processed[step_info].total_get_old_steps_time += float( + step_stats[_GET_OLD_STEPS_DURATION_SECS] + ) + step_already_processed[step_info].occurrence += 1 + + # Calculate the vertical step stats for the checkpoint manager blocking + # time. + save_statistics = self._save_statistics( + step_already_processed + ) + + return save_statistics + + def _restore_statistics( + self, processed_step_stats: Dict[str, RestoreProcessedStep] + ) -> RestoreCheckpointManagerVerticalStepStats: + """Calculates the vertical step stats.""" + if not processed_step_stats: + return RestoreCheckpointManagerVerticalStepStats() + broadcast_occurrence = 0 + for _, stats in processed_step_stats.items(): + stats.total_checkpoint_manager_time = ( + stats.total_checkpoint_manager_time / stats.occurrence + ) + stats.total_restore_time = stats.total_restore_time / stats.occurrence + if stats.broadcast_occurrence > 0: + stats.total_broadcast_time = ( + stats.total_broadcast_time / stats.broadcast_occurrence + ) + broadcast_occurrence += 1 + + vertical_step_stats = RestoreCheckpointManagerVerticalStepStats() + + # Record statistics for total time checkpoint manager spent on restore. + vertical_step_stats.total_checkpoint_manager_time = sum( + map( + lambda stats: stats.total_checkpoint_manager_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.average_checkpoint_manager_time = ( + vertical_step_stats.total_checkpoint_manager_time + / len(processed_step_stats) + ) + vertical_step_stats.minimum_checkpoint_manager_time = min( + map( + lambda stats: stats.total_checkpoint_manager_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.maximum_checkpoint_manager_time = max( + map( + lambda stats: stats.total_checkpoint_manager_time, + processed_step_stats.values(), + ) + ) + if len(processed_step_stats) > 1: + vertical_step_stats.standard_deviation_checkpoint_manager_time = ( + statistics.stdev( + map( + lambda stats: stats.total_checkpoint_manager_time, + processed_step_stats.values(), + ) + ) + ) + # Record statistics for restore time. + vertical_step_stats.total_restore_time = sum( + map( + lambda stats: stats.total_restore_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.average_restore_time = ( + vertical_step_stats.total_restore_time / len(processed_step_stats) + ) + vertical_step_stats.minimum_restore_time = min( + map( + lambda stats: stats.total_restore_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.maximum_restore_time = max( + map( + lambda stats: stats.total_restore_time, + processed_step_stats.values(), + ) + ) + if len(processed_step_stats) > 1: + vertical_step_stats.standard_deviation_restore_time = ( + statistics.stdev( + map( + lambda stats: stats.total_restore_time, + processed_step_stats.values(), + ) + ) + ) + + # Record statistics for broadcasting the restored checkpoint(Emergency + # restore only). + if broadcast_occurrence > 0: + vertical_step_stats.total_broadcast_time = sum( + map( + lambda stats: stats.total_broadcast_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.average_broadcast_time = ( + vertical_step_stats.total_broadcast_time / broadcast_occurrence + ) + vertical_step_stats.minimum_broadcast_time = min( + map( + lambda stats: stats.total_broadcast_time, + processed_step_stats.values(), + ) + ) + vertical_step_stats.maximum_broadcast_time = max( + map( + lambda stats: stats.total_broadcast_time, + processed_step_stats.values(), + ) + ) + if len(processed_step_stats) > 1: + vertical_step_stats.standard_deviation_broadcast_time = ( + statistics.stdev( + map( + lambda stats: stats.total_broadcast_time, + processed_step_stats.values(), + ) + ) + ) + + return vertical_step_stats + + def calculate_restore_operation_checkpoint_manager_blocking_time( + self, + operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, + ) -> RestoreCheckpointManagerVerticalStepStats: + """Gets checkpoint manager blocking time breakdown for restore operation.""" + self.entries = self.read_entries() + + step_already_processed: dict[str, RestoreProcessedStep] = dict() + for step_stats in self.entries: + if not self.is_valid_restore_stats(step_stats, operation_type): + continue + + # Create a step info to identify the step_stats whether local or + if self._is_local_operation(step_stats): + step_info = str(step_stats[_STEP]) + '-' + OPERATION_TYPE_LOCAL + else: + step_info = str(step_stats[_STEP]) + '-' + OPERATION_TYPE_PERSISTENT + + if step_already_processed.get(step_info) is None: + step_already_processed[step_info] = RestoreProcessedStep() + step_already_processed[step_info].step = step_info + + step_already_processed[step_info].total_checkpoint_manager_time = float( + step_stats[_CHECKPOINT_MANAGER_RESTORE_DURATION_SECS] + ) + step_already_processed[step_info].total_restore_time = float( + step_stats[_CHECKPOINTER_RESTORE_DURATION_SECS] + ) + if ( + step_stats.get(_BROADCAST_DURATION_SECS) + and step_stats[_BROADCAST_DURATION_SECS] is not None + ): + step_already_processed[step_info].total_broadcast_time = float( + step_stats[_BROADCAST_DURATION_SECS] + ) + step_already_processed[step_info].broadcast_occurrence = 1 + step_already_processed[step_info].occurrence = 1 + else: + step_already_processed[step_info].step = step_info + step_already_processed[ + step_info + ].total_checkpoint_manager_time += float( + step_stats[_CHECKPOINT_MANAGER_RESTORE_DURATION_SECS] + ) + step_already_processed[step_info].total_restore_time += float( + step_stats[_CHECKPOINTER_RESTORE_DURATION_SECS] + ) + if ( + step_stats.get(_BROADCAST_DURATION_SECS) + and step_stats[_BROADCAST_DURATION_SECS] is not None + ): + step_already_processed[step_info].total_broadcast_time += float( + step_stats[_BROADCAST_DURATION_SECS] + ) + step_already_processed[step_info].broadcast_occurrence += 1 + step_already_processed[step_info].occurrence += 1 + + # Calculate the vertical step stats for the checkpoint manager blocking + # time. + restore_statistics = self._restore_statistics(step_already_processed) + + return restore_statistics + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + options = CheckpointLoggerOptions() + parser.add_argument( + '--job_name', + type=str, + default=options.job_name, + help='The name of the job.', + ) + parser.add_argument( + '--logger_name', + type=str, + default=options.logger_name, + help='The name of the logger.', + ) + parser.add_argument( + '--client', + type=str, + default=options.client, + help='The name of the client.', + ) + parser.add_argument( + '--operation_type', + type=str, + default=OPERATION_TYPE_PERSISTENT_AND_LOCAL, + help='The operation type.', + ) + args = parser.parse_args() + options = CheckpointLoggerOptions( + job_name=args.job_name, + logger_name=args.logger_name, + client=args.client, + ) + checkpoint_badput_calculator = ( + CheckpointBadputCalculator(options) + ) + checkpoint_badput_calculator.calculate_save_operation_checkpoint_manager_blocking_time( + args.operation_type + ) + + + diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py b/ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py new file mode 100644 index 0000000..da570d9 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py @@ -0,0 +1,106 @@ +"""A generic class to send multiple metrics to GCP Cloud Monitoring in a batch with dynamic resources.""" +import enum +import logging +import time +from typing import Any, Dict + +from google.api_core import exceptions +from google.cloud import monitoring_v3 + +GoogleAPIError = exceptions.GoogleAPIError +Enum = enum.Enum + +logger = logging.getLogger(__name__) + + +class ValueType(Enum): + """Enum for metric value types.""" + + BOOL = "bool_value" + INT = "int64_value" + DOUBLE = "double_value" + STRING = "string_value" + DISTRIBUTION = "distribution_value" # Add other types as needed + + +class GCPMetrics: + """A generic class to send multiple metrics to GCP Cloud Monitoring in a batch with dynamic resources.""" + + def __init__(self, project_id: str): + """Initializes the GCPMetrics.""" + self.project_id = project_id + self.client = monitoring_v3.MetricServiceClient() + self.project_name = f"projects/{project_id}" + + def create_time_series( + self, + metric_type: str, + value, + value_type: ValueType, + metric_labels: Dict[str, str], + resource_type: str, + resource_labels: Dict[str, str], + seconds: int, + nanos: int, + ) -> monitoring_v3.TimeSeries: + """Creates a TimeSeries object for a single metric with dynamic resources.""" + series = monitoring_v3.TimeSeries() + series.metric.type = metric_type + series.resource.type = resource_type + series.resource.labels.update(resource_labels) + if metric_labels: + series.metric.labels.update(metric_labels) + + point = monitoring_v3.Point( + interval=monitoring_v3.TimeInterval( + end_time={"seconds": seconds, "nanos": nanos} + ), + value=monitoring_v3.TypedValue(**{value_type.value: value}), + ) + series.points.append(point) + + return series + + def send_metrics(self, metrics: list[Dict[str, Any]]): + """Sends multiple metrics to GCP Monitoring in a batch with dynamic resources. + + Args: + metrics: A list of dictionaries, where each dictionary represents + a metric. Each dictionary should have the following keys: + - 'metric_type': str + - 'value': The metric value. + - 'value_type': ValueType (e.g., ValueType.INT, + ValueType.DOUBLE) + - 'metric_labels': dict (optional) + - 'resource_type': str + - 'resource_labels': dict + """ + try: + now = time.time() + seconds = int(now) + nanos = int((now - seconds) * 10**9) + + time_series_list = [] + for metric in metrics: + try: + metric_labels = metric.get("metric_labels", {}) + series = self.create_time_series( + metric["metric_type"], + metric["value"], + metric["value_type"], + metric_labels, + metric["resource_type"], + metric["resource_labels"], + seconds, + nanos, + ) + time_series_list.append(series) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error("Failed to create time series: %s", e) + self.client.create_time_series( + name=self.project_name, time_series=time_series_list + ) + logger.info("Sent %d metrics to GCP Monitoring.", len(metrics)) + + except GoogleAPIError as e: + logger.error("Failed to send metrics: %s", e) diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/goodput.py b/ml-goodput-measurement/ml_goodput_measurement/src/goodput.py new file mode 100644 index 0000000..75859f3 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/src/goodput.py @@ -0,0 +1,1690 @@ +"""Goodput package API implementations. + +This file contains all the core implementations of the ml_goodput_measurement +library for users to measure and monitor Goodput, Badput and Step Time +Deviation. +""" + +import datetime +import logging +import threading +from typing import Any, Optional, Union + +from cloud_goodput.ml_goodput_measurement.src import checkpoint_badput_calculator +from cloud_goodput.ml_goodput_measurement.src import goodput_cache +from cloud_goodput.ml_goodput_measurement.src import goodput_utils + + +get_timestamp_from_log_entry = goodput_utils.get_timestamp_from_log_entry +get_extra_time_from_anomalous_steps = ( + goodput_utils.get_extra_time_from_anomalous_steps +) +compute_ideal_step_time = goodput_utils.compute_ideal_step_time + +BadputType = goodput_utils.BadputType +CheckpointLoggerOptions = checkpoint_badput_calculator.CheckpointLoggerOptions +CheckpointBadputCalculator = ( + checkpoint_badput_calculator.CheckpointBadputCalculator +) +GoodputType = goodput_utils.GoodputType +GoodputCache = goodput_cache.GoodputCache +GoodputInfo = goodput_utils.GoodputInfo +StepInfo = goodput_utils.StepInfo +# Data structure to store the type of unproductive time (BadputType) and the +# corresponding time in seconds. If the BadputType is CUSTOM_BADPUT_EVENTS, the +# value is a dictionary of user defined event type and the corresponding time +# in seconds. +UnproductiveTimeDict = dict[ + BadputType, Union[float, dict[str, float]] +] + +_JOB_NAME = 'job_name' +_STEP_COUNT = 'step_count' +_STEP_START_TIME = 'step_start_time' +_JOB_START_TIME = 'job_start_time' +_JOB_END_TIME = 'job_end_time' +_TPU_INIT_START_TIME = 'tpu_init_start_time' +_TPU_INIT_END_TIME = 'tpu_init_end_time' +_TRAINING_PREPARATION_START_TIME = 'training_prep_start_time' +_TRAINING_PREPARATION_END_TIME = 'training_prep_end_time' +_DATA_LOADING_START_TIME = 'data_loading_start_time' +_DATA_LOADING_END_TIME = 'data_loading_end_time' +_CUSTOM_BADPUT_EVENT_TYPE = 'custom_badput_event_type' +_CUSTOM_BADPUT_EVENT_START_TIME = 'custom_badput_event_start_time' +_CUSTOM_BADPUT_EVENT_END_TIME = 'custom_badput_event_end_time' + +_CLOUD_LOGGING_PAGE_SIZE = 1000000 + +logger = logging.getLogger(__name__) + + +class _CloudLogger: + """A helper class for reading and writing to Cloud Logging. + + Attributes: + job_name: Name of a specific job. + logger: The Cloud Logging logger object. + job_start_time: Start time of the job run. + """ + + def __init__(self, job_name: str, log_name: str): + """_CloudLogger constructor. + + Args: + job_name: Name of the job the _CloudLogger is for. + log_name: Name of the log being written. + """ + import google.cloud.logging # pylint: disable=g-import-not-at-top + + self.job_name = job_name + logging_client = google.cloud.logging.Client() + self.logger = logging_client.logger(log_name) + self.job_start_time = None + + def write_cloud_logging_entry(self, entry) -> None: + """Writes an entry to the Cloud Logging logger at INFO level. + + Args: + entry: JSON-serializable structured log dictionary. + """ + if entry is None: + return + if entry[_JOB_NAME] == self.job_name: + self.logger.log_struct( + entry, + severity='INFO', + ) + + def _get_filter_msg( + self, + start_time: Optional[datetime.datetime], + end_time: Optional[datetime.datetime], + ) -> str: + """Gets the filter message for the Cloud Logging query.""" + filter_entries = [ + 'severity=INFO', + f'jsonPayload.job_name="{self.job_name}"', + ] + # Add a filter to bind an end-time to the query window. + if end_time is None: + end_time = datetime.datetime.now(datetime.timezone.utc) + elif end_time.tzinfo is None: + end_time = end_time.replace(tzinfo=datetime.timezone.utc) + + filter_entries.append(f'timestamp<="{end_time.isoformat()}"') + + # Add a filter to bind a start-time to the query window (if available). + if start_time is None: + if self.job_start_time is not None: + start_time = self.job_start_time - datetime.timedelta(days=1) + + if start_time is not None: + if start_time.tzinfo is None: + start_time = start_time.replace(tzinfo=datetime.timezone.utc) + filter_entries.append(f'timestamp>"{start_time.isoformat()}"') + return ' AND '.join(filter_entries) + + def _update_job_start_time(self, entries: list[Any]): + if self.job_start_time: + return + for entry in entries: + if _JOB_START_TIME in entry and self.job_start_time is None: + self.job_start_time = datetime.datetime.fromtimestamp( + entry[_JOB_START_TIME] + ) + break + + def read_cloud_logging_entries( + self, + start_time: Optional[datetime.datetime] = None, + end_time: Optional[datetime.datetime] = None, + ): + """Queries Cloud Logging entries for the specific job. + + Args: + start_time: The start time of the query window. + end_time: The end time of the query window. + + Returns: + Filtered entries in ascending order of timestamp. + """ + import google.cloud.logging # pylint: disable=g-import-not-at-top + + entries = self.logger.list_entries( + filter_=self._get_filter_msg(start_time, end_time), + order_by=google.cloud.logging.ASCENDING, + page_size=_CLOUD_LOGGING_PAGE_SIZE, + ) + entry_payload = [entry.payload for entry in entries] + self._update_job_start_time(entry_payload) + return entry_payload + + +class GoodputRecorder: + """The Goodput recorder class, responsible for recording Goodput metrics from the user application. + + Attributes: + job_name: Name of the job the GoodputRecorder is for. + """ + + def __init__( + self, + job_name: str, + logger_name: str, + logging_enabled=False, + cloud_logger: Optional[_CloudLogger] = None, + ): + """GoodputRecorder constructor. + + Args: + job_name: Name of the job the GoodputRecorder is for. + logger_name: The name of the Cloud Logging logger object that the + application wants logs to be written to and read from. + logging_enabled: A boolean value to indicate whether the current process + should send logs to Cloud Logging or not. The application should set + this value to True if the Recorder is being called from TPU worker 0 and + the application's configurations request Goodput logging. + cloud_logger: Should never be passed directly by the user. + """ + self.job_name = job_name + # If logging is disabled for this process, do not create a _cloud_logger + # object and exit early if any record record_* API is called. + if not logging_enabled: + self._cloud_logger = None + logging.info('Logging is disabled for this process.') + return + + if cloud_logger is not None: + self._cloud_logger = cloud_logger + else: + self._cloud_logger = _CloudLogger(job_name, logger_name) + + def record_step_start_time( + self, step: int, start_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log an individual step's start time. + + Args: + step: The count of the step that timing information is recorded for. + start_time: Optional backfill start time of the training step. If + provided, it has to be in UTC time. + """ + if self._cloud_logger is None: + return + if start_time is None: + start_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _STEP_COUNT: int(step), + _STEP_START_TIME: start_time.timestamp(), + }) + + def record_checkpoint_progress(self, step, checkpoint_start_time): + """Main recorder function to log information on a successful checkpoint. + + This method is intended to log the progress for a checkpoint (last step + included in the checkpoint) and when the checkpoint starts. This information + will be retrieved in the future to determine whether training progress from + a completed step contributes to Goodput or wasted progress Badput. + + Args: + step: The step count of the last step included in the saved checkpoint. + checkpoint_start_time: Timestamp at which the checkpoint containing + progress upto "step" starts to save. + """ + pass + + def record_job_start_time( + self, start_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log a job's start time. + + Args: + start_time: Optional backfill start time of the job. If provided, it has + to be in UTC time. + """ + if self._cloud_logger is None: + return + if start_time is None: + start_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _JOB_START_TIME: start_time.timestamp(), + }) + + def record_job_end_time(self, end_time: Optional[datetime.datetime] = None): + """Main recorder function to log a job's end time. + + Args: + end_time: Optional backfull end time of the job. If provided, it has to be + in UTC time. + """ + if self._cloud_logger is None: + return + if end_time is None: + end_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _JOB_END_TIME: end_time.timestamp(), + }) + + def record_tpu_init_start_time( + self, start_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log the start time for TPU initialization. + + Note: TPU initialization may include the time spent in completing + jax.devices() which is responsible for device scanning and Slice Builder + initialization. + + Args: + start_time: Start time of TPU initialization. + """ + if self._cloud_logger is None: + return + if start_time is None: + start_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _TPU_INIT_START_TIME: start_time.timestamp(), + }) + + def record_tpu_init_end_time( + self, end_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log the end time for TPU initialization. + + Args: + end_time: End time of TPU initialization. + """ + if self._cloud_logger is None: + return + if end_time is None: + end_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _TPU_INIT_END_TIME: end_time.timestamp(), + }) + + def record_training_preparation_start_time( + self, start_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log the start time of training preparation before starting a training loop. + + Note: Training preparation may include the time spent in creation of + checkpoint managers, checkpoint loading, running mesh and model optimizers + etc. + + Args: + start_time: Start time of training preparation. + """ + if self._cloud_logger is None: + return + if start_time is None: + start_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _TRAINING_PREPARATION_START_TIME: start_time.timestamp(), + }) + + def record_training_preparation_end_time( + self, end_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log the end time of training preparation before starting a training loop. + + Args: + end_time: End time of training preparation. + """ + if self._cloud_logger is None: + return + if end_time is None: + end_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _TRAINING_PREPARATION_END_TIME: end_time.timestamp(), + }) + + def record_data_loading_start_time( + self, start_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log the start time of training's data loading. + + Args: + start_time: Start time of data loading. + """ + if self._cloud_logger is None: + return + if start_time is None: + start_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _DATA_LOADING_START_TIME: start_time.timestamp(), + }) + + def record_data_loading_end_time( + self, end_time: Optional[datetime.datetime] = None + ): + """Main recorder function to log the end time of training's data loading. + + Args: + end_time: End time of data loading. + """ + if self._cloud_logger is None: + return + if end_time is None: + end_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _DATA_LOADING_END_TIME: end_time.timestamp(), + }) + + def record_custom_badput_event_start_time( + self, + start_time: Optional[datetime.datetime] = None, + custom_badput_event_type: str = 'unknown', + ): + """Main recorder function to log the start time of a custom badput event. + + Use this function to record the start time of a custom badput event that + occurs inside the training loop and utilizes the accelerator resources, + and blocks training. + + For example, use this API to record the start time of the evaluation + loop or an SDC check if the the event blocks the training loop. + + Args: + start_time: Start time of the custom badput event. + custom_badput_event_type: Type of the custom badput event. + """ + if self._cloud_logger is None: + return + if start_time is None: + start_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _CUSTOM_BADPUT_EVENT_TYPE: custom_badput_event_type, + _CUSTOM_BADPUT_EVENT_START_TIME: start_time.timestamp(), + }) + + def record_custom_badput_event_end_time( + self, + end_time: Optional[datetime.datetime] = None, + custom_badput_event_type: str = 'unknown', + ): + """Main recorder function to log the end time of a custom badput event. + + Args: + end_time: End time of the custom badput event. + custom_badput_event_type: Type of the custom badput event. + """ + if self._cloud_logger is None: + return + if end_time is None: + end_time = datetime.datetime.now(datetime.timezone.utc) + + self._cloud_logger.write_cloud_logging_entry({ + _JOB_NAME: self.job_name, + _CUSTOM_BADPUT_EVENT_TYPE: custom_badput_event_type, + _CUSTOM_BADPUT_EVENT_END_TIME: end_time.timestamp(), + }) + + +class GoodputCalculator: + """The Goodput calculator class, responsible for querying necessary information and computing Goodput metrics to return to the user application. + + Attributes: + job_name: Name of the job the GoodputCalculator is for. + using_pathways: Whether or not the job uses Pathways. + """ + + def __init__( + self, + job_name: str, + logger_name: str, + cloud_logger: Optional[_CloudLogger] = None, + using_pathways: bool = False, + ): + """GoodputCalculator constructor. + + Args: + job_name: Name of the job the GoodputCalculator is for. + logger_name: Name of the log being written. + cloud_logger: Should never be passed directly by the user. + using_pathways: Whether or not the job uses Pathways. + """ + self.job_name = job_name + self.using_pathways = using_pathways + if cloud_logger is not None: + self._cloud_logger = cloud_logger + else: + self._cloud_logger = _CloudLogger(job_name, logger_name) + self._current_entries = [] + self._goodput_cache = GoodputCache() + self._goodput_cache_lock = threading.Lock() + self._interval_entries = [] + self._interval_start_time = None + self._interval_end_time = None + self._number_of_interruptions = 0 + self._gcm_last_recorded_timestamp = None + self._last_disruption_time = None + self._last_disrupted_step = None + + def _get_total_productive_and_unproductive_time( + self, new_entries: list[dict[str, Any]] + ) -> tuple[float, UnproductiveTimeDict, int]: + """Helper function to compute the total productive and unproductive time. + + Args: + new_entries: A list of new log entries to process. + + Returns: + A tuple of: + - total productive training time + - total unproductive time + - last recorded step + """ + # If no new entries are present, return last computed values. + if not new_entries: + cached_values = self._get_cached_productive_and_unproductive_time() + if cached_values is not None: + return cached_values + + return self._get_current_productive_and_unproductive_time() + + def _get_cached_productive_and_unproductive_time( + self, + ) -> tuple[float, UnproductiveTimeDict, int] | None: + """Helper function to retrieve the cached productive training time and unproductive time.""" + goodput_info = self._goodput_cache.get_goodput_info() + if not self._goodput_cache.is_cache_empty() and goodput_info is not None: + return ( + goodput_info.total_productive_time, + goodput_info.total_unproductive_time, + goodput_info.last_recorded_step, + ) + return None + + def _accumulate_unproductive_time( + self, + segment_unproductive_time: UnproductiveTimeDict, + total_unproductive_time: UnproductiveTimeDict, + ): + """Helper function to accumulate the segment unproductive time. + + Args: + segment_unproductive_time: A dictionary of unproductive time for a + segment. + total_unproductive_time: A dictionary of total unproductive time. + + Returns: + None. The function updates the total_unproductive_time dictionary. + """ + + for badput_type, unproductive_value in segment_unproductive_time.items(): + if isinstance(unproductive_value, dict): + if badput_type not in total_unproductive_time: + total_unproductive_time[badput_type] = dict(unproductive_value) + else: + existing_value = total_unproductive_time[badput_type] + if isinstance(existing_value, dict): + for sub_type, sub_value in unproductive_value.items(): + existing_value[sub_type] = ( + existing_value.get(sub_type, 0.0) + sub_value + ) + else: + if badput_type in total_unproductive_time: + existing_value = total_unproductive_time[badput_type] + if isinstance(existing_value, float): + total_unproductive_time[badput_type] = ( + existing_value + unproductive_value + ) + else: + total_unproductive_time[badput_type] = unproductive_value + + def _get_current_productive_and_unproductive_time( + self, interval_query: Optional[bool] = False + ) -> tuple[ + float, + UnproductiveTimeDict, + int, + ]: + """Helper function to compute the current productive training time, unproductive time and the last step recorded till now. + + Args: + interval_query: A boolean value to indicate whether the current query is + for an interval or not. + + Returns: + A tuple of the productive training time, the unproductive time + (dict of BadputType and unproductive time) and the last step recorded till + now based on the latest entries retrieved from Cloud Logging. + """ + def _extract_custom_sync_intervals( + entries: list[dict[str, Any]], + ) -> list[tuple[float, float, str]]: + """Extracts custom badput intervals from Cloud Logging entries. + + This helperfunction scans through a list of Cloud Logging entries to find + custom + badput start and end times, pairing them into intervals. + + Args: + entries: A list of dictionaries representing Cloud Logging entries. + Each entry may contain keys indicating the start or end of a custom + badput event. + + Returns: + A list of tuples, where each tuple consists of: + - start_time (float): The timestamp when the sync event started. + - end_time (float): The timestamp when the sync event ended. + - sync_type (str): The type of custom sync + event. + """ + intervals = [] + active_syncs = {} + + for entry in entries: + if _CUSTOM_BADPUT_EVENT_START_TIME in entry: + sync_type = entry[_CUSTOM_BADPUT_EVENT_TYPE].upper() + active_syncs[sync_type] = entry[_CUSTOM_BADPUT_EVENT_START_TIME] + elif _CUSTOM_BADPUT_EVENT_END_TIME in entry: + sync_type = entry[_CUSTOM_BADPUT_EVENT_TYPE].upper() + if sync_type in active_syncs: + start_time = active_syncs.pop(sync_type) + end_time = entry[_CUSTOM_BADPUT_EVENT_END_TIME] + if start_time < end_time: + intervals.append((start_time, end_time, sync_type)) + + return intervals + + def _compute_adjusted_segment_productive_and_unproductive_time( + step_items: list[tuple[int, float]], + curr_step: int, + min_step: int, + custom_sync_intervals: list[ + tuple[float, float, str] + ], + ) -> tuple[ + float, + float, + list[float], + float, + dict[str, float], + int, + ]: + """Computes adjusted productive and unproductive time for a segment of steps. + + This helper function calculates the total productive time, and the + breakdown of time lost due to custom badput events, as well as + wasted progress caused by disruptions. + + Args: + step_items: A list of tuples, where each tuple contains a step number + (int) and its start timestamp (float). + curr_step: The current step number indicating the end of the segment. + min_step: The minimum step number indicating the start of the segment. + custom_sync_intervals: A list of tuples, where each tuple consists of: + - start_time (float): Start timestamp of the sync event. + - end_time (float): End timestamp of the sync event. + - sync_type (str): The type of sync event. + + Returns: + A tuple containing: + - total_productive_time (float): Adjusted time excluding custom + sync durations. + - first_step_time (float): Adjusted duration of the first step in + the segment. + - step_times (list[float]): List of adjusted times for steps in the + segment excluding the first step. + - wasted_progress (float): Total unproductive time due to possible + disruptions. + - custom_sync_breakdown (dict[str, float]): + Breakdown of time spent in each custom sync type. + - steps_in_segment (int): Total number of steps considered in the + segment. + """ + total_productive_time = 0.0 + first_step_time = 0.0 + step_times = [] + wasted_progress = 0.0 + custom_sync_breakdown: dict[str, float] = {} + + steps_in_segment = 0 + + for i in range(1, len(step_items)): + prev_step, prev_time = step_items[i - 1] + curr_step_num, curr_time = step_items[i] + + raw_delta = curr_time - prev_time + if curr_step_num <= curr_step: + if curr_step_num - 1 != prev_step: + continue + + custom_sync_in_interval = 0.0 + for sync_start, sync_end, sync_type in custom_sync_intervals: + if prev_time <= sync_start and sync_end <= curr_time: + sync_duration = sync_end - sync_start + custom_sync_in_interval += sync_duration + custom_sync_breakdown[sync_type] = ( + custom_sync_breakdown.get(sync_type, 0.0) + sync_duration + ) + + adjusted_delta = max(0.0, raw_delta - custom_sync_in_interval) + total_productive_time += adjusted_delta + + if prev_step == min_step: + first_step_time = adjusted_delta + else: + step_times.append(adjusted_delta) + + steps_in_segment += 1 + + else: + # These steps are after curr_step, they are lost due to disruption. + wasted_progress += raw_delta + + return ( + total_productive_time, + first_step_time, + step_times, + wasted_progress, + custom_sync_breakdown, + steps_in_segment, + ) + + def _compute_segment_final_metrics( + adjusted_productive_time: float, + first_step_time: float, + step_times: list[float], + wasted_progress: float, + custom_sync_breakdown: dict[str, float], + ) -> tuple[ + float, + UnproductiveTimeDict, + ]: + """Computes final metrics for a segment, separating productive and unproductive time. + + This function takes adjusted productive time and calculates additional + badput sources such as program startup and wasted progress due to + disruptions. It returns the final productive time and a breakdown of all + unproductive time sources. + + Args: + adjusted_productive_time: Total productive time for the segment + first_step_time: Productive time for the first step in the segment. + step_times: Productive times for non-first steps in the segment. + wasted_progress: Total time lost due to step discontinuities. + custom_sync_breakdown: A dictionary mapping each custom sync type to + the total badput time it accounted for during the segment. + + Returns: + A tuple containing: + - final_productive_time (float) + - total_segment_unproductive_time (dict) + """ + steps_in_segment = len(step_times) + 1 # Including first step + + if steps_in_segment == 1: + return first_step_time, { + BadputType.WASTED_PROGRESS_FROM_DISRUPTION: wasted_progress, + BadputType.CUSTOM_BADPUT_EVENTS: custom_sync_breakdown, + BadputType.PROGRAM_STARTUP: 0.0, + } + + non_first_steps = steps_in_segment - 1 + non_first_total_time = adjusted_productive_time - first_step_time + average_step_time = ( + non_first_total_time / non_first_steps if non_first_steps > 0 else 0.0 + ) + first_step_extra_time = max(0.0, first_step_time - average_step_time) + final_productive_time = adjusted_productive_time - first_step_extra_time + + total_segment_unproductive_time = { + BadputType.PROGRAM_STARTUP: first_step_extra_time, + BadputType.WASTED_PROGRESS_FROM_DISRUPTION: wasted_progress, + BadputType.CUSTOM_BADPUT_EVENTS: custom_sync_breakdown, + } + + return final_productive_time, total_segment_unproductive_time + + def _get_segment_productive_and_unproductive_time( + step_start_data: dict[int, float], + curr_step: int, + entries_to_process: list[Any], + ) -> tuple[ + float, + UnproductiveTimeDict, + ]: + if curr_step == 0: + return 0.0, {} + + step_items = list(step_start_data.items()) + min_step = min(step_start_data.keys()) + + # Extract custom sync intervals + custom_sync_intervals = _extract_custom_sync_intervals(entries_to_process) + + # Compute adjusted segmentproductive and unproductive times + ( + total_productive_time, + first_step_time, + step_times, + wasted_progress_from_disruption, + custom_sync_breakdown, + steps_in_segment, + ) = _compute_adjusted_segment_productive_and_unproductive_time( + step_items, curr_step, min_step, custom_sync_intervals + ) + + if steps_in_segment == 0: + return 0.0, { + BadputType.WASTED_PROGRESS_FROM_DISRUPTION: ( + wasted_progress_from_disruption + ) + } + + # Compute adjusted averages and unproductive breakdown + ( + final_adjusted_productive_time, + total_segment_unproductive_time, + ) = _compute_segment_final_metrics( + total_productive_time, + first_step_time, + step_times, + wasted_progress_from_disruption, + custom_sync_breakdown, + ) + + return final_adjusted_productive_time, total_segment_unproductive_time + + # Build a deserialized dictionary from cloud logging entries to store step + # start times. The dictionary maps from step count to start time and will be + # used to each step's productive time by looking for its completion in the + # next step's start. + # Note in the instance where progress is lost due to a disruption and the + # last successful checkpoint did not include all the steps, the last set of + # records of the step information will be kept and the previous set will be + # overwritten by design so as to correct for the the previously computed + # additional time that was counted as productive but lost due to a + # disruption. + productive_training_time = 0.0 + total_unproductive_time = {} + step_start_data = {} + job_start_time = None + job_end_time = None + tpu_init_start_time = None + training_prep_start_time = None + data_loading_start_time = None + tpu_initialization_badput = 0.0 + training_prep_badput = 0.0 + data_loading_badput = 0.0 + sync_data_loading = True + current_sync_data_loading = None + if interval_query: + entries_to_process = self._interval_entries + else: + with self._goodput_cache_lock: + entries_to_process = list(self._goodput_cache.get_cached_entries()) + + self._number_of_interruptions = 0 + for payload in entries_to_process: + if _JOB_START_TIME in payload: + # Keep track of the latest start to compute badput due to disruption. + job_start_time = payload[_JOB_START_TIME] + if _STEP_START_TIME in payload: + curr_step = int(payload[_STEP_COUNT]) + if curr_step not in step_start_data: + step_start_data[curr_step] = payload[_STEP_START_TIME] + else: + # In this case, the job restarted from Step (curr_step). It means that + # all progress till Step (curr_step - 1) has been preserved. So we + # can get the productive time since the previous start/restart and + # then clear the step_start_data dict. + self._number_of_interruptions += 1 + self._last_disrupted_step = list(step_start_data.keys())[-1] + self._last_disruption_time = step_start_data[ + self._last_disrupted_step + ] + + # Compute segment productive and unproductive time. + segment_productive_time, segment_unproductive_time = ( + _get_segment_productive_and_unproductive_time( + step_start_data, curr_step, entries_to_process + ) + ) + # Accumulate the segment productive time. + productive_training_time += segment_productive_time + + # When the job restarts, data loading is synchronous. + sync_data_loading = True + if current_sync_data_loading is not None: + segment_unproductive_time[BadputType.DATA_LOADING_SYNC] = ( + segment_unproductive_time.get(BadputType.DATA_LOADING_SYNC, 0) + + current_sync_data_loading + ) + current_sync_data_loading = None + + # Since the current step has been recorded again, the progress + # between the previously recorded curr_step and recently recorded + # curr_step has been lost to a disruption and partially recovered + # due to a checkpoint of curr_step - 1. Accumulate the lost time in + # this segment as unproductive time. + # Note this unproductive time is divided into two buckets: + # 1. Wasted training progress after the last successfully + # checkpointed step and the disruption time until the job + # restarts. + # 2. TPU re-init, training prep, data loading, program startup, + # checkpoint loading etc. after the job restarts and before + # training progress resumes. + + # The first bucket can be calculated as the time between the start + # time of curr_step and the job restart time immediately prior. + if ( + job_start_time is not None + and self._last_disruption_time is not None + and job_start_time > self._last_disruption_time + ): + # Add the additional time it took for the job to restart after last + # interruption. These conditions are only met when the job is + # restarted after a disruption. + # TODO(dishaw): This is the infrastructure disruption Badput and can + # go into a separate bucket. + disruption_badput = job_start_time - self._last_disruption_time + if ( + BadputType.WASTED_PROGRESS_FROM_DISRUPTION + in segment_unproductive_time + ): + segment_unproductive_time[ + BadputType.WASTED_PROGRESS_FROM_DISRUPTION + ] += disruption_badput + else: + segment_unproductive_time[ + BadputType.WASTED_PROGRESS_FROM_DISRUPTION + ] = disruption_badput + + # The second bucket is individually computed either from recorded + # logs (TPU initialization, training preparation, data loading) or + # computed from the first step time after start or restart + # (segment unproductive time). All unproductive time is accumulated + # as we go. + self._accumulate_unproductive_time( + segment_unproductive_time, total_unproductive_time + ) + step_start_data = {curr_step: payload[_STEP_START_TIME]} + + if _JOB_END_TIME in payload: + # Locate the last instance of job's end time if the job has completed. + job_end_time = payload[_JOB_END_TIME] + + # Compute badput due to TPU initialization. + if _TPU_INIT_START_TIME in payload: + tpu_init_start_time = payload[_TPU_INIT_START_TIME] + elif _TPU_INIT_END_TIME in payload and tpu_init_start_time is not None: + tpu_initialization_badput += ( + payload[_TPU_INIT_END_TIME] - tpu_init_start_time + ) + tpu_init_start_time = None + + # Compute badput due to training preparation. + elif _TRAINING_PREPARATION_START_TIME in payload: + training_prep_start_time = payload[_TRAINING_PREPARATION_START_TIME] + elif ( + _TRAINING_PREPARATION_END_TIME in payload + and training_prep_start_time is not None + ): + training_prep_badput += ( + payload[_TRAINING_PREPARATION_END_TIME] - training_prep_start_time + ) + training_prep_start_time = None + + # Compute badput due to data loading. + elif _DATA_LOADING_START_TIME in payload: + data_loading_start_time = payload[_DATA_LOADING_START_TIME] + elif ( + _DATA_LOADING_END_TIME in payload + and data_loading_start_time is not None + ): + data_loading_end_time = payload[_DATA_LOADING_END_TIME] + current_sync_data_loading = ( + data_loading_end_time - data_loading_start_time + ) + data_loading_badput += current_sync_data_loading + if sync_data_loading: + # When the job starts, data loading is synchronous. + total_unproductive_time[BadputType.DATA_LOADING_SYNC] = ( + total_unproductive_time.get(BadputType.DATA_LOADING_SYNC, 0) + + current_sync_data_loading + ) + sync_data_loading = False + data_loading_start_time = None + + # Compute unproductive time from checkpoint manager save and restore. + checkpoint_logger_options = CheckpointLoggerOptions(use_goodput_logger=True) + checkpoint_badput_calc = CheckpointBadputCalculator( + checkpoint_logger_options + ) + checkpoint_badput_calc.entries = entries_to_process + checkpoint_manager_save_stats = ( + checkpoint_badput_calc.calculate_save_operation_checkpoint_manager_blocking_time() + ) + checkpoint_manager_save_badput = ( + checkpoint_manager_save_stats.total_checkpoint_manager_blocking_time + ) + checkpoint_manager_restore_stats = ( + checkpoint_badput_calc.calculate_restore_operation_checkpoint_manager_blocking_time() + ) + checkpoint_manager_restore_badput = ( + checkpoint_manager_restore_stats.total_checkpoint_manager_time + ) + + # Populate some Badput buckets in total_unproductive_time. + total_unproductive_time[BadputType.TPU_INITIALIZATION] = ( + tpu_initialization_badput + ) + total_unproductive_time[BadputType.TRAINING_PREP] = training_prep_badput + + # Populate async data loading badput. + async_data_loading_badput = ( + data_loading_badput + - total_unproductive_time.get(BadputType.DATA_LOADING_SYNC, 0) + ) + total_unproductive_time[BadputType.DATA_LOADING_ASYNC] = ( + async_data_loading_badput + ) + + # Populate checkpoint manager save and restore badput. + total_unproductive_time[BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME] = ( + checkpoint_manager_save_badput + ) + total_unproductive_time[BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME] = ( + checkpoint_manager_restore_badput + ) + + if not step_start_data: + return 0.0, total_unproductive_time, 0 + + last_step = max(list(step_start_data.keys())) + segment_productive_time, segment_unproductive_time = ( + _get_segment_productive_and_unproductive_time( + step_start_data, last_step, entries_to_process + ) + ) + productive_training_time += segment_productive_time + self._accumulate_unproductive_time( + segment_unproductive_time, total_unproductive_time + ) + + # Only consider the last step productive if the job has completed. + if job_end_time is not None: + productive_training_time += job_end_time - step_start_data[last_step] + + # Remove blocking checkpoint manager save time from productive time. + productive_training_time -= checkpoint_manager_save_badput + + # Return a tuple of the total productive training time, the total + # unproductive time (dict of BadputType and unproductive time) and the last + # step recorded. + return productive_training_time, total_unproductive_time, last_step + + def _get_total_job_time(self, query_time: datetime.datetime) -> float: + """Helper function to compute the current job runtime. + + Args: + query_time: The time at which the query is being made. + + Returns: + The job's total runtime computed based on the last retrieved logs. + """ + # Find the job's original start time from the cache. + start_time = self._goodput_cache.get_job_start_time() + end_time = self._goodput_cache.get_job_end_time() + if start_time: + if not end_time: + end_time = query_time + return end_time.timestamp() - start_time.timestamp() + + # De-serealize job start and end times from cloud logging entries. These + # will be used to compute total runtime of the job. + job_start_time = None + job_end_time = None + with self._goodput_cache_lock: + cached_entries = list(self._goodput_cache.get_cached_entries()) + for payload in cached_entries: + # Locate the earliest timestamp recorded for the job's start. + if _JOB_START_TIME in payload and job_start_time is None: + job_start_time = payload[_JOB_START_TIME] + # Locate the latest timestamp recorded for the job's end. + if _JOB_END_TIME in payload: + job_end_time = payload[_JOB_END_TIME] + + if job_start_time is not None: + if job_end_time is not None: + return job_end_time - job_start_time + # If the job's end time is missing then job has not yet completed, use + # current query time to compute total job time. + return query_time.timestamp() - job_start_time + # The the job's start time is missing so the total job time cannot be + # calculated. Caller of this function should raise an error if this happens. + return 0.0 + + def _fetch_new_entries(self, query_time: datetime.datetime) -> list[Any]: + """Thread-safe helper function to update and return new log entries.""" + with self._goodput_cache_lock: + if not self._goodput_cache.is_cache_empty(): + last_entry_timestamp = self._goodput_cache.get_last_entry_timestamp() + if query_time <= last_entry_timestamp: + return [] + new_entries = self._cloud_logger.read_cloud_logging_entries( + last_entry_timestamp, query_time + ) + else: + new_entries = self._cloud_logger.read_cloud_logging_entries() + + # Update the cache with the new log entries. + self._goodput_cache.update_cached_entries(new_entries) + return new_entries + + def _get_interval_log_entries( + self, start_time: datetime.datetime, end_time: datetime.datetime + ): + """Helper function to get log entries from an interval window.""" + if start_time is None or end_time is None: + raise ValueError( + 'Start and end times are required to get log entries from an interval' + ' window.' + ) + self._interval_entries = self._cloud_logger.read_cloud_logging_entries( # type: ignore + start_time, end_time + ) + logging.info( + 'Inspecting interval entries between %s and %s', start_time, end_time + ) + + if not self._interval_entries: + raise ValueError( + 'No log entries found within the interval window between %s and %s.' + % (start_time, end_time) + ) + + def _sanitize_unproductive_times( + self, + unproductive_times: UnproductiveTimeDict, + max_allowed: float, + ) -> None: + """Helper function to sanitize unproductive times.""" + for badput_type, value in unproductive_times.items(): + if isinstance(value, float): + if value < 0.0 or value > max_allowed: + logging.warning( + 'Unproductive time for %s could not be computed.', badput_type + ) + unproductive_times[badput_type] = 0.0 + elif isinstance(value, dict): + for sub_type, sub_value in value.items(): + if sub_value < 0.0 or sub_value > max_allowed: + logging.warning( + 'Unproductive time for %s[%s] could not be computed.', + badput_type, + sub_type, + ) + value[sub_type] = 0.0 + + def _calculate_total_flat_unproductive_time( + self, + unproductive_time_dict: UnproductiveTimeDict, + ) -> float: + """Helper function to calculate total flat unproductive time.""" + total = 0.0 + for badput_type, value in unproductive_time_dict.items(): + if badput_type in {BadputType.DATA_LOADING_ASYNC, BadputType.OTHER}: + continue + if isinstance(value, float): + total += value + elif isinstance(value, dict): + total += sum(value.values()) + return total + + def _compute_other_unproductive_time( + self, + total_job_time: float, + productive_training_time: float, + unproductive_time_dict: UnproductiveTimeDict, + ) -> float: + """Helper function to compute the "Unknown/Other" unproductive time.""" + other_unproductive_time = ( + total_job_time + - productive_training_time + - self._calculate_total_flat_unproductive_time(unproductive_time_dict) + ) + return max(0.0, other_unproductive_time) + + def _get_total_job_time_from_interval( + self, start_interval: datetime.datetime, end_interval: datetime.datetime + ) -> float: + """Helper function to compute the total job runtime from interval entries.""" + # Get the first and last entry's timestamps in the window + first_entry_timestamp = get_timestamp_from_log_entry( + self._interval_entries[0] + ) + last_entry_timestamp = get_timestamp_from_log_entry( + self._interval_entries[-1] + ) + + # Calculate effective start_time and end_time + self._interval_start_time = ( + max(start_interval, first_entry_timestamp) + if first_entry_timestamp + else start_interval + ) + self._interval_end_time = ( + min(end_interval, last_entry_timestamp) + if last_entry_timestamp + else end_interval + ) + + # Ensure start_time is not after end_time + if self._interval_start_time >= self._interval_end_time: + raise ValueError( + 'Start time is on or after end time, cannot compute total job time.' + ) + + return ( + self._interval_end_time.timestamp() + - self._interval_start_time.timestamp() + ) + + def get_job_goodput(self, include_badput_breakdown=False) -> tuple[ + float, + UnproductiveTimeDict, + int, + ]: + """Method to get the cumulative Goodput and Badput breakdown of the job computed until now. + + If the application is interested in retrieving the overall Goodput of the + job throughout its lifetime, this method provides the singular Goodput + computation for the entire job. + + This method also returns the Badput breakdown of the job if + `include_badput_breakdown` is set to True. + + Additionaly, this method returns the last step recorded for the job. This is + primarily used for improving monitoring and observability of the job's + overall Goodput as a function of number of executed steps. + + Args: + include_badput_breakdown: Whether or not to return the badput breakdown. + If False, returns {} for the badput breakdown. + + Returns: + A tuple of the job's Goodput, optionally the Badput breakdown and the last + step recorded for the job. + + Raises: + ValueError if computed total job time is zero. In this case, Goodput + cannot be computed. + ValueError if productive training time is invalid. + """ + query_time = datetime.datetime.now(datetime.timezone.utc) + + # Update the logs used to compute Goodput. + new_entries = self._fetch_new_entries(query_time) + + total_job_time = self._get_total_job_time(query_time) + # No calculations can be made if total job time is zero. This can happen if + # logs for the job are not present, sent to an invalid location or contain + # bad data. Raise a ValueError if this happens. + if total_job_time == 0.0: + raise ValueError( + 'Total job time is zero, Goodput cannot be calculated. Please fix the' + ' logging entries.' + ) + productive_training_time, total_unproductive_time, last_step = ( + self._get_total_productive_and_unproductive_time(new_entries) + ) + if ( + productive_training_time < 0.0 + or productive_training_time > total_job_time + ): + raise ValueError( + 'Productive training time is invalid. Please fix the logging entries.' + ) + + # Sanitize the unproductive times. + self._sanitize_unproductive_times(total_unproductive_time, total_job_time) + + # Compute the "Unknown/Other" unproductive time. + total_unproductive_time[BadputType.OTHER] = ( + self._compute_other_unproductive_time( + total_job_time, productive_training_time, total_unproductive_time + ) + ) + + # Compute the job Goodput and Badput breakdown. + job_goodput = (float(productive_training_time) / total_job_time) * 100 + job_badput_breakdown = ( + self._get_job_badput_breakdown(total_unproductive_time, total_job_time) + if include_badput_breakdown + else {} + ) + + # Update the Goodput cache with new information. + self._goodput_cache.update_goodput_info( + GoodputInfo( + total_productive_time=productive_training_time, + total_elapsed_time_since_start=total_job_time, + total_unproductive_time=total_unproductive_time, + last_recorded_step=last_step, + last_updated_timestamp=datetime.datetime.now(datetime.timezone.utc), + ) + ) + return job_goodput, job_badput_breakdown, last_step + + def get_job_goodput_interval( + self, interval_start: datetime.datetime, interval_end: datetime.datetime + ) -> tuple[ + float, + UnproductiveTimeDict, + int, + float, + int, + ]: + """Method to get the Goodput and Badput breakdown of the job within an interval window. + + If the application is interested in retrieving the Goodput of the job within + a specific window of time, this method provides the metrics computed between + the start and end of this window. + + Additionaly, this method returns the last step recorded for the job. This is + primarily used for improving monitoring and observability of the job's + overall Goodput as a function of number of executed steps. + + Args: + interval_start: The start time of the window for which Goodput is to be + computed. + interval_end: The end time of the window for which Goodput is to be + computed. + + Returns: + A tuple containing: + - The job's Goodput percentage with respect to the total job time within + the interval window. + - The Badput Breakdown percentages with respect to the total job time + within the interval window. + - The last step recorded for the job within the interval window. + - The total job time within the interval window. + - The number of disruptions within the interval window. + + Raises: + ValueError if computed total job time is zero. In this case, Goodput + cannot be computed. + ValueError if productive training or unproductive time is invalid. + """ + # Get the logs for the interval and validate the interval window. + self._get_interval_log_entries(interval_start, interval_end) + + total_job_time = self._get_total_job_time_from_interval( + interval_start, interval_end + ) + + productive_training_time, total_unproductive_time, last_step = ( + self._get_current_productive_and_unproductive_time(interval_query=True) + ) + if ( + productive_training_time < 0.0 + or productive_training_time > total_job_time + ): + raise ValueError( + 'Productive training time is invalid. Please fix the logging entries.' + ) + + # Sanitize unproductive times + self._sanitize_unproductive_times(total_unproductive_time, total_job_time) + + # Compute the "Unknown/Other" unproductive time + total_unproductive_time[BadputType.OTHER] = ( + self._compute_other_unproductive_time( + total_job_time, productive_training_time, total_unproductive_time + ) + ) + + # Compute the job Goodput and Badput breakdown. + job_goodput = (float(productive_training_time) / total_job_time) * 100 + job_badput_breakdown = self._get_job_badput_breakdown( + total_unproductive_time, total_job_time + ) + + return ( + job_goodput, + job_badput_breakdown, + last_step, + total_job_time, + self._number_of_interruptions, + ) + + def _get_step_times(self, entries: list[Any]): + """Helper function to compute step times from the log entries.""" + step_times = {} + previous_step_start_time = None + previous_step_count = None + for payload in entries: + if _STEP_START_TIME in payload: + step_start_time = payload[_STEP_START_TIME] + step_count = int(payload[_STEP_COUNT]) + if ( + previous_step_start_time is not None + and previous_step_count is not None + and step_count == previous_step_count + 1 + ): + step_times[previous_step_count] = ( + step_start_time - previous_step_start_time + ) + previous_step_count = step_count + previous_step_start_time = step_start_time + return step_times + + def _contains_step_entries(self, entries: list[Any]) -> bool: + return any(_STEP_START_TIME in entry for entry in entries) + + def get_step_deviation( + self, configured_ideal_step_time: Optional[float] = None + ) -> dict[int, float]: + """Method to get the step deviation of the current step based on the ideal step time. + + This method computes the ideal step time if one is not provided by the user + and returns the step deviation of the current step. + + Args: + configured_ideal_step_time: Optional user-defined ideal step time. + + Returns: + A dictionary of step deviation for each step. + """ + query_time = datetime.datetime.now(datetime.timezone.utc) + new_entries = self._fetch_new_entries(query_time) + with self._goodput_cache_lock: + step_info = self._goodput_cache.get_step_info() + + if ( + not self._contains_step_entries(new_entries) + and step_info + and step_info.step_deviations + ): + return step_info.step_deviations + + with self._goodput_cache_lock: + process_entries = self._goodput_cache.get_step_entries() + + step_times = self._get_step_times(process_entries) + + if not step_times: + raise ValueError( + 'No step times available and no previous step deviations found.' + ) + + # Compute ideal step time. + ideal_step_time = ( + configured_ideal_step_time + if configured_ideal_step_time is not None + else compute_ideal_step_time(list(step_times.values())) + ) + if not ideal_step_time: + raise ValueError( + 'No ideal step time available and no previous step deviations found.' + ) + + # Compute step deviation. + step_deviations = { + step_count: abs(step_time - ideal_step_time) + for step_count, step_time in step_times.items() + } + # Update the step information in the cache. + with self._goodput_cache_lock: + self._goodput_cache.update_step_info( + StepInfo( + ideal_step_time=ideal_step_time, + step_deviations=step_deviations, + ) + ) + return step_deviations + + def _get_job_badput_breakdown( + self, total_unproductive_time, total_job_time + ) -> UnproductiveTimeDict: + """Method to get the the Badput breakdown as percentage of total job time. + + This method provides a granular breakdown of the known components of Badput. + + Args: + total_unproductive_time: A dictionary of computed unproductive time of + each BadputType. + total_job_time: The total job time. + + Returns: + A dictionary of badput components and their percentage breakdown within + total job time. + """ + badput_breakdown: dict[ + BadputType, float | dict[str, float] + ] = {} + if total_job_time == 0.0: + raise ValueError( + 'Total job time is zero, Badput cannot be calculated. Please fix the' + ' logging entries.' + ) + + # TPU initialization badput. + tpu_init_badput = total_unproductive_time.get( + BadputType.TPU_INITIALIZATION, 0.0 + ) + badput_breakdown[BadputType.TPU_INITIALIZATION] = ( + (tpu_init_badput / total_job_time) * 100 + if 0 < tpu_init_badput < total_job_time + else 0.0 + ) + + # Training preparation badput. + training_prep_badput = total_unproductive_time.get( + BadputType.TRAINING_PREP, 0.0 + ) + badput_breakdown[BadputType.TRAINING_PREP] = ( + (training_prep_badput / total_job_time) * 100 + if 0 < training_prep_badput < total_job_time + else 0.0 + ) + + # Only synchronous data loading is badput. + # Sync data loading is accumulated after start and reset of the job and is + # blocking. + sync_data_loading_badput = total_unproductive_time.get( + BadputType.DATA_LOADING_SYNC, 0.0 + ) + # Async data loading is accumulated overlapping with training and is + # non-blocking, therefore is not unproductive time. + async_data_loading_badput = total_unproductive_time.get( + BadputType.DATA_LOADING_ASYNC, 0.0 + ) + badput_breakdown[BadputType.DATA_LOADING_SYNC] = ( + (sync_data_loading_badput / total_job_time) * 100 + if 0 < sync_data_loading_badput < total_job_time + else 0.0 + ) + badput_breakdown[BadputType.DATA_LOADING_ASYNC] = ( + (async_data_loading_badput / total_job_time) * 100 + if 0 < async_data_loading_badput < total_job_time + else 0.0 + ) + + # Unproductive checkpoint save time badput. + checkpoint_save_badput = total_unproductive_time.get( + BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME, 0.0 + ) + badput_breakdown[BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME] = ( + (checkpoint_save_badput / total_job_time) * 100 + if 0 < checkpoint_save_badput < total_job_time + else 0.0 + ) + + # Unproductive checkpoint restore time badput. + checkpoint_restore_badput = total_unproductive_time.get( + BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME, 0.0 + ) + badput_breakdown[BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME] = ( + (checkpoint_restore_badput / total_job_time) * 100 + if 0 < checkpoint_restore_badput < total_job_time + else 0.0 + ) + + # Program startup badput. + program_startup_badput = total_unproductive_time.get( + BadputType.PROGRAM_STARTUP, 0.0 + ) + badput_breakdown[BadputType.PROGRAM_STARTUP] = ( + (program_startup_badput / total_job_time) * 100 + if 0 < program_startup_badput < total_job_time + else 0.0 + ) + + # Wasted progress from disruption badput. + wasted_progress_from_disruption_badput = total_unproductive_time.get( + BadputType.WASTED_PROGRESS_FROM_DISRUPTION, 0.0 + ) + badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION] = ( + (wasted_progress_from_disruption_badput / total_job_time) * 100 + if 0 < wasted_progress_from_disruption_badput < total_job_time + else 0.0 + ) + + # Custom events badput. + badput_breakdown[BadputType.CUSTOM_BADPUT_EVENTS] = {} + custom_events_badput = total_unproductive_time.get( + BadputType.CUSTOM_BADPUT_EVENTS, {} + ) + + if isinstance(custom_events_badput, dict): + nested_breakdown = {} + for ( + custom_badput_type, + custom_events_badput_value, + ) in custom_events_badput.items(): + nested_breakdown[custom_badput_type] = ( + (custom_events_badput_value / total_job_time) * 100 + if 0 < custom_events_badput_value < total_job_time + else 0.0 + ) + badput_breakdown[BadputType.CUSTOM_BADPUT_EVENTS] = ( + nested_breakdown + ) + + # Populate the 'Other/Unknown' badput bucket. + other_badput = total_unproductive_time.get(BadputType.OTHER, 0.0) + badput_breakdown[BadputType.OTHER] = ( + (other_badput / total_job_time) * 100 + if 0 < other_badput < total_job_time + else 0.0 + ) + + return badput_breakdown + + def get_job_goodput_details( + self, + ) -> dict[ + str, + dict[ + Union[BadputType, GoodputType], + float | dict[str, float], + ], + ]: + """Method to get the productive and non-productive time with breakdown of the job computed until now.""" + + goodput_info = self._goodput_cache.get_goodput_info() + if goodput_info is None: + logger.warning( + 'Goodput information unavailable and will not be uploaded to GCM' + ) + return { + 'goodput_time_dict': {}, + 'badput_time_dict': {}, + } + + ( + productive_training_time, + total_unproductive_time, + cache_last_updated_timestamp, + ) = ( + goodput_info.total_productive_time, + goodput_info.total_unproductive_time, + goodput_info.last_updated_timestamp, + ) + + if ( + self._gcm_last_recorded_timestamp is not None # Ignore the first entry. + and self._gcm_last_recorded_timestamp >= cache_last_updated_timestamp + ): + logger.warning( + 'No new data, skipping upload to GCM. Cache Timestamp: %s, GCM' + ' Timestamp: %s', cache_last_updated_timestamp, + self._gcm_last_recorded_timestamp, + ) + return { + 'goodput_time_dict': {}, + 'badput_time_dict': {}, + } + + self._gcm_last_recorded_timestamp = datetime.datetime.now( + datetime.timezone.utc + ) + + # Currently productive_time is not split based on productive activities, it + # is just the total productive time. We will modify this to follow the same + # format as badput_breakdown. Please update this code accordingly in the + # future when we have more granular breakdown of productive time. + + total_productive_time = {GoodputType.TOTAL: productive_training_time} + + return { + 'goodput_time_dict': total_productive_time, + 'badput_time_dict': total_unproductive_time, + } + + def get_job_goodput_interval_details( + self, interval_start: datetime.datetime, interval_end: datetime.datetime + ) -> dict[ + str, + dict[ + Union[BadputType, GoodputType], + float | dict[str, float], + ], + ]: + """Method to get the productive and non-productive time with breakdown of the job computed within an interval window.""" + try: + goodput, badput_breakdown, _, total_job_time, _ = ( + self.get_job_goodput_interval(interval_start, interval_end) + ) + productive_time = goodput * total_job_time / 100 + total_unproductive_time = {} + for badput_type, badput_value in badput_breakdown.items(): + total_unproductive_time[badput_type] = ( + badput_value * total_job_time / 100 + ) + total_productive_time = {GoodputType.TOTAL: productive_time} + + return { + 'goodput_time_dict': total_productive_time, + 'badput_time_dict': total_unproductive_time, + } + except ValueError as e: + logger.warning('Failed to get job goodput interval details: %s', e) + return { + 'goodput_time_dict': {}, + 'badput_time_dict': {}, + } diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py b/ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py new file mode 100644 index 0000000..673f5a3 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py @@ -0,0 +1,119 @@ +"""Goodput Cache implementations.""" + +import datetime +from typing import Any + +from cloud_goodput.ml_goodput_measurement.src import goodput_utils + + +StepInfo = goodput_utils.StepInfo +GoodputInfo = goodput_utils.GoodputInfo +_TIME_ENTRY = 'time' +_JOB_START_TIME = 'job_start_time' +_JOB_END_TIME = 'job_end_time' +_STEP_START_TIME = 'step_start_time' + + +class GoodputCache: + """Goodput Cache.""" + + def __init__(self): + self._cached_entries = [] + self._step_entries = [] + self._goodput_info = None + self._last_entry_timestamp = None + self._job_start_time = None + self._job_end_time = None + self._step_info = None + + def update_step_info(self, step_info: StepInfo): + """Updates the step information.""" + self._step_info = step_info + + def update_cached_entries(self, entries: list[Any]): + """Updated the cached entries.""" + self._cached_entries.extend(entries) + self.update_last_entry_timestamp() + self.update_job_start_time() + self.update_job_end_time() + new_step_entries = [entry for entry in entries if _STEP_START_TIME in entry] + self._step_entries.extend(new_step_entries) + + def update_last_entry_timestamp(self): + """Helper function to store the timestamp of the last entry in the cache.""" + if self._cached_entries: + last_entry = self._cached_entries[-1] + last_entry_posix_time = [ + entry_value + for entry_label, entry_value in last_entry.items() + if _TIME_ENTRY in entry_label + ] + if last_entry_posix_time: + self._last_entry_timestamp = datetime.datetime.fromtimestamp( + last_entry_posix_time[0], tz=datetime.timezone.utc + ) + + def update_job_start_time(self): + """Updates the job start time.""" + # If the job start time is not set, try to find it in the cached entries. + if self._job_start_time is None and self._cached_entries: + for entry in self._cached_entries: + if _JOB_START_TIME in entry: + self._job_start_time = datetime.datetime.fromtimestamp( + entry[_JOB_START_TIME], tz=datetime.timezone.utc + ) + break + + def update_job_end_time(self): + """Updates the job end time.""" + # Overwrite the latest job end time if cached entries contain the job end + # time. + if self._job_end_time is None and self._cached_entries: + for entry in reversed(self._cached_entries): + if _JOB_END_TIME in entry: + self._job_end_time = datetime.datetime.fromtimestamp( + entry[_JOB_END_TIME], tz=datetime.timezone.utc + ) + break + + def update_goodput_info(self, goodput_info: GoodputInfo): + """Updates the last computed Goodput information.""" + self._goodput_info = goodput_info + + def get_cached_entries(self): + """Returns the cached entries.""" + return self._cached_entries + + def get_step_entries(self): + """Returns the step entries.""" + return self._step_entries + + def get_goodput_info(self): + """Returns the last computed Goodput information.""" + return self._goodput_info + + def get_job_start_time(self): + """Returns the job start time.""" + return self._job_start_time + + def get_job_end_time(self): + """Returns the job end time.""" + return self._job_end_time + + def get_last_entry_timestamp(self): + """Returns the timestamp of the last entry in the cache.""" + return self._last_entry_timestamp + + def get_step_info(self): + """Returns the step information.""" + return self._step_info + + def clear_cache(self): + """Clears the cache.""" + self._cached_entries = [] + self._goodput_info = None + self._last_entry_timestamp = None + + def is_cache_empty(self) -> bool: + """Checks if the cache is empty.""" + return not self._cached_entries diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py b/ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py new file mode 100644 index 0000000..9cd5e6c --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py @@ -0,0 +1,258 @@ +"""Goodput Utility Classes and Helpers.""" + +import dataclasses +import datetime +import enum +import logging +import math +from typing import Any, Optional + +import numpy as np +import requests +from scipy import stats +from urllib3.util import retry + + +Retry = retry.Retry +_TIME_ENTRY = 'time' +_METADATA_SERVER_URL = 'http://metadata.google.internal/computeMetadata/v1/' +_METADATA_HEADERS = {'Metadata-Flavor': 'Google'} + +MACHINE_TYPE_TO_ACCELERATOR_TYPE_MAPPING = { + 'ct6e': 'TPU-v6e', + 'ct5p': 'TPU-v5p', + 'ct5lp': 'TPU-v5e', + 'ct5l': 'TPU-v5e', + 'ct4p': 'TPU-v4p', + 'ct3p': 'TPU-v3', + 'ct3': 'TPU-v3', + 'tpu-v2': 'TPU-v2', + 'tpu': 'TPU', + 'a3-edgegpu': 'NVIDIA-H100', + 'a3-highgpu': 'NVIDIA-H100', + 'a3-megagpu': 'NVIDIA-H100', + 'a3-ultragpu': 'NVIDIA-H200', + 'a2': 'NVIDIA-A100', + 'gpu': 'GPU', +} + + +@dataclasses.dataclass +class GCPOptions: + project_id: Optional[str] = None + location: Optional[str] = None + replica_id: str = '0' + acc_type: Optional[str] = None + enable_gcp_goodput_metrics: bool = True + enable_gcp_step_deviation_metrics: bool = True + + +# Productive time is not broken down by activities yet. As such, we only have +# one type of Goodput which contributes to the total productive time. +class GoodputType(enum.Enum): + """The type of Goodput.""" + + TOTAL = 1 + + +class BadputType(enum.Enum): + """The type of Badput.""" + + TPU_INITIALIZATION = 1 + TRAINING_PREP = 2 + PROGRAM_STARTUP = 3 + DATA_LOADING_SYNC = 4 + DATA_LOADING_ASYNC = 5 + UNPRODUCTIVE_CHECKPOINT_SAVE_TIME = 6 + UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME = 7 + WASTED_PROGRESS_FROM_DISRUPTION = 8 + CUSTOM_BADPUT_EVENTS = 9 + OTHER = 10 + + +ACTIVITY_EXCLUSION_LIST = [ + # DATA_LOADING_ASYNC is not a non-productive activity as it is not + # blocking. Hence, we exclude it from calculating Goodput. + 'DATA_LOADING_ASYNC', +] + + +class GoodputInfo: + """Goodput Information.""" + + def __init__( + self, + total_productive_time: float = 0.0, + total_elapsed_time_since_start: float = 0.0, + total_unproductive_time: Optional[dict[BadputType, float]] = None, + last_recorded_step: int = 0, + last_updated_timestamp: datetime.datetime = datetime.datetime.now( + datetime.timezone.utc + ), + ): + self.total_productive_time = total_productive_time + self.total_elapsed_time_since_start = total_elapsed_time_since_start + + # We cannot use {} as the default argument directly because it's a mutable + # default argument. Mutable default arguments are shared between all + # instances of the class. If one instance modifies the default + # dictionary, it will affect all other instances. Instead, we use + # None as a sentinel value and create a new dictionary inside the + # __init__ method if no dictionary is provided. This ensures each + # instance gets its own dictionary. + self.total_unproductive_time = ( + total_unproductive_time or {} + ) + self.last_recorded_step = last_recorded_step + self.last_updated_timestamp = last_updated_timestamp + + +class StepInfo: + """Step Information.""" + + def __init__( + self, + ideal_step_time: float, + step_deviations: dict[int, float], + ): + self.ideal_step_time = ideal_step_time + self.step_deviations = step_deviations + + +def compute_ideal_step_time(step_times: list[float]) -> Optional[float]: + """Helper function to compute the ideal step time.""" + # Filter out step times that may be less than 1 second. + step_times = [step_time for step_time in step_times if step_time >= 1.0] + if not step_times: + return None + # Compute the median absolute deviation (MAD) and median of the step times + mad = stats.median_abs_deviation(step_times) + med = np.median(step_times) + + # Normalize the step times to the median + 3 * MAD. + normal_step_times = [ + step_time for step_time in step_times if step_time <= (med + mad * 3) + ] + return np.mean(normal_step_times) if normal_step_times else None + + +def get_anomalous_and_normal_step_times( + step_times: list[Any], +) -> tuple[list[Any], list[Any]]: + """Helper function to get anomalous and normal step times.""" + mad = stats.median_abs_deviation(step_times) + med = np.median(step_times) + + anomalous_step_times = [] + normal_step_times = [] + for step_time in step_times: + if step_time > (med + mad * 3): + anomalous_step_times.append(step_time) + else: + normal_step_times.append(step_time) + + return anomalous_step_times, normal_step_times + + +def get_extra_time_from_anomalous_steps(step_times: list[Any]) -> float: + anomalous_step_times, normal_step_times = get_anomalous_and_normal_step_times( + step_times + ) + normal_step_mean = np.mean(normal_step_times) + return sum(anomalous_step_times) - ( + len(anomalous_step_times) * normal_step_mean + ) + + +def get_timestamp_from_log_entry( + entry: dict[str, Any], +) -> Optional[datetime.datetime]: + """Helper function to get the timestamp from a log entry.""" + timestamp_posix_time = [ + entry_value + for entry_label, entry_value in entry.items() + if _TIME_ENTRY in entry_label + ] + if timestamp_posix_time: + return datetime.datetime.fromtimestamp( + timestamp_posix_time[0], datetime.timezone.utc + ) + return None + + +def get_gcp_metadata(category: str, attribute: str, timeout=5, retries=3): + """Fetch the specified attribute from GCP metadata server. + + Args: + category (str): The high-level metadata category (ex: 'instance', + 'project'). + attribute (str): The attribute to fetch under this category (ex: 'id', + 'zone'). + timeout (int): Timeout for the request in seconds. + retries (int): Number of retry attempts for transient failures. + + Returns: + str: The metadata value as a string, or None if the request fails. + """ + target_url = f'{_METADATA_SERVER_URL}{category}/{attribute}' + + session = requests.Session() + retry_strategy = Retry( + total=retries, + backoff_factor=0.5, + # Retry on the following status codes + status_forcelist=[429, 500, 502, 503, 504], + ) + adapter = requests.adapters.HTTPAdapter(max_retries=retry_strategy) + session.mount('http://', adapter) + + try: + response = session.get( + target_url, headers=_METADATA_HEADERS, timeout=timeout + ) + response.raise_for_status() + return response.text + except requests.exceptions.RequestException as e: + logging.warning( + 'Failed to retrieve metadata for %s/%s: %s', category, attribute, e + ) + return None + + +def get_gcp_project_id(): + """Returns the project id of the current GCP project.""" + return get_gcp_metadata('project', 'project-id') + + +def get_node_zone(): + """Returns the zone of the GCE instance.""" + zone_path = get_gcp_metadata('instance', 'zone') + # example zone_path: "projects/123456789/zones/us-central1-a" + return zone_path.rsplit('/', 1)[-1] if zone_path else None + + +def get_accelerator_type(): + """Retrieves the accelerator type from GCP metadata. + + For GKE TPU VMs, it extracts the type from the 'machine-type' metadata. + + Returns: + str: The accelerator type, or 'UNKNOWN' if not found. + """ + machine_type_url = get_gcp_metadata('instance', 'machine-type') + # example machine_type_url: "projects/123456789/machineTypes/a3-highgpu-8g" + machine_type_name = ( + machine_type_url.split('/')[-1] if machine_type_url else None + ) + + if not machine_type_name: + return 'UNKNOWN' + + for ( + prefix, + accelerator_type, + ) in MACHINE_TYPE_TO_ACCELERATOR_TYPE_MAPPING.items(): + if prefix.lower() in machine_type_name.lower(): + return accelerator_type + + return 'UNKNOWN' diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py b/ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py new file mode 100644 index 0000000..bb8e0a9 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py @@ -0,0 +1,638 @@ +"""Goodput monitoring API. + +This file contains all the utilities to monitor and upload goodput data of a +user workload to Tensorboard asynchronously. +""" + +import datetime +import logging +import math +import os +import threading +import time + +from cloud_goodput.ml_goodput_measurement.src import gcp_metrics +from cloud_goodput.ml_goodput_measurement.src import goodput +from cloud_goodput.ml_goodput_measurement.src import goodput_utils +from tensorboardX import writer + +BadputType = goodput_utils.BadputType +GCPOptions = goodput_utils.GCPOptions +GCPMetrics = gcp_metrics.GCPMetrics +GoodputCalculator = goodput.GoodputCalculator +ValueType = gcp_metrics.ValueType + +ACTIVITY_EXCLUSION_LIST = goodput_utils.ACTIVITY_EXCLUSION_LIST +_TENSORBOARD_GCS_SUBDIR = 'goodput' +_TENSORBOARD_GOODPUT_LABEL = 'goodput' +_TENSORBOARD_BADPUT_LABEL = 'badput' +_TENSORBOARD_STEP_DEVIATION_LABEL = 'step_deviation' +_GOODPUT_DETAILS_KEY = 'goodput_time_dict' +_BADPUT_DETAILS_KEY = 'badput_time_dict' + +logger = logging.getLogger(__name__) + + +class GoodputMonitor: + """Queries and uploads goodput data to Tensorboard at a regular interval.""" + + def __init__( + self, + job_name: str, + logger_name: str, + tensorboard_dir: str, + upload_interval: int, + monitoring_enabled: bool = False, + pathway_enabled: bool = False, + include_badput_breakdown=False, + include_step_deviation=False, + configured_ideal_step_time=None, + step_deviation_interval_seconds=10, + gcp_options: GCPOptions = GCPOptions(), + ): + """Initializes the GoodputMonitor. + + Args: + job_name: The name of the job to monitor. + logger_name: The name of the Google Cloud Logging logger to use. + tensorboard_dir: The directory to write TensorBoard data to. + upload_interval: The interval to upload data to TensorBoard and GCP + Monitoring. + monitoring_enabled: Whether to enable monitoring. If the application is + interested in monitoring Goodput, it should set this value to True if + monitoring from TPU worker 0 andthe application's configurations + request Goodput monitoring. + pathway_enabled: Whether the application is using Pathways. + include_badput_breakdown: Whether to query and upload badput breakdown + data to Tensorboard. + include_step_deviation: Whether to query and upload step deviation data + to Tensorboard. + configured_ideal_step_time: The optional ideal step time configured by + the user. + step_deviation_interval_seconds: The interval to query step deviation + data. + gcp_options: The options for Google Cloud Monitoring. + """ + if not monitoring_enabled: + logger.info( + 'Monitoring is disabled. Returning without initializing' + ' GoodputMonitor.' + ) + return + + # Common configurations. + self._job_name = job_name + self._logger_name = logger_name + self._tensorboard_dir = os.path.join( + tensorboard_dir, _TENSORBOARD_GCS_SUBDIR + ) + # Goodput configurations. + self._upload_interval = upload_interval + self._include_badput_breakdown = include_badput_breakdown + + # Step deviation configurations. + self._include_step_deviation = include_step_deviation + self._step_deviation_interval_seconds = step_deviation_interval_seconds + self._configured_ideal_step_time = configured_ideal_step_time + + # Initialize the GoodputCalculator. + self._goodput_calculator = GoodputCalculator( + job_name=self._job_name, + logger_name=self._logger_name, + using_pathways=pathway_enabled, + ) + self._writer = writer.SummaryWriter(self._tensorboard_dir) + + # Goodput uploader flags to signal the daemon thread if it exists when to + # initate shutdown and wait for termination. + self._goodput_uploader_thread_running = False + self._goodput_upload_thread = None + self._termination_event = threading.Event() + self._termination_event.clear() + + # Step deviation threading flags. + self._step_deviation_uploader_thread_running = False + self._step_deviation_upload_thread = None + self._step_deviation_termination_event = threading.Event() + self._step_deviation_termination_event.clear() + + # Google Cloud Monitoring configurations. + self._gcp_options = gcp_options + self._metrics_sender = None + + # If step deviation is not included, disable GCP step deviation metrics. + if not self._include_step_deviation: + self._gcp_options.enable_gcp_step_deviation_metrics = False + + if ( + self._gcp_options.enable_gcp_goodput_metrics + or self._gcp_options.enable_gcp_step_deviation_metrics + ): + if not self._gcp_options.project_id: + self._gcp_options.project_id = goodput_utils.get_gcp_project_id() + if not self._gcp_options.location: + self._gcp_options.location = goodput_utils.get_node_zone() + if not self._gcp_options.acc_type: + self._gcp_options.acc_type = goodput_utils.get_accelerator_type() + if self._gcp_options.project_id and self._gcp_options.location: + self._metrics_sender = GCPMetrics( + project_id=self._gcp_options.project_id + ) + else: + self._gcp_options.enable_gcp_goodput_metrics = False + self._gcp_options.enable_gcp_step_deviation_metrics = False + logger.warning( + 'Project ID or location is not set. GCP Monitoring will not be' + ' enabled.' + ) + # Goodput interval uploader flags. + self._interval_uploader_thread_running = False + self._interval_goodput_upload_thread = None + self._interval_termination_event = threading.Event() + self._interval_termination_event.clear() + self._interval_window_size_seconds = 0 + + def __del__(self): + try: + self.flush_and_stop_goodput_uploader() + self.flush_and_stop_step_deviation_uploader() + self.flush_and_stop_interval_goodput_uploader() + + except Exception: # pylint: disable=broad-exception-caught + pass + + def _log_tensorboard_scalars( + self, + label_prefix: str, + data: dict[str, float | dict[str, float]], + step: int, + ): + """Logs scalar values (flat or nested) to TensorBoard under a label prefix.""" + if self._writer is None: + return + + for data_type, data_value in data.items(): + if isinstance(data_value, dict): + for subtype, subval in data_value.items(): + full_label = f'{label_prefix}/{data_type}/{subtype}'.lower() + self._writer.add_scalar( + full_label, float(subval), step, display_name=subtype.lower() + ) + else: + full_label = f'{label_prefix}/{data_type.lower()}' + self._writer.add_scalar( + full_label, float(data_value), step, display_name=data_type.lower() + ) + + self._writer.flush() + + def _write_goodput_and_badput_data_to_tensorboard( + self, + job_goodput: float, + badput_breakdown: dict[BadputType, float], + last_step: int, + ): + """Writes goodput and badput breakdown to Tensorboard.""" + self._write_goodput_to_tensorboard(job_goodput, last_step) + if self._include_badput_breakdown: + self._write_badput_to_tensorboard(badput_breakdown, last_step) + + def _write_goodput_to_tensorboard(self, job_goodput: float, last_step: int): + self._log_tensorboard_scalars( + _TENSORBOARD_GOODPUT_LABEL, + {_TENSORBOARD_GOODPUT_LABEL: job_goodput}, + last_step, + ) + + def _write_badput_to_tensorboard( + self, + job_badput_breakdown: dict[BadputType, float | dict[str, float]], + last_step: int, + ): + """Writes badput breakdown to TensorBoard.""" + flattened_badput: dict[str, float | dict[str, float]] = {} + + for badput_type, badput_value in job_badput_breakdown.items(): + if isinstance(badput_value, dict): + flattened_badput[badput_type.name.lower()] = { + subtype.lower(): value for subtype, value in badput_value.items() + } + else: + flattened_badput[badput_type.name.lower()] = badput_value + + self._log_tensorboard_scalars( + _TENSORBOARD_BADPUT_LABEL, + flattened_badput, + last_step, + ) + + def _query_and_upload_goodput_to_tensorboard(self): + """Queries and uploads goodput data to Tensorboard.""" + try: + job_goodput, job_badput_breakdown, last_step = ( + self._goodput_calculator.get_job_goodput( + include_badput_breakdown=self._include_badput_breakdown + ) + ) + self._write_goodput_and_badput_data_to_tensorboard( + job_goodput, job_badput_breakdown, last_step + ) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + 'Error while querying and uploading goodput to Tensorboard. This' + ' will not impact the workload. Error: %s', + e, + ) + + def _flatten_badput_dict( + self, + badput_time_dict: dict[BadputType, float | dict[str, float]], + ) -> list[tuple[str, float]]: + """Flattens nested badput types into (label, value) pairs for export.""" + flat_badput = [] + for badput_type, val in badput_time_dict.items(): + if isinstance(val, dict): + for subtype, subval in val.items(): + flat_badput.append((f'{badput_type.name}.{subtype.upper()}', subval)) + else: + flat_badput.append((badput_type.name, val)) + return flat_badput + + def _send_goodput_metrics_to_gcp(self, goodput_details): + """Sends goodput and badput metrics to GCP Monitoring.""" + try: + gcp_goodput_metrics = [] + + for goodput_type, time_value in goodput_details[ + _GOODPUT_DETAILS_KEY + ].items(): + if goodput_type.name in ACTIVITY_EXCLUSION_LIST: + continue + gcp_goodput_metrics.append({ + 'metric_type': 'compute.googleapis.com/workload/goodput_time', + 'value': time_value, + 'value_type': ValueType.DOUBLE, + 'metric_labels': { + 'goodput_source': goodput_type.name, + 'accelerator_type': self._gcp_options.acc_type, + }, + 'resource_type': 'compute.googleapis.com/Workload', + 'resource_labels': { + 'location': self._gcp_options.location, + 'workload_id': self._job_name, + 'replica_id': self._gcp_options.replica_id, + }, + }) + for badput_label, time_value in self._flatten_badput_dict( + goodput_details[_BADPUT_DETAILS_KEY] + ): + if badput_label in ACTIVITY_EXCLUSION_LIST: + continue + gcp_goodput_metrics.append({ + 'metric_type': 'compute.googleapis.com/workload/badput_time', + 'value': time_value, + 'value_type': ValueType.DOUBLE, + 'metric_labels': { + 'badput_source': badput_label, + 'accelerator_type': self._gcp_options.acc_type, + }, + 'resource_type': 'compute.googleapis.com/Workload', + 'resource_labels': { + 'location': self._gcp_options.location, + 'workload_id': self._job_name, + 'replica_id': self._gcp_options.replica_id, + }, + }) + if self._metrics_sender and gcp_goodput_metrics: + self._metrics_sender.send_metrics(gcp_goodput_metrics) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + 'Error while sending goodput metrics to GCP Monitoring. This' + ' will not impact the workload. Error: %s', + e, + ) + + def _query_and_upload_goodput(self): + """Queries and uploads goodput data to Tensorboard.""" + while not self._termination_event.is_set(): + time.sleep(self._upload_interval) + self._query_and_upload_goodput_to_tensorboard() + if self._gcp_options.enable_gcp_goodput_metrics: + self._send_goodput_metrics_to_gcp( + self._goodput_calculator.get_job_goodput_details() + ) + + def _final_goodput_query_and_upload(self): + """Performs final goodput query and uploads data to Tensorboard & GCM.""" + logger.info( + 'Final goodput query and upload for job: %s and logger: %s', + self._job_name, + self._logger_name, + ) + try: + job_goodput, job_badput_breakdown, last_step = ( + self._goodput_calculator.get_job_goodput( + include_badput_breakdown=self._include_badput_breakdown + ) + ) + self._write_goodput_and_badput_data_to_tensorboard( + job_goodput, job_badput_breakdown, last_step + ) + if self._gcp_options.enable_gcp_goodput_metrics: + self._send_goodput_metrics_to_gcp( + self._goodput_calculator.get_job_goodput_details() + ) + logger.info( + 'Final goodput query and upload for job: %s and logger: %s completed' + ' with total goodput: %.2f%%, last step: %d', + self._job_name, + self._logger_name, + job_goodput, + last_step, + ) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + 'Error while performing final goodput query and upload for job: %s' + ' and logger: %s. This will not impact the workload. Error: %s', + self._job_name, + self._logger_name, + e, + ) + + def flush_and_stop_goodput_uploader(self): + """Stops uploader and performs a final goodput upload.""" + if self._goodput_uploader_thread_running: + self.stop_goodput_uploader() + self._final_goodput_query_and_upload() + + def start_goodput_uploader(self): + """Starts the goodput uploader thread.""" + if self._goodput_uploader_thread_running: + raise RuntimeError('Goodput uploader thread is already running.') + + self._termination_event.clear() + self._goodput_upload_thread = threading.Thread( + target=self._query_and_upload_goodput, daemon=True + ) + logger.info( + 'Starting goodput query and uploader thread in the background for job:' + ' %s and logger: %s', + self._job_name, + self._logger_name, + ) + self._goodput_upload_thread.start() + self._goodput_uploader_thread_running = True + + def stop_goodput_uploader(self): + """Stops the goodput uploader thread.""" + if not self._goodput_uploader_thread_running: + raise RuntimeError('Goodput uploader thread is not running.') + + self._termination_event.set() + if self._goodput_upload_thread is not None: + logger.info('Waiting for goodput query and uploader thread to complete.') + self._goodput_upload_thread.join() + self._goodput_upload_thread = None + logger.info( + 'Goodput query and uploader thread stopped. No more goodput data will' + ' be uploaded to Tensorboard or GCP Monitoring.' + ) + self._goodput_uploader_thread_running = False + + def _write_step_deviation_to_tensorboard( + self, step_deviation: dict[int, float] + ): + if self._writer is not None: + for step_count, step_deviation in step_deviation.items(): + self._writer.add_scalar( + _TENSORBOARD_STEP_DEVIATION_LABEL, + float(step_deviation), + step_count, + ) + self._writer.flush() + + def _send_step_deviation_metric_to_gcp(self, step_deviations): + """Sends step deviation metric to GCP Monitoring.""" + try: + if not step_deviations: + logger.warning( + 'Step deviation is empty. This will not impact the workload.' + ) + return + avg_step_deviation = sum(step_deviations.values()) / len(step_deviations) + + if math.isnan(avg_step_deviation): + logger.warning( + 'Step deviation is NaN. This will not impact the workload.' + ) + return + + perf_metric = [{ + 'metric_type': 'compute.googleapis.com/workload/performance', + 'value': avg_step_deviation, + 'value_type': ValueType.DOUBLE, + 'resource_type': 'compute.googleapis.com/Workload', + 'resource_labels': { + 'location': self._gcp_options.location, + 'workload_id': self._job_name, + 'replica_id': self._gcp_options.replica_id, + }, + }] + if self._metrics_sender: + self._metrics_sender.send_metrics(perf_metric) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + 'Error while sending step deviation to GCP Monitoring.' + ' This will not impact the workload. Error: %s', + e, + ) + + def _query_and_upload_step_deviation_to_tensorboard_and_gcp(self): + """Queries and uploads step deviation data to Tensorboard and GCP Monitoring.""" + try: + step_deviation = self._goodput_calculator.get_step_deviation( + self._configured_ideal_step_time + ) + self._write_step_deviation_to_tensorboard(step_deviation) + if self._gcp_options.enable_gcp_step_deviation_metrics: + self._send_step_deviation_metric_to_gcp(step_deviation) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + 'Error while querying and uploading step deviation to Tensorboard.' + ' This will not impact the workload. Error: %s', + e, + ) + + def _query_and_upload_step_deviation(self): + """Queries and uploads step deviation data to Tensorboard.""" + while not self._step_deviation_termination_event.is_set(): + time.sleep(self._step_deviation_interval_seconds) + self._query_and_upload_step_deviation_to_tensorboard_and_gcp() + + def _final_step_deviation_query_and_upload(self): + """Performs final step deviation query and uploads data to Tensorboard & GCM.""" + logger.info( + 'Final step deviation query and upload for job: %s and logger: %s', + self._job_name, + self._logger_name, + ) + try: + step_deviation = self._goodput_calculator.get_step_deviation( + self._configured_ideal_step_time + ) + self._write_step_deviation_to_tensorboard(step_deviation) + if self._gcp_options.enable_gcp_step_deviation_metrics: + self._send_step_deviation_metric_to_gcp(step_deviation) + logger.info( + 'Final step deviation query and upload for job: %s and logger: %s' + ' completed', + self._job_name, + self._logger_name, + ) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + 'Error while performing final step deviation query and upload for' + ' job: %s and logger: %s. This will not impact the workload. Error:' + ' %s', + self._job_name, + self._logger_name, + e, + ) + + def flush_and_stop_step_deviation_uploader(self): + """Stops uploader and performs a final step deviation upload.""" + if self._step_deviation_uploader_thread_running: + self.stop_step_deviation_uploader() + self._final_step_deviation_query_and_upload() + + def start_step_deviation_uploader(self): + """Starts the step deviation uploader thread.""" + if not self._include_step_deviation: + logger.info( + 'Step deviation monitoring is disabled. Returning without' + ' initializing step deviation uploader thread.' + ) + return + + if self._step_deviation_uploader_thread_running: + raise RuntimeError('Step deviation uploader thread is already running.') + + self._step_deviation_termination_event.clear() + self._step_deviation_upload_thread = threading.Thread( + target=self._query_and_upload_step_deviation, daemon=True + ) + logger.info( + 'Starting step deviation query and uploader thread in the background' + ' for job: %s and logger: %s', + self._job_name, + self._logger_name, + ) + self._step_deviation_upload_thread.start() + self._step_deviation_uploader_thread_running = True + + def stop_step_deviation_uploader(self): + """Stops the step deviation uploader thread.""" + if not self._step_deviation_uploader_thread_running: + raise RuntimeError('Step deviation uploader thread is not running.') + + self._step_deviation_termination_event.set() + if self._step_deviation_upload_thread is not None: + logger.info( + 'Waiting for step deviation query and uploader thread to complete.' + ) + self._step_deviation_upload_thread.join() + logger.info( + 'Step deviation query and uploader thread stopped. No more step' + ' deviation data will be uploaded to Tensorboard or GCP Monitoring.' + ) + self._step_deviation_uploader_thread_running = False + + def _query_and_upload_interval_goodput(self): + """Queries and uploads goodput interval data to Tensorboard.""" + while not self._interval_termination_event.is_set(): + time.sleep(self._upload_interval) + if self._gcp_options.enable_gcp_goodput_metrics: + window_end = datetime.datetime.now(datetime.timezone.utc) + window_start = window_end - datetime.timedelta( + seconds=self._interval_window_size_seconds + ) + # Add timezone since deltatime removes it. + window_start = window_start.replace(tzinfo=datetime.timezone.utc) + self._send_goodput_metrics_to_gcp( + self._goodput_calculator.get_job_goodput_interval_details( + window_start, window_end + ) + ) + + def _final_interval_goodput_query_and_upload(self): + """Performs final interval goodput query and uploads data to GCM.""" + logger.info( + 'Final interval goodput query and upload for job: %s and logger: %s', + self._job_name, + self._logger_name, + ) + try: + window_end = datetime.datetime.now(datetime.timezone.utc) + window_start = window_end - datetime.timedelta( + seconds=self._interval_window_size_seconds + ) + # Add timezone since deltatime removes it. + window_start = window_start.replace(tzinfo=datetime.timezone.utc) + self._send_goodput_metrics_to_gcp( + self._goodput_calculator.get_job_goodput_interval_details( + window_start, window_end + ) + ) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + 'Error while performing final interval goodput query and upload for' + ' job: %s and logger: %s. This will not impact the workload. Error:' + ' %s', + self._job_name, + self._logger_name, + e, + ) + + def flush_and_stop_interval_goodput_uploader(self): + """Stops uploader and performs a final interval goodput upload.""" + if self._interval_uploader_thread_running: + self.stop_goodput_interval_uploader() + self._final_interval_goodput_query_and_upload() + + def start_goodput_interval_uploader(self, window_size_seconds: float): + """Starts the goodput uploader thread for a user-specified interval window.""" + if self._interval_uploader_thread_running: + raise RuntimeError('Goodput interval uploader thread is already running.') + + self._interval_termination_event.clear() + self._interval_window_size_seconds = window_size_seconds + self._interval_goodput_upload_thread = threading.Thread( + target=self._query_and_upload_interval_goodput, + daemon=True, + ) + logger.info( + 'Starting goodput interval query and uploader thread in the background' + ' for job: %s and logger: %s', + self._job_name, + self._logger_name, + ) + self._interval_goodput_upload_thread.start() + self._interval_uploader_thread_running = True + + def stop_goodput_interval_uploader(self): + """Stops the goodput uploader thread.""" + if not self._interval_uploader_thread_running: + raise RuntimeError('Goodput intervaluploader thread is not running.') + + self._interval_termination_event.set() + if self._interval_goodput_upload_thread is not None: + logger.info( + 'Waiting for goodput interval query and uploader thread to complete.' + ) + self._interval_goodput_upload_thread.join() + self._interval_goodput_upload_thread = None + logger.info( + 'Goodput interval query and uploader thread stopped. No more goodput' + ' intervaldata will be uploaded to GCP Monitoring.' + ) + self._interval_uploader_thread_running = False diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py new file mode 100644 index 0000000..58b0d31 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py @@ -0,0 +1,446 @@ +"""Tests for checkpoint badput calculator.""" + +import dataclasses +from typing import Optional + +from absl.testing import absltest +from cloud_goodput.ml_goodput_measurement.src import checkpoint_badput_calculator +import google.cloud.logging as google_cloud_logging +import mock + + +_JOB_NAME = 'checkpoint_job' +_LOGGER_NAME = 'checkpoint_logger' + + +@dataclasses.dataclass +class MockSaveStepStatistics: + """Attributes for save step statistics. + + Attributes: + step: The step number. + event_type: The event type. + checkpoint_manager_blocking_start_time: The start time of checkpoint manager + blocking section. + directory: The directory of the checkpoint. + reached_preemption: Whether the event reached preemption. + preemption_received_at: The time when preemption was received. + wait_for_prev_start_time: The start time of waiting for previous checkpoint. + checkpointer_blocking_start_time: The start time of blocking time introduced + by checkpointer. + get_old_steps_start_time: The start time of getting old steps. + synchronous: Whether the event is synchronous. + wait_for_prev_duration_secs: The duration of waiting for previous + checkpoint. + checkpointer_blocking_duration_secs: The duration of blocking time + introduced by checkpointer. + get_old_steps_duration_secs: The duration of getting old steps. + checkpoint_manager_blocking_duration_secs: The duration of checkpoint + manager blocking section. + """ + + step: Optional[int] = None + event_type: Optional[str] = 'save' + directory: Optional[str] = None + reached_preemption: Optional[bool] = False + preemption_received_at: Optional[float] = None + synchronous: Optional[bool] = False + wait_for_prev_start_time: Optional[float] = None + wait_for_prev_duration_secs: Optional[float] = None + checkpointer_blocking_start_time: Optional[float] = None + checkpointer_blocking_duration_secs: Optional[float] = None + get_old_steps_start_time: Optional[float] = None + get_old_steps_duration_secs: Optional[float] = None + checkpoint_manager_blocking_start_time: Optional[float] = None + checkpoint_manager_blocking_duration_secs: Optional[float] = None + + +@dataclasses.dataclass +class MockRestoreStepStatistics: + """Attributes for restore step statistics. + + Attributes: + step: The step number. + event_type: The event type. + directory: The directory of the checkpoint. + checkpointer_start_time: The start time of restoring the checkpoint, while + using the checkpointer. + checkpointer_duration_secs: The total duration for restoring the checkpoint, + while using the checkpointer. + checkpoint_manager_start_time: The start time for restoring the checkpoint, + while using the checkpoint manager. + checkpoint_manager_duration_secs: The total duration for restoring the + checkpoint, while using the checkpoint manager. + """ + + step: Optional[int] = None + event_type: Optional[str] = 'restore' + directory: Optional[str] = None + checkpointer_start_time: Optional[float] = None + checkpointer_duration_secs: Optional[float] = None + checkpoint_manager_start_time: Optional[float] = None + checkpoint_manager_duration_secs: Optional[float] = None + + +@dataclasses.dataclass +class MockEmergencyRestoreStepStatistics: + """Attributes for emergency restore step statistics. + + Attributes: + step: The step number. + event_type: The event type. + checkpoint_manager_start_time: The start time of checkpoint manager + restore event. + directory: The directory of the checkpoint. + is_restoring_slice: Whether the event takes place on the slice responsible + for reading from the storage location. (Note that in_primary_slice=True + necessarily implies is_restoring_slice=True.) + in_primary_slice: Whether the event takes place on the slice designated as + primary (responsible for restoring from persistent storage). + checkpointer_start_time: The start time of restoring the checkpoint, while + using the checkpointer. + checkpointer_duration_secs: The total duration for restoring the checkpoint, + while using the checkpointer. + broadcast_start_time: The start time of broadcasting(Restore).The broadcast + operation performed by SingleReplicaArrayHandler won't be captured in this + context. + broadcast_duration_secs: The duration of broadcasting(Restore). + checkpoint_manager_duration_secs: The total duration of checkpoint + manager restore event. + """ + + step: Optional[int] = None + event_type: Optional[str] = 'emergency_restore' + checkpoint_manager_start_time: Optional[float] = None + directory: Optional[str] = None + is_restoring_slice: Optional[bool] = False + in_primary_slice: Optional[bool] = False + checkpointer_start_time: Optional[float] = None + checkpointer_duration_secs: Optional[float] = None + broadcast_start_time: Optional[float] = None + broadcast_duration_secs: Optional[float] = None + checkpoint_manager_duration_secs: Optional[float] = None + + +class CheckpointBadputCalculatorTest(absltest.TestCase): + + def setUp(self): + """Setup for the test.""" + super().setUp() + mock_gcloud_client = mock.create_autospec(google_cloud_logging.Client) + options = checkpoint_badput_calculator.CheckpointLoggerOptions( + job_name=_JOB_NAME, + logger_name=_LOGGER_NAME, + client=mock_gcloud_client, + use_goodput_logger=True, + ) + self.checkpoint_badput_calculator = ( + checkpoint_badput_calculator.CheckpointBadputCalculator(options) + ) + + def test_checkpoint_badput_calculator_persistent_save_operation(self): + """Test for persistent save operation.""" + step_count = 4 + default_cm_blocking_duration_secs = 4 + default_ckptr_blocking_duration_secs = 1 + default_gos_duration_secs = 1 + default_wfp_duration_secs = 2 + for i in range(1, step_count+1): + persistent_save_entry = dataclasses.asdict( + MockSaveStepStatistics( + step=i, + event_type='save', + directory='gs://bucket/path', + wait_for_prev_start_time=i * 10.0, + wait_for_prev_duration_secs=default_wfp_duration_secs, + checkpointer_blocking_start_time=i * 10.0 + 2, + checkpointer_blocking_duration_secs=default_ckptr_blocking_duration_secs, + get_old_steps_start_time=i * 10.0 + 3, + get_old_steps_duration_secs=default_gos_duration_secs, + checkpoint_manager_blocking_start_time=i * 10.0, + checkpoint_manager_blocking_duration_secs=default_cm_blocking_duration_secs, + reached_preemption=True, + preemption_received_at=i * 10.0, + synchronous=True, + ) + ) + self.checkpoint_badput_calculator.entries.append(persistent_save_entry) + + expected_breakdown = ( + checkpoint_badput_calculator.SaveCheckpointManagerVerticalStepStats() + ) + expected_breakdown.total_checkpoint_manager_blocking_time = ( + step_count * default_cm_blocking_duration_secs + ) + expected_breakdown.average_checkpoint_manager_blocking_time = ( + default_cm_blocking_duration_secs + ) + expected_breakdown.minimum_checkpoint_manager_blocking_time = ( + default_cm_blocking_duration_secs + ) + expected_breakdown.maximum_checkpoint_manager_blocking_time = ( + default_cm_blocking_duration_secs + ) + expected_breakdown.standard_deviation_checkpoint_manager_blocking_time = 0 + expected_breakdown.total_checkpointer_blocking_time = ( + step_count * default_ckptr_blocking_duration_secs + ) + expected_breakdown.average_checkpointer_blocking_time = ( + default_ckptr_blocking_duration_secs + ) + expected_breakdown.minimum_checkpointer_blocking_time = ( + default_ckptr_blocking_duration_secs + ) + expected_breakdown.maximum_checkpointer_blocking_time = ( + default_ckptr_blocking_duration_secs + ) + expected_breakdown.standard_deviation_checkpointer_blocking_time = 0 + expected_breakdown.total_wait_for_prev_time = ( + step_count * default_wfp_duration_secs + ) + expected_breakdown.average_wait_for_prev_time = default_wfp_duration_secs + expected_breakdown.minimum_wait_for_prev_time = default_wfp_duration_secs + expected_breakdown.maximum_wait_for_prev_time = default_wfp_duration_secs + expected_breakdown.standard_deviation_wait_for_prev_time = 0 + expected_breakdown.total_get_old_steps_time = ( + step_count * default_gos_duration_secs + ) + expected_breakdown.average_get_old_steps_time = default_gos_duration_secs + expected_breakdown.minimum_get_old_steps_time = default_gos_duration_secs + expected_breakdown.maximum_get_old_steps_time = default_gos_duration_secs + expected_breakdown.standard_deviation_get_old_steps_time = 0 + + cm_breakdown = ( + self.checkpoint_badput_calculator.calculate_save_operation_checkpoint_manager_blocking_time( + checkpoint_badput_calculator.OPERATION_TYPE_PERSISTENT + ) + ) + for field in dataclasses.fields(cm_breakdown): + value1 = getattr(cm_breakdown, field.name) + value2 = getattr(expected_breakdown, field.name) + if value1 != value2: + raise ValueError( + f"Mismatch in field '{field.name}':\n" + f" Actual: {value1}\n" + f" Expected: {value2}" + ) + + def test_checkpoint_badput_calculator_local_save_operation(self): + """Test for local save operation.""" + step_count = 4 + default_cm_blocking_duration_secs = 4 + default_ckptr_blocking_duration_secs = 1 + default_gos_duration_secs = 1 + default_wfp_duration_secs = 2 + for i in range(1, step_count+1): + local_save_entry = dataclasses.asdict( + MockSaveStepStatistics( + step=i, + event_type='save', + directory='local', + wait_for_prev_start_time=i * 10.0, + wait_for_prev_duration_secs=default_wfp_duration_secs, + checkpointer_blocking_start_time=i * 10.0 + 2, + checkpointer_blocking_duration_secs=default_ckptr_blocking_duration_secs, + get_old_steps_start_time=i * 10.0 + 3, + get_old_steps_duration_secs=default_gos_duration_secs, + checkpoint_manager_blocking_start_time=i * 10.0, + checkpoint_manager_blocking_duration_secs=default_cm_blocking_duration_secs, + reached_preemption=True, + preemption_received_at=i * 10.0, + synchronous=True, + ) + ) + self.checkpoint_badput_calculator.entries.append(local_save_entry) + + expected_breakdown = ( + checkpoint_badput_calculator.SaveCheckpointManagerVerticalStepStats() + ) + expected_breakdown.total_checkpoint_manager_blocking_time = ( + step_count * default_cm_blocking_duration_secs + ) + expected_breakdown.average_checkpoint_manager_blocking_time = ( + default_cm_blocking_duration_secs + ) + expected_breakdown.minimum_checkpoint_manager_blocking_time = ( + default_cm_blocking_duration_secs + ) + expected_breakdown.maximum_checkpoint_manager_blocking_time = ( + default_cm_blocking_duration_secs + ) + expected_breakdown.standard_deviation_checkpoint_manager_blocking_time = 0 + expected_breakdown.total_checkpointer_blocking_time = ( + step_count * default_ckptr_blocking_duration_secs + ) + expected_breakdown.average_checkpointer_blocking_time = ( + default_ckptr_blocking_duration_secs + ) + expected_breakdown.minimum_checkpointer_blocking_time = ( + default_ckptr_blocking_duration_secs + ) + expected_breakdown.maximum_checkpointer_blocking_time = ( + default_ckptr_blocking_duration_secs + ) + expected_breakdown.standard_deviation_checkpointer_blocking_time = 0 + expected_breakdown.total_wait_for_prev_time = ( + step_count * default_wfp_duration_secs + ) + expected_breakdown.average_wait_for_prev_time = default_wfp_duration_secs + expected_breakdown.minimum_wait_for_prev_time = default_wfp_duration_secs + expected_breakdown.maximum_wait_for_prev_time = default_wfp_duration_secs + expected_breakdown.standard_deviation_wait_for_prev_time = 0 + expected_breakdown.total_get_old_steps_time = ( + step_count * default_gos_duration_secs + ) + expected_breakdown.average_get_old_steps_time = default_gos_duration_secs + expected_breakdown.minimum_get_old_steps_time = default_gos_duration_secs + expected_breakdown.maximum_get_old_steps_time = default_gos_duration_secs + expected_breakdown.standard_deviation_get_old_steps_time = 0 + + cm_breakdown = ( + self.checkpoint_badput_calculator.calculate_save_operation_checkpoint_manager_blocking_time( + checkpoint_badput_calculator.OPERATION_TYPE_LOCAL + ) + ) + for field in dataclasses.fields(cm_breakdown): + value1 = getattr(cm_breakdown, field.name) + value2 = getattr(expected_breakdown, field.name) + if value1 != value2: + raise ValueError( + f"Mismatch in field '{field.name}':\n" + f" Actual: {value1}\n" + f" Expected: {value2}" + ) + + def test_checkpoint_badput_calculator_persistent_restore_operation(self): + """Test for persistent restore operation.""" + step_count = 4 + default_cm_duration_secs = 4 + default_ckptr_duration_secs = 1 + for i in range(1, step_count+1): + persitent_save_entry = dataclasses.asdict( + MockRestoreStepStatistics( + step=i, + event_type='restore', + directory='gs://bucket/path', + checkpointer_start_time=i * 10.0, + checkpointer_duration_secs=default_ckptr_duration_secs, + checkpoint_manager_start_time=i * 10.0 + 2, + checkpoint_manager_duration_secs=default_cm_duration_secs, + ) + ) + self.checkpoint_badput_calculator.entries.append(persitent_save_entry) + + expected_breakdown = ( + checkpoint_badput_calculator.RestoreCheckpointManagerVerticalStepStats() + ) + expected_breakdown.total_checkpoint_manager_time = ( + step_count * default_cm_duration_secs + ) + expected_breakdown.average_checkpoint_manager_time = ( + default_cm_duration_secs + ) + expected_breakdown.minimum_checkpoint_manager_time = ( + default_cm_duration_secs + ) + expected_breakdown.maximum_checkpoint_manager_time = ( + default_cm_duration_secs + ) + expected_breakdown.standard_deviation_checkpoint_manager_time = 0 + expected_breakdown.total_restore_time = ( + step_count * default_ckptr_duration_secs + ) + expected_breakdown.average_restore_time = default_ckptr_duration_secs + expected_breakdown.minimum_restore_time = default_ckptr_duration_secs + expected_breakdown.maximum_restore_time = default_ckptr_duration_secs + expected_breakdown.standard_deviation_restore_time = 0 + expected_breakdown.total_broadcast_time = 0 + expected_breakdown.average_broadcast_time = 0 + expected_breakdown.minimum_broadcast_time = 0 + expected_breakdown.maximum_broadcast_time = 0 + expected_breakdown.standard_deviation_broadcast_time = 0 + + cm_breakdown = ( + self.checkpoint_badput_calculator.calculate_restore_operation_checkpoint_manager_blocking_time( + checkpoint_badput_calculator.OPERATION_TYPE_PERSISTENT + ) + ) + for field in dataclasses.fields(cm_breakdown): + value1 = getattr(cm_breakdown, field.name) + value2 = getattr(expected_breakdown, field.name) + if value1 != value2: + raise ValueError( + f"Mismatch in field '{field.name}':\n" + f" Actual: {value1}\n" + f" Expected: {value2}" + ) + + def test_checkpoint_badput_calculator_local_restore_operation(self): + """Test for local restore operation.""" + step_count = 4 + default_cm_duration_secs = 4 + default_ckptr_duration_secs = 2 + default_broadcast_duration_secs = 2 + for i in range(1, step_count+1): + local_save_entry = dataclasses.asdict( + MockEmergencyRestoreStepStatistics( + step=i, + event_type='emergency_restore', + directory='local', + checkpointer_start_time=i * 10.0, + checkpointer_duration_secs=default_ckptr_duration_secs, + checkpoint_manager_start_time=i * 10.0 + 2, + checkpoint_manager_duration_secs=default_cm_duration_secs, + broadcast_start_time=i * 10.0 + 3, + broadcast_duration_secs=default_broadcast_duration_secs, + ) + ) + self.checkpoint_badput_calculator.entries.append(local_save_entry) + + expected_breakdown = ( + checkpoint_badput_calculator.RestoreCheckpointManagerVerticalStepStats() + ) + expected_breakdown.total_checkpoint_manager_time = ( + default_cm_duration_secs * step_count + ) + expected_breakdown.average_checkpoint_manager_time = ( + default_cm_duration_secs + ) + expected_breakdown.minimum_checkpoint_manager_time = ( + default_cm_duration_secs + ) + expected_breakdown.maximum_checkpoint_manager_time = ( + default_cm_duration_secs + ) + expected_breakdown.standard_deviation_checkpoint_manager_time = 0 + expected_breakdown.total_restore_time = ( + step_count * default_ckptr_duration_secs + ) + expected_breakdown.average_restore_time = default_ckptr_duration_secs + expected_breakdown.minimum_restore_time = default_ckptr_duration_secs + expected_breakdown.maximum_restore_time = default_ckptr_duration_secs + expected_breakdown.standard_deviation_restore_time = 0 + expected_breakdown.total_broadcast_time = ( + step_count * default_broadcast_duration_secs + ) + expected_breakdown.average_broadcast_time = default_broadcast_duration_secs + expected_breakdown.minimum_broadcast_time = default_broadcast_duration_secs + expected_breakdown.maximum_broadcast_time = default_broadcast_duration_secs + expected_breakdown.standard_deviation_broadcast_time = 0 + + cm_breakdown = ( + self.checkpoint_badput_calculator.calculate_restore_operation_checkpoint_manager_blocking_time( + checkpoint_badput_calculator.OPERATION_TYPE_LOCAL + ) + ) + for field in dataclasses.fields(cm_breakdown): + value1 = getattr(cm_breakdown, field.name) + value2 = getattr(expected_breakdown, field.name) + if value1 != value2: + raise ValueError( + f"Mismatch in field '{field.name}':\n" + f" Actual: {value1}\n" + f" Expected: {value2}" + ) +if __name__ == '__main__': + absltest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py new file mode 100644 index 0000000..4981a71 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py @@ -0,0 +1,150 @@ +"""Tests for GCP metrics.""" + +from unittest import mock + +from absl.testing import absltest +from cloud_goodput.ml_goodput_measurement.src import gcp_metrics +from google.api_core import exceptions +from google.cloud import monitoring_v3 + + +ValueType = gcp_metrics.ValueType +GCPMetrics = gcp_metrics.GCPMetrics +patch = mock.patch +GoogleAPIError = exceptions.GoogleAPIError + + +class GCPMetricsTest(absltest.TestCase): + + @patch("google.cloud.monitoring_v3.MetricServiceClient") + def setUp(self, mock_client): + super().setUp() + self.mock_client = mock_client.return_value + self.project_id = "test-project" + self.metrics_sender = GCPMetrics(self.project_id) + + def test_create_time_series(self): + metric_type = "compute.googleapis.com/workload/goodput_time" + value = 123.45 + value_type = ValueType.DOUBLE + metric_labels = { + "goodput_source": "TOTAL", + "accelerator_type": "tpu-v5p", + } + resource_type = "compute.googleapis.com/Workload" + resource_labels = { + "location": "us-central1", + "workload_id": "test-workload", + "replica_id": "0", + } + seconds = 1677347200 + nanos = 123456789 + + time_series = self.metrics_sender.create_time_series( + metric_type, + value, + value_type, + metric_labels, + resource_type, + resource_labels, + seconds, + nanos, + ) + + # Assertions to check if the TimeSeries object is created correctly + self.assertIsInstance(time_series, monitoring_v3.TimeSeries) + self.assertEqual(time_series.metric.type, metric_type) + self.assertEqual(time_series.resource.type, resource_type) + self.assertEqual(time_series.resource.labels, resource_labels) + self.assertEqual(time_series.metric.labels, metric_labels) + + # Correctly check the value based on value_type + if value_type == ValueType.BOOL: + self.assertEqual(time_series.points[0].value.bool_value, value) + elif value_type == ValueType.INT: + self.assertEqual(time_series.points[0].value.int64_value, value) + elif value_type == ValueType.DOUBLE: + self.assertEqual(time_series.points[0].value.double_value, value) + elif value_type == ValueType.STRING: + self.assertEqual(time_series.points[0].value.string_value, value) + elif value_type == ValueType.DISTRIBUTION: + self.assertEqual( + time_series.points[0].value.distribution_value, value + ) + + @patch("time.time") + def test_send_metrics(self, mock_time): + # Set a fixed return value for the mocked time.time() + mock_time.return_value = 1677347200.5 + + metrics_to_send = [ + { + "metric_type": "compute.googleapis.com/workload/goodput_time", + "value": 42.0, + "value_type": ValueType.DOUBLE, + "resource_type": "test_resource", + "resource_labels": {"loc": "us"}, + }, + { + "metric_type": "compute.googleapis.com/workload/badput_time", + "value": 10, + "value_type": ValueType.INT, + "metric_labels": {"source": "test2"}, + "resource_type": "test_resource", + "resource_labels": {"loc": "eu"}, + }, + ] + + self.metrics_sender.send_metrics(metrics_to_send) + + # Verify that create_time_series was called with the correct arguments + expected_name = f"projects/{self.project_id}" + expected_calls = [] + for metric in metrics_to_send: + metric_labels = metric.get("metric_labels", {}) + series = self.metrics_sender.create_time_series( + metric["metric_type"], + metric["value"], + metric["value_type"], + metric_labels, + metric["resource_type"], + metric["resource_labels"], + 1677347200, # seconds + 500000000, # nanos + ) + expected_calls.append(series) + + self.mock_client.create_time_series.assert_called_once() + _, kwargs = self.mock_client.create_time_series.call_args + self.assertEqual(kwargs["name"], expected_name) + # Check time series + actual_series = kwargs["time_series"] + self.assertEqual(len(actual_series), len(expected_calls)) + for actual, expected in zip(actual_series, expected_calls): + self.assertEqual(actual.metric.type, expected.metric.type) + self.assertEqual(actual.resource.type, expected.resource.type) + self.assertEqual(actual.resource.labels, expected.resource.labels) + self.assertEqual(actual.metric.labels, expected.metric.labels) + + @patch("cloud_goodput.ml_goodput_measurement.src.gcp_metrics.logger.error") + def test_send_metrics_failure(self, mock_logging_error): + + self.mock_client.create_time_series.side_effect = GoogleAPIError( + "Test Error" + ) + + metrics_to_send = [ + { + "metric_type": "compute.googleapis.com/workload/goodput_time", + "value": 42.0, + "value_type": ValueType.DOUBLE, + "resource_type": "test_resource", + "resource_labels": {"loc": "us"}, + } + ] + + self.metrics_sender.send_metrics(metrics_to_send) + mock_logging_error.assert_called_once() + +if __name__ == "__main__": + absltest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py new file mode 100644 index 0000000..0d0a690 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py @@ -0,0 +1,141 @@ +"""Tests to unit test GoodputCache class.""" + +import datetime +from unittest import mock + +from cloud_goodput.ml_goodput_measurement.src import goodput_cache +from cloud_goodput.ml_goodput_measurement.src import goodput_utils +from cloud_goodput.ml_goodput_measurement.src.goodput_utils import BadputType, GoodputInfo + +from google3.testing.pybase import googletest + + +class GoodputCacheTest(googletest.TestCase): + + def setUp(self): + super().setUp() + self.goodput_cache = goodput_cache.GoodputCache() + + def test_update_cached_entries(self): + mock_entries = [ + {'time': 1, 'step': 1}, + {'time': 2, 'step': 2}, + {'time': 3, 'step': 3}, + ] + self.goodput_cache.update_cached_entries(mock_entries) + self.assertFalse(self.goodput_cache.is_cache_empty()) + self.assertEqual(self.goodput_cache.get_cached_entries(), mock_entries) + + def test_update_goodput_info(self): + goodput_info = GoodputInfo( + total_productive_time=100, + total_elapsed_time_since_start=200, + total_unproductive_time={ + BadputType.TPU_INITIALIZATION: 10, + BadputType.TRAINING_PREP: 10, + BadputType.DATA_LOADING_SYNC: 30, + BadputType.PROGRAM_STARTUP: 10, + BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME: 20, + BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME: 10, + BadputType.WASTED_PROGRESS_FROM_DISRUPTION: 10, + BadputType.OTHER: 10, + }, + last_recorded_step=3, + ) + self.goodput_cache.update_goodput_info(goodput_info) + self.assertEqual(self.goodput_cache._goodput_info, goodput_info) + + def test_clear_cache(self): + mock_entries = [ + {'time': 1, 'step': 1}, + {'time': 2, 'step': 2}, + {'time': 3, 'step': 3}, + ] + self.goodput_cache.update_cached_entries(mock_entries) + self.goodput_cache.update_goodput_info( + GoodputInfo( + total_productive_time=100, + total_elapsed_time_since_start=200, + total_unproductive_time={ + BadputType.TPU_INITIALIZATION: 10, + BadputType.TRAINING_PREP: 10, + BadputType.DATA_LOADING_SYNC: 30, + BadputType.PROGRAM_STARTUP: 10, + BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME: 20, + BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME: 10, + BadputType.WASTED_PROGRESS_FROM_DISRUPTION: 10, + BadputType.OTHER: 10, + }, + last_recorded_step=3, + ) + ) + self.goodput_cache.clear_cache() + self.assertEqual(self.goodput_cache.get_cached_entries(), []) + self.assertIsNone(self.goodput_cache._goodput_info) + self.assertIsNone(self.goodput_cache._last_entry_timestamp) + + def test_is_cache_empty(self): + self.assertTrue(self.goodput_cache.is_cache_empty()) + self.goodput_cache.update_cached_entries([ + {'time': 1, 'step': 1}, + {'time': 2, 'step': 2}, + {'time': 3, 'step': 3}, + ]) + self.assertFalse(self.goodput_cache.is_cache_empty()) + + def test_get_last_entry_timestamp(self): + self.assertIsNone(self.goodput_cache._last_entry_timestamp) + self.goodput_cache.update_cached_entries([ + {'time': 1, 'step': 1}, + {'time': 2, 'step': 2}, + {'time': 3, 'step': 3}, + ]) + self.assertFalse(self.goodput_cache.is_cache_empty()) + self.assertEqual( + self.goodput_cache._last_entry_timestamp, + datetime.datetime.fromtimestamp(3, tz=datetime.timezone.utc), + ) + + def test_get_step_info(self): + step_info = goodput_utils.StepInfo( + step_deviations={1: 1.0, 2: 2.0}, + ideal_step_time=1.0, + ) + self.goodput_cache.update_step_info(step_info) + self.assertEqual(self.goodput_cache._step_info, step_info) + + def test_update_job_start_time(self): + self.assertIsNone(self.goodput_cache._job_start_time) + self.goodput_cache.update_cached_entries([ + {'step_start_time': 2, 'step': 1}, + {'step_start_time': 3, 'step': 2}, + {'job_end_time': 4}, + ]) + self.assertIsNone(self.goodput_cache._job_start_time) + self.goodput_cache.update_cached_entries([ + {'job_start_time': 1}, + {'job_start_time': 9}, + {'step_start_time': 2, 'step': 1}, + {'step_start_time': 3, 'step': 2}, + {'job_end_time': 4}, + ]) + self.assertEqual( + self.goodput_cache._job_start_time, + datetime.datetime.fromtimestamp(1, tz=datetime.timezone.utc), + ) + + def test_update_job_end_time(self): + self.assertIsNone(self.goodput_cache._job_end_time) + self.goodput_cache.update_cached_entries([ + {'job_end_time': 1}, + {'job_end_time': 2}, + {'job_end_time': 3}, + ]) + self.assertEqual( + self.goodput_cache._job_end_time, + datetime.datetime.fromtimestamp(3, tz=datetime.timezone.utc), + ) + + +if __name__ == '__main__': + googletest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py new file mode 100644 index 0000000..78515e6 --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py @@ -0,0 +1,2102 @@ +"""Goodput tests to validate Recorder, Calculator and Logger classes.""" + +import dataclasses +from dataclasses import asdict +import datetime +import random +import time +import threading +from typing import Optional + +from cloud_goodput.ml_goodput_measurement.src import goodput +from cloud_goodput.ml_goodput_measurement.src.goodput_utils import BadputType +from cloud_goodput.ml_goodput_measurement.src.goodput_utils import compute_ideal_step_time, get_timestamp_from_log_entry + +from google3.testing.pybase import googletest + + +# Fake job timeline information for test purposes. +_TEST_JOB_START_TIME = datetime.datetime( + year=2024, + month=1, + day=1, + hour=1, + minute=0, + second=0, + microsecond=0, + tzinfo=datetime.timezone.utc, +) +_TEST_PROGRAM_STARTUP_TIME = datetime.timedelta(seconds=5) +_TEST_TPU_INIT_TIME = datetime.timedelta(seconds=1) +_TEST_TRAINING_PREPARATION_TIME = datetime.timedelta(seconds=2) +_TEST_DATA_LOADING_TIME = datetime.timedelta(seconds=2) +_TEST_STEP_START_TIME = _TEST_JOB_START_TIME + _TEST_PROGRAM_STARTUP_TIME +_TEST_TOTAL_STEPS = 5 +_TEST_STEP_TIME = datetime.timedelta(seconds=3) +_TEST_JOB_END_TIME = _TEST_STEP_START_TIME + _TEST_STEP_TIME * _TEST_TOTAL_STEPS +# Badput time included in the first step time after start and restart. +_TEST_FIRST_STEP_EXTRA_TIME = datetime.timedelta(seconds=5) +# Anomalous large step times +_TEST_ANOMALOUS_STEP_TIME = datetime.timedelta(seconds=30) +# Custom badput event (overlapped with training) time +_TEST_CUSTOM_BADPUT_TIME = datetime.timedelta(seconds=10) + + +class MockCloudLogger: + + def __init__(self, job_name, logger_name): + self.job_name = job_name + self.logger_name = logger_name + self.entries = [] + + def write_cloud_logging_entry(self, entry): + timestamp = get_timestamp_from_log_entry(entry) + if timestamp is not None: + self.entries.append((timestamp, entry)) + + def read_cloud_logging_entries(self, start_time=None, end_time=None): + + def to_aware(dt): + return ( + dt.replace(tzinfo=datetime.timezone.utc) + if dt is not None and dt.tzinfo is None + else dt + ) + + start_time = to_aware(start_time) + end_time = to_aware(end_time) + return [ + entry + for timestamp, entry in self.entries + if (start_time is None or to_aware(timestamp) > start_time) + and (end_time is None or to_aware(timestamp) <= end_time) + ] + + +@dataclasses.dataclass +class MockSaveStepStatistics: + """Attributes for save step statistics. + + Attributes: + step: The step number. + event_type: The event type. + checkpoint_manager_blocking_start_time: The start time of checkpoint manager + blocking section. + directory: The directory of the checkpoint. + reached_preemption: Whether the event reached preemption. + preemption_received_at: The time when preemption was received. + wait_for_prev_start_time: The start time of waiting for previous checkpoint. + checkpointer_blocking_start_time: The start time of blocking time introduced + by checkpointer. + get_old_steps_start_time: The start time of getting old steps. + synchronous: Whether the event is synchronous. + wait_for_prev_duration_secs: The duration of waiting for previous + checkpoint. + checkpointer_blocking_duration_secs: The duration of blocking time + introduced by checkpointer. + get_old_steps_duration_secs: The duration of getting old steps. + checkpoint_manager_blocking_duration_secs: The duration of checkpoint + manager blocking section. + """ + + step: Optional[int] = None + event_type: Optional[str] = 'save' + directory: Optional[str] = None + reached_preemption: Optional[bool] = False + preemption_received_at: Optional[float] = None + synchronous: Optional[bool] = False + wait_for_prev_start_time: Optional[float] = None + wait_for_prev_duration_secs: Optional[float] = None + checkpointer_blocking_start_time: Optional[float] = None + checkpointer_blocking_duration_secs: Optional[float] = None + get_old_steps_start_time: Optional[float] = None + get_old_steps_duration_secs: Optional[float] = None + checkpoint_manager_blocking_start_time: Optional[float] = None + checkpoint_manager_blocking_duration_secs: Optional[float] = None + + +@dataclasses.dataclass +class MockRestoreStepStatistics: + """Attributes for restore step statistics. + + Attributes: + step: The step number. + event_type: The event type. + directory: The directory of the checkpoint. + checkpointer_start_time: The start time of restoring the checkpoint, while + using the checkpointer. + checkpointer_duration_secs: The total duration for restoring the checkpoint, + while using the checkpointer. + checkpoint_manager_start_time: The start time for restoring the checkpoint, + while using the checkpoint manager. + checkpoint_manager_duration_secs: The total duration for restoring the + checkpoint, while using the checkpoint manager. + """ + + step: Optional[int] = None + event_type: Optional[str] = 'restore' + directory: Optional[str] = None + checkpointer_start_time: Optional[float] = None + checkpointer_duration_secs: Optional[float] = None + checkpoint_manager_start_time: Optional[float] = None + checkpoint_manager_duration_secs: Optional[float] = None + + +class GoodputTest(googletest.TestCase): + + def setUp(self): + super().setUp() + self.job_name = 'test-run' + self.logger_name = 'test-log' + self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) + self.goodput_recorder = goodput.GoodputRecorder( + self.job_name, + self.logger_name, + True, + self.mock_cloud_logger, + ) + self.goodput_calculator = goodput.GoodputCalculator( + self.job_name, self.logger_name, self.mock_cloud_logger + ) + + def _mock_sample_program(self): + # Record job start time of the job: use a fake timestamp + self.goodput_recorder.record_job_start_time(_TEST_JOB_START_TIME) + + # Mock _TEST_TOTAL_STEPS steps of training + step_start_time = _TEST_STEP_START_TIME + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Record job end time + self.goodput_recorder.record_job_end_time(_TEST_JOB_END_TIME) + + def _mock_sample_program_with_badput(self): + mock_current_time = _TEST_JOB_START_TIME + delay = datetime.timedelta(seconds=1) + + # Record job start time of the job: use a fake timestamp + self.goodput_recorder.record_job_start_time(mock_current_time) + + # Mock TPU initialization time + mock_current_time += delay + self.goodput_recorder.record_tpu_init_start_time(mock_current_time) + mock_current_time += _TEST_TPU_INIT_TIME + self.goodput_recorder.record_tpu_init_end_time(mock_current_time) + + # Mock training preparation time + mock_current_time += delay + self.goodput_recorder.record_training_preparation_start_time( + mock_current_time + ) + mock_current_time += _TEST_TRAINING_PREPARATION_TIME + self.goodput_recorder.record_training_preparation_end_time( + mock_current_time + ) + + # Mock data loading time + mock_current_time += delay + self.goodput_recorder.record_data_loading_start_time(mock_current_time) + mock_current_time += _TEST_DATA_LOADING_TIME + self.goodput_recorder.record_data_loading_end_time(mock_current_time) + + # Mock _TEST_TOTAL_STEPS steps of training + mock_current_time += delay + custom_badput_event_frequency = 3 + for step in range(_TEST_TOTAL_STEPS): + step_start_time = mock_current_time + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + # Record synchronous data loading time + self.goodput_recorder.record_data_loading_start_time(mock_current_time) + mock_current_time += _TEST_DATA_LOADING_TIME + self.goodput_recorder.record_data_loading_end_time(mock_current_time) + # Record custom badput event time + if step % custom_badput_event_frequency == 0: + self.goodput_recorder.record_custom_badput_event_start_time( + mock_current_time, 'test_sync' + ) + mock_current_time += _TEST_CUSTOM_BADPUT_TIME + self.goodput_recorder.record_custom_badput_event_end_time( + mock_current_time, 'test_sync' + ) + mock_current_time += _TEST_STEP_TIME + # Record job end time + self.goodput_recorder.record_job_end_time(mock_current_time) + + def test_goodput_recorder(self): + """Test function to validate goodput recorder and logger.""" + # Emulate job run timeline. + self._mock_sample_program() + + # Ensure read returns the right number of entries. + validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() + # There should be one entry for each of the 5 steps, one job start + # and one job end entry. + self.assertLen(validate_entries, _TEST_TOTAL_STEPS + 2) + # Ensure payload contains the expected information. + for entry_payload in validate_entries: + self.assertIn(goodput._JOB_NAME, entry_payload) + self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) + if goodput._JOB_START_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._JOB_START_TIME], + _TEST_JOB_START_TIME.timestamp(), + ) + if goodput._JOB_END_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._JOB_END_TIME], + _TEST_JOB_END_TIME.timestamp(), + ) + if goodput._STEP_START_TIME in entry_payload: + step_count = entry_payload[goodput._STEP_COUNT] + expected_start_start_time = ( + _TEST_STEP_START_TIME + _TEST_STEP_TIME * step_count + ) + self.assertEqual( + entry_payload[goodput._STEP_START_TIME], + expected_start_start_time.timestamp(), + ) + + def test_goodput_recorder_badput(self): + """Test function to validate goodput recorder and logger.""" + # Emulate job run timeline. + self._mock_sample_program_with_badput() + + validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() + + # Ensure payload contains the required information. + expected_keys = { + goodput._JOB_NAME, + goodput._STEP_COUNT, + goodput._STEP_START_TIME, + goodput._JOB_START_TIME, + goodput._JOB_END_TIME, + goodput._TPU_INIT_START_TIME, + goodput._TPU_INIT_END_TIME, + goodput._TRAINING_PREPARATION_START_TIME, + goodput._TRAINING_PREPARATION_END_TIME, + goodput._DATA_LOADING_START_TIME, + goodput._DATA_LOADING_END_TIME, + goodput._CUSTOM_BADPUT_EVENT_TYPE, + goodput._CUSTOM_BADPUT_EVENT_START_TIME, + goodput._CUSTOM_BADPUT_EVENT_END_TIME, + } + # Ensure right number of entries are written. + found_keys = set() + for entry_payload in validate_entries: + self.assertIn(goodput._JOB_NAME, entry_payload) + self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) + found_keys.update(entry_payload.keys() & expected_keys) + + self.assertEqual(found_keys, expected_keys) + + def test_goodput_calculator(self): + """Test function to validate goodput calculator.""" + # Emulate job run timeline. + self._mock_sample_program() + # Get the computed Goodput from the library and compare with expected + # result. + computed_goodput, _, total_steps = self.goodput_calculator.get_job_goodput() + expected_goodput = ( + (_TEST_STEP_TIME * _TEST_TOTAL_STEPS) + / (_TEST_JOB_END_TIME - _TEST_JOB_START_TIME) + * 100 + ) + self.assertEqual(computed_goodput, expected_goodput) + self.assertEqual(total_steps, _TEST_TOTAL_STEPS - 1) + + def test_goodput_with_startup_badput(self): + """Test function to validate goodput with startup badput.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock _TEST_TOTAL_STEPS steps of training + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + + # All steps but first progress with average step time. + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Introduce startup badput during the first step + if step == 0: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + total_time = ( + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + _TEST_FIRST_STEP_EXTRA_TIME + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Get the computed Goodput from the library and compare with expected + # result. + + computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() + expected_goodput = ( + (_TEST_TOTAL_STEPS * _TEST_STEP_TIME.total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + + +class GoodputDisruptionCompleteRestartTest(googletest.TestCase): + + def setUp(self): + super().setUp() + self.job_name = 'test-run' + self.logger_name = 'test-log' + self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) + self.goodput_recorder = goodput.GoodputRecorder( + self.job_name, + self.logger_name, + True, + self.mock_cloud_logger, + ) + self.goodput_calculator = goodput.GoodputCalculator( + self.job_name, self.logger_name, self.mock_cloud_logger + ) + + def test_goodput_calculator(self): + """Test function to validate goodput calculator.""" + # It is not ideal to use non-deterministic timestamps in unit tests, but + # testing this complex scenario using deterministic timestamps is not + # straightforward. + # TODO(xfgu): Refactor this test. + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock _TEST_TOTAL_STEPS steps of training + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Simulate a disruption. + disruption_time = datetime.timedelta(seconds=5) + job_start_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_start_time) + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + + steps_before_query = _TEST_TOTAL_STEPS - 2 + for step in range(steps_before_query): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Get the computed Goodput from the library and compare with expected + # result. + + # The time from when the job first started to when the last step start was + # logged. + total_time = ( + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + disruption_time + + _TEST_PROGRAM_STARTUP_TIME + + (steps_before_query - 1) * _TEST_STEP_TIME + ) + seconds_before_query = 2 + query_time = total_time.total_seconds() + seconds_before_query + + time.sleep(query_time) + computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() + expected_goodput = ( + ( + (steps_before_query - 1) * _TEST_STEP_TIME.total_seconds() + ) + / query_time + * 100 + ) + + self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + + +class GoodputDisruptionPartialRestartTest(googletest.TestCase): + + def setUp(self): + super().setUp() + self.job_name = 'test-run' + self.logger_name = 'test-log' + self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) + self.goodput_recorder = goodput.GoodputRecorder( + self.job_name, + self.logger_name, + True, + self.mock_cloud_logger, + ) + self.goodput_calculator = goodput.GoodputCalculator( + self.job_name, self.logger_name, self.mock_cloud_logger + ) + + def test_goodput_calculator(self): + """Test function to validate goodput calculator.""" + # It is not ideal to use non-deterministic timestamps in unit tests, but + # testing this complex scenario using deterministic timestamps is not + # straightforward. + # TODO(xfgu): Refactor this test. + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock _TEST_TOTAL_STEPS steps of training + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Simulate a disruption. + disruption_time = datetime.timedelta(seconds=5) + job_start_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_start_time) + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + + restart_from_step = 2 + for step in range(restart_from_step, _TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Get the computed Goodput from the library and compare with expected + # result. + + # The time from when the job first started to when the last step start was + # logged. + total_time = ( + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + disruption_time + + _TEST_PROGRAM_STARTUP_TIME + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + seconds_before_query = 2 + query_time = total_time.total_seconds() + seconds_before_query + + time.sleep(query_time) + computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() + expected_goodput = ( + ((_TEST_TOTAL_STEPS - 1) * _TEST_STEP_TIME.total_seconds()) + / query_time + * 100 + ) + # Validate that the cache is updated correctly. + cached_goodput_info = ( + self.goodput_calculator._goodput_cache.get_goodput_info() + ) + expected_productive_time = ( + _TEST_TOTAL_STEPS - 1 + ) * _TEST_STEP_TIME.total_seconds() + self.assertAlmostEqual( + cached_goodput_info.total_productive_time, + expected_productive_time, + delta=0.1, + ) + + self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + + def test_goodput_with_startup_badput(self): + """Test function to validate goodput with startup badput.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock _TEST_TOTAL_STEPS steps of training + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + + # All steps but first progress with average step time. + for step in range(0, _TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Introduce startup badput during the first step + if step == 0: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + # Simulate a disruption. + disruption_time = datetime.timedelta(seconds=5) + job_start_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_start_time) + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + + restart_from_step = 2 + # All steps but first progress with average step time. + for step in range(restart_from_step, _TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Introduce badput during the first step after restart + if step == restart_from_step: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + # Get the computed Goodput from the library and compare with expected + # result. + + # The time from when the job first started to when the last step start was + # logged. + total_time = ( + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + _TEST_FIRST_STEP_EXTRA_TIME + + disruption_time + + _TEST_PROGRAM_STARTUP_TIME + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + ) + seconds_before_query = 2 + query_time = total_time.total_seconds() + seconds_before_query + + time.sleep(query_time) + computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() + expected_goodput = ( + ((_TEST_TOTAL_STEPS - 1) * _TEST_STEP_TIME.total_seconds()) + / query_time + * 100 + ) + + self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + + +class GoodputPathwaysTest(googletest.TestCase): + + def setUp(self): + super().setUp() + self.job_name = 'test-run' + self.logger_name = 'test-log' + self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) + self.goodput_recorder = goodput.GoodputRecorder( + self.job_name, + self.logger_name, + True, + self.mock_cloud_logger, + ) + self.goodput_calculator = goodput.GoodputCalculator( + self.job_name, self.logger_name, self.mock_cloud_logger, True + ) + + def test_goodput_with_anomalous_steps_single_disruption(self): + """Test function to validate goodput with anomalous step times due to a single disruption.""" + # This test simulates _TEST_TOTAL_STEPS training steps and a single + # disruption during the job's run time as follows: + # [0, 1, 2, Handled Disruption, 3, 4] + # The handled disruption will manifest as anomalously large step times. + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock some program startup time before the training steps + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + + # First few steps progress with normal step time. + for step in range(_TEST_TOTAL_STEPS - 3): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Introduce an anomalously large step time due to a disruption. + self.goodput_recorder.record_step_start_time( + _TEST_TOTAL_STEPS - 3, step_start_time + ) + step_start_time += _TEST_ANOMALOUS_STEP_TIME + _TEST_STEP_TIME + + # Remaining steps progress with normal step time. + for step in range(_TEST_TOTAL_STEPS - 2, _TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + job_end_time = ( + job_start_time + + _TEST_PROGRAM_STARTUP_TIME + + _TEST_ANOMALOUS_STEP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + ) + self.goodput_recorder.record_job_end_time(job_end_time) + + # The time from when the job first started to when the last step start was + # logged. + total_time = ( + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + _TEST_ANOMALOUS_STEP_TIME + ) + + computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() + expected_goodput = ( + (_TEST_TOTAL_STEPS * _TEST_STEP_TIME.total_seconds()) + / total_time.total_seconds() + * 100 + ) + # TODO(b/400837154): Add this back once the bug is fixed. + # self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + + def test_goodput_with_anomalous_steps_multiple_disruptions(self): + """Test function to validate goodput with anomalous step times due to multiple disruptions.""" + + # This test simulates _TEST_TOTAL_STEPS * 2 training steps and multiple + # disruptions during the job's run time as follows: + # [0, 1, 2, Handled Disruption, 3, 4, 5, 6, 7 Handled Disruption, 8, 9] + # The handled disruptions will manifest as anomalously large step times. + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock some program startup time before the training steps + step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME + + # First few steps progress with normal step time. + for step in range(_TEST_TOTAL_STEPS - 3): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Introduce an anomalously large step time due to a disruption. + self.goodput_recorder.record_step_start_time( + _TEST_TOTAL_STEPS - 3, step_start_time + ) + step_start_time += _TEST_ANOMALOUS_STEP_TIME + _TEST_STEP_TIME + + # A few more steps progress with normal step time. + for step in range(_TEST_TOTAL_STEPS - 2, _TEST_TOTAL_STEPS + 2): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + # Introduce an anomalously large step time due to a second disruption. + self.goodput_recorder.record_step_start_time( + _TEST_TOTAL_STEPS + 2, step_start_time + ) + step_start_time += _TEST_ANOMALOUS_STEP_TIME + _TEST_STEP_TIME + + # Remaining steps progress with normal step time. + for step in range(_TEST_TOTAL_STEPS + 3, _TEST_TOTAL_STEPS * 2): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + job_end_time = ( + job_start_time + + _TEST_PROGRAM_STARTUP_TIME + + _TEST_ANOMALOUS_STEP_TIME * 2 + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS * 2 + ) + self.goodput_recorder.record_job_end_time(job_end_time) + + # The time from when the job first started to when the last step start was + # logged. + total_time = ( + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS * 2 + + _TEST_ANOMALOUS_STEP_TIME * 2 + ) + + computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() + expected_goodput = ( + (2 * _TEST_TOTAL_STEPS * _TEST_STEP_TIME.total_seconds()) + / total_time.total_seconds() + * 100 + ) + # TODO(b/400837154): Add this back once the bug is fixed. + # self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + + +class BadputTest(googletest.TestCase): + + def setUp(self): + super().setUp() + self.job_name = 'test-run' + self.logger_name = 'test-log' + self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) + self.goodput_recorder = goodput.GoodputRecorder( + self.job_name, + self.logger_name, + True, + self.mock_cloud_logger, + ) + self.goodput_calculator = goodput.GoodputCalculator( + self.job_name, self.logger_name, self.mock_cloud_logger + ) + + def test_tpu_init_recorder(self): + """Test function to validate goodput recorder for TPU init.""" + # Record TPU init + self.goodput_recorder.record_tpu_init_start_time(_TEST_JOB_START_TIME) + self.goodput_recorder.record_tpu_init_end_time( + _TEST_JOB_START_TIME + _TEST_TPU_INIT_TIME + ) + + # Ensure read returns the right number of entries. + validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() + self.assertLen(validate_entries, 2) + # Ensure payload contains the expected information. + for entry_payload in validate_entries: + self.assertIn(goodput._JOB_NAME, entry_payload) + self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) + if goodput._TPU_INIT_START_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._TPU_INIT_START_TIME], + _TEST_JOB_START_TIME.timestamp(), + ) + if goodput._TPU_INIT_END_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._TPU_INIT_END_TIME], + (_TEST_JOB_START_TIME + _TEST_TPU_INIT_TIME).timestamp(), + ) + + def test_training_prep_recorder(self): + """Test function to validate goodput recorder for training preparation.""" + # Record training preparation time. + training_prep_start_time = _TEST_JOB_START_TIME + _TEST_TPU_INIT_TIME + training_prep_end_time = ( + _TEST_JOB_START_TIME + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_training_preparation_start_time( + training_prep_start_time + ) + self.goodput_recorder.record_training_preparation_end_time( + training_prep_end_time + ) + + # Ensure read returns the right number of entries. + validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() + self.assertLen(validate_entries, 2) + # Ensure payload contains the expected information. + for entry_payload in validate_entries: + self.assertIn(goodput._JOB_NAME, entry_payload) + self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) + if goodput._TRAINING_PREPARATION_START_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._TRAINING_PREPARATION_START_TIME], + training_prep_start_time.timestamp(), + ) + if goodput._TRAINING_PREPARATION_END_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._TRAINING_PREPARATION_END_TIME], + training_prep_end_time.timestamp(), + ) + + def test_training_prep_recorder_no_timestamps(self): + """Test function to validate goodput recorder for training preparation with no timestamps.""" + # Record training preparation time. + expected_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_training_preparation_start_time(None) + time.sleep(_TEST_TRAINING_PREPARATION_TIME.total_seconds()) + expected_end_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_training_preparation_end_time(None) + + # Ensure read returns the right number of entries. + validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() + self.assertLen(validate_entries, 2) + # Ensure payload contains the expected information. + for entry_payload in validate_entries: + self.assertIn(goodput._JOB_NAME, entry_payload) + self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) + if goodput._TRAINING_PREPARATION_START_TIME in entry_payload: + self.assertAlmostEqual( + entry_payload[goodput._TRAINING_PREPARATION_START_TIME], + expected_start_time.timestamp(), + delta=0.1, + ) + + if goodput._TRAINING_PREPARATION_END_TIME in entry_payload: + self.assertAlmostEqual( + entry_payload[goodput._TRAINING_PREPARATION_END_TIME], + expected_end_time.timestamp(), + delta=0.1, + ) + + def test_data_loading_recorder(self): + """Test function to validate goodput recorder for data loading.""" + # Record data loading time. + data_loading_start_time = ( + _TEST_JOB_START_TIME + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + ) + data_loading_end_time = ( + _TEST_JOB_START_TIME + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + self.goodput_recorder.record_data_loading_start_time( + data_loading_start_time + ) + self.goodput_recorder.record_data_loading_end_time(data_loading_end_time) + + # Ensure read returns the right number of entries. + validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() + self.assertLen(validate_entries, 2) + # Ensure payload contains the expected information. + for entry_payload in validate_entries: + self.assertIn(goodput._JOB_NAME, entry_payload) + self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) + if goodput._DATA_LOADING_START_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._DATA_LOADING_START_TIME], + data_loading_start_time.timestamp(), + ) + if goodput._DATA_LOADING_END_TIME in entry_payload: + self.assertEqual( + entry_payload[goodput._DATA_LOADING_END_TIME], + data_loading_end_time.timestamp(), + ) + + def test_data_loading_recorder_no_timestamps(self): + """Test function to validate goodput recorder for data loading.""" + # Record data loading time. + expected_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_data_loading_start_time(None) + time.sleep(_TEST_DATA_LOADING_TIME.total_seconds()) + expected_end_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_data_loading_end_time(None) + + # Ensure read returns the right number of entries. + validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() + self.assertLen(validate_entries, 2) + # Ensure payload contains the expected information. + for entry_payload in validate_entries: + self.assertIn(goodput._JOB_NAME, entry_payload) + self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) + if goodput._DATA_LOADING_START_TIME in entry_payload: + self.assertAlmostEqual( + entry_payload[goodput._DATA_LOADING_START_TIME], + expected_start_time.timestamp(), + delta=0.1, + ) + if goodput._DATA_LOADING_END_TIME in entry_payload: + self.assertAlmostEqual( + entry_payload[goodput._DATA_LOADING_END_TIME], + expected_end_time.timestamp(), + delta=0.1, + ) + + def test_badput_calculator_tpu_initialization(self): + """Test function to validate computation of badput due to TPU initialization.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + + # Mock _TEST_TOTAL_STEPS steps of training with built-in badput + # due to program startup. + step_start_time = ( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_PROGRAM_STARTUP_TIME + ) + for step in range(_TEST_TOTAL_STEPS): + # Record step time. + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + expected_badput_due_to_tpu_initialization = ( + (_TEST_TPU_INIT_TIME.total_seconds()) / total_time.total_seconds() * 100 + ) + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn(BadputType.TPU_INITIALIZATION, computed_badput_breakdown) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.TPU_INITIALIZATION], + expected_badput_due_to_tpu_initialization, + delta=0.1, + ) + + def test_badput_calculator_training_preparation(self): + """Test function to validate computation of badput due to training preparation.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + for step in range(_TEST_TOTAL_STEPS): + # Record step time. + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Badput with selection. + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + expected_badput_due_to_training_preparation = ( + (_TEST_TRAINING_PREPARATION_TIME.total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn(BadputType.TRAINING_PREP, computed_badput_breakdown) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.TRAINING_PREP], + expected_badput_due_to_training_preparation, + delta=0.1, + ) + + def test_badput_calculator_sync_data_loading(self): + """Test function to validate computation of badput due to data loading.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + # Mock data loading. + self.goodput_recorder.record_data_loading_start_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + for step in range(_TEST_TOTAL_STEPS): + # Record step time. + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Badput with selection. + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + expected_badput_due_to_sync_data_loading = ( + (_TEST_DATA_LOADING_TIME.total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn(BadputType.DATA_LOADING_SYNC, computed_badput_breakdown) + self.assertIn(BadputType.DATA_LOADING_ASYNC, computed_badput_breakdown) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.DATA_LOADING_SYNC], + expected_badput_due_to_sync_data_loading, + delta=0.1, + ) + + def test_badput_calculator_async_data_loading(self): + """Test function to validate computation of badput due to data loading.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + # Mock syncdata loading. + self.goodput_recorder.record_data_loading_start_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + for step in range(_TEST_TOTAL_STEPS): + # Record step time. + self.goodput_recorder.record_step_start_time(step, step_start_time) + # Record async (overlapped) data loading. + self.goodput_recorder.record_data_loading_start_time( + step_start_time + _TEST_STEP_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + step_start_time + _TEST_STEP_TIME + + _TEST_DATA_LOADING_TIME + ) + step_start_time += (_TEST_STEP_TIME + _TEST_DATA_LOADING_TIME) + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + (_TEST_STEP_TIME + _TEST_DATA_LOADING_TIME) * _TEST_TOTAL_STEPS + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Badput with selection. + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + + # Every step has overloaded (async) data loading. + expected_badput_due_to_async_data_loading = ( + ((_TEST_DATA_LOADING_TIME * _TEST_TOTAL_STEPS).total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn(BadputType.DATA_LOADING_ASYNC, computed_badput_breakdown) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.DATA_LOADING_ASYNC], + expected_badput_due_to_async_data_loading, + delta=0.1, + ) + + def test_badput_calculator_program_startup(self): + """Test function to validate computation of badput due to program startup.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + # Mock data loading. + self.goodput_recorder.record_data_loading_start_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + # All steps but first progress with average step time. + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Add startup badput during the first step + if step == 0: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Badput. + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + expected_badput_due_to_program_startup = ( + (_TEST_FIRST_STEP_EXTRA_TIME.total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn(BadputType.PROGRAM_STARTUP, computed_badput_breakdown) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.PROGRAM_STARTUP], + expected_badput_due_to_program_startup, + delta=0.1, + ) + + def test_badput_calculator_program_startup_with_disruptions(self): + """Validate computation of badput due to program startup after a disruption.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + # Mock data loading. + self.goodput_recorder.record_data_loading_start_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + # All steps but first progress with average step time. + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Add startup badput during the first step + if step == 0: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + # Simulate a disruption. + disruption_time = datetime.timedelta(seconds=5) + job_restart_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_restart_time) + step_start_time = ( + job_restart_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + restart_from_step = 2 + # All steps but first progress with average step time. + for step in range(restart_from_step, _TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + if step == restart_from_step: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + disruption_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Badput. + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + expected_badput_due_to_program_startup = ( + ((_TEST_FIRST_STEP_EXTRA_TIME * 2).total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn(BadputType.PROGRAM_STARTUP, computed_badput_breakdown) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.PROGRAM_STARTUP], + expected_badput_due_to_program_startup, + delta=0.1, + ) + + def test_badput_calculator_wasted_progress_and_disruptions(self): + """Validate computation of badput due to wasted progress and disruptions.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + # Mock data loading. + self.goodput_recorder.record_data_loading_start_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + # All steps but first progress with average step time. + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Add startup badput during the first step + if step == 0: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + # Simulate a disruption. + disruption_time = datetime.timedelta(seconds=5) + job_restart_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_restart_time) + step_start_time = ( + job_restart_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + restart_from_step = 2 + # All steps but first progress with average step time. + for step in range(restart_from_step, _TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + if step == restart_from_step: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + disruption_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Badput. + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + wasted_progress_and_disruption_time = ( + disruption_time + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + expected_badput_due_to_disruptions = ( + (wasted_progress_and_disruption_time.total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn( + BadputType.WASTED_PROGRESS_FROM_DISRUPTION, + computed_badput_breakdown, + ) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], + expected_badput_due_to_disruptions, + delta=0.1, + ) + + def test_badput_calculator_unknown_badput(self): + """Test function to validate unknown badput bucket.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + + # Mock _TEST_TOTAL_STEPS steps of training with built-in badput + # due to program startup. + step_start_time = ( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_PROGRAM_STARTUP_TIME + ) + for step in range(_TEST_TOTAL_STEPS): + # Record step time. + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + unknown_badput_time = datetime.timedelta(seconds=5) + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + unknown_badput_time + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn(BadputType.OTHER, computed_badput_breakdown) + + expected_badput_due_to_unknown = ( + (unknown_badput_time.total_seconds()) / total_time.total_seconds() * 100 + ) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.OTHER], + expected_badput_due_to_unknown, + delta=0.1, + ) + # Make sure this data is cached correctly. + cached_goodput_info = ( + self.goodput_calculator._goodput_cache.get_goodput_info() + ) + self.assertNotEmpty(cached_goodput_info.total_unproductive_time) + self.assertIn(BadputType.OTHER, cached_goodput_info.total_unproductive_time) + self.assertAlmostEqual( + cached_goodput_info.total_unproductive_time[BadputType.OTHER], + unknown_badput_time.total_seconds(), + delta=0.1, + ) + + def test_badput_calculator_checkpoint_badput(self): + """Validate computation of badput due to checkpoint manager time.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + # Mock data loading. + self.goodput_recorder.record_data_loading_start_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + # All steps but first progress with average step time. + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Add startup badput during the first step + if step == 0: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + # Mock a save operation. + save_stats = MockSaveStepStatistics( + step=1, + event_type='save', + directory='gs://bucket/path', + wait_for_prev_start_time=10.0, + wait_for_prev_duration_secs=1.0, + checkpointer_blocking_start_time=12.0, + checkpointer_blocking_duration_secs=2.0, + get_old_steps_start_time=13.0, + get_old_steps_duration_secs=3.0, + checkpoint_manager_blocking_start_time=10.0, + checkpoint_manager_blocking_duration_secs=6.0, + reached_preemption=True, + preemption_received_at=10.0, + synchronous=True, + ) + self.mock_cloud_logger.write_cloud_logging_entry(asdict(save_stats)) + + # Simulate a disruption. + disruption_time = datetime.timedelta(seconds=5) + job_restart_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_restart_time) + step_start_time = ( + job_restart_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + restart_from_step = 2 + # All steps but first progress with average step time. + for step in range(restart_from_step, _TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + if step == restart_from_step: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + disruption_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + restore_stats = MockRestoreStepStatistics( + step=1, + event_type='restore', + directory='gs://bucket/path', + checkpointer_start_time=10.0, + checkpointer_duration_secs=2.0, + checkpoint_manager_start_time=10.0, + checkpoint_manager_duration_secs=2.0, + ) + self.mock_cloud_logger.write_cloud_logging_entry(asdict(restore_stats)) + + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Badput. + _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( + include_badput_breakdown=True + ) + wasted_progress_and_disruption_time = ( + disruption_time + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + expected_badput_due_to_disruptions = ( + (wasted_progress_and_disruption_time.total_seconds()) + / total_time.total_seconds() + * 100 + ) + + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn( + BadputType.WASTED_PROGRESS_FROM_DISRUPTION, + computed_badput_breakdown, + ) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], + expected_badput_due_to_disruptions, + delta=0.1, + ) + self.assertIn( + BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME, computed_badput_breakdown + ) + + self.assertIn( + BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME, + computed_badput_breakdown, + ) + + expect_badput_due_to_checkpointing_save = ( + (save_stats.checkpoint_manager_blocking_duration_secs) + / total_time.total_seconds() + * 100 + ) + + expect_badput_due_to_checkpointing_restore = ( + (restore_stats.checkpoint_manager_duration_secs) + / total_time.total_seconds() + * 100 + ) + + self.assertEqual( + computed_badput_breakdown[BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME], + expect_badput_due_to_checkpointing_save, + ) + + self.assertEqual( + computed_badput_breakdown[ + BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME + ], + expect_badput_due_to_checkpointing_restore, + ) + + def test_goodput_badput_with_interval_query(self): + """Validate computation of goodput and badput with interval query.""" + + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + # Mock training preparation. + self.goodput_recorder.record_training_preparation_start_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + self.goodput_recorder.record_training_preparation_end_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + # Mock data loading. + self.goodput_recorder.record_data_loading_start_time( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME + ) + self.goodput_recorder.record_data_loading_end_time( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + # Mock training. + step_start_time = ( + job_start_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + # All steps but first progress with average step time. + for step in range(_TEST_TOTAL_STEPS): + # Record step time + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + # Add startup badput during the first step + if step == 0: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + intermediate_job_end_time = step_start_time + + # Simulate a disruption. + disruption_time = datetime.timedelta(seconds=5) + job_restart_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_restart_time) + step_start_time = ( + job_restart_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + ) + + restart_from_step = 2 + # All steps but first progress with average step time. + for step in range(restart_from_step, _TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + if step == restart_from_step: + step_start_time += _TEST_FIRST_STEP_EXTRA_TIME + + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS + + disruption_time + + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + # Compute Goodput and Badput with the interval query API. + ( + computed_goodput, + computed_badput_breakdown, + last_step, + total_job_time, + number_of_disruptions, + ) = self.goodput_calculator.get_job_goodput_interval( + job_start_time - datetime.timedelta(microseconds=1), job_end_time + ) + + productive_time = _TEST_STEP_TIME * _TEST_TOTAL_STEPS + expected_goodput = ( + (productive_time.total_seconds()) / total_time.total_seconds() * 100 + ) + wasted_progress_and_disruption_time = ( + disruption_time + + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME + ) + expected_badput_due_to_disruptions = ( + (wasted_progress_and_disruption_time.total_seconds()) + / total_time.total_seconds() + * 100 + ) + + # Validate last step + self.assertEqual(last_step, _TEST_TOTAL_STEPS - 1) + # Validate total job time + self.assertEqual(total_job_time, total_time.total_seconds()) + # Validate number of disruptions + self.assertEqual(number_of_disruptions, 1) + # Validate Goodput + self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + # Validate Badput + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn( + BadputType.WASTED_PROGRESS_FROM_DISRUPTION, + computed_badput_breakdown, + ) + self.assertAlmostEqual( + computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], + expected_badput_due_to_disruptions, + delta=0.1, + ) + + # Update the interval to exclude the disruption and validate new values. + ( + computed_goodput, + computed_badput_breakdown, + last_step, + total_job_time, + number_of_disruptions, + ) = self.goodput_calculator.get_job_goodput_interval( + job_start_time - datetime.timedelta(microseconds=1), intermediate_job_end_time + ) + + productive_time = _TEST_STEP_TIME * (_TEST_TOTAL_STEPS - 1) + expected_intermediate_total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_TRAINING_PREPARATION_TIME + + _TEST_DATA_LOADING_TIME + + _TEST_FIRST_STEP_EXTRA_TIME + + _TEST_STEP_TIME * (_TEST_TOTAL_STEPS - 1) + ) + expected_goodput = ( + (productive_time.total_seconds()) + / expected_intermediate_total_time.total_seconds() + * 100 + ) + + # Validate last step + self.assertEqual(last_step, _TEST_TOTAL_STEPS - 1) + # Validate total job time + self.assertEqual( + total_job_time, expected_intermediate_total_time.total_seconds() + ) + # There should be no disruptions in the interval. + self.assertEqual(number_of_disruptions, 0) + # Validate Goodput + self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + # Validate Badput + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn( + BadputType.WASTED_PROGRESS_FROM_DISRUPTION, + computed_badput_breakdown, + ) + self.assertEqual( + computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], 0 + ) + + def _generate_step_start_times(self, number_of_steps: int, start_time): + """Generate a list of n non-decreasing datetime objects.""" + max_step_seconds = 600 + step_start_times = [start_time] + for _ in range(1, number_of_steps): + increment = random.randint(1, max_step_seconds) + new_time = step_start_times[-1] + datetime.timedelta(seconds=increment) + step_start_times.append(new_time) + return step_start_times + + def test_get_step_deviation(self): + """Test function to validate step deviation computation.""" + job_start_time = datetime.datetime.now(datetime.timezone.utc) + self.goodput_recorder.record_job_start_time(job_start_time) + # Generate a list of 100 step start times with random step times. + step_count = 0 + max_steps = 100 + test_step_start_times = self._generate_step_start_times( + number_of_steps=max_steps, start_time=job_start_time + ) + + # Record step start times. + for step_start_time in test_step_start_times: + self.goodput_recorder.record_step_start_time(step_count, step_start_time) + step_count += 1 + + job_end_time = test_step_start_times[-1] + datetime.timedelta(seconds=10) + self.goodput_recorder.record_job_end_time(job_end_time) + + step_times = self.goodput_calculator._get_step_times(self.mock_cloud_logger.entries) + ideal_step_time = compute_ideal_step_time( + step_times=list(step_times.values()) + ) + computed_step_deviations = self.goodput_calculator.get_step_deviation() + expected_step_deviations = { + step_count: abs(step_time - ideal_step_time) + for step_count, step_time in step_times.items() + } + for step_count, expected_deviation in expected_step_deviations.items(): + computed_deviation = computed_step_deviations[step_count] + self.assertAlmostEqual( + expected_deviation, + computed_deviation, + delta=0.1, + ) + + def test_badput_calculator_custom_sync_badput(self): + """Test function to validate unknown badput bucket.""" + + job_start_time = _TEST_JOB_START_TIME + self.goodput_recorder.record_job_start_time(job_start_time) + + # Mock TPU initialization. + self.goodput_recorder.record_tpu_init_start_time(job_start_time) + self.goodput_recorder.record_tpu_init_end_time( + job_start_time + _TEST_TPU_INIT_TIME + ) + + # Mock _TEST_TOTAL_STEPS steps of training with built-in badput + # due to program startup. + step_start_time = ( + job_start_time + _TEST_TPU_INIT_TIME + _TEST_PROGRAM_STARTUP_TIME + ) + for step in range(_TEST_TOTAL_STEPS): + # Record step time. + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + eval_sync_badput_time = datetime.timedelta(seconds=5) + self.goodput_recorder.record_custom_badput_event_start_time( + step_start_time, 'eval_step' + ) + self.goodput_recorder.record_custom_badput_event_end_time( + step_start_time + eval_sync_badput_time, 'eval_step' + ) + step_start_time += eval_sync_badput_time + + # Continue training for _TEST_TOTAL_STEPS more steps. + for step in range(_TEST_TOTAL_STEPS, _TEST_TOTAL_STEPS * 2): + # Record step time. + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + total_time = ( + _TEST_TPU_INIT_TIME + + _TEST_PROGRAM_STARTUP_TIME + + _TEST_STEP_TIME * _TEST_TOTAL_STEPS * 2 + + eval_sync_badput_time + ) + job_end_time = job_start_time + total_time + self.goodput_recorder.record_job_end_time(job_end_time) + + computed_goodput, computed_badput_breakdown, _ = ( + self.goodput_calculator.get_job_goodput(include_badput_breakdown=True) + ) + # Validate Badput breakdown. + self.assertNotEmpty(computed_badput_breakdown) + self.assertIn( + BadputType.CUSTOM_BADPUT_EVENTS, computed_badput_breakdown + ) + self.assertIn( + 'EVAL_STEP', + computed_badput_breakdown[BadputType.CUSTOM_BADPUT_EVENTS], + ) + computed_badput_due_to_custom_sync = computed_badput_breakdown[ + BadputType.CUSTOM_BADPUT_EVENTS + ]['EVAL_STEP'] + + expected_badput_due_to_custom_sync = ( + (eval_sync_badput_time.total_seconds()) + / total_time.total_seconds() + * 100 + ) + self.assertAlmostEqual( + computed_badput_due_to_custom_sync, + expected_badput_due_to_custom_sync, + delta=0.1, + ) + # Validate Goodput. + expected_goodput = ( + (_TEST_STEP_TIME * (_TEST_TOTAL_STEPS * 2)).total_seconds() + / total_time.total_seconds() + * 100 + ) + self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) + # Make sure this data is cached correctly. + cached_goodput_info = ( + self.goodput_calculator._goodput_cache.get_goodput_info() + ) + self.assertNotEmpty(cached_goodput_info.total_unproductive_time) + self.assertIn( + BadputType.CUSTOM_BADPUT_EVENTS, + cached_goodput_info.total_unproductive_time, + ) + self.assertAlmostEqual( + cached_goodput_info.total_unproductive_time[ + BadputType.CUSTOM_BADPUT_EVENTS + ]['EVAL_STEP'], + eval_sync_badput_time.total_seconds(), + delta=0.1, + ) + + def test_goodput_with_disruption_and_caching(self): + """Test function to validate goodput with disruption and caching. + + Verifies that productive time is correctly computed when a disruption is + detected after the last cache update, and previous cached data is stale. + + Scenario: + - Initial productive steps (0-4) are cached before disruption. + - A disruption occurs and the job restarts from step 3. + - Delta between cached and new logs show steps 3-4 (latent disruption). + - Final computed and cached productive time should be correct at each query. + """ + job_start_time = _TEST_JOB_START_TIME + self.goodput_recorder.record_job_start_time(job_start_time) + + step_start_time = job_start_time + for step in range(_TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + disruption_time = datetime.timedelta(seconds=5) + job_start_time = step_start_time + disruption_time + self.goodput_recorder.record_job_start_time(job_start_time) + + # Query after restart but before any steps (emulate above scenario). + _, _, _ = self.goodput_calculator.get_job_goodput() + # Validate productive in the cache. + cached_goodput_info = ( + self.goodput_calculator._goodput_cache.get_goodput_info() + ) + self.assertAlmostEqual( + cached_goodput_info.total_productive_time, + (_TEST_STEP_TIME * (_TEST_TOTAL_STEPS - 1)).total_seconds(), + delta=0.1, + ) + + step_start_time = job_start_time + repeat_steps = 2 + restart_step = _TEST_TOTAL_STEPS - repeat_steps + for step in range(restart_step, _TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_start_time) + step_start_time += _TEST_STEP_TIME + + total_time = ( + +_TEST_STEP_TIME * _TEST_TOTAL_STEPS + + disruption_time + + (restart_step - 1) * _TEST_STEP_TIME + ) + self.goodput_recorder.record_job_end_time(_TEST_JOB_START_TIME + total_time) + # Compute Goodput and Badput. + _, _, _ = self.goodput_calculator.get_job_goodput() + + # Validate that the cache is updated correctly. + cached_goodput_info = ( + self.goodput_calculator._goodput_cache.get_goodput_info() + ) + # Validate productive time, + expected_productive_time = _TEST_STEP_TIME * _TEST_TOTAL_STEPS + self.assertAlmostEqual( + cached_goodput_info.total_productive_time, + expected_productive_time.total_seconds(), + ) + # Validate that previous progress is now unproductive and marked as + # wasted progress). + self.assertNotEmpty(cached_goodput_info.total_unproductive_time) + self.assertIn( + BadputType.WASTED_PROGRESS_FROM_DISRUPTION, + cached_goodput_info.total_unproductive_time, + ) + expected_unproductive_time = ( + total_time.total_seconds() - expected_productive_time.total_seconds() + ) + cached_unproductive_time = sum( + value if isinstance(value, float) else sum(value.values()) + for badput_type, value in cached_goodput_info.total_unproductive_time.items() + if badput_type != BadputType.DATA_LOADING_ASYNC + ) + + self.assertAlmostEqual( + cached_unproductive_time, + expected_unproductive_time, + delta=0.1, + ) + expected_wasted_progress_from_disruption = ( + disruption_time + (restart_step - 2) * _TEST_STEP_TIME + ) + self.assertAlmostEqual( + cached_goodput_info.total_unproductive_time[ + BadputType.WASTED_PROGRESS_FROM_DISRUPTION + ], + expected_wasted_progress_from_disruption.total_seconds(), + delta=0.1, + ) + + +class GoodputStepDeviationConcurrencyTest(googletest.TestCase): + + def setUp(self): + super().setUp() + self.job_name = 'test-concurrent-run' + self.logger_name = 'test-concurrent-log' + self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) + self.goodput_recorder = goodput.GoodputRecorder( + self.job_name, + self.logger_name, + True, + self.mock_cloud_logger, + ) + self.goodput_calculator = goodput.GoodputCalculator( + self.job_name, self.logger_name, self.mock_cloud_logger + ) + self._mock_sample_program() + + def _mock_sample_program(self): + self.goodput_recorder.record_job_start_time(_TEST_JOB_START_TIME) + step_time = _TEST_STEP_START_TIME + for step in range(_TEST_TOTAL_STEPS): + self.goodput_recorder.record_step_start_time(step, step_time) + step_time += _TEST_STEP_TIME + self.goodput_recorder.record_job_end_time(_TEST_JOB_END_TIME) + + def test_concurrent_goodput_and_step_deviation(self): + """Test concurrent access to Goodput and Step Deviation calculations.""" + errors = [] + + def compute_goodput(): + try: + for _ in range(10): + self.goodput_calculator.get_job_goodput() + except ( + ValueError, + TypeError, + KeyError, + ) as e: + errors.append(f'Goodput thread error: {e}') + + def compute_step_deviation(): + try: + for _ in range(10): + self.goodput_calculator.get_step_deviation() + except ( + ValueError, + TypeError, + ) as e: + errors.append(f'Step deviation thread error: {e}') + + threads = [] + thread_count = 5 + for _ in range(thread_count): + threads.append(threading.Thread(target=compute_goodput)) + threads.append(threading.Thread(target=compute_step_deviation)) + + for t in threads: + t.start() + for t in threads: + t.join() + self.assertEmpty(errors, msg=f'Errors occurred in concurrent threads: {errors}') + +if __name__ == '__main__': + googletest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py new file mode 100644 index 0000000..a97fcfc --- /dev/null +++ b/ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py @@ -0,0 +1,794 @@ +"""Tests to validate the monitoring module. + +This module tests the GoodputMonitor class and its functionality, specifically +the uploading of step deviation, goodput and badput data to Tensorboard. +""" + +from unittest import mock + +from absl.testing import absltest +from cloud_goodput.ml_goodput_measurement.src import gcp_metrics +from cloud_goodput.ml_goodput_measurement.src import goodput_utils +from cloud_goodput.ml_goodput_measurement.src import monitoring + +from google.cloud import monitoring_v3 + +BadputType = goodput_utils.BadputType +GCPOptions = goodput_utils.GCPOptions +GoodputMonitor = monitoring.GoodputMonitor +GoodputType = goodput_utils.GoodputType +MagicMock = mock.MagicMock +ValueType = gcp_metrics.ValueType + +patch = mock.patch +_TEST_UPLOAD_INTERVAL = 1 + + +class GoodputMonitorTests(absltest.TestCase): + """Tests for the GoodputMonitor class.""" + + def setUp(self): + super().setUp() + self.job_name = 'test-run' + self.logger_name = 'test-logger' + self.tensorboard_dir = 'test-dir' + + def _create_timeseries( + self, metric_type: str, labels: dict, value: float + ) -> monitoring_v3.TimeSeries: + ts = monitoring_v3.TimeSeries() + ts.metric.type = metric_type + ts.metric.labels.update(labels) + ts.resource.type = 'compute.googleapis.com/Workload' + ts.resource.labels.update({ + 'location': 'test-location', + 'workload_id': 'test-run', + 'replica_id': 'test-replica-id', + }) + ts.points.append( + monitoring_v3.Point( + value=monitoring_v3.TypedValue(double_value=value), + ) + ) + return ts + + def _compare_calls_ignore_time_series( + self, expected_call, actual_call + ) -> bool: + if ( + expected_call.args != actual_call.args + or expected_call.kwargs.keys() != actual_call.kwargs.keys() + ): + return False + + for key, expected_value in expected_call.kwargs.items(): + actual_value = actual_call.kwargs[key] + if key == 'time_series': + continue + if expected_value != actual_value: + return False + + return True + + def _setup_mock_goodput_monitor( + self, mock_logging_client, mock_summary_writer, mock_metric_service_client + ) -> GoodputMonitor: + mock_client = MagicMock() + mock_metric_service_client.return_value = mock_client + mock_logging_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + + gcp_options = GCPOptions( + enable_gcp_goodput_metrics=True, + project_id='test-project', + location='test-location', + acc_type='test-acc-type', + replica_id='test-replica-id', + ) + + return GoodputMonitor( + job_name='test-run', + logger_name='test-logger', + tensorboard_dir='/tmp', + upload_interval=1, + monitoring_enabled=True, + gcp_options=gcp_options, + ) + + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + def test_goodput_monitor_init(self, mock_logger_client, mock_summary_writer): + mock_summary_writer.return_value = MagicMock() + mock_logger_client.return_value = MagicMock() + goodput_monitor = GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + ) + # Objects should be initialized correctly. + self.assertIsNotNone(goodput_monitor) + self.assertIs(goodput_monitor._writer, mock_summary_writer.return_value) + self.assertIsNotNone(goodput_monitor._goodput_calculator) + + # Thread events should be initialized correctly. + self.assertIsNotNone(goodput_monitor._step_deviation_termination_event) + self.assertFalse(goodput_monitor._step_deviation_termination_event.is_set()) + self.assertFalse(goodput_monitor._step_deviation_uploader_thread_running) + self.assertIsNotNone(goodput_monitor._termination_event) + self.assertFalse(goodput_monitor._termination_event.is_set()) + self.assertFalse(goodput_monitor._goodput_uploader_thread_running) + + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_goodput_to_tensorboard' + ) + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + async def test_goodput_monitor_start_goodput_uploader_success( + self, mock_logger_client, mock_summary_writer, mock_goodput_to_tensorboard + ): + mock_summary_writer.return_value = MagicMock() + mock_goodput_to_tensorboard.return_value = MagicMock() + mock_logger_client.return_value = MagicMock() + goodput_monitor = monitoring.GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + ) + goodput_monitor.start_goodput_uploader() + self.assertTrue(goodput_monitor._uploader_thread_running) + self.assertIsNotNone(goodput_monitor._goodput_upload_thread) + self.assertFalse(goodput_monitor._termination_event.is_set()) + mock_goodput_to_tensorboard.assert_called_once() + mock_summary_writer.return_value.add_scalar.assert_called_once() + goodput_monitor.stop_goodput_uploader() + self.assertFalse(goodput_monitor._uploader_thread_running) + self.assertIsNone(goodput_monitor._goodput_upload_thread) + self.assertTrue(goodput_monitor._termination_event.is_set()) + + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_goodput_to_tensorboard' + ) + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + async def test_goodput_monitor_start_goodput_uploader_failure( + self, mock_logger_client, mock_summary_writer, mock_goodput_to_tensorboard + ): + mock_logger_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + mock_goodput_to_tensorboard.side_effect = ValueError('Test Error') + goodput_monitor = monitoring.GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + ) + goodput_monitor.start_goodput_uploader() + self.assertTrue(goodput_monitor._uploader_thread_running) + self.assertIsNotNone(goodput_monitor._goodput_upload_thread) + self.assertFalse(goodput_monitor._termination_event.is_set()) + mock_goodput_to_tensorboard.assert_called_once() + with self.assertRaisesRegex(ValueError, 'Test Error'): + goodput_monitor._query_and_upload_goodput() + mock_summary_writer.return_value.add_scalar.assert_not_called() + goodput_monitor.stop_goodput_uploader() + self.assertFalse(goodput_monitor._uploader_thread_running) + self.assertIsNone(goodput_monitor._goodput_upload_thread) + self.assertTrue(goodput_monitor._termination_event.is_set()) + + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_badput_to_tensorboard' + ) + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + async def test_goodput_monitor_start_badput_uploader_success( + self, mock_logger_client, mock_summary_writer, mock_badput_to_tensorboard + ): + mock_summary_writer.return_value = MagicMock() + mock_badput_to_tensorboard.return_value = MagicMock() + mock_logger_client.return_value = MagicMock() + goodput_monitor = monitoring.GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + include_badput_breakdown=True, + ) + + goodput_monitor.start_goodput_uploader() + self.assertTrue(goodput_monitor._uploader_thread_running) + self.assertIsNotNone(goodput_monitor._goodput_upload_thread) + self.assertFalse(goodput_monitor._termination_event.is_set()) + self.assertTrue(goodput_monitor._include_badput_breakdown) + + mock_badput_to_tensorboard.assert_called_once() + mock_summary_writer.return_value.add_scalar.assert_called_once() + + goodput_monitor.stop_goodput_uploader() + self.assertFalse(goodput_monitor._uploader_thread_running) + self.assertIsNone(goodput_monitor._goodput_upload_thread) + self.assertTrue(goodput_monitor._termination_event.is_set()) + + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_step_deviation_to_tensorboard' + ) + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + async def test_goodput_monitor_start_step_deviation_uploader_success( + self, + mock_logger_client, + mock_summary_writer, + mock_step_deviation_to_tensorboard, + ): + mock_logger_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + mock_step_deviation_to_tensorboard.return_value = MagicMock() + goodput_monitor = monitoring.GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + include_step_deviation=True, + ) + goodput_monitor.start_step_deviation_uploader() + self.assertTrue(goodput_monitor._step_deviation_uploader_thread_running) + self.assertIsNotNone(goodput_monitor._step_deviation_upload_thread) + self.assertFalse(goodput_monitor._step_deviation_termination_event.is_set()) + mock_step_deviation_to_tensorboard.assert_called_once() + mock_summary_writer.return_value.add_scalar.assert_called_once() + goodput_monitor.stop_step_deviation_uploader() + self.assertFalse(goodput_monitor._step_deviation_uploader_thread_running) + self.assertIsNone(goodput_monitor._step_deviation_upload_thread) + self.assertTrue(goodput_monitor._step_deviation_termination_event.is_set()) + + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_step_deviation_to_tensorboard' + ) + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + async def test_goodput_monitor_start_step_deviation_uploader_failure( + self, + mock_logger_client, + mock_summary_writer, + mock_query_and_upload_step_deviation, + ): + mock_logger_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + mock_query_and_upload_step_deviation.side_effect = ValueError('Test Error') + goodput_monitor = monitoring.GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + include_step_deviation=True, + ) + goodput_monitor.start_step_deviation_uploader() + self.assertTrue(goodput_monitor._step_deviation_uploader_thread_running) + self.assertIsNotNone(goodput_monitor._step_deviation_upload_thread) + self.assertFalse(goodput_monitor._step_deviation_termination_event.is_set()) + mock_query_and_upload_step_deviation.assert_called_once() + with self.assertRaisesRegex(ValueError, 'Test Error'): + goodput_monitor._query_and_upload_step_deviation() + mock_summary_writer.return_value.add_scalar.assert_not_called() + goodput_monitor.stop_step_deviation_uploader() + self.assertFalse(goodput_monitor._step_deviation_uploader_thread_running) + self.assertIsNone(goodput_monitor._step_deviation_upload_thread) + self.assertTrue(goodput_monitor._step_deviation_termination_event.is_set()) + + @patch('google.cloud.monitoring_v3.MetricServiceClient') + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + def test_send_goodput_metrics_to_gcp_success( + self, + mock_logging_client, + mock_summary_writer, + mock_metric_service_client, + ): + mock_client = MagicMock() + mock_metric_service_client.return_value = mock_client + mock_logging_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + + gcp_options = GCPOptions( + enable_gcp_goodput_metrics=True, + project_id='test-project', + location='test-location', + acc_type='test-acc-type', + replica_id='test-replica-id', + ) + + goodput_monitor = GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + gcp_options=gcp_options, + ) + + # Mock the get_job_goodput_details to return test data + goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( + return_value={ + 'goodput_time_dict': { + GoodputType.TOTAL: 10.0, + }, + 'badput_time_dict': { + BadputType.TPU_INITIALIZATION: 2.0, + BadputType.DATA_LOADING_SYNC: 1.0, + }, + } + ) + + goodput_monitor._send_goodput_metrics_to_gcp( + goodput_monitor._goodput_calculator.get_job_goodput_details() + ) + + expected_calls = [ + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/goodput_time', + { + 'goodput_source': 'TOTAL', + 'accelerator_type': 'test-acc-type', + }, + 10.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'TPU_INITIALIZATION', + 'accelerator_type': 'test-acc-type', + }, + 2.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'DATA_LOADING_SYNC', + 'accelerator_type': 'test-acc-type', + }, + 1.0, + ) + ], + ), + ] + + actual_calls = mock_client.create_time_series.call_args_list + + # Verify each call individually + for expected_call in expected_calls: + self.assertTrue( + any( + self._compare_calls_ignore_time_series(expected_call, actual) + for actual in actual_calls + ), + f'Expected call not found: {expected_call}', + ) + + @patch('google.cloud.monitoring_v3.MetricServiceClient') + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + def test_send_goodput_metrics_to_gcp_exception( + self, + mock_logging_client, + mock_summary_writer, + mock_metric_service_client, + ): + mock_client = MagicMock() + mock_client.create_time_series.side_effect = Exception('Test Exception') + mock_metric_service_client.return_value = mock_client + mock_logging_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + + gcp_options = GCPOptions( + enable_gcp_goodput_metrics=True, + project_id='test-project', + location='test-location', + acc_type='test-acc-type', + replica_id='test-replica-id', + ) + + goodput_monitor = GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + gcp_options=gcp_options, + ) + + # Mock the get_job_goodput_details to return test data + goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( + return_value={ + 'goodput_time_dict': { + GoodputType.TOTAL: 10.0, + }, + 'badput_time_dict': { + BadputType.DATA_LOADING_SYNC: 2.0, + }, + } + ) + + goodput_monitor._send_goodput_metrics_to_gcp( + goodput_monitor._goodput_calculator.get_job_goodput_details() + ) + + # Verify that create_time_series was called, even if it raised an exception + mock_client.create_time_series.assert_called_once() + + @patch('google.cloud.monitoring_v3.MetricServiceClient') + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + def test_send_goodput_metrics_to_gcp_exclusion( + self, + mock_logging_client, + mock_summary_writer, + mock_metric_service_client + ): + mock_client = MagicMock() + mock_metric_service_client.return_value = mock_client + mock_logging_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + + gcp_options = GCPOptions( + enable_gcp_goodput_metrics=True, + project_id='test-project', + location='test-location', + acc_type='test-acc-type', + replica_id='test-replica-id', + ) + + goodput_monitor = GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + gcp_options=gcp_options, + ) + + # Mock the get_job_goodput_details to return test data, including an + # excluded type + goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( + return_value={ + 'goodput_time_dict': { + GoodputType.TOTAL: 10.0, + }, + 'badput_time_dict': { + BadputType.TPU_INITIALIZATION: 2.0, + BadputType.DATA_LOADING_SYNC: 1.0, + BadputType.DATA_LOADING_ASYNC: ( + 3.0 + ), # DATA_LOADING_ASYNC is in ACTIVITY_EXCLUSION_LIST + }, + } + ) + + goodput_monitor._send_goodput_metrics_to_gcp( + goodput_monitor._goodput_calculator.get_job_goodput_details() + ) + + # Verify that create_time_series was called with the correct data, + # excluding DATA_LOADING_ASYNC + expected_calls = [ + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/goodput_time', + { + 'goodput_source': 'TOTAL', + 'accelerator_type': 'test-acc-type', + }, + 10.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'TPU_INITIALIZATION', + 'accelerator_type': 'test-acc-type', + }, + 2.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'DATA_LOADING_SYNC', + 'accelerator_type': 'test-acc-type', + }, + 1.0, + ) + ], + ), + ] + + actual_calls = mock_client.create_time_series.call_args_list + + # Verify each call individually + for expected_call in expected_calls: + self.assertTrue( + any( + self._compare_calls_ignore_time_series(expected_call, actual) + for actual in actual_calls + ), + f'Expected call not found: {expected_call}', + ) + # Verify unexpected calls are not made + for actual_call in actual_calls: + for ts in actual_call.kwargs.get('time_series', []): + if ( + 'badput_source' in ts.metric.labels + and ts.metric.labels['badput_source'] == 'DATA_LOADING_ASYNC' + ): + self.fail(f'Unexpected call found: {ts}') + + @patch('google.cloud.monitoring_v3.MetricServiceClient') + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + def test_send_interval_goodput_metrics_to_gcp( + self, + mock_logging_client, + mock_summary_writer, + mock_metric_service_client, + ): + mock_client = MagicMock() + mock_metric_service_client.return_value = mock_client + mock_logging_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + + gcp_options = GCPOptions( + enable_gcp_goodput_metrics=True, + project_id='test-project', + location='test-location', + acc_type='test-acc-type', + replica_id='test-replica-id', + ) + + goodput_monitor = GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + gcp_options=gcp_options, + ) + + # Mock the get_job_goodput_details to return test data + goodput_monitor._goodput_calculator.get_job_goodput_interval_details = ( + MagicMock( + return_value={ + 'goodput_time_dict': { + GoodputType.TOTAL: 10.0, + }, + 'badput_time_dict': { + BadputType.TPU_INITIALIZATION: 2.0, + BadputType.DATA_LOADING_SYNC: 1.0, + }, + } + ) + ) + + goodput_monitor._send_goodput_metrics_to_gcp( + goodput_monitor._goodput_calculator.get_job_goodput_interval_details() + ) + + expected_calls = [ + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/goodput_time', + { + 'goodput_source': 'TOTAL', + 'accelerator_type': 'test-acc-type', + }, + 10.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'TPU_INITIALIZATION', + 'accelerator_type': 'test-acc-type', + }, + 2.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'DATA_LOADING_SYNC', + 'accelerator_type': 'test-acc-type', + }, + 1.0, + ) + ], + ), + ] + + actual_calls = mock_client.create_time_series.call_args_list + + # Verify each call individually + for expected_call in expected_calls: + self.assertTrue( + any( + self._compare_calls_ignore_time_series(expected_call, actual) + for actual in actual_calls + ), + f'Expected call not found: {expected_call}', + ) + + @patch('google.cloud.monitoring_v3.MetricServiceClient') + @patch('tensorboardX.writer.SummaryWriter') + @patch('google.cloud.logging.Client') + def test_send_goodput_metrics_custom_sync_events( + self, mock_logging_client, mock_summary_writer, mock_metric_service_client + ): + mock_client = MagicMock() + mock_metric_service_client.return_value = mock_client + mock_logging_client.return_value = MagicMock() + mock_summary_writer.return_value = MagicMock() + + gcp_options = GCPOptions( + enable_gcp_goodput_metrics=True, + project_id='test-project', + location='test-location', + acc_type='test-acc-type', + replica_id='test-replica-id', + ) + + goodput_monitor = GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + gcp_options=gcp_options, + ) + + # Mock the get_job_goodput_details to return test data, including an + # excluded type + goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( + return_value={ + 'goodput_time_dict': { + GoodputType.TOTAL: 10.0, + }, + 'badput_time_dict': { + BadputType.TPU_INITIALIZATION: 2.0, + BadputType.DATA_LOADING_SYNC: 1.0, + BadputType.CUSTOM_BADPUT_EVENTS: { + 'EVAL_STEP': 3.0, + 'SDC_COMPILATION': 4.0, + }, + }, + } + ) + + goodput_monitor._send_goodput_metrics_to_gcp( + goodput_monitor._goodput_calculator.get_job_goodput_details() + ) + + expected_calls = [ + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/goodput_time', + { + 'goodput_source': 'TOTAL', + 'accelerator_type': 'test-acc-type', + }, + 10.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'TPU_INITIALIZATION', + 'accelerator_type': 'test-acc-type', + }, + 2.0, + ) + ], + ), + mock.call.create_time_series( + name='projects/test-project', + time_series=[ + self._create_timeseries( + 'compute.googleapis.com/workload/badput_time', + { + 'badput_source': 'DATA_LOADING_SYNC', + 'accelerator_type': 'test-acc-type', + }, + 1.0, + ) + ], + ), + ] + + actual_calls = mock_client.create_time_series.call_args_list + + # Verify each call individually + for expected_call in expected_calls: + self.assertTrue( + any( + self._compare_calls_ignore_time_series(expected_call, actual_call) + for actual_call in actual_calls + ), + f'Expected call not found: {expected_call}', + ) + + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._final_interval_goodput_query_and_upload' + ) + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._final_step_deviation_query_and_upload' + ) + @patch( + 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._final_goodput_query_and_upload' + ) + async def test_goodput_monitor_final_query_and_upload( + self, + mock_final_goodput_query_and_upload, + mock_final_step_deviation_query_and_upload, + mock_final_interval_goodput_query_and_upload, + ): + mock_final_goodput_query_and_upload.return_value = MagicMock() + mock_final_step_deviation_query_and_upload.return_value = MagicMock() + mock_final_interval_goodput_query_and_upload.return_value = MagicMock() + goodput_monitor = monitoring.GoodputMonitor( + self.job_name, + self.logger_name, + self.tensorboard_dir, + upload_interval=_TEST_UPLOAD_INTERVAL, + monitoring_enabled=True, + ) + goodput_monitor.__del__() + mock_final_goodput_query_and_upload.assert_called_once() + mock_final_step_deviation_query_and_upload.assert_called_once() + mock_final_interval_goodput_query_and_upload.assert_called_once() + + +if __name__ == '__main__': + absltest.main() diff --git a/ml-goodput-measurement/pyproject.toml b/ml-goodput-measurement/pyproject.toml new file mode 100644 index 0000000..ad696f4 --- /dev/null +++ b/ml-goodput-measurement/pyproject.toml @@ -0,0 +1,59 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +[project] +name = "ml_goodput_measurement" +version = "0.0.10" +authors = [ + { name="Cloud TPU Team", email="cloud-tpu-eng@google.com" }, +] +description = "Package to monitor Goodput, Badput and other metrics of ML workloads." +readme = "README.md" +requires-python = ">=3.8" +license = {text = "Apache-2.0"} +classifiers = [ + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", +] +keywords = [] + +# pip dependencies installed with `pip install -e .` +dependencies = [ + "google-api-core>=2.24.1", + "google-cloud-logging>=3.5.0", + "google-cloud-monitoring>=2.20.0", + "numpy", + "requests", + "scipy", + "tensorboardx", + "urllib3", +] + +[project.urls] +"Homepage" = "https://github.com/AI-Hypercomputer/ml-goodput-measurement" +"Bug Tracker" = "https://github.com/AI-Hypercomputer/ml-goodput-measurement/issues" + +[build-system] +# Build system specify which backend is used to build/install the project +requires = ["flit_core >=3.8,<4"] +build-backend = "flit_core.buildapi" + +[tool.flit.sdist] +# Flit specific options (files to exclude from the PyPI package) +exclude = [ + # Do not release tests files on PyPI + "tests/*_test.py", +] \ No newline at end of file From b328528507b3752f45e3c1cf9b906cc92d7c9d0c Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Tue, 27 May 2025 15:54:40 +0000 Subject: [PATCH 02/10] Here's a revised version of the commit message, written from my perspective as Jules: I've updated the GoodPut guide based on your feedback. This update significantly enhances GOODPUT_GUIDE.md to make it more comprehensive and address your specific requests: - **Generalization:** I've reframed the guide as a general document for GoodPut optimization, using the LLaMA3-1-70B recipe as a case study. - **Sources of BadPut:** I added a new section detailing common sources of inefficiency, including quantitative examples and references from the Google Cloud blog post. - **Enhanced Technical Details:** - The "Elastic Training Solutions" section now includes more in-depth explanations of the Supervisor components (Sensor, Controller, Actuator), Host Monitors, and their interactions. It also provides more specific links to configuration parameters in `values-supervisor.yaml`, `ksa-setup.yaml`, the supervisor Helm chart, and the NVIDIA Resiliency Extension. - The "Optimized Checkpointing Solutions" section offers more detailed descriptions of asynchronous and distributed checkpointing, and the role of GCS FUSE. It includes more specific links to flags in `values.yaml`, GCS configuration in `values-gcs.yaml`, the gcs-fuse Helm chart, and external PyTorch/GCS FUSE documentation. - **Link Precision:** I've made the links to files within the repository (e.g., `values.yaml`, `README.md`) in the "DIY" and "Goodput Analysis" sections more explicit and accurate. The guide now provides a more thorough overview of GoodPut concepts, practical implementation details based on the recipe, and pointers to relevant code, configurations, and external resources. --- .../GOODPUT_GUIDE.md | 184 ++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md new file mode 100644 index 0000000..6e4d3ff --- /dev/null +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -0,0 +1,184 @@ +# Maximizing ML Training Efficiency: A General Guide to Improving GoodPut + +Effective utilization of resources in large-scale machine learning (ML) training is crucial for both cost efficiency and rapid model development. A key metric for measuring this efficiency is **ML GoodPut**. As discussed in the Google Cloud blog post, "[Train AI for less: Improve ML Goodput with elastic training and optimized checkpointing](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput)," GoodPut represents the actual productive training time, excluding time lost to various inefficiencies. Even a small percentage improvement in GoodPut can lead to significant cost savings and faster time-to-market for your models. + +Achieving high GoodPut can be challenging due to several factors common in large distributed training environments: +* **Frequent Interruptions:** Hardware failures, preemptions, or other system issues can halt training, requiring restarts from the latest checkpoint and wasting valuable compute time. +* **Slow or Inefficient Checkpointing:** The process of saving model checkpoints can itself interrupt training or consume excessive resources if not optimized. +* **Limited Observability and Slow Recovery:** Difficulty in quickly detecting, diagnosing, and remediating failures or stragglers can extend downtime and further reduce GoodPut. + +This guide provides a general overview of techniques and tools to address these common challenges and maximize ML GoodPut. While the principles discussed are broadly applicable, we will use the [LLaMA3-1-70B pretraining recipe](https://github.com/AI-Hypercomputer/gpu-recipes/tree/main/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency) as a concrete case study to illustrate how these components can be implemented and customized for large-scale training workloads on Google Cloud. The goal is to showcase a "DIY" style product, where users can understand and selectively adopt these "Lego blocks" to build resilient and efficient training pipelines. + +## Understanding Sources of BadPut (Lost Efficiency) + +To effectively improve GoodPut, it's essential to understand the common culprits that lead to "BadPut" – the wasted time and resources during training. The previously mentioned [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput) highlights several of these. In a case study referenced in the article, involving 1,024 A3 Mega GPU instances, overall ML GoodPut was improved from around 80% to over 90% by addressing these factors. + +Key sources of BadPut include: + +1. **Hardware Failures and System Errors:** + * **Impact:** These can cause sudden job crashes, leading to lost training progress since the last checkpoint. The time taken to detect the failure, reprovision resources (if necessary), and restart the job contributes significantly to BadPut. + * **Example:** A GPU failing, a node becoming unresponsive, or critical system software errors. + +2. **Preemptions and Evictions:** + * **Impact:** In cloud environments or shared clusters, workloads might be preempted or evicted. Similar to hardware failures, this results in lost work and restart overhead. + * **Example:** Spot VMs/preemptible VMs being reclaimed, or higher-priority jobs displacing lower-priority ones. + +3. **Slow Checkpoint Save and Load Times:** + * **Impact:** If saving checkpoints (inline/synchronous) takes a long time, the GPUs are idle, directly reducing GoodPut. Similarly, slow loading of checkpoints after a restart extends downtime. + * **Example:** Saving large model states to slow storage, or inefficient serialization/deserialization of checkpoints. + +4. **Suboptimal Checkpoint Frequency:** + * **Impact:** + * *Too infrequent:* Leads to significant loss of work if a failure occurs late in a checkpoint interval. + * *Too frequent:* The cumulative time spent on checkpointing itself (even if asynchronous) can become a major overhead. + * **Example:** Setting a 4-hour checkpoint interval when failures occur every 2 hours, or checkpointing every 5 minutes with a process that takes 1 minute. + +5. **Stragglers and Performance Bottlenecks:** + * **Impact:** Slower nodes or processes (stragglers) can delay the entire training job, especially in synchronous training paradigms. This leads to underutilization of faster resources. + * **Example:** A single node with a faulty network connection slowing down data loading or gradient synchronization for all other nodes. + +6. **Lack of Rapid Failure Detection and Diagnosis:** + * **Impact:** The longer it takes to identify that a problem has occurred and what the root cause is, the longer the downtime and the greater the BadPut. + * **Example:** A silent error corrupting data without immediate detection, or lack of clear logs making diagnosis time-consuming. + +The blog post further provides a table (via an image link: `https://storage.googleapis.com/gweb-cloudblog-publish/images/3_BRK2-131_xtcNYDc.max-2200x2200.jpg`) that details the specific metric improvements and ML GoodPut contributions for different techniques applied in their case study. While the visual data from the image cannot be rendered here, it underscores that a multi-faceted approach targeting these BadPut sources is key to substantial GoodPut gains. + +## Addressing Interruptions: Elastic Training + +Elastic training is a core strategy for improving ML GoodPut by making training workloads resilient to interruptions. Instead of a job failing entirely when an issue occurs, elastic training allows the job to adapt to the changing environment. This could involve recovering from a transient error, transparently moving to different hardware, or adjusting the job size to continue training on available resources. + +The LLaMA3-1-70B recipe, as a case study, implements these elastic training principles through the **Google Cloud Resiliency library**. This library is designed to work with GKE and leverages the [NVIDIA Resiliency Extension](https://github.com/NVIDIA/nvidia-resiliency-ext) for certain low-level hardware interactions and failure signaling. + +Key components and concepts include: + +### 1. Failure Sensing and Mitigation: The Supervisor System + +A sophisticated supervisor system is deployed to monitor the health of the training cluster and the job itself. This system is crucial for quickly identifying issues and orchestrating a response. It consists of: + +* **Supervisor Components:** These typically run on a dedicated CPU node pool. + * **Sensor:** Actively monitors the training job and cluster components for failure signals, performance degradation, or straggler behavior. It might use heartbeat mechanisms (polling worker nodes) and receive signals from other sources like the Host Monitors. The `heartbeat_polling_period_s` and `heartbeat_timeout_s` in `values-supervisor.yaml` are critical for this. + * **Controller:** The central "brain" that receives event data from the Sensor. It consults a user-defined policy (or its internal logic) to decide on the appropriate remediation action. + * **Actuator:** Executes the remediation actions chosen by the Controller, such as initiating a job restart, requesting a node replacement, or triggering a scaling operation. + * The configuration for these components, including their Docker images and startup commands, can be found in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-supervisor.yaml`. + * The Kubernetes service accounts and roles required for the Supervisor to interact with GKE resources are defined in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/ksa-setup.yaml`. + * The underlying Helm chart that deploys these supervisor components is located in `src/helm-charts/resiliency/supervisor-chart/`. + +* **Host Monitors:** These are deployed as a Kubernetes DaemonSet, ensuring one runs on each GPU worker node (e.g., A3 Mega nodes). + * They provide granular, node-level health information and can detect local hardware issues (like GPU errors) more directly. + * They communicate with the central Supervisor, feeding it critical data for decision-making. Configuration details are also present in `values-supervisor.yaml` (see `host_daemon` section). + +The interaction between these components allows the system to automatically sense disruptions (e.g., using parameters like `pod_termination_threshold_s` and `jobset_downtime_threshold_s` from `values-supervisor.yaml`) and initiate mitigation procedures. The system also supports fault injection (`enable_fault_injection` in `values-supervisor.yaml`) for testing resiliency. + +### 2. Remediation Strategies + +The Google Cloud Resiliency library, leveraging the NVIDIA Resiliency Extension, is designed to support various remediation strategies. The exact policy and automation level can be customized: + +* **In-Job Restarts / GPU Reset:** For certain correctable errors (e.g., transient GPU issues), the NVIDIA library might enable an in-job restart or a GPU reset to restore functionality without full node replacement. +* **Node Hot Swap:** In case of unrecoverable hardware failures, the Supervisor can coordinate with GKE to replace the faulty node with a healthy one from a spare pool, then rejoin it to the training job. +* **Scaling Down (and Up):** If spare resources aren't immediately available, the job can be automatically scaled down (e.g., reducing the number of data-parallel replicas, configured via `num_dp_replicas` and `num_nodes_per_dp` in `values-supervisor.yaml`) to continue training on the remaining healthy nodes. When replacement nodes become available, the system is designed to allow the training job to scale back up, maximizing resource utilization. User-defined callbacks (typically part of the training framework integration) can help adjust hyperparameters like learning rate and batch size during such elasticity events. + +### State of Support + +The Elastic Training features provided by the Google Cloud Resiliency library, as demonstrated in the LLaMA3-1-70B recipe (Supervisor, Host Monitors, integration with GKE and NVIDIA Resiliency Extension), are considered **Production-ready** components. They provide a robust framework for improving the resilience of large-scale training jobs on Google Cloud. The specific remediation policies and their triggers can be further customized. +## Minimizing Downtime: Optimized Checkpointing + +Checkpointing is vital for fault tolerance, allowing training to resume from a saved state. However, the checkpointing process itself can consume valuable time and, if not optimized, reduce GoodPut. The LLaMA3-1-70B recipe, as an example, incorporates several strategies for optimized checkpointing, aligning with principles from the [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput). + +These strategies focus on making checkpointing faster, less intrusive, and more resilient. + +### 1. Asynchronous Checkpointing + +To prevent training pauses during checkpoint saves, this recipe leverages asynchronous checkpointing. This means the training process (e.g., GPU computation) can continue while checkpoints are being written to storage in the background. This is typically achieved by first copying the checkpoint data from GPU memory to host CPU memory, which is a fast operation, and then the host CPU handles the slower write to persistent storage. + +* This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml`: + * `--enable-async-ckpt`: Enables the basic asynchronous checkpointing feature. + * `--enable-optimized-async-ckpt`: Enables further optimizations for the asynchronous checkpointing mechanism, potentially improving the efficiency of offloading data from GPU HBM to host memory and managing the subsequent save. + * `--ckpt-threads-per-rank=2`: (Example from `values.yaml`) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. + +### 2. Distributed Checkpointing + +For large models trained across many GPUs, saving and loading checkpoints can be a bottleneck if handled by a single process or node. Distributed checkpointing, often a feature of the training framework (like PyTorch, which NeMo builds upon), addresses this by parallelizing the save/load operations across multiple workers/nodes. Each rank or a subset of ranks saves its portion of the model state concurrently. + +* The `--enable-dist-ckpt` flag in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` activates this feature. +* For more details on PyTorch's distributed checkpointing capabilities, refer to the [PyTorch Distributed Documentation](https://pytorch.org/docs/stable/distributed.html) (specific links may vary by PyTorch version, search for "distributed checkpointing" or "state_dict"). + +### 3. Multi-Tier Checkpointing Strategy (Leveraging GCS with FUSE) + +The blog post describes an ideal multi-tiered approach (local node storage, peer node storage, cloud storage) for balancing speed and resilience. The LLaMA3-1-70B recipe prominently features Google Cloud Storage (GCS) as a robust and scalable tier for durable checkpoint storage, accessed via the [Cloud Storage FUSE CSI driver](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/cloud-storage-fuse-csi-driver). + +* **GCS for Checkpoints:** + * The `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml` file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). + * The main `README.md` of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). + * The `infrastructure.enable_gcsfuse: true` setting in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` ensures that GCS FUSE is utilized for the job. + * The underlying Helm chart for GCS FUSE setup can be found in `src/helm-charts/storage/gcs-fuse/`. +* **How GCS FUSE Helps:** GCS FUSE allows Kubernetes Pods to mount a GCS bucket as a local filesystem. This simplifies access for training frameworks, as they can read/write checkpoints to what appears to be a local path, while the data is actually persisted to GCS. This is crucial for both saving checkpoints and for restoring them during job recovery. +* While this recipe focuses on GCS as the primary persistent checkpointing backend, advanced configurations within NeMo/PyTorch might allow for staging checkpoints on local SSDs before asynchronous upload to GCS, achieving a multi-tier behavior. + +### 4. Configurable Checkpoint Frequency + +The optimal frequency for saving checkpoints is a balance: too infrequent, and you risk losing significant work; too frequent, and the overhead (even if async) can become substantial. + +* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` allows users to tune this. +* Other related flags like `--topk-ckpt=-1` (from `values.yaml`, meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. + +### State of Support + +The Optimized Checkpointing features showcased in this recipe, including asynchronous and distributed checkpointing via NeMo/PyTorch flags and the use of GCS with GCS FUSE for durable checkpoint storage, are considered **Production-ready**. These are well-established techniques for improving I/O performance and resilience in large-scale training. Tuning these parameters appropriately for your specific model size, training duration, and failure rates is key to maximizing their benefit. +## A "DIY" Approach: Composable Lego Blocks for GoodPut + +While this guide and the accompanying LLaMA3-1-70B recipe demonstrate a +comprehensive solution for maximizing GoodPut, it's important to view the +underlying technologies as a collection of "Lego blocks." You can choose, +configure, and combine these components to best suit your specific ML training +workload and infrastructure. + +The key takeaway is that achieving high GoodPut isn't about a single monolithic +solution, but rather about strategically applying various techniques. Here's how +the components discussed can be seen as reusable blocks: + +* **Google Cloud Resiliency Library (Supervisor & Host Monitors):** + * This is a powerful, standalone system for adding failure detection and + automated remediation to jobs running on GKE. While shown here with + NeMo, its principles and components (Sensor, Controller, Actuator) can + be adapted for other training frameworks. + * Customization is primarily done through its configuration (as seen in + `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-supervisor.yaml`) and the policies you define for remediation. + +* **Optimized Checkpointing Techniques:** + * **Asynchronous and Distributed Checkpointing:** These are features often + provided by the training framework itself (like NeMo/PyTorch in this + case). You can enable and tune them using framework-specific parameters + (e.g., the `--enable-async-ckpt`, `--enable-dist-ckpt` flags in + `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml`). + * **Google Cloud Storage (GCS) with GCS FUSE:** This provides a robust, + scalable, and accessible storage backend for your checkpoints, + regardless of the training framework. The setup using PV/PVC and the GCS + FUSE CSI driver (detailed in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md` and configured via + `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml`) is a pattern applicable to many scenarios. + +* **Helm Chart Configuration:** + * The use of Helm charts (evident from the `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml`, `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-supervisor.yaml`, and `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml` files, and the `helm install` commands in the `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md`) is a prime example of the "DIY" + approach. Helm allows you to parameterize and manage the deployment of + these components, making it easier to tailor the setup to different + models, cluster sizes, or resiliency requirements. You can modify these + values files or create your own Helm charts to integrate these GoodPut + strategies into your existing MLOps pipelines. + +By understanding each component's role, you can decide which ones are most +relevant to your pain points and integrate them incrementally. For instance, you +might start by optimizing your checkpointing strategy with GCS and then later +implement the full Supervisor system for enhanced elastic training capabilities. +## Measuring Success: Goodput Analysis + +Improving GoodPut is an ongoing process, and being able to measure it is critical to understanding the impact of the strategies you implement. The `gpu-recipes` repository provides a utility to help with this analysis. + +* **Resiliency Metrics Tool:** + * Located in the `src/utils/resiliency_metrics/` directory (relative to the root of the `gpu-recipes` repository), the `calculator.py` script is designed to analyze training job logs and calculate various metrics, including the overall GoodPut percentage. + * The main `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md` for the LLaMA3-1-70B recipe includes instructions on how to set up and run this tool. It typically involves parsing logs to identify events like job starts, checkpoint loads/saves, and total runtime to derive the effective computation time versus total time. + +Using this tool, or similar log analysis techniques, allows you to quantify the benefits of elastic training and optimized checkpointing, identify remaining bottlenecks, and further tune your setup for maximum efficiency. +## Conclusion: Towards More Efficient and Resilient Training + +Maximizing ML GoodPut is essential for controlling costs and accelerating innovation in large-scale AI model development. By implementing robust elastic training mechanisms and optimized checkpointing strategies, as demonstrated in this LLaMA3-1-70B recipe, you can significantly reduce wasted compute time and improve the overall efficiency and resilience of your training pipelines. + +The Google Cloud Resiliency library, combined with features within frameworks like NVIDIA NeMo and PyTorch, and leveraging Google Cloud infrastructure like GKE and GCS, provides a powerful toolkit. We encourage you to explore these "Lego blocks," adapt them to your specific needs, and continuously measure and refine your setup to achieve the best possible GoodPut for your demanding training workloads. From 708e2c39f9aab0401cfc7eaae5aad648b861b57c Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Tue, 27 May 2025 16:27:33 +0000 Subject: [PATCH 03/10] Update GOODPUT_GUIDE.md with embedded image and links This commit addresses several improvements to the GOODPUT_GUIDE.md: - Downloads and embeds the previously externally linked image. - Converts various plain text key references (e.g., `heartbeat_polling_period_s`) and file paths (e.g., `values-supervisor.yaml`, `ksa-setup.yaml`, Helm chart paths) into relative markdown links. - Integrates the "DIY/Lego blocks" concept into existing sections rather than having a standalone section. - Adds a new subsection explaining how to use the Supervisor system with a custom model, detailing deployment, configuration, Actuator integration, and checkpointing/resumption needs. --- .../GOODPUT_GUIDE.md | 80 ++++++------------ .../images/goodput_blog_image.jpg | Bin 0 -> 166526 bytes 2 files changed, 27 insertions(+), 53 deletions(-) create mode 100644 training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/images/goodput_blog_image.jpg diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md index 6e4d3ff..9f0980a 100644 --- a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -41,7 +41,7 @@ Key sources of BadPut include: * **Impact:** The longer it takes to identify that a problem has occurred and what the root cause is, the longer the downtime and the greater the BadPut. * **Example:** A silent error corrupting data without immediate detection, or lack of clear logs making diagnosis time-consuming. -The blog post further provides a table (via an image link: `https://storage.googleapis.com/gweb-cloudblog-publish/images/3_BRK2-131_xtcNYDc.max-2200x2200.jpg`) that details the specific metric improvements and ML GoodPut contributions for different techniques applied in their case study. While the visual data from the image cannot be rendered here, it underscores that a multi-faceted approach targeting these BadPut sources is key to substantial GoodPut gains. +The blog post further provides a table (via an image link: ![ML GoodPut Contributions](images/goodput_blog_image.jpg)) that details the specific metric improvements and ML GoodPut contributions for different techniques applied in their case study. While the visual data from the image cannot be rendered here, it underscores that a multi-faceted approach targeting these BadPut sources is key to substantial GoodPut gains. ## Addressing Interruptions: Elastic Training @@ -56,18 +56,36 @@ Key components and concepts include: A sophisticated supervisor system is deployed to monitor the health of the training cluster and the job itself. This system is crucial for quickly identifying issues and orchestrating a response. It consists of: * **Supervisor Components:** These typically run on a dedicated CPU node pool. - * **Sensor:** Actively monitors the training job and cluster components for failure signals, performance degradation, or straggler behavior. It might use heartbeat mechanisms (polling worker nodes) and receive signals from other sources like the Host Monitors. The `heartbeat_polling_period_s` and `heartbeat_timeout_s` in `values-supervisor.yaml` are critical for this. + * **Sensor:** Actively monitors the training job and cluster components for failure signals, performance degradation, or straggler behavior. It might use heartbeat mechanisms (polling worker nodes) and receive signals from other sources like the Host Monitors. The [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) in `values-supervisor.yaml` are critical for this. * **Controller:** The central "brain" that receives event data from the Sensor. It consults a user-defined policy (or its internal logic) to decide on the appropriate remediation action. * **Actuator:** Executes the remediation actions chosen by the Controller, such as initiating a job restart, requesting a node replacement, or triggering a scaling operation. - * The configuration for these components, including their Docker images and startup commands, can be found in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-supervisor.yaml`. - * The Kubernetes service accounts and roles required for the Supervisor to interact with GKE resources are defined in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/ksa-setup.yaml`. - * The underlying Helm chart that deploys these supervisor components is located in `src/helm-charts/resiliency/supervisor-chart/`. + * The configuration for these components, including their Docker images and startup commands, can be found in [values-supervisor.yaml](values-supervisor.yaml). + * The Kubernetes service accounts and roles required for the Supervisor to interact with GKE resources are defined in [ksa-setup.yaml](ksa-setup.yaml). + * The underlying Helm chart that deploys these supervisor components is located in [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). + +This entire Supervisor system (Sensor, Controller, Actuator, and Host Monitors) is designed as a modular 'Lego block'. While showcased here with NeMo, its components and principles can be adapted for other training frameworks by customizing the interaction points, primarily through the Actuator's remediation scripts and the policies defined in `values-supervisor.yaml`. + +#### Using the Supervisor with Your Custom Model +This Supervisor system can be integrated with your custom training frameworks or models beyond the LLaMA3-1-70B NeMo example. Here's a general guide: + +* **Deployment:** The Supervisor system (Supervisor controllers and Host Monitor DaemonSet) is deployed via its dedicated Helm chart, found at [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). +* **Configuration:** Crucially, you'll need to customize the [values-supervisor.yaml](values-supervisor.yaml) file. This includes: + * Defining your GKE cluster setup (node pools, etc.). + * Setting appropriate monitoring parameters like heartbeat intervals, timeouts, and failure detection thresholds ([`heartbeat_polling_period_s`](values-supervisor.yaml), [`heartbeat_timeout_s`](values-supervisor.yaml), [`pod_termination_threshold_s`](values-supervisor.yaml), [`jobset_downtime_threshold_s`](values-supervisor.yaml)) to match your job's behavior. + * Specifying the remediation policies and scripts the Actuator should use for events like job restarts, node replacements, or scaling. +* **Actuator Integration:** The core of the integration lies in how the Supervisor's Actuator component interacts with your custom training application. Your application must be controllable via external commands or signals that the Actuator can trigger. This might involve: + * The Actuator executing custom scripts that interact with your job (e.g., to stop, start, or send signals). + * Your training framework exposing APIs that the Actuator can call. + * Using signals (e.g., SIGUSR1, SIGTERM) that your application traps to initiate actions like saving a checkpoint and exiting, or re-evaluating cluster membership. +* **Checkpointing and Resumption:** Your custom application must implement robust checkpointing and the ability to resume training from these checkpoints. This is essential because Supervisor-initiated actions (like restarting a job after a failure or preemption) will rely on your application's capability to continue from the last known good state. + +By carefully configuring these aspects, you can leverage the Google Cloud Resiliency library's Supervisor system to bring enhanced fault tolerance and elastic training capabilities to a wide range of ML workloads. * **Host Monitors:** These are deployed as a Kubernetes DaemonSet, ensuring one runs on each GPU worker node (e.g., A3 Mega nodes). * They provide granular, node-level health information and can detect local hardware issues (like GPU errors) more directly. - * They communicate with the central Supervisor, feeding it critical data for decision-making. Configuration details are also present in `values-supervisor.yaml` (see `host_daemon` section). + * They communicate with the central Supervisor, feeding it critical data for decision-making. Configuration details are also present in `values-supervisor.yaml` (see [`host_daemon` section](values-supervisor.yaml)). -The interaction between these components allows the system to automatically sense disruptions (e.g., using parameters like `pod_termination_threshold_s` and `jobset_downtime_threshold_s` from `values-supervisor.yaml`) and initiate mitigation procedures. The system also supports fault injection (`enable_fault_injection` in `values-supervisor.yaml`) for testing resiliency. +The interaction between these components allows the system to automatically sense disruptions (e.g., using parameters like [`pod_termination_threshold_s`](values-supervisor.yaml) and [`jobset_downtime_threshold_s`](values-supervisor.yaml) from `values-supervisor.yaml`) and initiate mitigation procedures. The system also supports fault injection ([`enable_fault_injection`](values-supervisor.yaml) in `values-supervisor.yaml`) for testing resiliency. ### 2. Remediation Strategies @@ -75,7 +93,7 @@ The Google Cloud Resiliency library, leveraging the NVIDIA Resiliency Extension, * **In-Job Restarts / GPU Reset:** For certain correctable errors (e.g., transient GPU issues), the NVIDIA library might enable an in-job restart or a GPU reset to restore functionality without full node replacement. * **Node Hot Swap:** In case of unrecoverable hardware failures, the Supervisor can coordinate with GKE to replace the faulty node with a healthy one from a spare pool, then rejoin it to the training job. -* **Scaling Down (and Up):** If spare resources aren't immediately available, the job can be automatically scaled down (e.g., reducing the number of data-parallel replicas, configured via `num_dp_replicas` and `num_nodes_per_dp` in `values-supervisor.yaml`) to continue training on the remaining healthy nodes. When replacement nodes become available, the system is designed to allow the training job to scale back up, maximizing resource utilization. User-defined callbacks (typically part of the training framework integration) can help adjust hyperparameters like learning rate and batch size during such elasticity events. +* **Scaling Down (and Up):** If spare resources aren't immediately available, the job can be automatically scaled down (e.g., reducing the number of data-parallel replicas, configured via [`num_dp_replicas`](values-supervisor.yaml) and [`num_nodes_per_dp`](values-supervisor.yaml) in `values-supervisor.yaml`) to continue training on the remaining healthy nodes. When replacement nodes become available, the system is designed to allow the training job to scale back up, maximizing resource utilization. User-defined callbacks (typically part of the training framework integration) can help adjust hyperparameters like learning rate and batch size during such elasticity events. ### State of Support @@ -84,7 +102,7 @@ The Elastic Training features provided by the Google Cloud Resiliency library, a Checkpointing is vital for fault tolerance, allowing training to resume from a saved state. However, the checkpointing process itself can consume valuable time and, if not optimized, reduce GoodPut. The LLaMA3-1-70B recipe, as an example, incorporates several strategies for optimized checkpointing, aligning with principles from the [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput). -These strategies focus on making checkpointing faster, less intrusive, and more resilient. +These strategies focus on making checkpointing faster, less intrusive, and more resilient. These strategies—asynchronous operations, distributed saves/loads, and leveraging robust cloud storage via FUSE—are themselves modular 'Lego blocks' that can be adopted independently or combined to enhance the I/O performance and resilience of various training setups, not limited to NeMo or this specific recipe. ### 1. Asynchronous Checkpointing @@ -124,50 +142,6 @@ The optimal frequency for saving checkpoints is a balance: too infrequent, and y ### State of Support The Optimized Checkpointing features showcased in this recipe, including asynchronous and distributed checkpointing via NeMo/PyTorch flags and the use of GCS with GCS FUSE for durable checkpoint storage, are considered **Production-ready**. These are well-established techniques for improving I/O performance and resilience in large-scale training. Tuning these parameters appropriately for your specific model size, training duration, and failure rates is key to maximizing their benefit. -## A "DIY" Approach: Composable Lego Blocks for GoodPut - -While this guide and the accompanying LLaMA3-1-70B recipe demonstrate a -comprehensive solution for maximizing GoodPut, it's important to view the -underlying technologies as a collection of "Lego blocks." You can choose, -configure, and combine these components to best suit your specific ML training -workload and infrastructure. - -The key takeaway is that achieving high GoodPut isn't about a single monolithic -solution, but rather about strategically applying various techniques. Here's how -the components discussed can be seen as reusable blocks: - -* **Google Cloud Resiliency Library (Supervisor & Host Monitors):** - * This is a powerful, standalone system for adding failure detection and - automated remediation to jobs running on GKE. While shown here with - NeMo, its principles and components (Sensor, Controller, Actuator) can - be adapted for other training frameworks. - * Customization is primarily done through its configuration (as seen in - `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-supervisor.yaml`) and the policies you define for remediation. - -* **Optimized Checkpointing Techniques:** - * **Asynchronous and Distributed Checkpointing:** These are features often - provided by the training framework itself (like NeMo/PyTorch in this - case). You can enable and tune them using framework-specific parameters - (e.g., the `--enable-async-ckpt`, `--enable-dist-ckpt` flags in - `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml`). - * **Google Cloud Storage (GCS) with GCS FUSE:** This provides a robust, - scalable, and accessible storage backend for your checkpoints, - regardless of the training framework. The setup using PV/PVC and the GCS - FUSE CSI driver (detailed in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md` and configured via - `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml`) is a pattern applicable to many scenarios. - -* **Helm Chart Configuration:** - * The use of Helm charts (evident from the `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml`, `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-supervisor.yaml`, and `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml` files, and the `helm install` commands in the `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md`) is a prime example of the "DIY" - approach. Helm allows you to parameterize and manage the deployment of - these components, making it easier to tailor the setup to different - models, cluster sizes, or resiliency requirements. You can modify these - values files or create your own Helm charts to integrate these GoodPut - strategies into your existing MLOps pipelines. - -By understanding each component's role, you can decide which ones are most -relevant to your pain points and integrate them incrementally. For instance, you -might start by optimizing your checkpointing strategy with GCS and then later -implement the full Supervisor system for enhanced elastic training capabilities. ## Measuring Success: Goodput Analysis Improving GoodPut is an ongoing process, and being able to measure it is critical to understanding the impact of the strategies you implement. 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zK$xRooZ7Qq1$HHk9Hk%+F{KyL)89xiUp%TQ@i@l8`1Jd;91P08j-y^05Mm-^v~Tf_ zZks#Fzl+4?wr||obg;GAt7x%O&s0XwkcFBH>LXG1L8Bb~;rewA)2s#c2Tx7a3RxMn o)QhfE{0mAf3|-d6m_r!1`;8ow3@BhCE0Fb{2IRF3v literal 0 HcmV?d00001 From dc912c960a08448a5440a517bc928897d88d48c3 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Tue, 27 May 2025 16:38:31 +0000 Subject: [PATCH 04/10] I've updated GOODPUT_GUIDE.md to extend the Supervisor section style and add a checkpointing preamble. This change addresses your request to: - Extend the detailed, link-rich style of the Supervisor section to other relevant sections, particularly Remediation Strategies and Goodput Analysis. - Add a preamble to the Optimized Checkpointing section to guide you on choosing between asynchronous, distributed checkpointing, and how these relate to the overall resiliency managed by the Supervisor system. - Enhance descriptions of various checkpointing parameters to clarify their use and tunability. - Remove all 'State of Support' sections for brevity. - Remove the 'Conclusion' section as requested. --- .../GOODPUT_GUIDE.md | 45 +++++++++++-------- 1 file changed, 26 insertions(+), 19 deletions(-) diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md index 9f0980a..c330ad0 100644 --- a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -91,19 +91,31 @@ The interaction between these components allows the system to automatically sens The Google Cloud Resiliency library, leveraging the NVIDIA Resiliency Extension, is designed to support various remediation strategies. The exact policy and automation level can be customized: -* **In-Job Restarts / GPU Reset:** For certain correctable errors (e.g., transient GPU issues), the NVIDIA library might enable an in-job restart or a GPU reset to restore functionality without full node replacement. -* **Node Hot Swap:** In case of unrecoverable hardware failures, the Supervisor can coordinate with GKE to replace the faulty node with a healthy one from a spare pool, then rejoin it to the training job. -* **Scaling Down (and Up):** If spare resources aren't immediately available, the job can be automatically scaled down (e.g., reducing the number of data-parallel replicas, configured via [`num_dp_replicas`](values-supervisor.yaml) and [`num_nodes_per_dp`](values-supervisor.yaml) in `values-supervisor.yaml`) to continue training on the remaining healthy nodes. When replacement nodes become available, the system is designed to allow the training job to scale back up, maximizing resource utilization. User-defined callbacks (typically part of the training framework integration) can help adjust hyperparameters like learning rate and batch size during such elasticity events. +* **In-Job Restarts / GPU Reset:** For certain correctable errors (e.g., transient GPU issues identified by lower-level hardware monitoring), the NVIDIA library might enable an in-job restart or a GPU reset to restore functionality. Following such recovery attempts, the Supervisor system orchestrates the restart of the affected training job components (e.g., Kubernetes pods) to ensure they rejoin the training process and resume from the last valid checkpoint. The primary goal from the Supervisor's perspective is to bring the job back to a healthy, training state. +* **Node Hot Swap:** This is a core capability of the Supervisor system. When the Sensor (using health-check parameters like [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) from `values-supervisor.yaml`) and the Host Monitors detect an unrecoverable node failure, the Controller evaluates the situation based on its configured policies. If a node replacement is deemed necessary, the Actuator component interacts with GKE to de-allocate the failed node and provision a new one from the available resource pool. The training job, often managed by a higher-level controller like JobSet, subsequently resumes on the reconstituted set of nodes, loading from the latest checkpoint. +* **Scaling Down (and Up):** The target size of the training job is defined by parameters such as [`num_dp_replicas`](values-supervisor.yaml) and [`num_nodes_per_dp`](values-supervisor.yaml) in `values-supervisor.yaml`. If nodes fail and replacement resources are not immediately available, the Supervisor's Controller can decide to scale down the job to continue training on the remaining healthy nodes. In such scenarios, the Actuator would modify the job specification (e.g., by updating the JobSet resource if Kubernetes JobSet is being used, or by interacting with the specific training framework's scaling mechanisms). The system is designed to scale back up to its target size if new resources become available or previously failed nodes are restored. The Supervisor components facilitating these actions are deployed via a Helm chart, available at [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). -### State of Support +#### Customizing Remediation Logic +While `values-supervisor.yaml` defines the monitoring parameters (like heartbeats and timeouts) and high-level remediation policies (e.g., whether to attempt a node swap or scale down), the precise commands and mechanisms for interacting with the *specific training application* during remediation are typically implemented within the Actuator component or scripts called by the Actuator. For instance, the exact command to gracefully stop a NeMo pod, instruct MaxText to save an emergency checkpoint, or re-launch a specific training script with an updated list of participating nodes resides in this layer. Users can customize these Actuator scripts or provide their own implementations to integrate the Supervisor system seamlessly with their chosen training framework's operational needs, thus making the resiliency solution highly adaptable. -The Elastic Training features provided by the Google Cloud Resiliency library, as demonstrated in the LLaMA3-1-70B recipe (Supervisor, Host Monitors, integration with GKE and NVIDIA Resiliency Extension), are considered **Production-ready** components. They provide a robust framework for improving the resilience of large-scale training jobs on Google Cloud. The specific remediation policies and their triggers can be further customized. ## Minimizing Downtime: Optimized Checkpointing Checkpointing is vital for fault tolerance, allowing training to resume from a saved state. However, the checkpointing process itself can consume valuable time and, if not optimized, reduce GoodPut. The LLaMA3-1-70B recipe, as an example, incorporates several strategies for optimized checkpointing, aligning with principles from the [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput). These strategies focus on making checkpointing faster, less intrusive, and more resilient. These strategies—asynchronous operations, distributed saves/loads, and leveraging robust cloud storage via FUSE—are themselves modular 'Lego blocks' that can be adopted independently or combined to enhance the I/O performance and resilience of various training setups, not limited to NeMo or this specific recipe. +Choosing the right checkpointing strategy, or combination of strategies, is crucial for both minimizing training disruption and ensuring robust recovery. The methods described below—asynchronous, distributed, and multi-tier storage—can be seen as complementary building blocks. Your choice will depend on factors like model size, training scale, and infrastructure characteristics. + +Consider the following when making your decision: + +* **Asynchronous Checkpointing:** This is generally recommended for most training jobs. By offloading the checkpoint save operation to background processes (typically on the CPU), it allows the GPUs to continue training with minimal interruption. This directly improves GoodPut by reducing idle GPU time. It's effective for both single-node and multi-node training. + +* **Distributed Checkpointing:** When training very large models across a significant number of nodes and GPUs, the process of gathering and saving the model state can still be time-consuming, even if asynchronous. Distributed checkpointing parallelizes the save (and load) process itself, where each worker or a subset of workers handles its portion of the model state concurrently. This is often used in conjunction with asynchronous checkpointing to further reduce the critical path of saving checkpoints. + +* **Integration with the Supervisor System:** The Supervisor system (detailed in the "Elastic Training" section) acts as the overall training controller and relies on a robust and efficient checkpointing mechanism to enable automated recovery from hardware failures or preemptions. When the Supervisor restarts a job or a pod, it depends on the training application's ability to quickly load the latest checkpoint. Therefore, selecting fast and reliable checkpointing methods (like asynchronous and distributed, saved to resilient storage like GCS) is key to minimizing downtime when the Supervisor needs to intervene. The goal is a synergistic relationship: checkpointing provides the recovery points, and the Supervisor automates the recovery process. + +These strategies can often be combined. For instance, a large distributed training job would ideally use both distributed checkpointing (to quickly gather state from all workers) and asynchronous checkpointing (to offload the writing to persistent storage without stalling GPUs), all while being monitored by the Supervisor for fault tolerance. + ### 1. Asynchronous Checkpointing To prevent training pauses during checkpoint saves, this recipe leverages asynchronous checkpointing. This means the training process (e.g., GPU computation) can continue while checkpoints are being written to storage in the background. This is typically achieved by first copying the checkpoint data from GPU memory to host CPU memory, which is a fast operation, and then the host CPU handles the slower write to persistent storage. @@ -111,7 +123,7 @@ To prevent training pauses during checkpoint saves, this recipe leverages asynch * This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml`: * `--enable-async-ckpt`: Enables the basic asynchronous checkpointing feature. * `--enable-optimized-async-ckpt`: Enables further optimizations for the asynchronous checkpointing mechanism, potentially improving the efficiency of offloading data from GPU HBM to host memory and managing the subsequent save. - * `--ckpt-threads-per-rank=2`: (Example from `values.yaml`) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. + * `--ckpt-threads-per-rank=2`: (Example from `values.yaml`) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. Users can tune the `--ckpt-threads-per-rank` value; increasing it may improve checkpointing speed if the process is I/O bound and sufficient CPU resources are available, but excessive threads could also lead to contention. Optimal values should be determined through experimentation. ### 2. Distributed Checkpointing @@ -125,7 +137,7 @@ For large models trained across many GPUs, saving and loading checkpoints can be The blog post describes an ideal multi-tiered approach (local node storage, peer node storage, cloud storage) for balancing speed and resilience. The LLaMA3-1-70B recipe prominently features Google Cloud Storage (GCS) as a robust and scalable tier for durable checkpoint storage, accessed via the [Cloud Storage FUSE CSI driver](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/cloud-storage-fuse-csi-driver). * **GCS for Checkpoints:** - * The `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml` file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). + * The `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml` file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). Users should ensure this GCS bucket is provisioned in the same region as their GKE cluster, has appropriate write/read permissions for the training job's service account, and has Hierarchical Namespace enabled for potentially better performance, as detailed in the main recipe `README.md`. * The main `README.md` of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). * The `infrastructure.enable_gcsfuse: true` setting in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` ensures that GCS FUSE is utilized for the job. * The underlying Helm chart for GCS FUSE setup can be found in `src/helm-charts/storage/gcs-fuse/`. @@ -136,23 +148,18 @@ The blog post describes an ideal multi-tiered approach (local node storage, peer The optimal frequency for saving checkpoints is a balance: too infrequent, and you risk losing significant work; too frequent, and the overhead (even if async) can become substantial. -* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` allows users to tune this. -* Other related flags like `--topk-ckpt=-1` (from `values.yaml`, meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. - -### State of Support +* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` allows users to tune this. This value is specified in terms of training steps. The optimal interval is a trade-off: smaller intervals reduce the amount of lost computation in case of a failure but increase the aggregate time spent on checkpointing. Larger intervals minimize checkpointing overhead but risk more lost work. Users should tune this based on their specific job's typical step duration and observed failure rates. +* Other related flags like `--topk-ckpt=-1` (from `values.yaml`, meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. A value of `-1` (as shown in the example) means all checkpoints are kept, which can consume considerable storage over long runs. Users should set this to a positive integer to keep only the latest 'k' checkpoints, balancing recovery needs with storage costs. -The Optimized Checkpointing features showcased in this recipe, including asynchronous and distributed checkpointing via NeMo/PyTorch flags and the use of GCS with GCS FUSE for durable checkpoint storage, are considered **Production-ready**. These are well-established techniques for improving I/O performance and resilience in large-scale training. Tuning these parameters appropriately for your specific model size, training duration, and failure rates is key to maximizing their benefit. ## Measuring Success: Goodput Analysis Improving GoodPut is an ongoing process, and being able to measure it is critical to understanding the impact of the strategies you implement. The `gpu-recipes` repository provides a utility to help with this analysis. * **Resiliency Metrics Tool:** - * Located in the `src/utils/resiliency_metrics/` directory (relative to the root of the `gpu-recipes` repository), the `calculator.py` script is designed to analyze training job logs and calculate various metrics, including the overall GoodPut percentage. - * The main `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md` for the LLaMA3-1-70B recipe includes instructions on how to set up and run this tool. It typically involves parsing logs to identify events like job starts, checkpoint loads/saves, and total runtime to derive the effective computation time versus total time. + * Located in the [`src/utils/resiliency_metrics/`](../../../../src/utils/resiliency_metrics/) directory (relative to the root of the `gpu-recipes` repository), the [`calculator.py`](../../../../src/utils/resiliency_metrics/calculator.py) script is designed to analyze training job logs and calculate various metrics, including the overall GoodPut percentage. + * The main `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md` for the LLaMA3-1-70B recipe includes detailed instructions on how to set up and run this tool (see the [Goodput Analysis for the job](README.md#goodput-analysis-for-the-job) section). Generally, using the tool involves these key steps: + * Navigating to the `src/utils/resiliency_metrics/` directory. + * Creating a Python virtual environment and installing required packages from `requirements.txt`. + * Executing the `python3 calculator.py` script with necessary arguments, such as `--job-name ` (which can be found using `kubectl get jobsets`), and parameters for log lookback periods (e.g., `--gcloud-logging-lookback-days 1`) and reference step times. Using this tool, or similar log analysis techniques, allows you to quantify the benefits of elastic training and optimized checkpointing, identify remaining bottlenecks, and further tune your setup for maximum efficiency. -## Conclusion: Towards More Efficient and Resilient Training - -Maximizing ML GoodPut is essential for controlling costs and accelerating innovation in large-scale AI model development. By implementing robust elastic training mechanisms and optimized checkpointing strategies, as demonstrated in this LLaMA3-1-70B recipe, you can significantly reduce wasted compute time and improve the overall efficiency and resilience of your training pipelines. - -The Google Cloud Resiliency library, combined with features within frameworks like NVIDIA NeMo and PyTorch, and leveraging Google Cloud infrastructure like GKE and GCS, provides a powerful toolkit. We encourage you to explore these "Lego blocks," adapt them to your specific needs, and continuously measure and refine your setup to achieve the best possible GoodPut for your demanding training workloads. From e285fdfcfb7dbc702279017473187d4eaabc44e8 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Tue, 27 May 2025 16:52:52 +0000 Subject: [PATCH 05/10] Improve GOODPUT_GUIDE.md with TLDR and clickable links This commit addresses several improvements for the GoodPut guide: 1. **Adds a TLDR Section:** A new "TLDR: Recommended Lego Blocks for Your Deployment" section has been added after the introduction. This section provides a concise, actionable summary of key strategies (Supervisor System, Asynchronous Checkpointing, Distributed Checkpointing, GCS with FUSE, and Checkpoint Frequency Tuning) to help you quickly understand how to improve GoodPut for your own workloads. 2. **Updates File References to Clickable Links:** All plain text references to internal files (e.g., `values.yaml`, `values-supervisor.yaml`, `README.md`, Helm chart paths) and relevant external documentation URLs within the guide have been converted into clickable Markdown links. This improves navigability and makes it easier for you to access referenced resources and code. Relative paths for internal files have been carefully constructed. These changes aim to make the guide more user-friendly, easier to navigate, and more actionable for customers looking to apply these resiliency and efficiency techniques to their ML training deployments. --- .../GOODPUT_GUIDE.md | 78 +++++++++++++------ 1 file changed, 54 insertions(+), 24 deletions(-) diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md index c330ad0..faef396 100644 --- a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -9,6 +9,36 @@ Achieving high GoodPut can be challenging due to several factors common in large This guide provides a general overview of techniques and tools to address these common challenges and maximize ML GoodPut. While the principles discussed are broadly applicable, we will use the [LLaMA3-1-70B pretraining recipe](https://github.com/AI-Hypercomputer/gpu-recipes/tree/main/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency) as a concrete case study to illustrate how these components can be implemented and customized for large-scale training workloads on Google Cloud. The goal is to showcase a "DIY" style product, where users can understand and selectively adopt these "Lego blocks" to build resilient and efficient training pipelines. +## TLDR: Recommended Lego Blocks for Your Deployment +For customers looking to improve GoodPut on their own ML training workloads, here’s a concise guide to the key strategies discussed in this document, presented as 'Lego blocks' you can implement: + +1. **Implement a Robust Supervisor System (Elastic Training):** + * **Why:** This is the foundational step to make your training resilient to hardware failures, preemptions, and other interruptions. It automates detection and recovery. + * **How:** Adapt or implement a supervisor system like the one detailed in the "Elastic Training" section. Focus on failure sensing, policy-based remediation (like node hot-swapping), and ensuring your training job can be controlled externally (start/stop/checkpoint). The Google Cloud Resiliency library components (Sensor, Controller, Actuator, Host Monitors) provide a strong template. + * **Key for your workload:** Ensure your custom training application can gracefully handle external signals for checkpointing and resumption. + +2. **Optimize Checkpointing - Start with Asynchronous Checkpointing:** + * **Why:** Minimize GPU idle time by offloading checkpoint saves to CPU/background processes. This directly boosts GoodPut. + * **How:** Enable asynchronous checkpointing features in your training framework (e.g., `--enable-async-ckpt` in NeMo). Ensure you have sufficient CPU and memory resources on host machines for this. + * **Key for your workload:** Verify that your checkpointing mechanism can indeed save without halting GPU computation. + +3. **Leverage Cloud Storage with FUSE for Checkpoints:** + * **Why:** Provides durable, accessible, and scalable storage for your checkpoints, crucial for recovery across different nodes or after failures. + * **How:** Use a service like Google Cloud Storage (GCS) with the Cloud Storage FUSE CSI driver to mount GCS buckets as local filesystems. Configure your training job to save checkpoints to this mounted path. + * **Key for your workload:** Ensure appropriate permissions and regional alignment between your compute and storage. Consider enabling Hierarchical Namespace on GCS buckets for potentially better performance. + +4. **Consider Distributed Checkpointing (For Very Large Models/Setups):** + * **Why:** If asynchronous checkpointing is still too slow due to massive model size or a large number of distributed workers, parallelize the checkpoint save/load process itself. + * **How:** Utilize distributed checkpointing features within your framework (e.g., `--enable-dist-ckpt` in NeMo/PyTorch). This typically involves each worker saving its shard of the model. + * **Key for your workload:** This adds complexity, so evaluate if the benefits outweigh it based on your scale. Often used in conjunction with asynchronous checkpointing. + +5. **Tune Checkpoint Frequency:** + * **Why:** Balance the risk of lost work against the overhead of checkpointing. + * **How:** Configure how often checkpoints are saved (e.g., based on training steps or time). Monitor your failure rates and checkpoint durations to find an optimal balance. + * **Key for your workload:** There's no one-size-fits-all; this needs empirical tuning. + +Start with implementing a supervisor system (Step 1) as it provides the core resiliency. Then, optimize your checkpointing process (Steps 2-4), choosing the techniques most relevant to your workload's scale and characteristics. Finally, continuously tune your checkpoint frequency (Step 5) and monitor your GoodPut to measure improvements. + ## Understanding Sources of BadPut (Lost Efficiency) To effectively improve GoodPut, it's essential to understand the common culprits that lead to "BadPut" – the wasted time and resources during training. The previously mentioned [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput) highlights several of these. In a case study referenced in the article, involving 1,024 A3 Mega GPU instances, overall ML GoodPut was improved from around 80% to over 90% by addressing these factors. @@ -56,20 +86,20 @@ Key components and concepts include: A sophisticated supervisor system is deployed to monitor the health of the training cluster and the job itself. This system is crucial for quickly identifying issues and orchestrating a response. It consists of: * **Supervisor Components:** These typically run on a dedicated CPU node pool. - * **Sensor:** Actively monitors the training job and cluster components for failure signals, performance degradation, or straggler behavior. It might use heartbeat mechanisms (polling worker nodes) and receive signals from other sources like the Host Monitors. The [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) in `values-supervisor.yaml` are critical for this. + * **Sensor:** Actively monitors the training job and cluster components for failure signals, performance degradation, or straggler behavior. It might use heartbeat mechanisms (polling worker nodes) and receive signals from other sources like the Host Monitors. The [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) in `[values-supervisor.yaml](values-supervisor.yaml)` are critical for this. * **Controller:** The central "brain" that receives event data from the Sensor. It consults a user-defined policy (or its internal logic) to decide on the appropriate remediation action. * **Actuator:** Executes the remediation actions chosen by the Controller, such as initiating a job restart, requesting a node replacement, or triggering a scaling operation. - * The configuration for these components, including their Docker images and startup commands, can be found in [values-supervisor.yaml](values-supervisor.yaml). - * The Kubernetes service accounts and roles required for the Supervisor to interact with GKE resources are defined in [ksa-setup.yaml](ksa-setup.yaml). - * The underlying Helm chart that deploys these supervisor components is located in [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). + * The configuration for these components, including their Docker images and startup commands, can be found in `[values-supervisor.yaml](values-supervisor.yaml)`. + * The Kubernetes service accounts and roles required for the Supervisor to interact with GKE resources are defined in `[ksa-setup.yaml](ksa-setup.yaml)`. + * The underlying Helm chart that deploys these supervisor components is located in `[src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/)`. -This entire Supervisor system (Sensor, Controller, Actuator, and Host Monitors) is designed as a modular 'Lego block'. While showcased here with NeMo, its components and principles can be adapted for other training frameworks by customizing the interaction points, primarily through the Actuator's remediation scripts and the policies defined in `values-supervisor.yaml`. +This entire Supervisor system (Sensor, Controller, Actuator, and Host Monitors) is designed as a modular 'Lego block'. While showcased here with NeMo, its components and principles can be adapted for other training frameworks by customizing the interaction points, primarily through the Actuator's remediation scripts and the policies defined in `[values-supervisor.yaml](values-supervisor.yaml)`. #### Using the Supervisor with Your Custom Model This Supervisor system can be integrated with your custom training frameworks or models beyond the LLaMA3-1-70B NeMo example. Here's a general guide: -* **Deployment:** The Supervisor system (Supervisor controllers and Host Monitor DaemonSet) is deployed via its dedicated Helm chart, found at [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). -* **Configuration:** Crucially, you'll need to customize the [values-supervisor.yaml](values-supervisor.yaml) file. This includes: +* **Deployment:** The Supervisor system (Supervisor controllers and Host Monitor DaemonSet) is deployed via its dedicated Helm chart, found at `[src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/)`. +* **Configuration:** Crucially, you'll need to customize the `[values-supervisor.yaml](values-supervisor.yaml)` file. This includes: * Defining your GKE cluster setup (node pools, etc.). * Setting appropriate monitoring parameters like heartbeat intervals, timeouts, and failure detection thresholds ([`heartbeat_polling_period_s`](values-supervisor.yaml), [`heartbeat_timeout_s`](values-supervisor.yaml), [`pod_termination_threshold_s`](values-supervisor.yaml), [`jobset_downtime_threshold_s`](values-supervisor.yaml)) to match your job's behavior. * Specifying the remediation policies and scripts the Actuator should use for events like job restarts, node replacements, or scaling. @@ -83,9 +113,9 @@ By carefully configuring these aspects, you can leverage the Google Cloud Resili * **Host Monitors:** These are deployed as a Kubernetes DaemonSet, ensuring one runs on each GPU worker node (e.g., A3 Mega nodes). * They provide granular, node-level health information and can detect local hardware issues (like GPU errors) more directly. - * They communicate with the central Supervisor, feeding it critical data for decision-making. Configuration details are also present in `values-supervisor.yaml` (see [`host_daemon` section](values-supervisor.yaml)). + * They communicate with the central Supervisor, feeding it critical data for decision-making. Configuration details are also present in `[values-supervisor.yaml](values-supervisor.yaml)` (see [`host_daemon` section](values-supervisor.yaml)). -The interaction between these components allows the system to automatically sense disruptions (e.g., using parameters like [`pod_termination_threshold_s`](values-supervisor.yaml) and [`jobset_downtime_threshold_s`](values-supervisor.yaml) from `values-supervisor.yaml`) and initiate mitigation procedures. The system also supports fault injection ([`enable_fault_injection`](values-supervisor.yaml) in `values-supervisor.yaml`) for testing resiliency. +The interaction between these components allows the system to automatically sense disruptions (e.g., using parameters like [`pod_termination_threshold_s`](values-supervisor.yaml) and [`jobset_downtime_threshold_s`](values-supervisor.yaml) from `[values-supervisor.yaml](values-supervisor.yaml)`) and initiate mitigation procedures. The system also supports fault injection ([`enable_fault_injection`](values-supervisor.yaml) in `[values-supervisor.yaml](values-supervisor.yaml)`) for testing resiliency. ### 2. Remediation Strategies @@ -93,10 +123,10 @@ The Google Cloud Resiliency library, leveraging the NVIDIA Resiliency Extension, * **In-Job Restarts / GPU Reset:** For certain correctable errors (e.g., transient GPU issues identified by lower-level hardware monitoring), the NVIDIA library might enable an in-job restart or a GPU reset to restore functionality. Following such recovery attempts, the Supervisor system orchestrates the restart of the affected training job components (e.g., Kubernetes pods) to ensure they rejoin the training process and resume from the last valid checkpoint. The primary goal from the Supervisor's perspective is to bring the job back to a healthy, training state. * **Node Hot Swap:** This is a core capability of the Supervisor system. When the Sensor (using health-check parameters like [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) from `values-supervisor.yaml`) and the Host Monitors detect an unrecoverable node failure, the Controller evaluates the situation based on its configured policies. If a node replacement is deemed necessary, the Actuator component interacts with GKE to de-allocate the failed node and provision a new one from the available resource pool. The training job, often managed by a higher-level controller like JobSet, subsequently resumes on the reconstituted set of nodes, loading from the latest checkpoint. -* **Scaling Down (and Up):** The target size of the training job is defined by parameters such as [`num_dp_replicas`](values-supervisor.yaml) and [`num_nodes_per_dp`](values-supervisor.yaml) in `values-supervisor.yaml`. If nodes fail and replacement resources are not immediately available, the Supervisor's Controller can decide to scale down the job to continue training on the remaining healthy nodes. In such scenarios, the Actuator would modify the job specification (e.g., by updating the JobSet resource if Kubernetes JobSet is being used, or by interacting with the specific training framework's scaling mechanisms). The system is designed to scale back up to its target size if new resources become available or previously failed nodes are restored. The Supervisor components facilitating these actions are deployed via a Helm chart, available at [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). +* **Scaling Down (and Up):** The target size of the training job is defined by parameters such as [`num_dp_replicas`](values-supervisor.yaml) and [`num_nodes_per_dp`](values-supervisor.yaml) in `[values-supervisor.yaml](values-supervisor.yaml)`. If nodes fail and replacement resources are not immediately available, the Supervisor's Controller can decide to scale down the job to continue training on the remaining healthy nodes. In such scenarios, the Actuator would modify the job specification (e.g., by updating the JobSet resource if Kubernetes JobSet is being used, or by interacting with the specific training framework's scaling mechanisms). The system is designed to scale back up to its target size if new resources become available or previously failed nodes are restored. The Supervisor components facilitating these actions are deployed via a Helm chart, available at `[src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/)`. #### Customizing Remediation Logic -While `values-supervisor.yaml` defines the monitoring parameters (like heartbeats and timeouts) and high-level remediation policies (e.g., whether to attempt a node swap or scale down), the precise commands and mechanisms for interacting with the *specific training application* during remediation are typically implemented within the Actuator component or scripts called by the Actuator. For instance, the exact command to gracefully stop a NeMo pod, instruct MaxText to save an emergency checkpoint, or re-launch a specific training script with an updated list of participating nodes resides in this layer. Users can customize these Actuator scripts or provide their own implementations to integrate the Supervisor system seamlessly with their chosen training framework's operational needs, thus making the resiliency solution highly adaptable. +While `[values-supervisor.yaml](values-supervisor.yaml)` defines the monitoring parameters (like heartbeats and timeouts) and high-level remediation policies (e.g., whether to attempt a node swap or scale down), the precise commands and mechanisms for interacting with the *specific training application* during remediation are typically implemented within the Actuator component or scripts called by the Actuator. For instance, the exact command to gracefully stop a NeMo pod, instruct MaxText to save an emergency checkpoint, or re-launch a specific training script with an updated list of participating nodes resides in this layer. Users can customize these Actuator scripts or provide their own implementations to integrate the Supervisor system seamlessly with their chosen training framework's operational needs, thus making the resiliency solution highly adaptable. ## Minimizing Downtime: Optimized Checkpointing @@ -120,16 +150,16 @@ These strategies can often be combined. For instance, a large distributed traini To prevent training pauses during checkpoint saves, this recipe leverages asynchronous checkpointing. This means the training process (e.g., GPU computation) can continue while checkpoints are being written to storage in the background. This is typically achieved by first copying the checkpoint data from GPU memory to host CPU memory, which is a fast operation, and then the host CPU handles the slower write to persistent storage. -* This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml`: +* This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of `[values.yaml](values.yaml)`: * `--enable-async-ckpt`: Enables the basic asynchronous checkpointing feature. * `--enable-optimized-async-ckpt`: Enables further optimizations for the asynchronous checkpointing mechanism, potentially improving the efficiency of offloading data from GPU HBM to host memory and managing the subsequent save. - * `--ckpt-threads-per-rank=2`: (Example from `values.yaml`) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. Users can tune the `--ckpt-threads-per-rank` value; increasing it may improve checkpointing speed if the process is I/O bound and sufficient CPU resources are available, but excessive threads could also lead to contention. Optimal values should be determined through experimentation. + * `--ckpt-threads-per-rank=2`: (Example from `[values.yaml](values.yaml)`) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. Users can tune the `--ckpt-threads-per-rank` value; increasing it may improve checkpointing speed if the process is I/O bound and sufficient CPU resources are available, but excessive threads could also lead to contention. Optimal values should be determined through experimentation. ### 2. Distributed Checkpointing For large models trained across many GPUs, saving and loading checkpoints can be a bottleneck if handled by a single process or node. Distributed checkpointing, often a feature of the training framework (like PyTorch, which NeMo builds upon), addresses this by parallelizing the save/load operations across multiple workers/nodes. Each rank or a subset of ranks saves its portion of the model state concurrently. -* The `--enable-dist-ckpt` flag in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` activates this feature. +* The `--enable-dist-ckpt` flag in `[values.yaml](values.yaml)` activates this feature. * For more details on PyTorch's distributed checkpointing capabilities, refer to the [PyTorch Distributed Documentation](https://pytorch.org/docs/stable/distributed.html) (specific links may vary by PyTorch version, search for "distributed checkpointing" or "state_dict"). ### 3. Multi-Tier Checkpointing Strategy (Leveraging GCS with FUSE) @@ -137,10 +167,10 @@ For large models trained across many GPUs, saving and loading checkpoints can be The blog post describes an ideal multi-tiered approach (local node storage, peer node storage, cloud storage) for balancing speed and resilience. The LLaMA3-1-70B recipe prominently features Google Cloud Storage (GCS) as a robust and scalable tier for durable checkpoint storage, accessed via the [Cloud Storage FUSE CSI driver](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/cloud-storage-fuse-csi-driver). * **GCS for Checkpoints:** - * The `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values-gcs.yaml` file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). Users should ensure this GCS bucket is provisioned in the same region as their GKE cluster, has appropriate write/read permissions for the training job's service account, and has Hierarchical Namespace enabled for potentially better performance, as detailed in the main recipe `README.md`. - * The main `README.md` of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). - * The `infrastructure.enable_gcsfuse: true` setting in `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` ensures that GCS FUSE is utilized for the job. - * The underlying Helm chart for GCS FUSE setup can be found in `src/helm-charts/storage/gcs-fuse/`. + * The `[values-gcs.yaml](values-gcs.yaml)` file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). Users should ensure this GCS bucket is provisioned in the same region as their GKE cluster, has appropriate write/read permissions for the training job's service account, and has Hierarchical Namespace enabled for potentially better performance, as detailed in the main recipe `[README.md](README.md)`. + * The main `[README.md](README.md)` of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). + * The `infrastructure.enable_gcsfuse: true` setting in `[values.yaml](values.yaml)` ensures that GCS FUSE is utilized for the job. + * The underlying Helm chart for GCS FUSE setup can be found in `[src/helm-charts/storage/gcs-fuse/](../../../../src/helm-charts/storage/gcs-fuse/)`. * **How GCS FUSE Helps:** GCS FUSE allows Kubernetes Pods to mount a GCS bucket as a local filesystem. This simplifies access for training frameworks, as they can read/write checkpoints to what appears to be a local path, while the data is actually persisted to GCS. This is crucial for both saving checkpoints and for restoring them during job recovery. * While this recipe focuses on GCS as the primary persistent checkpointing backend, advanced configurations within NeMo/PyTorch might allow for staging checkpoints on local SSDs before asynchronous upload to GCS, achieving a multi-tier behavior. @@ -148,18 +178,18 @@ The blog post describes an ideal multi-tiered approach (local node storage, peer The optimal frequency for saving checkpoints is a balance: too infrequent, and you risk losing significant work; too frequent, and the overhead (even if async) can become substantial. -* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/values.yaml` allows users to tune this. This value is specified in terms of training steps. The optimal interval is a trade-off: smaller intervals reduce the amount of lost computation in case of a failure but increase the aggregate time spent on checkpointing. Larger intervals minimize checkpointing overhead but risk more lost work. Users should tune this based on their specific job's typical step duration and observed failure rates. -* Other related flags like `--topk-ckpt=-1` (from `values.yaml`, meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. A value of `-1` (as shown in the example) means all checkpoints are kept, which can consume considerable storage over long runs. Users should set this to a positive integer to keep only the latest 'k' checkpoints, balancing recovery needs with storage costs. +* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of `[values.yaml](values.yaml)` allows users to tune this. This value is specified in terms of training steps. The optimal interval is a trade-off: smaller intervals reduce the amount of lost computation in case of a failure but increase the aggregate time spent on checkpointing. Larger intervals minimize checkpointing overhead but risk more lost work. Users should tune this based on their specific job's typical step duration and observed failure rates. +* Other related flags like `--topk-ckpt=-1` (from `[values.yaml](values.yaml)`, meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. A value of `-1` (as shown in the example) means all checkpoints are kept, which can consume considerable storage over long runs. Users should set this to a positive integer to keep only the latest 'k' checkpoints, balancing recovery needs with storage costs. ## Measuring Success: Goodput Analysis Improving GoodPut is an ongoing process, and being able to measure it is critical to understanding the impact of the strategies you implement. The `gpu-recipes` repository provides a utility to help with this analysis. * **Resiliency Metrics Tool:** - * Located in the [`src/utils/resiliency_metrics/`](../../../../src/utils/resiliency_metrics/) directory (relative to the root of the `gpu-recipes` repository), the [`calculator.py`](../../../../src/utils/resiliency_metrics/calculator.py) script is designed to analyze training job logs and calculate various metrics, including the overall GoodPut percentage. - * The main `training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/README.md` for the LLaMA3-1-70B recipe includes detailed instructions on how to set up and run this tool (see the [Goodput Analysis for the job](README.md#goodput-analysis-for-the-job) section). Generally, using the tool involves these key steps: - * Navigating to the `src/utils/resiliency_metrics/` directory. - * Creating a Python virtual environment and installing required packages from `requirements.txt`. + * Located in the `[src/utils/resiliency_metrics/](../../../../src/utils/resiliency_metrics/)` directory (relative to the root of the `gpu-recipes` repository), the `[calculator.py](../../../../src/utils/resiliency_metrics/calculator.py)` script is designed to analyze training job logs and calculate various metrics, including the overall GoodPut percentage. + * The main `[README.md](README.md#goodput-analysis-for-the-job)` for the LLaMA3-1-70B recipe includes detailed instructions on how to set up and run this tool (see the [Goodput Analysis for the job](README.md#goodput-analysis-for-the-job) section). Generally, using the tool involves these key steps: + * Navigating to the `[src/utils/resiliency_metrics/](../../../../src/utils/resiliency_metrics/)` directory. + * Creating a Python virtual environment and installing required packages from `[requirements.txt](../../../../src/utils/resiliency_metrics/requirements.txt)`. * Executing the `python3 calculator.py` script with necessary arguments, such as `--job-name ` (which can be found using `kubectl get jobsets`), and parameters for log lookback periods (e.g., `--gcloud-logging-lookback-days 1`) and reference step times. Using this tool, or similar log analysis techniques, allows you to quantify the benefits of elastic training and optimized checkpointing, identify remaining bottlenecks, and further tune your setup for maximum efficiency. From 09d314e27aa24ce8b137196e4e5f49a8a5b4717d Mon Sep 17 00:00:00 2001 From: Viacheslav Kovalevskyi Date: Tue, 27 May 2025 09:54:38 -0700 Subject: [PATCH 06/10] Update GOODPUT_GUIDE.md --- .../GOODPUT_GUIDE.md | 48 +++++++++---------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md index faef396..a5c6680 100644 --- a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -86,20 +86,20 @@ Key components and concepts include: A sophisticated supervisor system is deployed to monitor the health of the training cluster and the job itself. This system is crucial for quickly identifying issues and orchestrating a response. It consists of: * **Supervisor Components:** These typically run on a dedicated CPU node pool. - * **Sensor:** Actively monitors the training job and cluster components for failure signals, performance degradation, or straggler behavior. It might use heartbeat mechanisms (polling worker nodes) and receive signals from other sources like the Host Monitors. The [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) in `[values-supervisor.yaml](values-supervisor.yaml)` are critical for this. + * **Sensor:** Actively monitors the training job and cluster components for failure signals, performance degradation, or straggler behavior. It might use heartbeat mechanisms (polling worker nodes) and receive signals from other sources like the Host Monitors. The [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) in [values-supervisor.yaml](values-supervisor.yaml) are critical for this. * **Controller:** The central "brain" that receives event data from the Sensor. It consults a user-defined policy (or its internal logic) to decide on the appropriate remediation action. * **Actuator:** Executes the remediation actions chosen by the Controller, such as initiating a job restart, requesting a node replacement, or triggering a scaling operation. - * The configuration for these components, including their Docker images and startup commands, can be found in `[values-supervisor.yaml](values-supervisor.yaml)`. - * The Kubernetes service accounts and roles required for the Supervisor to interact with GKE resources are defined in `[ksa-setup.yaml](ksa-setup.yaml)`. - * The underlying Helm chart that deploys these supervisor components is located in `[src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/)`. + * The configuration for these components, including their Docker images and startup commands, can be found in [values-supervisor.yaml](values-supervisor.yaml). + * The Kubernetes service accounts and roles required for the Supervisor to interact with GKE resources are defined in [ksa-setup.yaml](ksa-setup.yaml). + * The underlying Helm chart that deploys these supervisor components is located in [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). -This entire Supervisor system (Sensor, Controller, Actuator, and Host Monitors) is designed as a modular 'Lego block'. While showcased here with NeMo, its components and principles can be adapted for other training frameworks by customizing the interaction points, primarily through the Actuator's remediation scripts and the policies defined in `[values-supervisor.yaml](values-supervisor.yaml)`. +This entire Supervisor system (Sensor, Controller, Actuator, and Host Monitors) is designed as a modular 'Lego block'. While showcased here with NeMo, its components and principles can be adapted for other training frameworks by customizing the interaction points, primarily through the Actuator's remediation scripts and the policies defined in [values-supervisor.yaml](values-supervisor.yaml). #### Using the Supervisor with Your Custom Model This Supervisor system can be integrated with your custom training frameworks or models beyond the LLaMA3-1-70B NeMo example. Here's a general guide: -* **Deployment:** The Supervisor system (Supervisor controllers and Host Monitor DaemonSet) is deployed via its dedicated Helm chart, found at `[src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/)`. -* **Configuration:** Crucially, you'll need to customize the `[values-supervisor.yaml](values-supervisor.yaml)` file. This includes: +* **Deployment:** The Supervisor system (Supervisor controllers and Host Monitor DaemonSet) is deployed via its dedicated Helm chart, found at [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). +* **Configuration:** Crucially, you'll need to customize the [values-supervisor.yaml](values-supervisor.yaml) file. This includes: * Defining your GKE cluster setup (node pools, etc.). * Setting appropriate monitoring parameters like heartbeat intervals, timeouts, and failure detection thresholds ([`heartbeat_polling_period_s`](values-supervisor.yaml), [`heartbeat_timeout_s`](values-supervisor.yaml), [`pod_termination_threshold_s`](values-supervisor.yaml), [`jobset_downtime_threshold_s`](values-supervisor.yaml)) to match your job's behavior. * Specifying the remediation policies and scripts the Actuator should use for events like job restarts, node replacements, or scaling. @@ -113,9 +113,9 @@ By carefully configuring these aspects, you can leverage the Google Cloud Resili * **Host Monitors:** These are deployed as a Kubernetes DaemonSet, ensuring one runs on each GPU worker node (e.g., A3 Mega nodes). * They provide granular, node-level health information and can detect local hardware issues (like GPU errors) more directly. - * They communicate with the central Supervisor, feeding it critical data for decision-making. Configuration details are also present in `[values-supervisor.yaml](values-supervisor.yaml)` (see [`host_daemon` section](values-supervisor.yaml)). + * They communicate with the central Supervisor, feeding it critical data for decision-making. Configuration details are also present in [values-supervisor.yaml](values-supervisor.yaml) (see [`host_daemon` section](values-supervisor.yaml)). -The interaction between these components allows the system to automatically sense disruptions (e.g., using parameters like [`pod_termination_threshold_s`](values-supervisor.yaml) and [`jobset_downtime_threshold_s`](values-supervisor.yaml) from `[values-supervisor.yaml](values-supervisor.yaml)`) and initiate mitigation procedures. The system also supports fault injection ([`enable_fault_injection`](values-supervisor.yaml) in `[values-supervisor.yaml](values-supervisor.yaml)`) for testing resiliency. +The interaction between these components allows the system to automatically sense disruptions (e.g., using parameters like [`pod_termination_threshold_s`](values-supervisor.yaml) and [`jobset_downtime_threshold_s`](values-supervisor.yaml) from [values-supervisor.yaml](values-supervisor.yaml)) and initiate mitigation procedures. The system also supports fault injection ([`enable_fault_injection`](values-supervisor.yaml) in [values-supervisor.yaml](values-supervisor.yaml)) for testing resiliency. ### 2. Remediation Strategies @@ -123,10 +123,10 @@ The Google Cloud Resiliency library, leveraging the NVIDIA Resiliency Extension, * **In-Job Restarts / GPU Reset:** For certain correctable errors (e.g., transient GPU issues identified by lower-level hardware monitoring), the NVIDIA library might enable an in-job restart or a GPU reset to restore functionality. Following such recovery attempts, the Supervisor system orchestrates the restart of the affected training job components (e.g., Kubernetes pods) to ensure they rejoin the training process and resume from the last valid checkpoint. The primary goal from the Supervisor's perspective is to bring the job back to a healthy, training state. * **Node Hot Swap:** This is a core capability of the Supervisor system. When the Sensor (using health-check parameters like [`heartbeat_polling_period_s`](values-supervisor.yaml) and [`heartbeat_timeout_s`](values-supervisor.yaml) from `values-supervisor.yaml`) and the Host Monitors detect an unrecoverable node failure, the Controller evaluates the situation based on its configured policies. If a node replacement is deemed necessary, the Actuator component interacts with GKE to de-allocate the failed node and provision a new one from the available resource pool. The training job, often managed by a higher-level controller like JobSet, subsequently resumes on the reconstituted set of nodes, loading from the latest checkpoint. -* **Scaling Down (and Up):** The target size of the training job is defined by parameters such as [`num_dp_replicas`](values-supervisor.yaml) and [`num_nodes_per_dp`](values-supervisor.yaml) in `[values-supervisor.yaml](values-supervisor.yaml)`. If nodes fail and replacement resources are not immediately available, the Supervisor's Controller can decide to scale down the job to continue training on the remaining healthy nodes. In such scenarios, the Actuator would modify the job specification (e.g., by updating the JobSet resource if Kubernetes JobSet is being used, or by interacting with the specific training framework's scaling mechanisms). The system is designed to scale back up to its target size if new resources become available or previously failed nodes are restored. The Supervisor components facilitating these actions are deployed via a Helm chart, available at `[src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/)`. +* **Scaling Down (and Up):** The target size of the training job is defined by parameters such as [`num_dp_replicas`](values-supervisor.yaml) and [`num_nodes_per_dp`](values-supervisor.yaml) in [values-supervisor.yaml](values-supervisor.yaml). If nodes fail and replacement resources are not immediately available, the Supervisor's Controller can decide to scale down the job to continue training on the remaining healthy nodes. In such scenarios, the Actuator would modify the job specification (e.g., by updating the JobSet resource if Kubernetes JobSet is being used, or by interacting with the specific training framework's scaling mechanisms). The system is designed to scale back up to its target size if new resources become available or previously failed nodes are restored. The Supervisor components facilitating these actions are deployed via a Helm chart, available at [src/helm-charts/resiliency/supervisor-chart/](../../../../src/helm-charts/resiliency/supervisor-chart/). #### Customizing Remediation Logic -While `[values-supervisor.yaml](values-supervisor.yaml)` defines the monitoring parameters (like heartbeats and timeouts) and high-level remediation policies (e.g., whether to attempt a node swap or scale down), the precise commands and mechanisms for interacting with the *specific training application* during remediation are typically implemented within the Actuator component or scripts called by the Actuator. For instance, the exact command to gracefully stop a NeMo pod, instruct MaxText to save an emergency checkpoint, or re-launch a specific training script with an updated list of participating nodes resides in this layer. Users can customize these Actuator scripts or provide their own implementations to integrate the Supervisor system seamlessly with their chosen training framework's operational needs, thus making the resiliency solution highly adaptable. +While [values-supervisor.yaml](values-supervisor.yaml) defines the monitoring parameters (like heartbeats and timeouts) and high-level remediation policies (e.g., whether to attempt a node swap or scale down), the precise commands and mechanisms for interacting with the *specific training application* during remediation are typically implemented within the Actuator component or scripts called by the Actuator. For instance, the exact command to gracefully stop a NeMo pod, instruct MaxText to save an emergency checkpoint, or re-launch a specific training script with an updated list of participating nodes resides in this layer. Users can customize these Actuator scripts or provide their own implementations to integrate the Supervisor system seamlessly with their chosen training framework's operational needs, thus making the resiliency solution highly adaptable. ## Minimizing Downtime: Optimized Checkpointing @@ -150,16 +150,16 @@ These strategies can often be combined. For instance, a large distributed traini To prevent training pauses during checkpoint saves, this recipe leverages asynchronous checkpointing. This means the training process (e.g., GPU computation) can continue while checkpoints are being written to storage in the background. This is typically achieved by first copying the checkpoint data from GPU memory to host CPU memory, which is a fast operation, and then the host CPU handles the slower write to persistent storage. -* This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of `[values.yaml](values.yaml)`: +* This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of [values.yaml](values.yaml): * `--enable-async-ckpt`: Enables the basic asynchronous checkpointing feature. * `--enable-optimized-async-ckpt`: Enables further optimizations for the asynchronous checkpointing mechanism, potentially improving the efficiency of offloading data from GPU HBM to host memory and managing the subsequent save. - * `--ckpt-threads-per-rank=2`: (Example from `[values.yaml](values.yaml)`) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. Users can tune the `--ckpt-threads-per-rank` value; increasing it may improve checkpointing speed if the process is I/O bound and sufficient CPU resources are available, but excessive threads could also lead to contention. Optimal values should be determined through experimentation. + * `--ckpt-threads-per-rank=2`: (Example from [values.yaml](values.yaml)) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. Users can tune the `--ckpt-threads-per-rank` value; increasing it may improve checkpointing speed if the process is I/O bound and sufficient CPU resources are available, but excessive threads could also lead to contention. Optimal values should be determined through experimentation. ### 2. Distributed Checkpointing For large models trained across many GPUs, saving and loading checkpoints can be a bottleneck if handled by a single process or node. Distributed checkpointing, often a feature of the training framework (like PyTorch, which NeMo builds upon), addresses this by parallelizing the save/load operations across multiple workers/nodes. Each rank or a subset of ranks saves its portion of the model state concurrently. -* The `--enable-dist-ckpt` flag in `[values.yaml](values.yaml)` activates this feature. +* The `--enable-dist-ckpt` flag in [values.yaml](values.yaml) activates this feature. * For more details on PyTorch's distributed checkpointing capabilities, refer to the [PyTorch Distributed Documentation](https://pytorch.org/docs/stable/distributed.html) (specific links may vary by PyTorch version, search for "distributed checkpointing" or "state_dict"). ### 3. Multi-Tier Checkpointing Strategy (Leveraging GCS with FUSE) @@ -167,10 +167,10 @@ For large models trained across many GPUs, saving and loading checkpoints can be The blog post describes an ideal multi-tiered approach (local node storage, peer node storage, cloud storage) for balancing speed and resilience. The LLaMA3-1-70B recipe prominently features Google Cloud Storage (GCS) as a robust and scalable tier for durable checkpoint storage, accessed via the [Cloud Storage FUSE CSI driver](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/cloud-storage-fuse-csi-driver). * **GCS for Checkpoints:** - * The `[values-gcs.yaml](values-gcs.yaml)` file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). Users should ensure this GCS bucket is provisioned in the same region as their GKE cluster, has appropriate write/read permissions for the training job's service account, and has Hierarchical Namespace enabled for potentially better performance, as detailed in the main recipe `[README.md](README.md)`. - * The main `[README.md](README.md)` of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). - * The `infrastructure.enable_gcsfuse: true` setting in `[values.yaml](values.yaml)` ensures that GCS FUSE is utilized for the job. - * The underlying Helm chart for GCS FUSE setup can be found in `[src/helm-charts/storage/gcs-fuse/](../../../../src/helm-charts/storage/gcs-fuse/)`. + * The [values-gcs.yaml](values-gcs.yaml) file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). Users should ensure this GCS bucket is provisioned in the same region as their GKE cluster, has appropriate write/read permissions for the training job's service account, and has Hierarchical Namespace enabled for potentially better performance, as detailed in the main recipe [README.md](README.md). + * The main [README.md](README.md) of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). + * The `infrastructure.enable_gcsfuse: true` setting in [values.yaml](values.yaml) ensures that GCS FUSE is utilized for the job. + * The underlying Helm chart for GCS FUSE setup can be found in [src/helm-charts/storage/gcs-fuse/](../../../../src/helm-charts/storage/gcs-fuse/). * **How GCS FUSE Helps:** GCS FUSE allows Kubernetes Pods to mount a GCS bucket as a local filesystem. This simplifies access for training frameworks, as they can read/write checkpoints to what appears to be a local path, while the data is actually persisted to GCS. This is crucial for both saving checkpoints and for restoring them during job recovery. * While this recipe focuses on GCS as the primary persistent checkpointing backend, advanced configurations within NeMo/PyTorch might allow for staging checkpoints on local SSDs before asynchronous upload to GCS, achieving a multi-tier behavior. @@ -178,18 +178,18 @@ The blog post describes an ideal multi-tiered approach (local node storage, peer The optimal frequency for saving checkpoints is a balance: too infrequent, and you risk losing significant work; too frequent, and the overhead (even if async) can become substantial. -* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of `[values.yaml](values.yaml)` allows users to tune this. This value is specified in terms of training steps. The optimal interval is a trade-off: smaller intervals reduce the amount of lost computation in case of a failure but increase the aggregate time spent on checkpointing. Larger intervals minimize checkpointing overhead but risk more lost work. Users should tune this based on their specific job's typical step duration and observed failure rates. -* Other related flags like `--topk-ckpt=-1` (from `[values.yaml](values.yaml)`, meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. A value of `-1` (as shown in the example) means all checkpoints are kept, which can consume considerable storage over long runs. Users should set this to a positive integer to keep only the latest 'k' checkpoints, balancing recovery needs with storage costs. +* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of [values.yaml](values.yaml) allows users to tune this. This value is specified in terms of training steps. The optimal interval is a trade-off: smaller intervals reduce the amount of lost computation in case of a failure but increase the aggregate time spent on checkpointing. Larger intervals minimize checkpointing overhead but risk more lost work. Users should tune this based on their specific job's typical step duration and observed failure rates. +* Other related flags like `--topk-ckpt=-1` (from [values.yaml](values.yaml), meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. A value of `-1` (as shown in the example) means all checkpoints are kept, which can consume considerable storage over long runs. Users should set this to a positive integer to keep only the latest 'k' checkpoints, balancing recovery needs with storage costs. ## Measuring Success: Goodput Analysis Improving GoodPut is an ongoing process, and being able to measure it is critical to understanding the impact of the strategies you implement. The `gpu-recipes` repository provides a utility to help with this analysis. * **Resiliency Metrics Tool:** - * Located in the `[src/utils/resiliency_metrics/](../../../../src/utils/resiliency_metrics/)` directory (relative to the root of the `gpu-recipes` repository), the `[calculator.py](../../../../src/utils/resiliency_metrics/calculator.py)` script is designed to analyze training job logs and calculate various metrics, including the overall GoodPut percentage. - * The main `[README.md](README.md#goodput-analysis-for-the-job)` for the LLaMA3-1-70B recipe includes detailed instructions on how to set up and run this tool (see the [Goodput Analysis for the job](README.md#goodput-analysis-for-the-job) section). Generally, using the tool involves these key steps: - * Navigating to the `[src/utils/resiliency_metrics/](../../../../src/utils/resiliency_metrics/)` directory. - * Creating a Python virtual environment and installing required packages from `[requirements.txt](../../../../src/utils/resiliency_metrics/requirements.txt)`. + * Located in the [src/utils/resiliency_metrics/](../../../../src/utils/resiliency_metrics/) directory (relative to the root of the `gpu-recipes` repository), the [calculator.py](../../../../src/utils/resiliency_metrics/calculator.py) script is designed to analyze training job logs and calculate various metrics, including the overall GoodPut percentage. + * The main [README.md](README.md#goodput-analysis-for-the-job) for the LLaMA3-1-70B recipe includes detailed instructions on how to set up and run this tool (see the [Goodput Analysis for the job](README.md#goodput-analysis-for-the-job) section). Generally, using the tool involves these key steps: + * Navigating to the [src/utils/resiliency_metrics/](../../../../src/utils/resiliency_metrics/) directory. + * Creating a Python virtual environment and installing required packages from [requirements.txt](../../../../src/utils/resiliency_metrics/requirements.txt). * Executing the `python3 calculator.py` script with necessary arguments, such as `--job-name ` (which can be found using `kubectl get jobsets`), and parameters for log lookback periods (e.g., `--gcloud-logging-lookback-days 1`) and reference step times. Using this tool, or similar log analysis techniques, allows you to quantify the benefits of elastic training and optimized checkpointing, identify remaining bottlenecks, and further tune your setup for maximum efficiency. From 629f989561a279cf6139038e4e25ad33bd9492a0 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Tue, 27 May 2025 17:11:28 +0000 Subject: [PATCH 07/10] Refactor GOODPUT_GUIDE.md for clarity and impact This commit significantly refactors the GOODPUT_GUIDE.md to improve its clarity, provide more concrete examples, and enhance its overall impact based on your feedback. Key changes include: - Enhanced Introduction: Added a concrete example with calculations to quantify the cost of wasted GPU hours due to low GoodPut. - Added BadPut Summary Table: Introduced a table at the beginning of the guide, listing common sources of BadPut, their descriptions, and estimated potential GoodPut loss percentages. This provides an upfront overview of challenges. - Restructured "TLDR: Recommended Lego Blocks": Reordered the "Lego Blocks" to prioritize checkpointing strategies, followed by the supervisor system, as checkpointing often offers significant initial GoodPut improvements. Added quantitative justifications (potential GoodPut recovery) to these key blocks. - Streamlined Content: Removed the redundant "Understanding Sources of BadPut (Lost Efficiency)" section, as its content was integrated into the new summary table. - Added Concluding Section: A new section "Tying It All Together: A Holistic Approach" was added to summarize the guide's message and emphasize the synergistic application of the discussed strategies. - Comprehensive Review: Ensured all examples, references, and internal links remain accurate and contextually appropriate after the restructuring. These changes aim to make the guide more actionable and persuasive for you when looking to improve your ML training efficiency. --- .../GOODPUT_GUIDE.md | 73 ++++++------------- 1 file changed, 24 insertions(+), 49 deletions(-) diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md index a5c6680..139e499 100644 --- a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -1,77 +1,49 @@ # Maximizing ML Training Efficiency: A General Guide to Improving GoodPut -Effective utilization of resources in large-scale machine learning (ML) training is crucial for both cost efficiency and rapid model development. A key metric for measuring this efficiency is **ML GoodPut**. As discussed in the Google Cloud blog post, "[Train AI for less: Improve ML Goodput with elastic training and optimized checkpointing](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput)," GoodPut represents the actual productive training time, excluding time lost to various inefficiencies. Even a small percentage improvement in GoodPut can lead to significant cost savings and faster time-to-market for your models. +Effective utilization of resources in large-scale machine learning (ML) training is crucial for both cost efficiency and rapid model development. A key metric for measuring this efficiency is **ML GoodPut**. As discussed in the Google Cloud blog post, "[Train AI for less: Improve ML Goodput with elastic training and optimized checkpointing](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput)," GoodPut represents the actual productive training time, excluding time lost to various inefficiencies. Even a small percentage improvement in GoodPut can lead to significant cost savings and faster time-to-market for your models. For instance, consider a large training job utilizing 1024 GPUs. If this job runs for 30 days, the total available GPU hours are 1024 GPUs * 30 days * 24 hours/day = 737,280 GPU hours. If the GoodPut is only 50%, this means 368,640 GPU hours are wasted due to inefficiencies. Improving GoodPut by just 10% (from 50% to 60%) would reclaim 73,728 GPU hours, potentially saving hundreds of thousands of dollars and accelerating research by weeks. -Achieving high GoodPut can be challenging due to several factors common in large distributed training environments: -* **Frequent Interruptions:** Hardware failures, preemptions, or other system issues can halt training, requiring restarts from the latest checkpoint and wasting valuable compute time. -* **Slow or Inefficient Checkpointing:** The process of saving model checkpoints can itself interrupt training or consume excessive resources if not optimized. -* **Limited Observability and Slow Recovery:** Difficulty in quickly detecting, diagnosing, and remediating failures or stragglers can extend downtime and further reduce GoodPut. +Achieving high GoodPut can be challenging due to several factors common in large distributed training environments. The table below outlines the main sources of BadPut and their potential impact: + +| Source of BadPut | Description/Impact | Potential GoodPut Loss (Example %) | +| :------------------------------------------- | :------------------------------------------------------------------------------------------ | :--------------------------------- | +| **Hardware Failures and System Errors** | Causes crashes, lost progress, time to detect/reprovision/restart. | 5-15% | +| **Preemptions and Evictions** | Similar to hardware failures, results in lost work and restart overhead. | 5-10% | +| **Slow Checkpoint Save and Load Times** | GPUs idle during synchronous saves; slow loads extend downtime. | 3-10% | +| **Suboptimal Checkpoint Frequency** | Too infrequent leads to large work loss; too frequent causes high overhead. | 2-8% | +| **Stragglers and Performance Bottlenecks** | Slower nodes delay the entire job, underutilizing resources. | 3-7% | +| **Lack of Rapid Failure Detection and Diagnosis** | Longer detection/diagnosis time increases downtime. | 2-5% | This guide provides a general overview of techniques and tools to address these common challenges and maximize ML GoodPut. While the principles discussed are broadly applicable, we will use the [LLaMA3-1-70B pretraining recipe](https://github.com/AI-Hypercomputer/gpu-recipes/tree/main/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency) as a concrete case study to illustrate how these components can be implemented and customized for large-scale training workloads on Google Cloud. The goal is to showcase a "DIY" style product, where users can understand and selectively adopt these "Lego blocks" to build resilient and efficient training pipelines. ## TLDR: Recommended Lego Blocks for Your Deployment For customers looking to improve GoodPut on their own ML training workloads, here’s a concise guide to the key strategies discussed in this document, presented as 'Lego blocks' you can implement: -1. **Implement a Robust Supervisor System (Elastic Training):** - * **Why:** This is the foundational step to make your training resilient to hardware failures, preemptions, and other interruptions. It automates detection and recovery. - * **How:** Adapt or implement a supervisor system like the one detailed in the "Elastic Training" section. Focus on failure sensing, policy-based remediation (like node hot-swapping), and ensuring your training job can be controlled externally (start/stop/checkpoint). The Google Cloud Resiliency library components (Sensor, Controller, Actuator, Host Monitors) provide a strong template. - * **Key for your workload:** Ensure your custom training application can gracefully handle external signals for checkpointing and resumption. - -2. **Optimize Checkpointing - Start with Asynchronous Checkpointing:** - * **Why:** Minimize GPU idle time by offloading checkpoint saves to CPU/background processes. This directly boosts GoodPut. +1. **Optimize Checkpointing - Start with Asynchronous Checkpointing:** + * **Why:** Minimize GPU idle time by offloading checkpoint saves to CPU/background processes. This directly boosts GoodPut. This can directly recover a significant portion of GoodPut, potentially 3-10%, by minimizing GPU idle time during saves. * **How:** Enable asynchronous checkpointing features in your training framework (e.g., `--enable-async-ckpt` in NeMo). Ensure you have sufficient CPU and memory resources on host machines for this. * **Key for your workload:** Verify that your checkpointing mechanism can indeed save without halting GPU computation. -3. **Leverage Cloud Storage with FUSE for Checkpoints:** +2. **Leverage Cloud Storage with FUSE for Checkpoints:** * **Why:** Provides durable, accessible, and scalable storage for your checkpoints, crucial for recovery across different nodes or after failures. * **How:** Use a service like Google Cloud Storage (GCS) with the Cloud Storage FUSE CSI driver to mount GCS buckets as local filesystems. Configure your training job to save checkpoints to this mounted path. * **Key for your workload:** Ensure appropriate permissions and regional alignment between your compute and storage. Consider enabling Hierarchical Namespace on GCS buckets for potentially better performance. -4. **Consider Distributed Checkpointing (For Very Large Models/Setups):** +3. **Consider Distributed Checkpointing (For Very Large Models/Setups):** * **Why:** If asynchronous checkpointing is still too slow due to massive model size or a large number of distributed workers, parallelize the checkpoint save/load process itself. * **How:** Utilize distributed checkpointing features within your framework (e.g., `--enable-dist-ckpt` in NeMo/PyTorch). This typically involves each worker saving its shard of the model. * **Key for your workload:** This adds complexity, so evaluate if the benefits outweigh it based on your scale. Often used in conjunction with asynchronous checkpointing. -5. **Tune Checkpoint Frequency:** +4. **Tune Checkpoint Frequency:** * **Why:** Balance the risk of lost work against the overhead of checkpointing. * **How:** Configure how often checkpoints are saved (e.g., based on training steps or time). Monitor your failure rates and checkpoint durations to find an optimal balance. * **Key for your workload:** There's no one-size-fits-all; this needs empirical tuning. -Start with implementing a supervisor system (Step 1) as it provides the core resiliency. Then, optimize your checkpointing process (Steps 2-4), choosing the techniques most relevant to your workload's scale and characteristics. Finally, continuously tune your checkpoint frequency (Step 5) and monitor your GoodPut to measure improvements. - -## Understanding Sources of BadPut (Lost Efficiency) - -To effectively improve GoodPut, it's essential to understand the common culprits that lead to "BadPut" – the wasted time and resources during training. The previously mentioned [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput) highlights several of these. In a case study referenced in the article, involving 1,024 A3 Mega GPU instances, overall ML GoodPut was improved from around 80% to over 90% by addressing these factors. - -Key sources of BadPut include: - -1. **Hardware Failures and System Errors:** - * **Impact:** These can cause sudden job crashes, leading to lost training progress since the last checkpoint. The time taken to detect the failure, reprovision resources (if necessary), and restart the job contributes significantly to BadPut. - * **Example:** A GPU failing, a node becoming unresponsive, or critical system software errors. - -2. **Preemptions and Evictions:** - * **Impact:** In cloud environments or shared clusters, workloads might be preempted or evicted. Similar to hardware failures, this results in lost work and restart overhead. - * **Example:** Spot VMs/preemptible VMs being reclaimed, or higher-priority jobs displacing lower-priority ones. - -3. **Slow Checkpoint Save and Load Times:** - * **Impact:** If saving checkpoints (inline/synchronous) takes a long time, the GPUs are idle, directly reducing GoodPut. Similarly, slow loading of checkpoints after a restart extends downtime. - * **Example:** Saving large model states to slow storage, or inefficient serialization/deserialization of checkpoints. - -4. **Suboptimal Checkpoint Frequency:** - * **Impact:** - * *Too infrequent:* Leads to significant loss of work if a failure occurs late in a checkpoint interval. - * *Too frequent:* The cumulative time spent on checkpointing itself (even if asynchronous) can become a major overhead. - * **Example:** Setting a 4-hour checkpoint interval when failures occur every 2 hours, or checkpointing every 5 minutes with a process that takes 1 minute. - -5. **Stragglers and Performance Bottlenecks:** - * **Impact:** Slower nodes or processes (stragglers) can delay the entire training job, especially in synchronous training paradigms. This leads to underutilization of faster resources. - * **Example:** A single node with a faulty network connection slowing down data loading or gradient synchronization for all other nodes. - -6. **Lack of Rapid Failure Detection and Diagnosis:** - * **Impact:** The longer it takes to identify that a problem has occurred and what the root cause is, the longer the downtime and the greater the BadPut. - * **Example:** A silent error corrupting data without immediate detection, or lack of clear logs making diagnosis time-consuming. +5. **Implement a Robust Supervisor System (Elastic Training):** + * **Why:** This is foundational for resilience, addressing BadPut from hardware failures and preemptions, which can account for 5-15% of lost GoodPut. It automates detection and recovery. + * **How:** Adapt or implement a supervisor system like the one detailed in the "Elastic Training" section. Focus on failure sensing, policy-based remediation (like node hot-swapping), and ensuring your training job can be controlled externally (start/stop/checkpoint). The Google Cloud Resiliency library components (Sensor, Controller, Actuator, Host Monitors) provide a strong template. + * **Key for your workload:** Ensure your custom training application can gracefully handle external signals for checkpointing and resumption. -The blog post further provides a table (via an image link: ![ML GoodPut Contributions](images/goodput_blog_image.jpg)) that details the specific metric improvements and ML GoodPut contributions for different techniques applied in their case study. While the visual data from the image cannot be rendered here, it underscores that a multi-faceted approach targeting these BadPut sources is key to substantial GoodPut gains. +Begin by optimizing your checkpointing process (Steps 1-4), choosing the techniques most relevant to your workload's scale and characteristics, as this often provides the most immediate GoodPut gains. Then, implement a robust supervisor system (Step 5) to build upon this with comprehensive resilience against interruptions. Finally, continuously monitor your GoodPut to measure improvements. ## Addressing Interruptions: Elastic Training @@ -193,3 +165,6 @@ Improving GoodPut is an ongoing process, and being able to measure it is critica * Executing the `python3 calculator.py` script with necessary arguments, such as `--job-name ` (which can be found using `kubectl get jobsets`), and parameters for log lookback periods (e.g., `--gcloud-logging-lookback-days 1`) and reference step times. Using this tool, or similar log analysis techniques, allows you to quantify the benefits of elastic training and optimized checkpointing, identify remaining bottlenecks, and further tune your setup for maximum efficiency. + +## Tying It All Together: A Holistic Approach +Achieving and maintaining high ML GoodPut is an ongoing journey rather than a one-time setup. It demands a holistic strategy that thoughtfully combines resilient infrastructure, efficient training processes, and continuous operational diligence. The 'Lego blocks' detailed in this guide—from robust supervisor systems for elastic training to multifaceted checkpointing optimizations (asynchronous, distributed, and leveraging cloud storage)—are designed to be synergistic. By understanding how these components interact and adapting them to your specific workload and environment, you can build a truly efficient and resilient training pipeline. Remember that the strategies for minimizing BadPut and maximizing GoodPut are not static; continuous measurement, analysis (as discussed in 'Measuring Success: Goodput Analysis'), and refinement are key to unlocking sustained efficiency gains, faster model delivery, and optimized resource utilization in your large-scale ML endeavors. From 825da18953b2dc3cc1527dcfbdea9ea3884177e4 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Tue, 27 May 2025 17:28:00 +0000 Subject: [PATCH 08/10] Docs: Add Table of Contents to GOODPUT_GUIDE.md This commit adds a Table of Contents (TOC) to the `GOODPUT_GUIDE.md` document to improve navigation. The TOC is placed after the initial introductory paragraph and includes links to all major H2 and H3 sections. Output: --- .../GOODPUT_GUIDE.md | 134 ++++++++++-------- 1 file changed, 71 insertions(+), 63 deletions(-) diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md index 139e499..5ef3573 100644 --- a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -2,6 +2,19 @@ Effective utilization of resources in large-scale machine learning (ML) training is crucial for both cost efficiency and rapid model development. A key metric for measuring this efficiency is **ML GoodPut**. As discussed in the Google Cloud blog post, "[Train AI for less: Improve ML Goodput with elastic training and optimized checkpointing](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput)," GoodPut represents the actual productive training time, excluding time lost to various inefficiencies. Even a small percentage improvement in GoodPut can lead to significant cost savings and faster time-to-market for your models. For instance, consider a large training job utilizing 1024 GPUs. If this job runs for 30 days, the total available GPU hours are 1024 GPUs * 30 days * 24 hours/day = 737,280 GPU hours. If the GoodPut is only 50%, this means 368,640 GPU hours are wasted due to inefficiencies. Improving GoodPut by just 10% (from 50% to 60%) would reclaim 73,728 GPU hours, potentially saving hundreds of thousands of dollars and accelerating research by weeks. +## Table of Contents +- [TLDR: Recommended Lego Blocks for Your Deployment](#tldr-recommended-lego-blocks-for-your-deployment) +- [Minimizing Downtime: Optimized Checkpointing](#minimizing-downtime-optimized-checkpointing) + - [1. Asynchronous Checkpointing](#1-asynchronous-checkpointing) + - [2. Multi-Tier Checkpointing Strategy (Leveraging GCS with FUSE)](#2-multi-tier-checkpointing-strategy-leveraging-gcs-with-fuse) + - [3. Distributed Checkpointing](#3-distributed-checkpointing) + - [4. Configurable Checkpoint Frequency](#4-configurable-checkpoint-frequency) +- [Addressing Interruptions: Elastic Training](#addressing-interruptions-elastic-training) + - [1. Failure Sensing and Mitigation: The Supervisor System](#1-failure-sensing-and-mitigation-the-supervisor-system) + - [2. Remediation Strategies](#2-remediation-strategies) +- [Measuring Success: Goodput Analysis](#measuring-success-goodput-analysis) +- [Tying It All Together: A Holistic Approach](#tying-it-all-together-a-holistic-approach) + Achieving high GoodPut can be challenging due to several factors common in large distributed training environments. The table below outlines the main sources of BadPut and their potential impact: | Source of BadPut | Description/Impact | Potential GoodPut Loss (Example %) | @@ -20,31 +33,79 @@ For customers looking to improve GoodPut on their own ML training workloads, her 1. **Optimize Checkpointing - Start with Asynchronous Checkpointing:** * **Why:** Minimize GPU idle time by offloading checkpoint saves to CPU/background processes. This directly boosts GoodPut. This can directly recover a significant portion of GoodPut, potentially 3-10%, by minimizing GPU idle time during saves. - * **How:** Enable asynchronous checkpointing features in your training framework (e.g., `--enable-async-ckpt` in NeMo). Ensure you have sufficient CPU and memory resources on host machines for this. - * **Key for your workload:** Verify that your checkpointing mechanism can indeed save without halting GPU computation. + * **How:** Enable asynchronous checkpointing features in your training framework (e.g., `--enable-async-ckpt` in NeMo). Ensure you have sufficient CPU and memory resources on host machines for this. See [Asynchronous Checkpointing](#1-asynchronous-checkpointing) for details. 2. **Leverage Cloud Storage with FUSE for Checkpoints:** * **Why:** Provides durable, accessible, and scalable storage for your checkpoints, crucial for recovery across different nodes or after failures. - * **How:** Use a service like Google Cloud Storage (GCS) with the Cloud Storage FUSE CSI driver to mount GCS buckets as local filesystems. Configure your training job to save checkpoints to this mounted path. - * **Key for your workload:** Ensure appropriate permissions and regional alignment between your compute and storage. Consider enabling Hierarchical Namespace on GCS buckets for potentially better performance. + * **How:** Use a service like Google Cloud Storage (GCS) with the Cloud Storage FUSE CSI driver to mount GCS buckets as local filesystems. Configure your training job to save checkpoints to this mounted path. More details can be found in [Multi-Tier Checkpointing Strategy (Leveraging GCS with FUSE)](#2-multi-tier-checkpointing-strategy-leveraging-gcs-with-fuse). 3. **Consider Distributed Checkpointing (For Very Large Models/Setups):** * **Why:** If asynchronous checkpointing is still too slow due to massive model size or a large number of distributed workers, parallelize the checkpoint save/load process itself. - * **How:** Utilize distributed checkpointing features within your framework (e.g., `--enable-dist-ckpt` in NeMo/PyTorch). This typically involves each worker saving its shard of the model. - * **Key for your workload:** This adds complexity, so evaluate if the benefits outweigh it based on your scale. Often used in conjunction with asynchronous checkpointing. + * **How:** Utilize distributed checkpointing features within your framework (e.g., `--enable-dist-ckpt` in NeMo/PyTorch). This typically involves each worker saving its shard of the model. Refer to [Distributed Checkpointing](#3-distributed-checkpointing) for more information. 4. **Tune Checkpoint Frequency:** * **Why:** Balance the risk of lost work against the overhead of checkpointing. - * **How:** Configure how often checkpoints are saved (e.g., based on training steps or time). Monitor your failure rates and checkpoint durations to find an optimal balance. - * **Key for your workload:** There's no one-size-fits-all; this needs empirical tuning. + * **How:** Configure how often checkpoints are saved (e.g., based on training steps or time). Monitor your failure rates and checkpoint durations to find an optimal balance. See [Configurable Checkpoint Frequency](#4-configurable-checkpoint-frequency) for guidance. 5. **Implement a Robust Supervisor System (Elastic Training):** * **Why:** This is foundational for resilience, addressing BadPut from hardware failures and preemptions, which can account for 5-15% of lost GoodPut. It automates detection and recovery. - * **How:** Adapt or implement a supervisor system like the one detailed in the "Elastic Training" section. Focus on failure sensing, policy-based remediation (like node hot-swapping), and ensuring your training job can be controlled externally (start/stop/checkpoint). The Google Cloud Resiliency library components (Sensor, Controller, Actuator, Host Monitors) provide a strong template. - * **Key for your workload:** Ensure your custom training application can gracefully handle external signals for checkpointing and resumption. + * **How:** Adapt or implement a supervisor system like the one detailed in the 'Elastic Training' section. Focus on failure sensing, policy-based remediation (like node hot-swapping), and ensuring your training job can be controlled externally (start/stop/checkpoint). The Google Cloud Resiliency library components (Sensor, Controller, Actuator, Host Monitors) provide a strong template. Detailed implementation strategies are discussed in [Addressing Interruptions: Elastic Training](#addressing-interruptions-elastic-training). Begin by optimizing your checkpointing process (Steps 1-4), choosing the techniques most relevant to your workload's scale and characteristics, as this often provides the most immediate GoodPut gains. Then, implement a robust supervisor system (Step 5) to build upon this with comprehensive resilience against interruptions. Finally, continuously monitor your GoodPut to measure improvements. +## Minimizing Downtime: Optimized Checkpointing + +Checkpointing is vital for fault tolerance, allowing training to resume from a saved state. However, the checkpointing process itself can consume valuable time and, if not optimized, reduce GoodPut. The LLaMA3-1-70B recipe, as an example, incorporates several strategies for optimized checkpointing, aligning with principles from the [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput). + +These strategies focus on making checkpointing faster, less intrusive, and more resilient. These strategies—asynchronous operations, distributed saves/loads, and leveraging robust cloud storage via FUSE—are themselves modular 'Lego blocks' that can be adopted independently or combined to enhance the I/O performance and resilience of various training setups, not limited to NeMo or this specific recipe. + +Choosing the right checkpointing strategy, or combination of strategies, is crucial for both minimizing training disruption and ensuring robust recovery. The methods described below—asynchronous, distributed, and multi-tier storage—can be seen as complementary building blocks. Your choice will depend on factors like model size, training scale, and infrastructure characteristics. + +Consider the following when making your decision: + +* **Asynchronous Checkpointing:** This is generally recommended for most training jobs. By offloading the checkpoint save operation to background processes (typically on the CPU), it allows the GPUs to continue training with minimal interruption. This directly improves GoodPut by reducing idle GPU time. It's effective for both single-node and multi-node training. + +* **Distributed Checkpointing:** When training very large models across a significant number of nodes and GPUs, the process of gathering and saving the model state can still be time-consuming, even if asynchronous. Distributed checkpointing parallelizes the save (and load) process itself, where each worker or a subset of workers handles its portion of the model state concurrently. This is often used in conjunction with asynchronous checkpointing to further reduce the critical path of saving checkpoints. + +* **Integration with the Supervisor System:** The Supervisor system (detailed in the "Elastic Training" section) acts as the overall training controller and relies on a robust and efficient checkpointing mechanism to enable automated recovery from hardware failures or preemptions. When the Supervisor restarts a job or a pod, it depends on the training application's ability to quickly load the latest checkpoint. Therefore, selecting fast and reliable checkpointing methods (like asynchronous and distributed, saved to resilient storage like GCS) is key to minimizing downtime when the Supervisor needs to intervene. The goal is a synergistic relationship: checkpointing provides the recovery points, and the Supervisor automates the recovery process. + +These strategies can often be combined. For instance, a large distributed training job would ideally use both distributed checkpointing (to quickly gather state from all workers) and asynchronous checkpointing (to offload the writing to persistent storage without stalling GPUs), all while being monitored by the Supervisor for fault tolerance. + +### 1. Asynchronous Checkpointing + +To prevent training pauses during checkpoint saves, this recipe leverages asynchronous checkpointing. This means the training process (e.g., GPU computation) can continue while checkpoints are being written to storage in the background. This is typically achieved by first copying the checkpoint data from GPU memory to host CPU memory, which is a fast operation, and then the host CPU handles the slower write to persistent storage. + +* This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of [values.yaml](values.yaml): + * `--enable-async-ckpt`: Enables the basic asynchronous checkpointing feature. + * `--enable-optimized-async-ckpt`: Enables further optimizations for the asynchronous checkpointing mechanism, potentially improving the efficiency of offloading data from GPU HBM to host memory and managing the subsequent save. + * `--ckpt-threads-per-rank=2`: (Example from [values.yaml](values.yaml)) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. Users can tune the `--ckpt-threads-per-rank` value; increasing it may improve checkpointing speed if the process is I/O bound and sufficient CPU resources are available, but excessive threads could also lead to contention. Optimal values should be determined through experimentation. + +### 2. Multi-Tier Checkpointing Strategy (Leveraging GCS with FUSE) + +The blog post describes an ideal multi-tiered approach (local node storage, peer node storage, cloud storage) for balancing speed and resilience. The LLaMA3-1-70B recipe prominently features Google Cloud Storage (GCS) as a robust and scalable tier for durable checkpoint storage, accessed via the [Cloud Storage FUSE CSI driver](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/cloud-storage-fuse-csi-driver). + +* **GCS for Checkpoints:** + * The [values-gcs.yaml](values-gcs.yaml) file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). Users should ensure this GCS bucket is provisioned in the same region as their GKE cluster, has appropriate write/read permissions for the training job's service account, and has Hierarchical Namespace enabled for potentially better performance, as detailed in the main recipe [README.md](README.md). + * The main [README.md](README.md) of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). + * The `infrastructure.enable_gcsfuse: true` setting in [values.yaml](values.yaml) ensures that GCS FUSE is utilized for the job. + * The underlying Helm chart for GCS FUSE setup can be found in [src/helm-charts/storage/gcs-fuse/](../../../../src/helm-charts/storage/gcs-fuse/). +* **How GCS FUSE Helps:** GCS FUSE allows Kubernetes Pods to mount a GCS bucket as a local filesystem. This simplifies access for training frameworks, as they can read/write checkpoints to what appears to be a local path, while the data is actually persisted to GCS. This is crucial for both saving checkpoints and for restoring them during job recovery. +* While this recipe focuses on GCS as the primary persistent checkpointing backend, advanced configurations within NeMo/PyTorch might allow for staging checkpoints on local SSDs before asynchronous upload to GCS, achieving a multi-tier behavior. + +### 3. Distributed Checkpointing + +For large models trained across many GPUs, saving and loading checkpoints can be a bottleneck if handled by a single process or node. Distributed checkpointing, often a feature of the training framework (like PyTorch, which NeMo builds upon), addresses this by parallelizing the save/load operations across multiple workers/nodes. Each rank or a subset of ranks saves its portion of the model state concurrently. + +* The `--enable-dist-ckpt` flag in [values.yaml](values.yaml) activates this feature. +* For more details on PyTorch's distributed checkpointing capabilities, refer to the [PyTorch Distributed Documentation](https://pytorch.org/docs/stable/distributed.html) (specific links may vary by PyTorch version, search for "distributed checkpointing" or "state_dict"). + +### 4. Configurable Checkpoint Frequency + +The optimal frequency for saving checkpoints is a balance: too infrequent, and you risk losing significant work; too frequent, and the overhead (even if async) can become substantial. + +* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of [values.yaml](values.yaml) allows users to tune this. This value is specified in terms of training steps. The optimal interval is a trade-off: smaller intervals reduce the amount of lost computation in case of a failure but increase the aggregate time spent on checkpointing. Larger intervals minimize checkpointing overhead but risk more lost work. Users should tune this based on their specific job's typical step duration and observed failure rates. +* Other related flags like `--topk-ckpt=-1` (from [values.yaml](values.yaml), meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. A value of `-1` (as shown in the example) means all checkpoints are kept, which can consume considerable storage over long runs. Users should set this to a positive integer to keep only the latest 'k' checkpoints, balancing recovery needs with storage costs. + ## Addressing Interruptions: Elastic Training Elastic training is a core strategy for improving ML GoodPut by making training workloads resilient to interruptions. Instead of a job failing entirely when an issue occurs, elastic training allows the job to adapt to the changing environment. This could involve recovering from a transient error, transparently moving to different hardware, or adjusting the job size to continue training on available resources. @@ -100,59 +161,6 @@ The Google Cloud Resiliency library, leveraging the NVIDIA Resiliency Extension, #### Customizing Remediation Logic While [values-supervisor.yaml](values-supervisor.yaml) defines the monitoring parameters (like heartbeats and timeouts) and high-level remediation policies (e.g., whether to attempt a node swap or scale down), the precise commands and mechanisms for interacting with the *specific training application* during remediation are typically implemented within the Actuator component or scripts called by the Actuator. For instance, the exact command to gracefully stop a NeMo pod, instruct MaxText to save an emergency checkpoint, or re-launch a specific training script with an updated list of participating nodes resides in this layer. Users can customize these Actuator scripts or provide their own implementations to integrate the Supervisor system seamlessly with their chosen training framework's operational needs, thus making the resiliency solution highly adaptable. -## Minimizing Downtime: Optimized Checkpointing - -Checkpointing is vital for fault tolerance, allowing training to resume from a saved state. However, the checkpointing process itself can consume valuable time and, if not optimized, reduce GoodPut. The LLaMA3-1-70B recipe, as an example, incorporates several strategies for optimized checkpointing, aligning with principles from the [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput). - -These strategies focus on making checkpointing faster, less intrusive, and more resilient. These strategies—asynchronous operations, distributed saves/loads, and leveraging robust cloud storage via FUSE—are themselves modular 'Lego blocks' that can be adopted independently or combined to enhance the I/O performance and resilience of various training setups, not limited to NeMo or this specific recipe. - -Choosing the right checkpointing strategy, or combination of strategies, is crucial for both minimizing training disruption and ensuring robust recovery. The methods described below—asynchronous, distributed, and multi-tier storage—can be seen as complementary building blocks. Your choice will depend on factors like model size, training scale, and infrastructure characteristics. - -Consider the following when making your decision: - -* **Asynchronous Checkpointing:** This is generally recommended for most training jobs. By offloading the checkpoint save operation to background processes (typically on the CPU), it allows the GPUs to continue training with minimal interruption. This directly improves GoodPut by reducing idle GPU time. It's effective for both single-node and multi-node training. - -* **Distributed Checkpointing:** When training very large models across a significant number of nodes and GPUs, the process of gathering and saving the model state can still be time-consuming, even if asynchronous. Distributed checkpointing parallelizes the save (and load) process itself, where each worker or a subset of workers handles its portion of the model state concurrently. This is often used in conjunction with asynchronous checkpointing to further reduce the critical path of saving checkpoints. - -* **Integration with the Supervisor System:** The Supervisor system (detailed in the "Elastic Training" section) acts as the overall training controller and relies on a robust and efficient checkpointing mechanism to enable automated recovery from hardware failures or preemptions. When the Supervisor restarts a job or a pod, it depends on the training application's ability to quickly load the latest checkpoint. Therefore, selecting fast and reliable checkpointing methods (like asynchronous and distributed, saved to resilient storage like GCS) is key to minimizing downtime when the Supervisor needs to intervene. The goal is a synergistic relationship: checkpointing provides the recovery points, and the Supervisor automates the recovery process. - -These strategies can often be combined. For instance, a large distributed training job would ideally use both distributed checkpointing (to quickly gather state from all workers) and asynchronous checkpointing (to offload the writing to persistent storage without stalling GPUs), all while being monitored by the Supervisor for fault tolerance. - -### 1. Asynchronous Checkpointing - -To prevent training pauses during checkpoint saves, this recipe leverages asynchronous checkpointing. This means the training process (e.g., GPU computation) can continue while checkpoints are being written to storage in the background. This is typically achieved by first copying the checkpoint data from GPU memory to host CPU memory, which is a fast operation, and then the host CPU handles the slower write to persistent storage. - -* This capability is enabled in the NeMo framework (used in the LLaMA3-1-70B recipe) via flags in the main `workload.flags` section of [values.yaml](values.yaml): - * `--enable-async-ckpt`: Enables the basic asynchronous checkpointing feature. - * `--enable-optimized-async-ckpt`: Enables further optimizations for the asynchronous checkpointing mechanism, potentially improving the efficiency of offloading data from GPU HBM to host memory and managing the subsequent save. - * `--ckpt-threads-per-rank=2`: (Example from [values.yaml](values.yaml)) Configures the number of threads per rank dedicated to checkpointing operations, which can help parallelize and speed up the process. Users can tune the `--ckpt-threads-per-rank` value; increasing it may improve checkpointing speed if the process is I/O bound and sufficient CPU resources are available, but excessive threads could also lead to contention. Optimal values should be determined through experimentation. - -### 2. Distributed Checkpointing - -For large models trained across many GPUs, saving and loading checkpoints can be a bottleneck if handled by a single process or node. Distributed checkpointing, often a feature of the training framework (like PyTorch, which NeMo builds upon), addresses this by parallelizing the save/load operations across multiple workers/nodes. Each rank or a subset of ranks saves its portion of the model state concurrently. - -* The `--enable-dist-ckpt` flag in [values.yaml](values.yaml) activates this feature. -* For more details on PyTorch's distributed checkpointing capabilities, refer to the [PyTorch Distributed Documentation](https://pytorch.org/docs/stable/distributed.html) (specific links may vary by PyTorch version, search for "distributed checkpointing" or "state_dict"). - -### 3. Multi-Tier Checkpointing Strategy (Leveraging GCS with FUSE) - -The blog post describes an ideal multi-tiered approach (local node storage, peer node storage, cloud storage) for balancing speed and resilience. The LLaMA3-1-70B recipe prominently features Google Cloud Storage (GCS) as a robust and scalable tier for durable checkpoint storage, accessed via the [Cloud Storage FUSE CSI driver](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/cloud-storage-fuse-csi-driver). - -* **GCS for Checkpoints:** - * The [values-gcs.yaml](values-gcs.yaml) file defines the GCS bucket to be used (e.g., `gcs-checkpoints`). Users should ensure this GCS bucket is provisioned in the same region as their GKE cluster, has appropriate write/read permissions for the training job's service account, and has Hierarchical Namespace enabled for potentially better performance, as detailed in the main recipe [README.md](README.md). - * The main [README.md](README.md) of the recipe details setting up the GCS bucket (Hierarchical Namespace recommended) and configuring access via a Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC). - * The `infrastructure.enable_gcsfuse: true` setting in [values.yaml](values.yaml) ensures that GCS FUSE is utilized for the job. - * The underlying Helm chart for GCS FUSE setup can be found in [src/helm-charts/storage/gcs-fuse/](../../../../src/helm-charts/storage/gcs-fuse/). -* **How GCS FUSE Helps:** GCS FUSE allows Kubernetes Pods to mount a GCS bucket as a local filesystem. This simplifies access for training frameworks, as they can read/write checkpoints to what appears to be a local path, while the data is actually persisted to GCS. This is crucial for both saving checkpoints and for restoring them during job recovery. -* While this recipe focuses on GCS as the primary persistent checkpointing backend, advanced configurations within NeMo/PyTorch might allow for staging checkpoints on local SSDs before asynchronous upload to GCS, achieving a multi-tier behavior. - -### 4. Configurable Checkpoint Frequency - -The optimal frequency for saving checkpoints is a balance: too infrequent, and you risk losing significant work; too frequent, and the overhead (even if async) can become substantial. - -* The `--checkpoint-interval=25` (by default, measured in training steps) in the `workload.flags` section of [values.yaml](values.yaml) allows users to tune this. This value is specified in terms of training steps. The optimal interval is a trade-off: smaller intervals reduce the amount of lost computation in case of a failure but increase the aggregate time spent on checkpointing. Larger intervals minimize checkpointing overhead but risk more lost work. Users should tune this based on their specific job's typical step duration and observed failure rates. -* Other related flags like `--topk-ckpt=-1` (from [values.yaml](values.yaml), meaning keep all checkpoints in this case) also play a role in the checkpointing strategy. A value of `-1` (as shown in the example) means all checkpoints are kept, which can consume considerable storage over long runs. Users should set this to a positive integer to keep only the latest 'k' checkpoints, balancing recovery needs with storage costs. - ## Measuring Success: Goodput Analysis Improving GoodPut is an ongoing process, and being able to measure it is critical to understanding the impact of the strategies you implement. The `gpu-recipes` repository provides a utility to help with this analysis. From 9149a5f1900c683b4e08da3dfd20c21d12437891 Mon Sep 17 00:00:00 2001 From: Viacheslav Kovalevskyi Date: Tue, 27 May 2025 10:39:23 -0700 Subject: [PATCH 09/10] Update GOODPUT_GUIDE.md --- .../nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md index 5ef3573..fe2d678 100644 --- a/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md +++ b/training/a3mega/llama3-1-70b/nemo-pretraining-gke-resiliency/GOODPUT_GUIDE.md @@ -57,8 +57,6 @@ Begin by optimizing your checkpointing process (Steps 1-4), choosing the techniq Checkpointing is vital for fault tolerance, allowing training to resume from a saved state. However, the checkpointing process itself can consume valuable time and, if not optimized, reduce GoodPut. The LLaMA3-1-70B recipe, as an example, incorporates several strategies for optimized checkpointing, aligning with principles from the [Google Cloud blog post](https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput). -These strategies focus on making checkpointing faster, less intrusive, and more resilient. These strategies—asynchronous operations, distributed saves/loads, and leveraging robust cloud storage via FUSE—are themselves modular 'Lego blocks' that can be adopted independently or combined to enhance the I/O performance and resilience of various training setups, not limited to NeMo or this specific recipe. - Choosing the right checkpointing strategy, or combination of strategies, is crucial for both minimizing training disruption and ensuring robust recovery. The methods described below—asynchronous, distributed, and multi-tier storage—can be seen as complementary building blocks. Your choice will depend on factors like model size, training scale, and infrastructure characteristics. Consider the following when making your decision: @@ -173,6 +171,3 @@ Improving GoodPut is an ongoing process, and being able to measure it is critica * Executing the `python3 calculator.py` script with necessary arguments, such as `--job-name ` (which can be found using `kubectl get jobsets`), and parameters for log lookback periods (e.g., `--gcloud-logging-lookback-days 1`) and reference step times. Using this tool, or similar log analysis techniques, allows you to quantify the benefits of elastic training and optimized checkpointing, identify remaining bottlenecks, and further tune your setup for maximum efficiency. - -## Tying It All Together: A Holistic Approach -Achieving and maintaining high ML GoodPut is an ongoing journey rather than a one-time setup. It demands a holistic strategy that thoughtfully combines resilient infrastructure, efficient training processes, and continuous operational diligence. The 'Lego blocks' detailed in this guide—from robust supervisor systems for elastic training to multifaceted checkpointing optimizations (asynchronous, distributed, and leveraging cloud storage)—are designed to be synergistic. By understanding how these components interact and adapting them to your specific workload and environment, you can build a truly efficient and resilient training pipeline. Remember that the strategies for minimizing BadPut and maximizing GoodPut are not static; continuous measurement, analysis (as discussed in 'Measuring Success: Goodput Analysis'), and refinement are key to unlocking sustained efficiency gains, faster model delivery, and optimized resource utilization in your large-scale ML endeavors. From 289a263229f6a973297a1394ebbc3d34d3c09f1f Mon Sep 17 00:00:00 2001 From: Slava Kovalevskyi Date: Tue, 27 May 2025 10:43:50 -0700 Subject: [PATCH 10/10] Revert "adding good put lib code" This reverts commit 793436503da6cb83ec30d9cfa0b1fa85aac1db2a. --- ml-goodput-measurement/CHANGELOG.md | 96 - ml-goodput-measurement/CONTRIBUTING.md | 33 - ml-goodput-measurement/LICENSE | 201 -- ml-goodput-measurement/README.md | 697 ------ .../ml_goodput_measurement/__init__.py | 20 - .../src/checkpoint_badput_calculator.py | 676 ------ .../ml_goodput_measurement/src/gcp_metrics.py | 106 - .../ml_goodput_measurement/src/goodput.py | 1690 ------------- .../src/goodput_cache.py | 119 - .../src/goodput_utils.py | 258 -- .../ml_goodput_measurement/src/monitoring.py | 638 ----- .../checkpoint_badput_calculator_test.py | 446 ---- .../tests/gcp_metrics_test.py | 150 -- .../tests/goodput_cache_test.py | 141 -- .../tests/goodput_test.py | 2102 ----------------- .../tests/monitoring_test.py | 794 ------- ml-goodput-measurement/pyproject.toml | 59 - 17 files changed, 8226 deletions(-) delete mode 100644 ml-goodput-measurement/CHANGELOG.md delete mode 100644 ml-goodput-measurement/CONTRIBUTING.md delete mode 100644 ml-goodput-measurement/LICENSE delete mode 100644 ml-goodput-measurement/README.md delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/__init__.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/goodput.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py delete mode 100644 ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py delete mode 100644 ml-goodput-measurement/pyproject.toml diff --git a/ml-goodput-measurement/CHANGELOG.md b/ml-goodput-measurement/CHANGELOG.md deleted file mode 100644 index 79a8b95..0000000 --- a/ml-goodput-measurement/CHANGELOG.md +++ /dev/null @@ -1,96 +0,0 @@ -# Changelog - - -## [0.0.10] - 2025-04-28 - -* Support for custom badput events which are synchronous and training-overlapped. -* Handling of edge case caching scenario. - -## [0.0.9] - SKIPPED - -* Used for external testing. Please upgrade to 0.0.10. - -## [0.0.8] - 2025-04-03 - -* Fix computation of ideal step time when step_times is empty. - -## [0.0.7] - 2025-03-24 - -* Cache updates to Other/Unknown Badput. -* Exclude monitoring asynchronous Badput types in GCM. -* Total and last step updates with hidden events. -* Interval Query Monitoring in GCM. - -## [0.0.6] - 2025-03-17 - -* Updates to data loading Badput buckets (Separated into Async & Sync). -* Short term fix to Pathways SuspendResume anomalous step time detection. -* Updates to account for Pathways Elastic Training. -* Automatic asynchronous upload of goodput, badput and step time deviation metrics to GCM. - -## [0.0.5] - 2025-02-03 - -* Goodput Cache and library improvements. -* Query and Monitor API support for checkpoint save and restore. -* Interval Query API support. -* Query and Monitor API support for step time deviation. - -## [0.0.4] - 2024-09-13 - -* Add Badput breakdown to GoodputMonitor. -* Add Checkpoint Badput Calculator backend. -* Return last recorded step from Goodput query API. -* Bug Fixes - * Fix a potential race-condition with Tensorboard write to GCS. - * Fix zero job time issue on long running jobs - -## [0.0.3] - 2024-05-28 - -* Compute and discount Badput from first step after start or restart. -* Compute and discount Badput due to anomalous step times (Pathways only). -* Badput recording APIs -* Some Badput computation APIs (TPU initialization , training preparation, data loading, program startup) -* Goodput monitoring API to asynchronously query and upload Goodput to Tensorboard. -* Bug Fixes - * Fix Goodput calculation with disruptions - * Fix some Cloud Logging latency and batching issues. - -## [0.0.2] - 2024-02-29 - -* Bug Fixes - * Fixes a typing mismatch in total step time calculation. -* Code and documentation cleanup - -## [0.0.1] - 2024-02-26 - -* Initial release of ML Goodput Measurement PyPi package -* Feature: Contains the Goodput module which allows logging and retrieval of training job's overall productive Goodput - -[0.0.10]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.8...v0.0.10 -[0.0.8]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.7...v0.0.8 -[0.0.7]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.6...v0.0.7 -[0.0.6]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.5...v0.0.6 -[0.0.5]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.4...v0.0.5 -[0.0.4]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.3...v0.0.4 -[0.0.3]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.2...v0.0.3 -[0.0.2]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/compare/v0.0.1...v0.0.2 -[0.0.1]: https://github.com/AI-Hypercomputer/ml-goodput-measurement/releases/tag/v0.0.1 \ No newline at end of file diff --git a/ml-goodput-measurement/CONTRIBUTING.md b/ml-goodput-measurement/CONTRIBUTING.md deleted file mode 100644 index bc23aae..0000000 --- a/ml-goodput-measurement/CONTRIBUTING.md +++ /dev/null @@ -1,33 +0,0 @@ -# How to contribute - -We'd love to accept your patches and contributions to this project. - -## Before you begin - -### Sign our Contributor License Agreement - -Contributions to this project must be accompanied by a -[Contributor License Agreement](https://cla.developers.google.com/about) (CLA). -You (or your employer) retain the copyright to your contribution; this simply -gives us permission to use and redistribute your contributions as part of the -project. - -If you or your current employer have already signed the Google CLA (even if it -was for a different project), you probably don't need to do it again. - -Visit to see your current agreements or to -sign a new one. - -### Review our community guidelines - -This project follows -[Google's Open Source Community Guidelines](https://opensource.google/conduct/). - -## Contribution process - -### Code reviews - -All submissions, including submissions by project members, require review. 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It - can be pip installed to import its modules, and retrieve information about a - training job's overall productive Goodput and sources of Badput. The package - exposes API interfaces to log useful information from the user application and - query Goodput for the job run, gain insight into the productivity of ML - workloads and utilization of compute resources. - - The package also exposes Goodput Monitoring APIs which allow asynchronous query - and export of the job's Goodput, Badput and Step Time Deviation to Tensorboard - with configurable upload interval. - -## Components - - - The ML Goodput Measurement library consists of the following main components: - - - `GoodputRecorder` - - - `GoodputCalculator` - - `GoodputMonitor` - - `GoodputCache` - - - The `GoodputRecorder` - exposes APIs to the client to export key timestamps while a training job makes - progress, namely APIs that allow logging of productive step time and total job - run time. The library will serialize and store this data in Google Cloud - Logging. - - The `GoodputCalculator` exposes APIs to compute Goodput based on the - recorded data. Cloud Logging handles its internal operations asynchronously. - The recommended way to compute Goodput is to run an analysis program separate - from the training application, either on a CPU instance or on the users' - development machine. - - Under the hood, the `GoodputCalculator` uses a `GoodputCache` which is an - internal component that locally caches pre-computations and useful logs such - that repeated computations can be made inexpensive. - - The `GoodputMonitor` exposes APIs to query and upload goodput and step time - deviation data to Tensorboard asynchronously. It does this by instantiating a - `GoodputCaluclator` under the hood. - -## Installation - - To install the ML Goodput Measurement package, run the following command on the - VM or machine you want to query or monitor your workload from: - - ```bash - pip install ml-goodput-measurement - ``` - -## Usage - -The usage of this package requires the setup of a Google Cloud project with -billing enabled to properly use Google Cloud Logging. If you don't have a Google -Cloud project, or if you don't have billing enabled for your Google Cloud -project, then do the following: - -1. In the Google Cloud console, on the project selector page, - [select or create a Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects). - -2. Make sure that billing is enabled for your Google Cloud project. Instructions can be found [here](https://cloud.google.com/billing/docs/how-to/verify-billing-enabled#console) - -3. [Enable](https://console.cloud.google.com/flows/enableapi?apiid=logging.googleapis.com&_ga=2.27841276.1571868865.1726250448-123998259.1726107009) the Cloud Logging API. - - To run your training on Cloud accelerator, set up the environment by following - instructions [here](https://cloud.google.com/tpu/docs/setup-gcp-account). - - To learn more about Google Cloud Logging, visit this [page](https://cloud.google.com/logging/docs). - -### Access Scopes - - You will need both read and write access scopes for cloud logging on both the - GPU or TPU and CPU node pools. Full cloud logging access is granted by the - following access scope during node pool creation: - - - `https://www.googleapis.com/auth/cloud-platform` - - XPK adds this access scope to the GPU, TPU and CPU node pools, so XPK is the - recommended method to create clusters and node-pools in you intend to run - your workloads on GKE. - - Instructions on how to create clusters using XPK can be - found [here](https://github.com/AI-Hypercomputer/xpk/blob/main/README.md#cluster-create) - and how to create workloads using XPK can be found - [here](https://github.com/AI-Hypercomputer/xpk/blob/main/README.md#workload-create). - - > **_NOTE:_** Access Scopes are immutable and workloads can only be migrated - to new node pools with required access scopes. Access scopes on already created - clusters cannot be updated. - -### Import - - To use this package, import the `goodput` module: - - ```python - from ml_goodput_measurement import goodput - from ml_goodput_measurement import monitoring - ``` - -### Define the name of the Google Cloud Logging logger. - - Create a run-specific logger name where Cloud Logging entries can be written to - and read from. - - > **IMPORTANT:** Please use a unique `run_name` for each individual experiment - or workload that you intend to monitor separately. If you unintentionally re-use - `run_name` or `goodput_logger_name` in the same storage bucket of a GCP project, - your cumulative Goodput metrics may be inaccurately taking previous runs into - account. - - For example: - - ```python - goodput_logger_name = f'goodput_{config.run_name}' # Here run_name is unique. - ``` - -### Create a `GoodputRecorder` object - - Next, create a recorder object with the following parameters: - - 1. `job_name`: The full run name of the job. - 2. `logger_name`: The name of the Cloud Logging logger object (created in the previous step). - 3. `logging_enabled`: Whether or not this process has Cloud Logging enabled. - - > **_NOTE:_** For a multi-worker setup, please ensure that only one worker - writes the logs to avoid the duplication. In JAX, for example, the check - could be `if jax.process_index() == 0` - - > **_NOTE:_** `logging_enabled` defaults to `False` and Goodput computations - cannot be completed if no logs are ever written. - - For example: - - ```python - goodput_recorder = goodput.GoodputRecorder( - job_name=config.run_name, - logger_name=goodput_logger_name, - logging_enabled=(jax.process_index() == 0) - ) - ``` - - > **_NOTE:_** JAX initialization should be complete before this call. - -### Record Data with `GoodputRecorder` - -#### Record Job Start and End Time - - Use the recorder object to record the job's overall start and end time. - - For example: - - ```python - def main(argv: Sequence[str]) -> None: - # Initialize configs… - goodput_recorder.record_job_start_time(datetime.datetime.now()) - # Device Initialization and device scanning… - # Set up other things for the main training loop… - # Main training loop - train_loop(config) - goodput_recorder.record_job_end_time(datetime.datetime.now()) - ``` - -#### Record Step Time - - Use the recorder object to record a step's start time using - `record_step_start_time(step_count)`: - -For example: - - ```python - def train_loop(config, state=None): - # Set up mesh, model, state, checkpoint manager… - - # Initialize functional train arguments and model parameters… - - # Define the compilation - - for step in np.arange(start_step, config.steps): - goodput_recorder.record_step_start_time(step) - # Training step… - - return state - ``` - -#### Record Device Initialization, Training Preparation and Data Loading Time - - - Use the recorder object to record Device Initialization time using - `record_tpu_init_start_time` and `record_tpu_init_end_time`. - - Use the recorder object to record Training Preparation time using - `record_training_preparation_start_time` and - `record_training_preparation_end_time`. - - Use the recorder object to record Data Loading time using - `record_data_loading_start_time` and `record_data_loading_end_time`. - - For example: - - ```python - def train_loop(config, state=None): - goodput_recorder.record_tpu_init_start_time() - # Set up mesh, model, state, checkpoint manager… - goodput_recorder.record_tpu_init_end_time() - goodput_recorder.record_training_preparation_start_time() - # Set up training set, initialize functional train args and model parameters… - # Define the compilation - # Set up any metrics collectors - goodput_recorder.record_training_preparation_end_time() - - for step in np.arange(start_step, config.steps): - goodput_recorder.record_data_loading_start_time() - example_batch = load_next_batch(data_iterator, example_batch, config) - goodput_recorder.record_data_loading_end_time() - goodput_recorder.record_step_start_time(step) - # Training step… - - return state - ``` - -#### Record Custom Badput Events (e.g., Evaluation, SDC Checks) - -- Use the recorder object to record the **start** of a custom badput event using - `record_custom_badput_event_start_time(custom_badput_event_type='your_event_name')`. -- Use the recorder object to record the **end** of a custom badput event using - `record_custom_badput_event_end_time(custom_badput_event_type='your_event_name')`. - -Use these APIs when you want to account for time spent on operations that -block the training loop and use accelerator resources, do not contribute to -productive training and occur while training is in progress — such as step -evaluations, SDC checks, or re-compilations. - -For example: - -```python -def train_loop(config, state=None): - goodput_recorder.record_training_preparation_start_time() - # Initialize training config, setup model, load checkpoint... - goodput_recorder.record_training_preparation_end_time() - - for step in range(config.steps): - goodput_recorder.record_data_loading_start_time() - batch = load_batch(train_data) - goodput_recorder.record_data_loading_end_time() - - goodput_recorder.record_step_start_time(step) - # Run training step... - run_train_step(step, state) - - if step % config.eval_interval == 0: - # Record a custom badput event for evaluation - goodput_recorder.record_custom_badput_event_start_time( - custom_badput_event_type="eval_step") - run_step_evaluation(model, val_data) - goodput_recorder.record_custom_badput_event_end_time( - custom_badput_event_type="eval_step") - - if step % config.sdc_check_interval == 0: - # Record a custom badput event for SDC check - goodput_recorder.record_custom_badput_event_start_time( - custom_badput_event_type="sdc_check") - run_sdc_check(state) - goodput_recorder.record_custom_badput_event_end_time( - custom_badput_event_type="sdc_check") - - return state -``` - -> **_NOTE:_** The `custom_badput_event_type` string should be descriptive and -consistent (e.g., "eval_step", "sdc_check"), to ensure accurate aggregation and -reporting in badput breakdowns. - -### Retrieve Goodput with `GoodputCalculator` - -In order to retrieve the Goodput of a job run, all you need to do is instantiate -a `GoodputCalculator` object with the job's run name and the Cloud Logging -logger name used to record data for that job run. Then call the -`get_job_goodput` API to get the computed Goodput for the job run. - -It is recommended to make the `get_job_goodput` calls for a job run from an -instance that runs elsewhere from your training machine. - -#### Create a `GoodputCalculator` object - -Create the calculator object: - -```python -goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. -goodput_calculator = goodput.GoodputCalculator(job_name=config.run_name, logger_name=goodput_logger_name) -``` - -If you want to enable Pathways, turn on the `using_pathways` flag: - -```python -goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. -goodput_calculator = goodput.GoodputCalculator(job_name=config.run_name, logger_name=goodput_logger_name, using_pathways=True) -``` - -#### Retrieve Goodput - -Finally, call the `get_job_goodput` API to retrieve Goodput for the entire job -run. This API takes an optional parameter `include_badput_breakdown`. which -defaults to `False`. - -The returned result is a tuple of the job’s Goodput at query-time, a dictionary -mapping various sources of Badput and their corresponding percentages and the -last recorded step. If `include_badput_breakdown` is not set, an empty -dictionary for Badput is returned. - -If you are only interested in Goodput: - -```python -total_goodput, _, _ = goodput_calculator.get_job_goodput() -print(f"Total job goodput: {total_goodput:.2f}%") -``` - -#### Retrieve Badput Breakdown - -Badput breakdown is dictionary representation of various sources of Badput -mapped to its corresponding value. Badput is the percentage of time spent by the -job doing work that is not training to the total lifetime of the job. This -includes time spent doing device initialization, training preparation, -program startup, checkpoint loading, compilation or re-compilation, data loading, -checkpoint saving, custom badput events, wasted progress and time lost due -to disruptions. - -Following Badput Breakdown buckets are supported by the library at this time: - -```python -# Supported Badput Types -class BadputType(enum.Enum): - """The type of Badput.""" - - TPU_INITIALIZATION = 1 - TRAINING_PREP = 2 - PROGRAM_STARTUP = 3 - DATA_LOADING_SYNC = 4 - DATA_LOADING_ASYNC = 5 # This does not affect Goodput - UNPRODUCTIVE_CHECKPOINT_SAVE_TIME = 6 - UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME = 7 - WASTED_PROGRESS_FROM_DISRUPTION = 8 - CUSTOM_BADPUT_EVENTS = 9 - OTHER = 10 -``` - -#### Badput Breakdown Details - - - Accelerator Initialization Time (TPU_INITIALIZATION) - - This is the time spent on device discovery, slice initialization, - device driver re-initialization and reset, security setup, initialization of - pre-mapped buffers and more. - - - Training Preparation Time (TRAINING_PREP) - - This is the time spent on the creation of checkpoint managers, checkpoint - loading, running mesh and model optimizers and more. - - - Program Startup Time (PROGRAM_STARTUP) - - This is the time spent on framework specific function transformations - (such as JAX tracing), compilation tasks, runtime initialization etc. - - - Data Loading Time (DATA_LOADING_SYNC) - - This is the time spent on loading each batch of data for the training at a - step to continue. This should be a small contribution to Badput if parallel - data loading is used. - - Async data loading is accumulated overlapping with training steps and is - non-blocking, therefore is not unproductive time. The time spent on overlapped - data loading is stored in BadputType.DATA_LOADING_ASYNC, but does **not** - affect overall Goodput of the workload. - - - Checkpointing Time (UNPRODUCTIVE_CHECKPOINT_SAVE_TIME, UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME) - - This is the time spent on saving a checkpoint and restoring a checkpoint. - - Depending on the type of checkpointing technology used by the program, there - could be unproductive time while saving a checkpoint. When checkpointing is - synchronous, the save operation will block training progress until it is complete. - - During asynchronous checkpointing, the model parameters or weights have to be - transferred from the device memory to the host memory which is a blocking - operation on the device. After the transfer, the device can proceed with model - training while the CPU saves the checkpoint to storage in the background. The - first blocking operation contributes to unproductive checkpoint time. - - If auto checkpointing is used, the checkpoint save operation is initiated upon - detection of a planned disruption signal. The save operation in type of - checkpointing is synchronous resulting in time lost to Badput. - - - Wasted Progress due to Disruption (WASTED_PROGRESS_FROM_DISRUPTION) - - Based on checkpointing frequency, a disruption may result in time lost in the - form of wasted progress, i.e. time that was spent on productive training but - lost after restart as well as time lost for the infrastructure to restart the - workload. - - When there is a disruption, Badput is expected to accumulate in - each of the following buckets after restart: - - - Accelerator Initialization - - Training Preparation - - Program Startup - - Wasted Progress due to Disruption - - - Custom Badput Events (CUSTOM_BADPUT_EVENTS) - - Your application can optionally use record and monitor badput from custom - synchronous (blocking training) and overlapping (between training steps) - events. These events are are generally used for useful non-training activity on - the accelerator while training is in progress such as performing SDC checks - or evaluations. - -If you are interested in retrieving Badput Breakdown along with Goodput: - -```python -goodput, badput_breakdown, last_step = goodput_calculator.get_job_goodput(include_badput_breakdown=True) -print(f"Last step recorded: {last_step}") -print(f"Goodput: {goodput:.2f}%") -print(f"Badput due to TPU initialization: {badput_breakdown[goodput.BadputType.TPU_INITIALIZATION]:.2f}%") -print(f"Badput due to training preparation: {badput_breakdown[goodput.BadputType.TRAINING_PREP]:.2f}%") -print(f"Badput due to program startup: {badput_breakdown[goodput.BadputType.PROGRAM_STARTUP]:.2f}%") -print(f"Badput due to data loading: {badput_breakdown[goodput.BadputType.DATA_LOADING_SYNC]:.2f}%") -print(f"Badput due to disruption and wasted progress: {badput_breakdown[goodput.BadputType.WASTED_PROGRESS_FROM_DISRUPTION]:.2f}%") -print(f"Badput due to checkpoint save: {badput_breakdown[goodput.BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME]:.2f}%") -print(f"Badput due to checkpoint restore: {badput_breakdown[goodput.BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME]:.2f}%") -print(f"Badput due to step evaluation: {badput_breakdown[goodput.BadputType.CUSTOM_BADPUT_EVENTS].get('EVAL_STEP', 0.0):.2f}%") -print(f"Badput due to SDC checks: {badput_breakdown[goodput.BadputType.CUSTOM_BADPUT_EVENTS].get('SDC_CHECK', 0.0):.2f}%") -print(f"Badput from unknown source: {badput_breakdown[goodput.BadputType.OTHER]:.2f}%") -``` - -#### Interval Query Goodput and Badput - -If you are interested in retrieving Goodput and Badput of the workload within a -specific window of time, the `GoodputCalculator` exposes the -`get_job_goodput_interval` API which computes metrics between the start and end -of this window. - -This API also returns the last step recorded for the job. the total job time in -this window and the number of disruptions within the interval window. - -> **_IMPORTANT:_** **Use this API if** you know the exact window of time within - the workload's total run time that you are interested in. - -> **_IMPORTANT:_** **Do NOT use this API if** your workload has been manually - disrupted. - -> **_IMPORTANT:_** **Do NOT use this API if** you have accidentally re-used a - previous `run_name`. - -```python -# Example usage -start_time_str = "2024-12-16 1:05:00" -start_time_utc = convert_pst_to_utc(start_time_str) -end_time_str = "2024-12-17 2:00:00" -end_time_utc = convert_pst_to_utc(end_time_str) -current_goodput, badput_breakdown, last_step, total_time, disruptions = goodput_calculator.get_job_goodput_interval(start_time_utc, end_time_utc) -``` - -### Monitor Goodput with `GoodputMonitor` - -In order to monitor the Goodput of a job run on Tensorboard, all you need to do -is instantiate a `GoodputMonitor` object with the job's run name, cloud logger -name and Goodput monitoring configurations (as described below). Then call the -`start_goodput_uploader` API to asynchronously query and upload measured Goodput -to the specified Tensorboard directory. - -#### Create a `GoodputMonitor` object - -Create a `GoodputMonitor` object with the following parameters: - - 1. `job_name`: The full run name of the job. - 2. `logger_name`: The name of the Cloud Logging logger object (created in the previous step). - 3. `tensorboard_dir`: The directory to write TensorBoard data to. - 4. `upload_interval`: The time interval at which to query and upload data to TensorBoard. - 5. `monitoring_enabled`: Whether or not monitoring is enabled. - If the application is interested in monitoring Goodput, it should set - this value to True. Only one worker should enable monitoring. - 6. `include_badput_breakdown`: Whether to query and upload badput breakdown - data to Tensorboard. - -> **_NOTE:_** Please ensure that only **one** worker enables monitoring of Goodput. - In JAX, for example, the check could be `if jax.process_index() == 0` - -For example: - -```python -goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. -goodput_monitoring_enabled = config.monitor_goodput and jax.process_index() == 0 # Check for configs whether or not the enable monitoring. - -goodput_monitor = monitoring.GoodputMonitor( - job_name=config.run_name, - logger_name=logger_name, - tensorboard_dir=config.tensorboard_dir, - upload_interval=config.goodput_upload_interval_seconds, - monitoring_enabled=True, - include_badput_breakdown=True, - ) -``` - -If you want to enable Pathways, turn on the `pathway_enabled` flag: - -```python -goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. -goodput_monitoring_enabled = config.monitor_goodput and jax.process_index() == 0 # Check for configs whether or not the enable monitoring. - -goodput_monitor = monitoring.GoodputMonitor( - job_name=config.run_name, - logger_name=logger_name, - tensorboard_dir=config.tensorboard_dir, - upload_interval=config.goodput_upload_interval_seconds, - monitoring_enabled=True, - include_badput_breakdown=True, - pathway_enabled=True - ) -``` - -If you want to monitor Step Time Deviation, configure the `GoodputMonitor` -as follows: - -```python -goodput_logger_name = f'goodput_{config.run_name}' # You can choose your own logger name. -goodput_monitoring_enabled = config.monitor_goodput and jax.process_index() == 0 # Check for configs whether or not the enable monitoring. - -goodput_monitor = monitoring.GoodputMonitor( - job_name=config.run_name, - logger_name=logger_name, - tensorboard_dir=config.tensorboard_dir, - upload_interval=config.goodput_upload_interval_seconds, - monitoring_enabled=True, - include_badput_breakdown=True, - include_step_deviation=True, - configured_ideal_step_time=None # Optional, the library will compute ideal step time if it is not provided - ) -``` - -#### Start asynchronous "query and upload" of Goodput - -Call the `start_goodput_uploader` API to spin off a thread which continuously -queries and uploads Goodput. - -Note: This will upload Goodput and Badput data to Google Cloud Monitoring -by default. - -```python -goodput_monitor.start_goodput_uploader() -``` - -#### Start asynchronous "query and upload" of Step Time Deviation - -Call the `start_step_deviation_uploader` API to spin off a thread which -continuously queries and uploads step time deviation. - -Note: This will upload Step Time Deviation data to Google Cloud Monitoring -by default. - -```python -goodput_monitor.start_step_deviation_uploader() -``` - -#### Visualize on Tensorboard - -1. Make sure you have `tensorboard-plugin-profile`, `tensorflow` and `tensorboard` packages installed -2. Follow instructions [here](https://cloud.google.com/tpu/docs/profile-tpu-vm#start_profiling_the_model_training) to start the Tensorboard server - -#### Access Goodput, Badput and Step Deviation on Google Cloud Monitoring - -By default, performance data ([goodput](https://cloud.google.com/monitoring/api/metrics_gcp#:~:text=workload/goodput_time), [badput](https://cloud.google.com/monitoring/api/metrics_gcp#:~:text=workload/badput_time), and [step deviation](https://cloud.google.com/monitoring/api/metrics_gcp#:~:text=workload/performance)) is automatically sent to Google Cloud Monitoring, enabling visualization on dashboards. - -This feature leverages Google VM metadata (project ID, location, accelerator type) -and supports replica IDs for uniquely identifying workloads in multi-replica -deployments. - -```python - -gcp_options = goodput_utils.GCPOptions( - project_id=None, # If None, the library will automatically identify from GCE internal metadata - location=None, # If None, the library will automatically identify from GCE internal metadata - replica_id='0', # Default is '0' - acc_type=None, # If None, the library will automatically identify from GCE internal metadata - enable_gcp_goodput_metrics=True, - enable_gcp_step_deviation_metrics=True, - ) - -goodput_monitor = monitoring.GoodputMonitor( - job_name=config.run_name, - logger_name=logger_name, - tensorboard_dir=config.tensorboard_dir, - upload_interval=config.goodput_upload_interval_seconds, - monitoring_enabled=True, - include_badput_breakdown=True, - include_step_deviation=True, - configured_ideal_step_time=None, # Optional, the library will compute ideal step time if it is not provided - gcp_options=gcp_options - ) -``` - -If you do not wish to send metrics to Google Cloud Monitoring then please set -the flag `enable_gcp_goodput_metrics` to `False` for disabling goodput metrics -and `enable_gcp_step_deviation_metrics` to `False` for disabling step deviation -metrics while creating the GCPOptions object. - -Setting `monitoring_enabled` to `False` will disable both tensorboard and GCM -monitoring. - -```python - -gcp_options = goodput_utils.GCPOptions( - project_id=None, # If None, the library will automatically identify from GCE internal metadata - location=None, # If None, the library will automatically identify from GCE internal metadata - replica_id='0', # Default is '0' - acc_type=None, # If None, the library will automatically identify from GCE internal metadata - enable_gcp_goodput_metrics=False, - enable_gcp_step_deviation_metrics=False, - ) - - -goodput_monitor = monitoring.GoodputMonitor( - job_name=config.run_name, - logger_name=logger_name, - tensorboard_dir=config.tensorboard_dir, - upload_interval=config.goodput_upload_interval_seconds, - monitoring_enabled=True, - include_badput_breakdown=True, - include_step_deviation=True, - configured_ideal_step_time=None, - gcp_options=gcp_options, - ) -``` - -If you want to monitor Goodput and Badput metrics computed in a specific window -of time, you can use the `start_goodput_interval_uploader` monitoring API. - -#### Create the `GoodputMonitor` with `enable_gcp_goodput_metrics` set to `True` in `GCPOptions` - -```python - -gcp_options = goodput_utils.GCPOptions( - project_id=None, # If None, the library will automatically identify from GCE internal metadata - location=None, # If None, the library will automatically identify from GCE internal metadata - replica_id='0', # Default is '0' - acc_type=None, # If None, the library will automatically identify from GCE internal metadata - enable_gcp_goodput_metrics=True, - ) - -goodput_monitor = monitoring.GoodputMonitor( - job_name=config.run_name, - logger_name=logger_name, - tensorboard_dir=config.tensorboard_dir, - upload_interval=config.goodput_upload_interval_seconds, - monitoring_enabled=True, - include_badput_breakdown=True, - gcp_options=gcp_options - ) -``` - -#### Start asynchronous "query and upload" of Interval Goodput to GCM. - -Call the `start_goodput_interval_uploader` API and specify `window_size_seconds` -to compute Goodput and Badput metrics only in the sliding time window. -The interval starts `window_size_seconds` prior to time of query, ends at time -of query, and moves ahead by `upload_interval` seconds. - -This call is asynchronous and will only upload Goodput and Badput data to -Google Cloud Monitoring, and not to Tensorboard. - -```python -# Set the window size to be 12h -goodput_monitor.start_goodput_interval_uploader(window_size_seconds = 43200) -``` - -Note: Google Cloud Monitoring will allow you to view all the metrics reported -during the entire workload. GCM will also allow you to filter by any time window -(irrespective of `window_size_seconds`). Each data point that is reported by -this API will correspond to computation only within the sliding window of size -`window_size_seconds`. \ No newline at end of file diff --git a/ml-goodput-measurement/ml_goodput_measurement/__init__.py b/ml-goodput-measurement/ml_goodput_measurement/__init__.py deleted file mode 100644 index 88fceac..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from cloud_goodput.ml_goodput_measurement.src import checkpoint_badput_calculator -from cloud_goodput.ml_goodput_measurement.src import gcp_metrics -from cloud_goodput.ml_goodput_measurement.src import goodput -from cloud_goodput.ml_goodput_measurement.src import goodput_cache -from cloud_goodput.ml_goodput_measurement.src import goodput_utils -from cloud_goodput.ml_goodput_measurement.src import monitoring diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py b/ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py deleted file mode 100644 index 34a2110..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/src/checkpoint_badput_calculator.py +++ /dev/null @@ -1,676 +0,0 @@ -"""Checkpoint Badput Calculator class.""" - -import argparse -import dataclasses -import statistics -from typing import Dict, List, Optional - -import google.cloud.logging as google_cloud_logging - - -_JOB_NAME = 'checkpoint_job' -_LOGGER_NAME = 'checkpoint_logger' - -_STEP = 'step' -_EVENT_TYPE = 'event_type' -_DIRECTORY = 'directory' - -_WAIT_FOR_PREV_DURATION_SECS = 'wait_for_prev_duration_secs' - -_CHECKPOINTER_SAVE_DURATION_SECS = 'checkpointer_blocking_duration_secs' -_CHECKPOINTER_RESTORE_DURATION_SECS = 'checkpointer_duration_secs' - -_GET_OLD_STEPS_DURATION_SECS = 'get_old_steps_duration_secs' - -_CHECKPOINT_MANAGER_SAVE_DURATION_SECS = 'checkpoint_manager_blocking_duration_secs' -_CHECKPOINT_MANAGER_RESTORE_DURATION_SECS = 'checkpoint_manager_duration_secs' - -_BROADCAST_DURATION_SECS = 'broadcast_duration_secs' - -OPERATION_TYPE_SAVE = 'save' -OPERATION_TYPE_RESTORE = 'restore' -OPERATION_TYPE_EMERGENCY_RESTORE = 'emergency_restore' - -OPERATION_TYPE_LOCAL = 'local' -OPERATION_TYPE_PERSISTENT = 'persistent' -OPERATION_TYPE_PERSISTENT_AND_LOCAL = 'persistent_and_local' - -_CLOUD_LOGGING_PAGE_SIZE = 10000 - - -@dataclasses.dataclass -class SaveCheckpointManagerVerticalStepStats: - """Vertical step statistics for save operation.""" - total_checkpoint_manager_blocking_time: float = 0.0 - average_checkpoint_manager_blocking_time: float = 0.0 - minimum_checkpoint_manager_blocking_time: float = 0.0 - maximum_checkpoint_manager_blocking_time: float = 0.0 - standard_deviation_checkpoint_manager_blocking_time: float = 0.0 - - total_checkpointer_blocking_time: float = 0.0 - average_checkpointer_blocking_time: float = 0.0 - minimum_checkpointer_blocking_time: float = 0.0 - maximum_checkpointer_blocking_time: float = 0.0 - standard_deviation_checkpointer_blocking_time: float = 0.0 - - total_wait_for_prev_time: float = 0.0 - average_wait_for_prev_time: float = 0.0 - minimum_wait_for_prev_time: float = 0.0 - maximum_wait_for_prev_time: float = 0.0 - standard_deviation_wait_for_prev_time: float = 0.0 - - total_get_old_steps_time: float = 0.0 - average_get_old_steps_time: float = 0.0 - minimum_get_old_steps_time: float = 0.0 - maximum_get_old_steps_time: float = 0.0 - standard_deviation_get_old_steps_time: float = 0.0 - - -@dataclasses.dataclass -class RestoreCheckpointManagerVerticalStepStats: - """Vertical step statistics for restore operation.""" - total_checkpoint_manager_time: float = 0.0 - average_checkpoint_manager_time: float = 0.0 - minimum_checkpoint_manager_time: float = 0.0 - maximum_checkpoint_manager_time: float = 0.0 - standard_deviation_checkpoint_manager_time: float = 0.0 - - total_restore_time: float = 0.0 - average_restore_time: float = 0.0 - minimum_restore_time: float = 0.0 - maximum_restore_time: float = 0.0 - standard_deviation_restore_time: float = 0.0 - - total_broadcast_time: float = 0.0 - average_broadcast_time: float = 0.0 - minimum_broadcast_time: float = 0.0 - maximum_broadcast_time: float = 0.0 - standard_deviation_broadcast_time: float = 0.0 - - -@dataclasses.dataclass -class SaveProcessedStep: - """Horizontal save step stats for a processed step.""" - step: str = '' - total_checkpoint_manager_blocking_time: float = 0.0 - total_checkpointer_blocking_time: float = 0.0 - total_wait_for_prev_time: float = 0.0 - total_get_old_steps_time: float = 0.0 - occurrence: int = 0 - - -@dataclasses.dataclass -class RestoreProcessedStep: - """Horizontal restore step stats for a processed step.""" - step: str = '' - total_checkpoint_manager_time: float = 0.0 - total_restore_time: float = 0.0 - total_broadcast_time: float = 0.0 - broadcast_occurrence: int = 0 - occurrence: int = 0 - - -@dataclasses.dataclass -class CheckpointLoggerOptions: - """Checkpoint logger options.""" - job_name: str = _JOB_NAME - logger_name: str = _LOGGER_NAME - client: Optional[google_cloud_logging.Client] = None - use_goodput_logger: bool = False - - -class CheckpointBadputCalculator: - """Checkpoint Badput Calculator class.""" - - def __init__( - self, options: CheckpointLoggerOptions = CheckpointLoggerOptions() - ): - self._options = options - if not options.use_goodput_logger: - if options.client is None: - self.logging_client = google_cloud_logging.Client() - else: - self.logging_client = options.client - self._logger = self.logging_client.logger(options.logger_name) - self._use_goodput_logger = options.use_goodput_logger - self.entries = [] - - def read_entries(self) -> List[Dict[str, str]]: - """Queries Cloud Logging entries for the specific job. - - Returns: - Filtered entries in ascending order of timestamp. - """ - if self._use_goodput_logger: - return self.entries - - filter_entries = [ - 'severity=INFO', - f'jsonPayload.job_name="{self._options.job_name}"', - ] - - event_type_filter = ( - '(jsonPayload.event_type=save OR jsonPayload.event_type=restore OR' - ' jsonPayload.event_type=emergency_restore)' - ) - filter_entries.append(event_type_filter) - - filter_entries = ' AND '.join(filter_entries) - - entries = self._logger.list_entries( - filter_=filter_entries, - order_by=google_cloud_logging.ASCENDING, - page_size=_CLOUD_LOGGING_PAGE_SIZE, - ) - entry_payload = [entry.payload for entry in entries] - return entry_payload - - def _is_local_operation(self, step_stats: Dict[str, str]): - if (step_stats[_DIRECTORY]).startswith('gs://'): - return False - else: - return True - - def is_valid_save_stats( - self, - step_stats: Dict[str, str], - operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, - ): - """Checks if the step stats is valid. - - Args: - step_stats: The step stats to check. - operation_type: whether to check for local or persistent or both. - - Returns: - Boolean indicating whether the step stats is valid. - """ - if ( - _EVENT_TYPE not in step_stats - or step_stats[_EVENT_TYPE] != OPERATION_TYPE_SAVE - ): - return False - elif operation_type == OPERATION_TYPE_LOCAL: - return self._is_local_operation(step_stats) - elif operation_type == OPERATION_TYPE_PERSISTENT: - return not self._is_local_operation(step_stats) - else: - return True - - def is_valid_restore_stats( - self, - step_stats: Dict[str, str], - operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, - ): - """Checks if the step stats is valid. - - Args: - step_stats: The step stats to check. - operation_type: whether to check for local or persistent or both. - - Returns: - Boolean indicating whether the step stats is valid. - - """ - if _EVENT_TYPE not in step_stats: - return False - elif step_stats[_EVENT_TYPE] not in [ - OPERATION_TYPE_RESTORE, - OPERATION_TYPE_EMERGENCY_RESTORE, - ]: - return False - elif operation_type == OPERATION_TYPE_LOCAL: - return step_stats[_EVENT_TYPE] == OPERATION_TYPE_EMERGENCY_RESTORE - elif operation_type == OPERATION_TYPE_PERSISTENT: - return step_stats[_EVENT_TYPE] == OPERATION_TYPE_RESTORE - else: - return True - - def _save_statistics( - self, processed_step_stats: Dict[str, SaveProcessedStep] - ) -> SaveCheckpointManagerVerticalStepStats: - """Gets the processed step stats.""" - if not processed_step_stats: - return SaveCheckpointManagerVerticalStepStats() - - for _, stats in processed_step_stats.items(): - if stats.occurrence > 0: - stats.total_checkpoint_manager_blocking_time = ( - stats.total_checkpoint_manager_blocking_time / stats.occurrence - ) - stats.total_checkpointer_blocking_time = ( - stats.total_checkpointer_blocking_time / stats.occurrence - ) - stats.total_wait_for_prev_time = ( - stats.total_wait_for_prev_time / stats.occurrence - ) - stats.total_get_old_steps_time = ( - stats.total_get_old_steps_time / stats.occurrence - ) - - vertical_step_stats = SaveCheckpointManagerVerticalStepStats() - - # Record statistics for checkpoint_manager_blocking_time. - vertical_step_stats.total_checkpoint_manager_blocking_time = sum( - map( - lambda stats: stats.total_checkpoint_manager_blocking_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.average_checkpoint_manager_blocking_time = ( - vertical_step_stats.total_checkpoint_manager_blocking_time - / len(processed_step_stats) - ) - vertical_step_stats.minimum_checkpoint_manager_blocking_time = min( - map( - lambda stats: stats.total_checkpoint_manager_blocking_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.maximum_checkpoint_manager_blocking_time = max( - map( - lambda stats: stats.total_checkpoint_manager_blocking_time, - processed_step_stats.values(), - ) - ) - if len(processed_step_stats) > 1: - vertical_step_stats.standard_deviation_checkpoint_manager_blocking_time = ( - statistics.stdev( - map( - lambda stats: stats.total_checkpoint_manager_blocking_time, - processed_step_stats.values(), - ) - ) - ) - - # Record statistics for checkpointer_blocking_time. - vertical_step_stats.total_checkpointer_blocking_time = sum( - map( - lambda stats: stats.total_checkpointer_blocking_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.average_checkpointer_blocking_time = ( - vertical_step_stats.total_checkpointer_blocking_time - / len(processed_step_stats) - ) - vertical_step_stats.minimum_checkpointer_blocking_time = min( - map( - lambda stats: stats.total_checkpointer_blocking_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.maximum_checkpointer_blocking_time = max( - map( - lambda stats: stats.total_checkpointer_blocking_time, - processed_step_stats.values(), - ) - ) - if len(processed_step_stats) > 1: - vertical_step_stats.standard_deviation_checkpointer_blocking_time = ( - statistics.stdev( - map( - lambda stats: stats.total_checkpointer_blocking_time, - processed_step_stats.values(), - ) - ) - ) - - # Record statistics for wait_for_prev_time. - vertical_step_stats.total_wait_for_prev_time = sum( - map( - lambda stats: stats.total_wait_for_prev_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.average_wait_for_prev_time = ( - vertical_step_stats.total_wait_for_prev_time - / len(processed_step_stats) - ) - vertical_step_stats.minimum_wait_for_prev_time = min( - map( - lambda stats: stats.total_wait_for_prev_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.maximum_wait_for_prev_time = max( - map( - lambda stats: stats.total_wait_for_prev_time, - processed_step_stats.values(), - ) - ) - if len(processed_step_stats) > 1: - vertical_step_stats.standard_deviation_wait_for_prev_time = ( - statistics.stdev( - map( - lambda stats: stats.total_wait_for_prev_time, - processed_step_stats.values(), - ) - ) - ) - - # Record statistics for get_old_steps_time. - vertical_step_stats.total_get_old_steps_time = sum( - map( - lambda stats: stats.total_get_old_steps_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.average_get_old_steps_time = ( - vertical_step_stats.total_get_old_steps_time / len(processed_step_stats) - ) - vertical_step_stats.minimum_get_old_steps_time = min( - map( - lambda stats: stats.total_get_old_steps_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.maximum_get_old_steps_time = max( - map( - lambda stats: stats.total_get_old_steps_time, - processed_step_stats.values(), - ) - ) - if len(processed_step_stats) > 1: - vertical_step_stats.standard_deviation_get_old_steps_time = ( - statistics.stdev( - map( - lambda stats: stats.total_get_old_steps_time, - processed_step_stats.values(), - ) - ) - ) - return vertical_step_stats - - def calculate_save_operation_checkpoint_manager_blocking_time( - self, operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, - ) -> SaveCheckpointManagerVerticalStepStats: - """Gets checkpoint manager blocking time breakdown for save operation.""" - self.entries = self.read_entries() - - step_already_processed: dict[str, SaveProcessedStep] = dict() - for step_stats in self.entries: - if ( - not self.is_valid_save_stats(step_stats, operation_type) - ): - continue - - # Create a step info to identify the step_statistics whether local or - # persistent. - if self._is_local_operation(step_stats): - step_info = str(step_stats[_STEP]) + '-' + OPERATION_TYPE_LOCAL - else: - step_info = ( - str(step_stats[_STEP]) + '-' + OPERATION_TYPE_PERSISTENT - ) - if step_already_processed.get(step_info) is None: - step_already_processed[step_info] = SaveProcessedStep() - step_already_processed[step_info].step = step_info - step_already_processed[ - step_info - ].total_checkpoint_manager_blocking_time = float( - step_stats[_CHECKPOINT_MANAGER_SAVE_DURATION_SECS] - ) - step_already_processed[step_info].total_checkpointer_blocking_time = ( - float(step_stats[_CHECKPOINTER_SAVE_DURATION_SECS]) - ) - step_already_processed[step_info].total_wait_for_prev_time = float( - step_stats[_WAIT_FOR_PREV_DURATION_SECS] - ) - step_already_processed[step_info].total_get_old_steps_time = float( - step_stats[_GET_OLD_STEPS_DURATION_SECS] - ) - step_already_processed[step_info].occurrence = 1 - else: - step_already_processed[step_info].step = step_info - step_already_processed[ - step_info - ].total_checkpoint_manager_blocking_time += float( - step_stats[_CHECKPOINT_MANAGER_SAVE_DURATION_SECS] - ) - step_already_processed[ - step_info - ].total_checkpointer_blocking_time += float( - step_stats[_CHECKPOINTER_SAVE_DURATION_SECS] - ) - step_already_processed[step_info].total_wait_for_prev_time += float( - step_stats[_WAIT_FOR_PREV_DURATION_SECS] - ) - step_already_processed[step_info].total_get_old_steps_time += float( - step_stats[_GET_OLD_STEPS_DURATION_SECS] - ) - step_already_processed[step_info].occurrence += 1 - - # Calculate the vertical step stats for the checkpoint manager blocking - # time. - save_statistics = self._save_statistics( - step_already_processed - ) - - return save_statistics - - def _restore_statistics( - self, processed_step_stats: Dict[str, RestoreProcessedStep] - ) -> RestoreCheckpointManagerVerticalStepStats: - """Calculates the vertical step stats.""" - if not processed_step_stats: - return RestoreCheckpointManagerVerticalStepStats() - broadcast_occurrence = 0 - for _, stats in processed_step_stats.items(): - stats.total_checkpoint_manager_time = ( - stats.total_checkpoint_manager_time / stats.occurrence - ) - stats.total_restore_time = stats.total_restore_time / stats.occurrence - if stats.broadcast_occurrence > 0: - stats.total_broadcast_time = ( - stats.total_broadcast_time / stats.broadcast_occurrence - ) - broadcast_occurrence += 1 - - vertical_step_stats = RestoreCheckpointManagerVerticalStepStats() - - # Record statistics for total time checkpoint manager spent on restore. - vertical_step_stats.total_checkpoint_manager_time = sum( - map( - lambda stats: stats.total_checkpoint_manager_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.average_checkpoint_manager_time = ( - vertical_step_stats.total_checkpoint_manager_time - / len(processed_step_stats) - ) - vertical_step_stats.minimum_checkpoint_manager_time = min( - map( - lambda stats: stats.total_checkpoint_manager_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.maximum_checkpoint_manager_time = max( - map( - lambda stats: stats.total_checkpoint_manager_time, - processed_step_stats.values(), - ) - ) - if len(processed_step_stats) > 1: - vertical_step_stats.standard_deviation_checkpoint_manager_time = ( - statistics.stdev( - map( - lambda stats: stats.total_checkpoint_manager_time, - processed_step_stats.values(), - ) - ) - ) - # Record statistics for restore time. - vertical_step_stats.total_restore_time = sum( - map( - lambda stats: stats.total_restore_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.average_restore_time = ( - vertical_step_stats.total_restore_time / len(processed_step_stats) - ) - vertical_step_stats.minimum_restore_time = min( - map( - lambda stats: stats.total_restore_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.maximum_restore_time = max( - map( - lambda stats: stats.total_restore_time, - processed_step_stats.values(), - ) - ) - if len(processed_step_stats) > 1: - vertical_step_stats.standard_deviation_restore_time = ( - statistics.stdev( - map( - lambda stats: stats.total_restore_time, - processed_step_stats.values(), - ) - ) - ) - - # Record statistics for broadcasting the restored checkpoint(Emergency - # restore only). - if broadcast_occurrence > 0: - vertical_step_stats.total_broadcast_time = sum( - map( - lambda stats: stats.total_broadcast_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.average_broadcast_time = ( - vertical_step_stats.total_broadcast_time / broadcast_occurrence - ) - vertical_step_stats.minimum_broadcast_time = min( - map( - lambda stats: stats.total_broadcast_time, - processed_step_stats.values(), - ) - ) - vertical_step_stats.maximum_broadcast_time = max( - map( - lambda stats: stats.total_broadcast_time, - processed_step_stats.values(), - ) - ) - if len(processed_step_stats) > 1: - vertical_step_stats.standard_deviation_broadcast_time = ( - statistics.stdev( - map( - lambda stats: stats.total_broadcast_time, - processed_step_stats.values(), - ) - ) - ) - - return vertical_step_stats - - def calculate_restore_operation_checkpoint_manager_blocking_time( - self, - operation_type: Optional[str] = OPERATION_TYPE_PERSISTENT_AND_LOCAL, - ) -> RestoreCheckpointManagerVerticalStepStats: - """Gets checkpoint manager blocking time breakdown for restore operation.""" - self.entries = self.read_entries() - - step_already_processed: dict[str, RestoreProcessedStep] = dict() - for step_stats in self.entries: - if not self.is_valid_restore_stats(step_stats, operation_type): - continue - - # Create a step info to identify the step_stats whether local or - if self._is_local_operation(step_stats): - step_info = str(step_stats[_STEP]) + '-' + OPERATION_TYPE_LOCAL - else: - step_info = str(step_stats[_STEP]) + '-' + OPERATION_TYPE_PERSISTENT - - if step_already_processed.get(step_info) is None: - step_already_processed[step_info] = RestoreProcessedStep() - step_already_processed[step_info].step = step_info - - step_already_processed[step_info].total_checkpoint_manager_time = float( - step_stats[_CHECKPOINT_MANAGER_RESTORE_DURATION_SECS] - ) - step_already_processed[step_info].total_restore_time = float( - step_stats[_CHECKPOINTER_RESTORE_DURATION_SECS] - ) - if ( - step_stats.get(_BROADCAST_DURATION_SECS) - and step_stats[_BROADCAST_DURATION_SECS] is not None - ): - step_already_processed[step_info].total_broadcast_time = float( - step_stats[_BROADCAST_DURATION_SECS] - ) - step_already_processed[step_info].broadcast_occurrence = 1 - step_already_processed[step_info].occurrence = 1 - else: - step_already_processed[step_info].step = step_info - step_already_processed[ - step_info - ].total_checkpoint_manager_time += float( - step_stats[_CHECKPOINT_MANAGER_RESTORE_DURATION_SECS] - ) - step_already_processed[step_info].total_restore_time += float( - step_stats[_CHECKPOINTER_RESTORE_DURATION_SECS] - ) - if ( - step_stats.get(_BROADCAST_DURATION_SECS) - and step_stats[_BROADCAST_DURATION_SECS] is not None - ): - step_already_processed[step_info].total_broadcast_time += float( - step_stats[_BROADCAST_DURATION_SECS] - ) - step_already_processed[step_info].broadcast_occurrence += 1 - step_already_processed[step_info].occurrence += 1 - - # Calculate the vertical step stats for the checkpoint manager blocking - # time. - restore_statistics = self._restore_statistics(step_already_processed) - - return restore_statistics - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - options = CheckpointLoggerOptions() - parser.add_argument( - '--job_name', - type=str, - default=options.job_name, - help='The name of the job.', - ) - parser.add_argument( - '--logger_name', - type=str, - default=options.logger_name, - help='The name of the logger.', - ) - parser.add_argument( - '--client', - type=str, - default=options.client, - help='The name of the client.', - ) - parser.add_argument( - '--operation_type', - type=str, - default=OPERATION_TYPE_PERSISTENT_AND_LOCAL, - help='The operation type.', - ) - args = parser.parse_args() - options = CheckpointLoggerOptions( - job_name=args.job_name, - logger_name=args.logger_name, - client=args.client, - ) - checkpoint_badput_calculator = ( - CheckpointBadputCalculator(options) - ) - checkpoint_badput_calculator.calculate_save_operation_checkpoint_manager_blocking_time( - args.operation_type - ) - - - diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py b/ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py deleted file mode 100644 index da570d9..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/src/gcp_metrics.py +++ /dev/null @@ -1,106 +0,0 @@ -"""A generic class to send multiple metrics to GCP Cloud Monitoring in a batch with dynamic resources.""" -import enum -import logging -import time -from typing import Any, Dict - -from google.api_core import exceptions -from google.cloud import monitoring_v3 - -GoogleAPIError = exceptions.GoogleAPIError -Enum = enum.Enum - -logger = logging.getLogger(__name__) - - -class ValueType(Enum): - """Enum for metric value types.""" - - BOOL = "bool_value" - INT = "int64_value" - DOUBLE = "double_value" - STRING = "string_value" - DISTRIBUTION = "distribution_value" # Add other types as needed - - -class GCPMetrics: - """A generic class to send multiple metrics to GCP Cloud Monitoring in a batch with dynamic resources.""" - - def __init__(self, project_id: str): - """Initializes the GCPMetrics.""" - self.project_id = project_id - self.client = monitoring_v3.MetricServiceClient() - self.project_name = f"projects/{project_id}" - - def create_time_series( - self, - metric_type: str, - value, - value_type: ValueType, - metric_labels: Dict[str, str], - resource_type: str, - resource_labels: Dict[str, str], - seconds: int, - nanos: int, - ) -> monitoring_v3.TimeSeries: - """Creates a TimeSeries object for a single metric with dynamic resources.""" - series = monitoring_v3.TimeSeries() - series.metric.type = metric_type - series.resource.type = resource_type - series.resource.labels.update(resource_labels) - if metric_labels: - series.metric.labels.update(metric_labels) - - point = monitoring_v3.Point( - interval=monitoring_v3.TimeInterval( - end_time={"seconds": seconds, "nanos": nanos} - ), - value=monitoring_v3.TypedValue(**{value_type.value: value}), - ) - series.points.append(point) - - return series - - def send_metrics(self, metrics: list[Dict[str, Any]]): - """Sends multiple metrics to GCP Monitoring in a batch with dynamic resources. - - Args: - metrics: A list of dictionaries, where each dictionary represents - a metric. Each dictionary should have the following keys: - - 'metric_type': str - - 'value': The metric value. - - 'value_type': ValueType (e.g., ValueType.INT, - ValueType.DOUBLE) - - 'metric_labels': dict (optional) - - 'resource_type': str - - 'resource_labels': dict - """ - try: - now = time.time() - seconds = int(now) - nanos = int((now - seconds) * 10**9) - - time_series_list = [] - for metric in metrics: - try: - metric_labels = metric.get("metric_labels", {}) - series = self.create_time_series( - metric["metric_type"], - metric["value"], - metric["value_type"], - metric_labels, - metric["resource_type"], - metric["resource_labels"], - seconds, - nanos, - ) - time_series_list.append(series) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error("Failed to create time series: %s", e) - self.client.create_time_series( - name=self.project_name, time_series=time_series_list - ) - logger.info("Sent %d metrics to GCP Monitoring.", len(metrics)) - - except GoogleAPIError as e: - logger.error("Failed to send metrics: %s", e) diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/goodput.py b/ml-goodput-measurement/ml_goodput_measurement/src/goodput.py deleted file mode 100644 index 75859f3..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/src/goodput.py +++ /dev/null @@ -1,1690 +0,0 @@ -"""Goodput package API implementations. - -This file contains all the core implementations of the ml_goodput_measurement -library for users to measure and monitor Goodput, Badput and Step Time -Deviation. -""" - -import datetime -import logging -import threading -from typing import Any, Optional, Union - -from cloud_goodput.ml_goodput_measurement.src import checkpoint_badput_calculator -from cloud_goodput.ml_goodput_measurement.src import goodput_cache -from cloud_goodput.ml_goodput_measurement.src import goodput_utils - - -get_timestamp_from_log_entry = goodput_utils.get_timestamp_from_log_entry -get_extra_time_from_anomalous_steps = ( - goodput_utils.get_extra_time_from_anomalous_steps -) -compute_ideal_step_time = goodput_utils.compute_ideal_step_time - -BadputType = goodput_utils.BadputType -CheckpointLoggerOptions = checkpoint_badput_calculator.CheckpointLoggerOptions -CheckpointBadputCalculator = ( - checkpoint_badput_calculator.CheckpointBadputCalculator -) -GoodputType = goodput_utils.GoodputType -GoodputCache = goodput_cache.GoodputCache -GoodputInfo = goodput_utils.GoodputInfo -StepInfo = goodput_utils.StepInfo -# Data structure to store the type of unproductive time (BadputType) and the -# corresponding time in seconds. If the BadputType is CUSTOM_BADPUT_EVENTS, the -# value is a dictionary of user defined event type and the corresponding time -# in seconds. -UnproductiveTimeDict = dict[ - BadputType, Union[float, dict[str, float]] -] - -_JOB_NAME = 'job_name' -_STEP_COUNT = 'step_count' -_STEP_START_TIME = 'step_start_time' -_JOB_START_TIME = 'job_start_time' -_JOB_END_TIME = 'job_end_time' -_TPU_INIT_START_TIME = 'tpu_init_start_time' -_TPU_INIT_END_TIME = 'tpu_init_end_time' -_TRAINING_PREPARATION_START_TIME = 'training_prep_start_time' -_TRAINING_PREPARATION_END_TIME = 'training_prep_end_time' -_DATA_LOADING_START_TIME = 'data_loading_start_time' -_DATA_LOADING_END_TIME = 'data_loading_end_time' -_CUSTOM_BADPUT_EVENT_TYPE = 'custom_badput_event_type' -_CUSTOM_BADPUT_EVENT_START_TIME = 'custom_badput_event_start_time' -_CUSTOM_BADPUT_EVENT_END_TIME = 'custom_badput_event_end_time' - -_CLOUD_LOGGING_PAGE_SIZE = 1000000 - -logger = logging.getLogger(__name__) - - -class _CloudLogger: - """A helper class for reading and writing to Cloud Logging. - - Attributes: - job_name: Name of a specific job. - logger: The Cloud Logging logger object. - job_start_time: Start time of the job run. - """ - - def __init__(self, job_name: str, log_name: str): - """_CloudLogger constructor. - - Args: - job_name: Name of the job the _CloudLogger is for. - log_name: Name of the log being written. - """ - import google.cloud.logging # pylint: disable=g-import-not-at-top - - self.job_name = job_name - logging_client = google.cloud.logging.Client() - self.logger = logging_client.logger(log_name) - self.job_start_time = None - - def write_cloud_logging_entry(self, entry) -> None: - """Writes an entry to the Cloud Logging logger at INFO level. - - Args: - entry: JSON-serializable structured log dictionary. - """ - if entry is None: - return - if entry[_JOB_NAME] == self.job_name: - self.logger.log_struct( - entry, - severity='INFO', - ) - - def _get_filter_msg( - self, - start_time: Optional[datetime.datetime], - end_time: Optional[datetime.datetime], - ) -> str: - """Gets the filter message for the Cloud Logging query.""" - filter_entries = [ - 'severity=INFO', - f'jsonPayload.job_name="{self.job_name}"', - ] - # Add a filter to bind an end-time to the query window. - if end_time is None: - end_time = datetime.datetime.now(datetime.timezone.utc) - elif end_time.tzinfo is None: - end_time = end_time.replace(tzinfo=datetime.timezone.utc) - - filter_entries.append(f'timestamp<="{end_time.isoformat()}"') - - # Add a filter to bind a start-time to the query window (if available). - if start_time is None: - if self.job_start_time is not None: - start_time = self.job_start_time - datetime.timedelta(days=1) - - if start_time is not None: - if start_time.tzinfo is None: - start_time = start_time.replace(tzinfo=datetime.timezone.utc) - filter_entries.append(f'timestamp>"{start_time.isoformat()}"') - return ' AND '.join(filter_entries) - - def _update_job_start_time(self, entries: list[Any]): - if self.job_start_time: - return - for entry in entries: - if _JOB_START_TIME in entry and self.job_start_time is None: - self.job_start_time = datetime.datetime.fromtimestamp( - entry[_JOB_START_TIME] - ) - break - - def read_cloud_logging_entries( - self, - start_time: Optional[datetime.datetime] = None, - end_time: Optional[datetime.datetime] = None, - ): - """Queries Cloud Logging entries for the specific job. - - Args: - start_time: The start time of the query window. - end_time: The end time of the query window. - - Returns: - Filtered entries in ascending order of timestamp. - """ - import google.cloud.logging # pylint: disable=g-import-not-at-top - - entries = self.logger.list_entries( - filter_=self._get_filter_msg(start_time, end_time), - order_by=google.cloud.logging.ASCENDING, - page_size=_CLOUD_LOGGING_PAGE_SIZE, - ) - entry_payload = [entry.payload for entry in entries] - self._update_job_start_time(entry_payload) - return entry_payload - - -class GoodputRecorder: - """The Goodput recorder class, responsible for recording Goodput metrics from the user application. - - Attributes: - job_name: Name of the job the GoodputRecorder is for. - """ - - def __init__( - self, - job_name: str, - logger_name: str, - logging_enabled=False, - cloud_logger: Optional[_CloudLogger] = None, - ): - """GoodputRecorder constructor. - - Args: - job_name: Name of the job the GoodputRecorder is for. - logger_name: The name of the Cloud Logging logger object that the - application wants logs to be written to and read from. - logging_enabled: A boolean value to indicate whether the current process - should send logs to Cloud Logging or not. The application should set - this value to True if the Recorder is being called from TPU worker 0 and - the application's configurations request Goodput logging. - cloud_logger: Should never be passed directly by the user. - """ - self.job_name = job_name - # If logging is disabled for this process, do not create a _cloud_logger - # object and exit early if any record record_* API is called. - if not logging_enabled: - self._cloud_logger = None - logging.info('Logging is disabled for this process.') - return - - if cloud_logger is not None: - self._cloud_logger = cloud_logger - else: - self._cloud_logger = _CloudLogger(job_name, logger_name) - - def record_step_start_time( - self, step: int, start_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log an individual step's start time. - - Args: - step: The count of the step that timing information is recorded for. - start_time: Optional backfill start time of the training step. If - provided, it has to be in UTC time. - """ - if self._cloud_logger is None: - return - if start_time is None: - start_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _STEP_COUNT: int(step), - _STEP_START_TIME: start_time.timestamp(), - }) - - def record_checkpoint_progress(self, step, checkpoint_start_time): - """Main recorder function to log information on a successful checkpoint. - - This method is intended to log the progress for a checkpoint (last step - included in the checkpoint) and when the checkpoint starts. This information - will be retrieved in the future to determine whether training progress from - a completed step contributes to Goodput or wasted progress Badput. - - Args: - step: The step count of the last step included in the saved checkpoint. - checkpoint_start_time: Timestamp at which the checkpoint containing - progress upto "step" starts to save. - """ - pass - - def record_job_start_time( - self, start_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log a job's start time. - - Args: - start_time: Optional backfill start time of the job. If provided, it has - to be in UTC time. - """ - if self._cloud_logger is None: - return - if start_time is None: - start_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _JOB_START_TIME: start_time.timestamp(), - }) - - def record_job_end_time(self, end_time: Optional[datetime.datetime] = None): - """Main recorder function to log a job's end time. - - Args: - end_time: Optional backfull end time of the job. If provided, it has to be - in UTC time. - """ - if self._cloud_logger is None: - return - if end_time is None: - end_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _JOB_END_TIME: end_time.timestamp(), - }) - - def record_tpu_init_start_time( - self, start_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log the start time for TPU initialization. - - Note: TPU initialization may include the time spent in completing - jax.devices() which is responsible for device scanning and Slice Builder - initialization. - - Args: - start_time: Start time of TPU initialization. - """ - if self._cloud_logger is None: - return - if start_time is None: - start_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _TPU_INIT_START_TIME: start_time.timestamp(), - }) - - def record_tpu_init_end_time( - self, end_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log the end time for TPU initialization. - - Args: - end_time: End time of TPU initialization. - """ - if self._cloud_logger is None: - return - if end_time is None: - end_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _TPU_INIT_END_TIME: end_time.timestamp(), - }) - - def record_training_preparation_start_time( - self, start_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log the start time of training preparation before starting a training loop. - - Note: Training preparation may include the time spent in creation of - checkpoint managers, checkpoint loading, running mesh and model optimizers - etc. - - Args: - start_time: Start time of training preparation. - """ - if self._cloud_logger is None: - return - if start_time is None: - start_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _TRAINING_PREPARATION_START_TIME: start_time.timestamp(), - }) - - def record_training_preparation_end_time( - self, end_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log the end time of training preparation before starting a training loop. - - Args: - end_time: End time of training preparation. - """ - if self._cloud_logger is None: - return - if end_time is None: - end_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _TRAINING_PREPARATION_END_TIME: end_time.timestamp(), - }) - - def record_data_loading_start_time( - self, start_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log the start time of training's data loading. - - Args: - start_time: Start time of data loading. - """ - if self._cloud_logger is None: - return - if start_time is None: - start_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _DATA_LOADING_START_TIME: start_time.timestamp(), - }) - - def record_data_loading_end_time( - self, end_time: Optional[datetime.datetime] = None - ): - """Main recorder function to log the end time of training's data loading. - - Args: - end_time: End time of data loading. - """ - if self._cloud_logger is None: - return - if end_time is None: - end_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _DATA_LOADING_END_TIME: end_time.timestamp(), - }) - - def record_custom_badput_event_start_time( - self, - start_time: Optional[datetime.datetime] = None, - custom_badput_event_type: str = 'unknown', - ): - """Main recorder function to log the start time of a custom badput event. - - Use this function to record the start time of a custom badput event that - occurs inside the training loop and utilizes the accelerator resources, - and blocks training. - - For example, use this API to record the start time of the evaluation - loop or an SDC check if the the event blocks the training loop. - - Args: - start_time: Start time of the custom badput event. - custom_badput_event_type: Type of the custom badput event. - """ - if self._cloud_logger is None: - return - if start_time is None: - start_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _CUSTOM_BADPUT_EVENT_TYPE: custom_badput_event_type, - _CUSTOM_BADPUT_EVENT_START_TIME: start_time.timestamp(), - }) - - def record_custom_badput_event_end_time( - self, - end_time: Optional[datetime.datetime] = None, - custom_badput_event_type: str = 'unknown', - ): - """Main recorder function to log the end time of a custom badput event. - - Args: - end_time: End time of the custom badput event. - custom_badput_event_type: Type of the custom badput event. - """ - if self._cloud_logger is None: - return - if end_time is None: - end_time = datetime.datetime.now(datetime.timezone.utc) - - self._cloud_logger.write_cloud_logging_entry({ - _JOB_NAME: self.job_name, - _CUSTOM_BADPUT_EVENT_TYPE: custom_badput_event_type, - _CUSTOM_BADPUT_EVENT_END_TIME: end_time.timestamp(), - }) - - -class GoodputCalculator: - """The Goodput calculator class, responsible for querying necessary information and computing Goodput metrics to return to the user application. - - Attributes: - job_name: Name of the job the GoodputCalculator is for. - using_pathways: Whether or not the job uses Pathways. - """ - - def __init__( - self, - job_name: str, - logger_name: str, - cloud_logger: Optional[_CloudLogger] = None, - using_pathways: bool = False, - ): - """GoodputCalculator constructor. - - Args: - job_name: Name of the job the GoodputCalculator is for. - logger_name: Name of the log being written. - cloud_logger: Should never be passed directly by the user. - using_pathways: Whether or not the job uses Pathways. - """ - self.job_name = job_name - self.using_pathways = using_pathways - if cloud_logger is not None: - self._cloud_logger = cloud_logger - else: - self._cloud_logger = _CloudLogger(job_name, logger_name) - self._current_entries = [] - self._goodput_cache = GoodputCache() - self._goodput_cache_lock = threading.Lock() - self._interval_entries = [] - self._interval_start_time = None - self._interval_end_time = None - self._number_of_interruptions = 0 - self._gcm_last_recorded_timestamp = None - self._last_disruption_time = None - self._last_disrupted_step = None - - def _get_total_productive_and_unproductive_time( - self, new_entries: list[dict[str, Any]] - ) -> tuple[float, UnproductiveTimeDict, int]: - """Helper function to compute the total productive and unproductive time. - - Args: - new_entries: A list of new log entries to process. - - Returns: - A tuple of: - - total productive training time - - total unproductive time - - last recorded step - """ - # If no new entries are present, return last computed values. - if not new_entries: - cached_values = self._get_cached_productive_and_unproductive_time() - if cached_values is not None: - return cached_values - - return self._get_current_productive_and_unproductive_time() - - def _get_cached_productive_and_unproductive_time( - self, - ) -> tuple[float, UnproductiveTimeDict, int] | None: - """Helper function to retrieve the cached productive training time and unproductive time.""" - goodput_info = self._goodput_cache.get_goodput_info() - if not self._goodput_cache.is_cache_empty() and goodput_info is not None: - return ( - goodput_info.total_productive_time, - goodput_info.total_unproductive_time, - goodput_info.last_recorded_step, - ) - return None - - def _accumulate_unproductive_time( - self, - segment_unproductive_time: UnproductiveTimeDict, - total_unproductive_time: UnproductiveTimeDict, - ): - """Helper function to accumulate the segment unproductive time. - - Args: - segment_unproductive_time: A dictionary of unproductive time for a - segment. - total_unproductive_time: A dictionary of total unproductive time. - - Returns: - None. The function updates the total_unproductive_time dictionary. - """ - - for badput_type, unproductive_value in segment_unproductive_time.items(): - if isinstance(unproductive_value, dict): - if badput_type not in total_unproductive_time: - total_unproductive_time[badput_type] = dict(unproductive_value) - else: - existing_value = total_unproductive_time[badput_type] - if isinstance(existing_value, dict): - for sub_type, sub_value in unproductive_value.items(): - existing_value[sub_type] = ( - existing_value.get(sub_type, 0.0) + sub_value - ) - else: - if badput_type in total_unproductive_time: - existing_value = total_unproductive_time[badput_type] - if isinstance(existing_value, float): - total_unproductive_time[badput_type] = ( - existing_value + unproductive_value - ) - else: - total_unproductive_time[badput_type] = unproductive_value - - def _get_current_productive_and_unproductive_time( - self, interval_query: Optional[bool] = False - ) -> tuple[ - float, - UnproductiveTimeDict, - int, - ]: - """Helper function to compute the current productive training time, unproductive time and the last step recorded till now. - - Args: - interval_query: A boolean value to indicate whether the current query is - for an interval or not. - - Returns: - A tuple of the productive training time, the unproductive time - (dict of BadputType and unproductive time) and the last step recorded till - now based on the latest entries retrieved from Cloud Logging. - """ - def _extract_custom_sync_intervals( - entries: list[dict[str, Any]], - ) -> list[tuple[float, float, str]]: - """Extracts custom badput intervals from Cloud Logging entries. - - This helperfunction scans through a list of Cloud Logging entries to find - custom - badput start and end times, pairing them into intervals. - - Args: - entries: A list of dictionaries representing Cloud Logging entries. - Each entry may contain keys indicating the start or end of a custom - badput event. - - Returns: - A list of tuples, where each tuple consists of: - - start_time (float): The timestamp when the sync event started. - - end_time (float): The timestamp when the sync event ended. - - sync_type (str): The type of custom sync - event. - """ - intervals = [] - active_syncs = {} - - for entry in entries: - if _CUSTOM_BADPUT_EVENT_START_TIME in entry: - sync_type = entry[_CUSTOM_BADPUT_EVENT_TYPE].upper() - active_syncs[sync_type] = entry[_CUSTOM_BADPUT_EVENT_START_TIME] - elif _CUSTOM_BADPUT_EVENT_END_TIME in entry: - sync_type = entry[_CUSTOM_BADPUT_EVENT_TYPE].upper() - if sync_type in active_syncs: - start_time = active_syncs.pop(sync_type) - end_time = entry[_CUSTOM_BADPUT_EVENT_END_TIME] - if start_time < end_time: - intervals.append((start_time, end_time, sync_type)) - - return intervals - - def _compute_adjusted_segment_productive_and_unproductive_time( - step_items: list[tuple[int, float]], - curr_step: int, - min_step: int, - custom_sync_intervals: list[ - tuple[float, float, str] - ], - ) -> tuple[ - float, - float, - list[float], - float, - dict[str, float], - int, - ]: - """Computes adjusted productive and unproductive time for a segment of steps. - - This helper function calculates the total productive time, and the - breakdown of time lost due to custom badput events, as well as - wasted progress caused by disruptions. - - Args: - step_items: A list of tuples, where each tuple contains a step number - (int) and its start timestamp (float). - curr_step: The current step number indicating the end of the segment. - min_step: The minimum step number indicating the start of the segment. - custom_sync_intervals: A list of tuples, where each tuple consists of: - - start_time (float): Start timestamp of the sync event. - - end_time (float): End timestamp of the sync event. - - sync_type (str): The type of sync event. - - Returns: - A tuple containing: - - total_productive_time (float): Adjusted time excluding custom - sync durations. - - first_step_time (float): Adjusted duration of the first step in - the segment. - - step_times (list[float]): List of adjusted times for steps in the - segment excluding the first step. - - wasted_progress (float): Total unproductive time due to possible - disruptions. - - custom_sync_breakdown (dict[str, float]): - Breakdown of time spent in each custom sync type. - - steps_in_segment (int): Total number of steps considered in the - segment. - """ - total_productive_time = 0.0 - first_step_time = 0.0 - step_times = [] - wasted_progress = 0.0 - custom_sync_breakdown: dict[str, float] = {} - - steps_in_segment = 0 - - for i in range(1, len(step_items)): - prev_step, prev_time = step_items[i - 1] - curr_step_num, curr_time = step_items[i] - - raw_delta = curr_time - prev_time - if curr_step_num <= curr_step: - if curr_step_num - 1 != prev_step: - continue - - custom_sync_in_interval = 0.0 - for sync_start, sync_end, sync_type in custom_sync_intervals: - if prev_time <= sync_start and sync_end <= curr_time: - sync_duration = sync_end - sync_start - custom_sync_in_interval += sync_duration - custom_sync_breakdown[sync_type] = ( - custom_sync_breakdown.get(sync_type, 0.0) + sync_duration - ) - - adjusted_delta = max(0.0, raw_delta - custom_sync_in_interval) - total_productive_time += adjusted_delta - - if prev_step == min_step: - first_step_time = adjusted_delta - else: - step_times.append(adjusted_delta) - - steps_in_segment += 1 - - else: - # These steps are after curr_step, they are lost due to disruption. - wasted_progress += raw_delta - - return ( - total_productive_time, - first_step_time, - step_times, - wasted_progress, - custom_sync_breakdown, - steps_in_segment, - ) - - def _compute_segment_final_metrics( - adjusted_productive_time: float, - first_step_time: float, - step_times: list[float], - wasted_progress: float, - custom_sync_breakdown: dict[str, float], - ) -> tuple[ - float, - UnproductiveTimeDict, - ]: - """Computes final metrics for a segment, separating productive and unproductive time. - - This function takes adjusted productive time and calculates additional - badput sources such as program startup and wasted progress due to - disruptions. It returns the final productive time and a breakdown of all - unproductive time sources. - - Args: - adjusted_productive_time: Total productive time for the segment - first_step_time: Productive time for the first step in the segment. - step_times: Productive times for non-first steps in the segment. - wasted_progress: Total time lost due to step discontinuities. - custom_sync_breakdown: A dictionary mapping each custom sync type to - the total badput time it accounted for during the segment. - - Returns: - A tuple containing: - - final_productive_time (float) - - total_segment_unproductive_time (dict) - """ - steps_in_segment = len(step_times) + 1 # Including first step - - if steps_in_segment == 1: - return first_step_time, { - BadputType.WASTED_PROGRESS_FROM_DISRUPTION: wasted_progress, - BadputType.CUSTOM_BADPUT_EVENTS: custom_sync_breakdown, - BadputType.PROGRAM_STARTUP: 0.0, - } - - non_first_steps = steps_in_segment - 1 - non_first_total_time = adjusted_productive_time - first_step_time - average_step_time = ( - non_first_total_time / non_first_steps if non_first_steps > 0 else 0.0 - ) - first_step_extra_time = max(0.0, first_step_time - average_step_time) - final_productive_time = adjusted_productive_time - first_step_extra_time - - total_segment_unproductive_time = { - BadputType.PROGRAM_STARTUP: first_step_extra_time, - BadputType.WASTED_PROGRESS_FROM_DISRUPTION: wasted_progress, - BadputType.CUSTOM_BADPUT_EVENTS: custom_sync_breakdown, - } - - return final_productive_time, total_segment_unproductive_time - - def _get_segment_productive_and_unproductive_time( - step_start_data: dict[int, float], - curr_step: int, - entries_to_process: list[Any], - ) -> tuple[ - float, - UnproductiveTimeDict, - ]: - if curr_step == 0: - return 0.0, {} - - step_items = list(step_start_data.items()) - min_step = min(step_start_data.keys()) - - # Extract custom sync intervals - custom_sync_intervals = _extract_custom_sync_intervals(entries_to_process) - - # Compute adjusted segmentproductive and unproductive times - ( - total_productive_time, - first_step_time, - step_times, - wasted_progress_from_disruption, - custom_sync_breakdown, - steps_in_segment, - ) = _compute_adjusted_segment_productive_and_unproductive_time( - step_items, curr_step, min_step, custom_sync_intervals - ) - - if steps_in_segment == 0: - return 0.0, { - BadputType.WASTED_PROGRESS_FROM_DISRUPTION: ( - wasted_progress_from_disruption - ) - } - - # Compute adjusted averages and unproductive breakdown - ( - final_adjusted_productive_time, - total_segment_unproductive_time, - ) = _compute_segment_final_metrics( - total_productive_time, - first_step_time, - step_times, - wasted_progress_from_disruption, - custom_sync_breakdown, - ) - - return final_adjusted_productive_time, total_segment_unproductive_time - - # Build a deserialized dictionary from cloud logging entries to store step - # start times. The dictionary maps from step count to start time and will be - # used to each step's productive time by looking for its completion in the - # next step's start. - # Note in the instance where progress is lost due to a disruption and the - # last successful checkpoint did not include all the steps, the last set of - # records of the step information will be kept and the previous set will be - # overwritten by design so as to correct for the the previously computed - # additional time that was counted as productive but lost due to a - # disruption. - productive_training_time = 0.0 - total_unproductive_time = {} - step_start_data = {} - job_start_time = None - job_end_time = None - tpu_init_start_time = None - training_prep_start_time = None - data_loading_start_time = None - tpu_initialization_badput = 0.0 - training_prep_badput = 0.0 - data_loading_badput = 0.0 - sync_data_loading = True - current_sync_data_loading = None - if interval_query: - entries_to_process = self._interval_entries - else: - with self._goodput_cache_lock: - entries_to_process = list(self._goodput_cache.get_cached_entries()) - - self._number_of_interruptions = 0 - for payload in entries_to_process: - if _JOB_START_TIME in payload: - # Keep track of the latest start to compute badput due to disruption. - job_start_time = payload[_JOB_START_TIME] - if _STEP_START_TIME in payload: - curr_step = int(payload[_STEP_COUNT]) - if curr_step not in step_start_data: - step_start_data[curr_step] = payload[_STEP_START_TIME] - else: - # In this case, the job restarted from Step (curr_step). It means that - # all progress till Step (curr_step - 1) has been preserved. So we - # can get the productive time since the previous start/restart and - # then clear the step_start_data dict. - self._number_of_interruptions += 1 - self._last_disrupted_step = list(step_start_data.keys())[-1] - self._last_disruption_time = step_start_data[ - self._last_disrupted_step - ] - - # Compute segment productive and unproductive time. - segment_productive_time, segment_unproductive_time = ( - _get_segment_productive_and_unproductive_time( - step_start_data, curr_step, entries_to_process - ) - ) - # Accumulate the segment productive time. - productive_training_time += segment_productive_time - - # When the job restarts, data loading is synchronous. - sync_data_loading = True - if current_sync_data_loading is not None: - segment_unproductive_time[BadputType.DATA_LOADING_SYNC] = ( - segment_unproductive_time.get(BadputType.DATA_LOADING_SYNC, 0) - + current_sync_data_loading - ) - current_sync_data_loading = None - - # Since the current step has been recorded again, the progress - # between the previously recorded curr_step and recently recorded - # curr_step has been lost to a disruption and partially recovered - # due to a checkpoint of curr_step - 1. Accumulate the lost time in - # this segment as unproductive time. - # Note this unproductive time is divided into two buckets: - # 1. Wasted training progress after the last successfully - # checkpointed step and the disruption time until the job - # restarts. - # 2. TPU re-init, training prep, data loading, program startup, - # checkpoint loading etc. after the job restarts and before - # training progress resumes. - - # The first bucket can be calculated as the time between the start - # time of curr_step and the job restart time immediately prior. - if ( - job_start_time is not None - and self._last_disruption_time is not None - and job_start_time > self._last_disruption_time - ): - # Add the additional time it took for the job to restart after last - # interruption. These conditions are only met when the job is - # restarted after a disruption. - # TODO(dishaw): This is the infrastructure disruption Badput and can - # go into a separate bucket. - disruption_badput = job_start_time - self._last_disruption_time - if ( - BadputType.WASTED_PROGRESS_FROM_DISRUPTION - in segment_unproductive_time - ): - segment_unproductive_time[ - BadputType.WASTED_PROGRESS_FROM_DISRUPTION - ] += disruption_badput - else: - segment_unproductive_time[ - BadputType.WASTED_PROGRESS_FROM_DISRUPTION - ] = disruption_badput - - # The second bucket is individually computed either from recorded - # logs (TPU initialization, training preparation, data loading) or - # computed from the first step time after start or restart - # (segment unproductive time). All unproductive time is accumulated - # as we go. - self._accumulate_unproductive_time( - segment_unproductive_time, total_unproductive_time - ) - step_start_data = {curr_step: payload[_STEP_START_TIME]} - - if _JOB_END_TIME in payload: - # Locate the last instance of job's end time if the job has completed. - job_end_time = payload[_JOB_END_TIME] - - # Compute badput due to TPU initialization. - if _TPU_INIT_START_TIME in payload: - tpu_init_start_time = payload[_TPU_INIT_START_TIME] - elif _TPU_INIT_END_TIME in payload and tpu_init_start_time is not None: - tpu_initialization_badput += ( - payload[_TPU_INIT_END_TIME] - tpu_init_start_time - ) - tpu_init_start_time = None - - # Compute badput due to training preparation. - elif _TRAINING_PREPARATION_START_TIME in payload: - training_prep_start_time = payload[_TRAINING_PREPARATION_START_TIME] - elif ( - _TRAINING_PREPARATION_END_TIME in payload - and training_prep_start_time is not None - ): - training_prep_badput += ( - payload[_TRAINING_PREPARATION_END_TIME] - training_prep_start_time - ) - training_prep_start_time = None - - # Compute badput due to data loading. - elif _DATA_LOADING_START_TIME in payload: - data_loading_start_time = payload[_DATA_LOADING_START_TIME] - elif ( - _DATA_LOADING_END_TIME in payload - and data_loading_start_time is not None - ): - data_loading_end_time = payload[_DATA_LOADING_END_TIME] - current_sync_data_loading = ( - data_loading_end_time - data_loading_start_time - ) - data_loading_badput += current_sync_data_loading - if sync_data_loading: - # When the job starts, data loading is synchronous. - total_unproductive_time[BadputType.DATA_LOADING_SYNC] = ( - total_unproductive_time.get(BadputType.DATA_LOADING_SYNC, 0) - + current_sync_data_loading - ) - sync_data_loading = False - data_loading_start_time = None - - # Compute unproductive time from checkpoint manager save and restore. - checkpoint_logger_options = CheckpointLoggerOptions(use_goodput_logger=True) - checkpoint_badput_calc = CheckpointBadputCalculator( - checkpoint_logger_options - ) - checkpoint_badput_calc.entries = entries_to_process - checkpoint_manager_save_stats = ( - checkpoint_badput_calc.calculate_save_operation_checkpoint_manager_blocking_time() - ) - checkpoint_manager_save_badput = ( - checkpoint_manager_save_stats.total_checkpoint_manager_blocking_time - ) - checkpoint_manager_restore_stats = ( - checkpoint_badput_calc.calculate_restore_operation_checkpoint_manager_blocking_time() - ) - checkpoint_manager_restore_badput = ( - checkpoint_manager_restore_stats.total_checkpoint_manager_time - ) - - # Populate some Badput buckets in total_unproductive_time. - total_unproductive_time[BadputType.TPU_INITIALIZATION] = ( - tpu_initialization_badput - ) - total_unproductive_time[BadputType.TRAINING_PREP] = training_prep_badput - - # Populate async data loading badput. - async_data_loading_badput = ( - data_loading_badput - - total_unproductive_time.get(BadputType.DATA_LOADING_SYNC, 0) - ) - total_unproductive_time[BadputType.DATA_LOADING_ASYNC] = ( - async_data_loading_badput - ) - - # Populate checkpoint manager save and restore badput. - total_unproductive_time[BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME] = ( - checkpoint_manager_save_badput - ) - total_unproductive_time[BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME] = ( - checkpoint_manager_restore_badput - ) - - if not step_start_data: - return 0.0, total_unproductive_time, 0 - - last_step = max(list(step_start_data.keys())) - segment_productive_time, segment_unproductive_time = ( - _get_segment_productive_and_unproductive_time( - step_start_data, last_step, entries_to_process - ) - ) - productive_training_time += segment_productive_time - self._accumulate_unproductive_time( - segment_unproductive_time, total_unproductive_time - ) - - # Only consider the last step productive if the job has completed. - if job_end_time is not None: - productive_training_time += job_end_time - step_start_data[last_step] - - # Remove blocking checkpoint manager save time from productive time. - productive_training_time -= checkpoint_manager_save_badput - - # Return a tuple of the total productive training time, the total - # unproductive time (dict of BadputType and unproductive time) and the last - # step recorded. - return productive_training_time, total_unproductive_time, last_step - - def _get_total_job_time(self, query_time: datetime.datetime) -> float: - """Helper function to compute the current job runtime. - - Args: - query_time: The time at which the query is being made. - - Returns: - The job's total runtime computed based on the last retrieved logs. - """ - # Find the job's original start time from the cache. - start_time = self._goodput_cache.get_job_start_time() - end_time = self._goodput_cache.get_job_end_time() - if start_time: - if not end_time: - end_time = query_time - return end_time.timestamp() - start_time.timestamp() - - # De-serealize job start and end times from cloud logging entries. These - # will be used to compute total runtime of the job. - job_start_time = None - job_end_time = None - with self._goodput_cache_lock: - cached_entries = list(self._goodput_cache.get_cached_entries()) - for payload in cached_entries: - # Locate the earliest timestamp recorded for the job's start. - if _JOB_START_TIME in payload and job_start_time is None: - job_start_time = payload[_JOB_START_TIME] - # Locate the latest timestamp recorded for the job's end. - if _JOB_END_TIME in payload: - job_end_time = payload[_JOB_END_TIME] - - if job_start_time is not None: - if job_end_time is not None: - return job_end_time - job_start_time - # If the job's end time is missing then job has not yet completed, use - # current query time to compute total job time. - return query_time.timestamp() - job_start_time - # The the job's start time is missing so the total job time cannot be - # calculated. Caller of this function should raise an error if this happens. - return 0.0 - - def _fetch_new_entries(self, query_time: datetime.datetime) -> list[Any]: - """Thread-safe helper function to update and return new log entries.""" - with self._goodput_cache_lock: - if not self._goodput_cache.is_cache_empty(): - last_entry_timestamp = self._goodput_cache.get_last_entry_timestamp() - if query_time <= last_entry_timestamp: - return [] - new_entries = self._cloud_logger.read_cloud_logging_entries( - last_entry_timestamp, query_time - ) - else: - new_entries = self._cloud_logger.read_cloud_logging_entries() - - # Update the cache with the new log entries. - self._goodput_cache.update_cached_entries(new_entries) - return new_entries - - def _get_interval_log_entries( - self, start_time: datetime.datetime, end_time: datetime.datetime - ): - """Helper function to get log entries from an interval window.""" - if start_time is None or end_time is None: - raise ValueError( - 'Start and end times are required to get log entries from an interval' - ' window.' - ) - self._interval_entries = self._cloud_logger.read_cloud_logging_entries( # type: ignore - start_time, end_time - ) - logging.info( - 'Inspecting interval entries between %s and %s', start_time, end_time - ) - - if not self._interval_entries: - raise ValueError( - 'No log entries found within the interval window between %s and %s.' - % (start_time, end_time) - ) - - def _sanitize_unproductive_times( - self, - unproductive_times: UnproductiveTimeDict, - max_allowed: float, - ) -> None: - """Helper function to sanitize unproductive times.""" - for badput_type, value in unproductive_times.items(): - if isinstance(value, float): - if value < 0.0 or value > max_allowed: - logging.warning( - 'Unproductive time for %s could not be computed.', badput_type - ) - unproductive_times[badput_type] = 0.0 - elif isinstance(value, dict): - for sub_type, sub_value in value.items(): - if sub_value < 0.0 or sub_value > max_allowed: - logging.warning( - 'Unproductive time for %s[%s] could not be computed.', - badput_type, - sub_type, - ) - value[sub_type] = 0.0 - - def _calculate_total_flat_unproductive_time( - self, - unproductive_time_dict: UnproductiveTimeDict, - ) -> float: - """Helper function to calculate total flat unproductive time.""" - total = 0.0 - for badput_type, value in unproductive_time_dict.items(): - if badput_type in {BadputType.DATA_LOADING_ASYNC, BadputType.OTHER}: - continue - if isinstance(value, float): - total += value - elif isinstance(value, dict): - total += sum(value.values()) - return total - - def _compute_other_unproductive_time( - self, - total_job_time: float, - productive_training_time: float, - unproductive_time_dict: UnproductiveTimeDict, - ) -> float: - """Helper function to compute the "Unknown/Other" unproductive time.""" - other_unproductive_time = ( - total_job_time - - productive_training_time - - self._calculate_total_flat_unproductive_time(unproductive_time_dict) - ) - return max(0.0, other_unproductive_time) - - def _get_total_job_time_from_interval( - self, start_interval: datetime.datetime, end_interval: datetime.datetime - ) -> float: - """Helper function to compute the total job runtime from interval entries.""" - # Get the first and last entry's timestamps in the window - first_entry_timestamp = get_timestamp_from_log_entry( - self._interval_entries[0] - ) - last_entry_timestamp = get_timestamp_from_log_entry( - self._interval_entries[-1] - ) - - # Calculate effective start_time and end_time - self._interval_start_time = ( - max(start_interval, first_entry_timestamp) - if first_entry_timestamp - else start_interval - ) - self._interval_end_time = ( - min(end_interval, last_entry_timestamp) - if last_entry_timestamp - else end_interval - ) - - # Ensure start_time is not after end_time - if self._interval_start_time >= self._interval_end_time: - raise ValueError( - 'Start time is on or after end time, cannot compute total job time.' - ) - - return ( - self._interval_end_time.timestamp() - - self._interval_start_time.timestamp() - ) - - def get_job_goodput(self, include_badput_breakdown=False) -> tuple[ - float, - UnproductiveTimeDict, - int, - ]: - """Method to get the cumulative Goodput and Badput breakdown of the job computed until now. - - If the application is interested in retrieving the overall Goodput of the - job throughout its lifetime, this method provides the singular Goodput - computation for the entire job. - - This method also returns the Badput breakdown of the job if - `include_badput_breakdown` is set to True. - - Additionaly, this method returns the last step recorded for the job. This is - primarily used for improving monitoring and observability of the job's - overall Goodput as a function of number of executed steps. - - Args: - include_badput_breakdown: Whether or not to return the badput breakdown. - If False, returns {} for the badput breakdown. - - Returns: - A tuple of the job's Goodput, optionally the Badput breakdown and the last - step recorded for the job. - - Raises: - ValueError if computed total job time is zero. In this case, Goodput - cannot be computed. - ValueError if productive training time is invalid. - """ - query_time = datetime.datetime.now(datetime.timezone.utc) - - # Update the logs used to compute Goodput. - new_entries = self._fetch_new_entries(query_time) - - total_job_time = self._get_total_job_time(query_time) - # No calculations can be made if total job time is zero. This can happen if - # logs for the job are not present, sent to an invalid location or contain - # bad data. Raise a ValueError if this happens. - if total_job_time == 0.0: - raise ValueError( - 'Total job time is zero, Goodput cannot be calculated. Please fix the' - ' logging entries.' - ) - productive_training_time, total_unproductive_time, last_step = ( - self._get_total_productive_and_unproductive_time(new_entries) - ) - if ( - productive_training_time < 0.0 - or productive_training_time > total_job_time - ): - raise ValueError( - 'Productive training time is invalid. Please fix the logging entries.' - ) - - # Sanitize the unproductive times. - self._sanitize_unproductive_times(total_unproductive_time, total_job_time) - - # Compute the "Unknown/Other" unproductive time. - total_unproductive_time[BadputType.OTHER] = ( - self._compute_other_unproductive_time( - total_job_time, productive_training_time, total_unproductive_time - ) - ) - - # Compute the job Goodput and Badput breakdown. - job_goodput = (float(productive_training_time) / total_job_time) * 100 - job_badput_breakdown = ( - self._get_job_badput_breakdown(total_unproductive_time, total_job_time) - if include_badput_breakdown - else {} - ) - - # Update the Goodput cache with new information. - self._goodput_cache.update_goodput_info( - GoodputInfo( - total_productive_time=productive_training_time, - total_elapsed_time_since_start=total_job_time, - total_unproductive_time=total_unproductive_time, - last_recorded_step=last_step, - last_updated_timestamp=datetime.datetime.now(datetime.timezone.utc), - ) - ) - return job_goodput, job_badput_breakdown, last_step - - def get_job_goodput_interval( - self, interval_start: datetime.datetime, interval_end: datetime.datetime - ) -> tuple[ - float, - UnproductiveTimeDict, - int, - float, - int, - ]: - """Method to get the Goodput and Badput breakdown of the job within an interval window. - - If the application is interested in retrieving the Goodput of the job within - a specific window of time, this method provides the metrics computed between - the start and end of this window. - - Additionaly, this method returns the last step recorded for the job. This is - primarily used for improving monitoring and observability of the job's - overall Goodput as a function of number of executed steps. - - Args: - interval_start: The start time of the window for which Goodput is to be - computed. - interval_end: The end time of the window for which Goodput is to be - computed. - - Returns: - A tuple containing: - - The job's Goodput percentage with respect to the total job time within - the interval window. - - The Badput Breakdown percentages with respect to the total job time - within the interval window. - - The last step recorded for the job within the interval window. - - The total job time within the interval window. - - The number of disruptions within the interval window. - - Raises: - ValueError if computed total job time is zero. In this case, Goodput - cannot be computed. - ValueError if productive training or unproductive time is invalid. - """ - # Get the logs for the interval and validate the interval window. - self._get_interval_log_entries(interval_start, interval_end) - - total_job_time = self._get_total_job_time_from_interval( - interval_start, interval_end - ) - - productive_training_time, total_unproductive_time, last_step = ( - self._get_current_productive_and_unproductive_time(interval_query=True) - ) - if ( - productive_training_time < 0.0 - or productive_training_time > total_job_time - ): - raise ValueError( - 'Productive training time is invalid. Please fix the logging entries.' - ) - - # Sanitize unproductive times - self._sanitize_unproductive_times(total_unproductive_time, total_job_time) - - # Compute the "Unknown/Other" unproductive time - total_unproductive_time[BadputType.OTHER] = ( - self._compute_other_unproductive_time( - total_job_time, productive_training_time, total_unproductive_time - ) - ) - - # Compute the job Goodput and Badput breakdown. - job_goodput = (float(productive_training_time) / total_job_time) * 100 - job_badput_breakdown = self._get_job_badput_breakdown( - total_unproductive_time, total_job_time - ) - - return ( - job_goodput, - job_badput_breakdown, - last_step, - total_job_time, - self._number_of_interruptions, - ) - - def _get_step_times(self, entries: list[Any]): - """Helper function to compute step times from the log entries.""" - step_times = {} - previous_step_start_time = None - previous_step_count = None - for payload in entries: - if _STEP_START_TIME in payload: - step_start_time = payload[_STEP_START_TIME] - step_count = int(payload[_STEP_COUNT]) - if ( - previous_step_start_time is not None - and previous_step_count is not None - and step_count == previous_step_count + 1 - ): - step_times[previous_step_count] = ( - step_start_time - previous_step_start_time - ) - previous_step_count = step_count - previous_step_start_time = step_start_time - return step_times - - def _contains_step_entries(self, entries: list[Any]) -> bool: - return any(_STEP_START_TIME in entry for entry in entries) - - def get_step_deviation( - self, configured_ideal_step_time: Optional[float] = None - ) -> dict[int, float]: - """Method to get the step deviation of the current step based on the ideal step time. - - This method computes the ideal step time if one is not provided by the user - and returns the step deviation of the current step. - - Args: - configured_ideal_step_time: Optional user-defined ideal step time. - - Returns: - A dictionary of step deviation for each step. - """ - query_time = datetime.datetime.now(datetime.timezone.utc) - new_entries = self._fetch_new_entries(query_time) - with self._goodput_cache_lock: - step_info = self._goodput_cache.get_step_info() - - if ( - not self._contains_step_entries(new_entries) - and step_info - and step_info.step_deviations - ): - return step_info.step_deviations - - with self._goodput_cache_lock: - process_entries = self._goodput_cache.get_step_entries() - - step_times = self._get_step_times(process_entries) - - if not step_times: - raise ValueError( - 'No step times available and no previous step deviations found.' - ) - - # Compute ideal step time. - ideal_step_time = ( - configured_ideal_step_time - if configured_ideal_step_time is not None - else compute_ideal_step_time(list(step_times.values())) - ) - if not ideal_step_time: - raise ValueError( - 'No ideal step time available and no previous step deviations found.' - ) - - # Compute step deviation. - step_deviations = { - step_count: abs(step_time - ideal_step_time) - for step_count, step_time in step_times.items() - } - # Update the step information in the cache. - with self._goodput_cache_lock: - self._goodput_cache.update_step_info( - StepInfo( - ideal_step_time=ideal_step_time, - step_deviations=step_deviations, - ) - ) - return step_deviations - - def _get_job_badput_breakdown( - self, total_unproductive_time, total_job_time - ) -> UnproductiveTimeDict: - """Method to get the the Badput breakdown as percentage of total job time. - - This method provides a granular breakdown of the known components of Badput. - - Args: - total_unproductive_time: A dictionary of computed unproductive time of - each BadputType. - total_job_time: The total job time. - - Returns: - A dictionary of badput components and their percentage breakdown within - total job time. - """ - badput_breakdown: dict[ - BadputType, float | dict[str, float] - ] = {} - if total_job_time == 0.0: - raise ValueError( - 'Total job time is zero, Badput cannot be calculated. Please fix the' - ' logging entries.' - ) - - # TPU initialization badput. - tpu_init_badput = total_unproductive_time.get( - BadputType.TPU_INITIALIZATION, 0.0 - ) - badput_breakdown[BadputType.TPU_INITIALIZATION] = ( - (tpu_init_badput / total_job_time) * 100 - if 0 < tpu_init_badput < total_job_time - else 0.0 - ) - - # Training preparation badput. - training_prep_badput = total_unproductive_time.get( - BadputType.TRAINING_PREP, 0.0 - ) - badput_breakdown[BadputType.TRAINING_PREP] = ( - (training_prep_badput / total_job_time) * 100 - if 0 < training_prep_badput < total_job_time - else 0.0 - ) - - # Only synchronous data loading is badput. - # Sync data loading is accumulated after start and reset of the job and is - # blocking. - sync_data_loading_badput = total_unproductive_time.get( - BadputType.DATA_LOADING_SYNC, 0.0 - ) - # Async data loading is accumulated overlapping with training and is - # non-blocking, therefore is not unproductive time. - async_data_loading_badput = total_unproductive_time.get( - BadputType.DATA_LOADING_ASYNC, 0.0 - ) - badput_breakdown[BadputType.DATA_LOADING_SYNC] = ( - (sync_data_loading_badput / total_job_time) * 100 - if 0 < sync_data_loading_badput < total_job_time - else 0.0 - ) - badput_breakdown[BadputType.DATA_LOADING_ASYNC] = ( - (async_data_loading_badput / total_job_time) * 100 - if 0 < async_data_loading_badput < total_job_time - else 0.0 - ) - - # Unproductive checkpoint save time badput. - checkpoint_save_badput = total_unproductive_time.get( - BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME, 0.0 - ) - badput_breakdown[BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME] = ( - (checkpoint_save_badput / total_job_time) * 100 - if 0 < checkpoint_save_badput < total_job_time - else 0.0 - ) - - # Unproductive checkpoint restore time badput. - checkpoint_restore_badput = total_unproductive_time.get( - BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME, 0.0 - ) - badput_breakdown[BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME] = ( - (checkpoint_restore_badput / total_job_time) * 100 - if 0 < checkpoint_restore_badput < total_job_time - else 0.0 - ) - - # Program startup badput. - program_startup_badput = total_unproductive_time.get( - BadputType.PROGRAM_STARTUP, 0.0 - ) - badput_breakdown[BadputType.PROGRAM_STARTUP] = ( - (program_startup_badput / total_job_time) * 100 - if 0 < program_startup_badput < total_job_time - else 0.0 - ) - - # Wasted progress from disruption badput. - wasted_progress_from_disruption_badput = total_unproductive_time.get( - BadputType.WASTED_PROGRESS_FROM_DISRUPTION, 0.0 - ) - badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION] = ( - (wasted_progress_from_disruption_badput / total_job_time) * 100 - if 0 < wasted_progress_from_disruption_badput < total_job_time - else 0.0 - ) - - # Custom events badput. - badput_breakdown[BadputType.CUSTOM_BADPUT_EVENTS] = {} - custom_events_badput = total_unproductive_time.get( - BadputType.CUSTOM_BADPUT_EVENTS, {} - ) - - if isinstance(custom_events_badput, dict): - nested_breakdown = {} - for ( - custom_badput_type, - custom_events_badput_value, - ) in custom_events_badput.items(): - nested_breakdown[custom_badput_type] = ( - (custom_events_badput_value / total_job_time) * 100 - if 0 < custom_events_badput_value < total_job_time - else 0.0 - ) - badput_breakdown[BadputType.CUSTOM_BADPUT_EVENTS] = ( - nested_breakdown - ) - - # Populate the 'Other/Unknown' badput bucket. - other_badput = total_unproductive_time.get(BadputType.OTHER, 0.0) - badput_breakdown[BadputType.OTHER] = ( - (other_badput / total_job_time) * 100 - if 0 < other_badput < total_job_time - else 0.0 - ) - - return badput_breakdown - - def get_job_goodput_details( - self, - ) -> dict[ - str, - dict[ - Union[BadputType, GoodputType], - float | dict[str, float], - ], - ]: - """Method to get the productive and non-productive time with breakdown of the job computed until now.""" - - goodput_info = self._goodput_cache.get_goodput_info() - if goodput_info is None: - logger.warning( - 'Goodput information unavailable and will not be uploaded to GCM' - ) - return { - 'goodput_time_dict': {}, - 'badput_time_dict': {}, - } - - ( - productive_training_time, - total_unproductive_time, - cache_last_updated_timestamp, - ) = ( - goodput_info.total_productive_time, - goodput_info.total_unproductive_time, - goodput_info.last_updated_timestamp, - ) - - if ( - self._gcm_last_recorded_timestamp is not None # Ignore the first entry. - and self._gcm_last_recorded_timestamp >= cache_last_updated_timestamp - ): - logger.warning( - 'No new data, skipping upload to GCM. Cache Timestamp: %s, GCM' - ' Timestamp: %s', cache_last_updated_timestamp, - self._gcm_last_recorded_timestamp, - ) - return { - 'goodput_time_dict': {}, - 'badput_time_dict': {}, - } - - self._gcm_last_recorded_timestamp = datetime.datetime.now( - datetime.timezone.utc - ) - - # Currently productive_time is not split based on productive activities, it - # is just the total productive time. We will modify this to follow the same - # format as badput_breakdown. Please update this code accordingly in the - # future when we have more granular breakdown of productive time. - - total_productive_time = {GoodputType.TOTAL: productive_training_time} - - return { - 'goodput_time_dict': total_productive_time, - 'badput_time_dict': total_unproductive_time, - } - - def get_job_goodput_interval_details( - self, interval_start: datetime.datetime, interval_end: datetime.datetime - ) -> dict[ - str, - dict[ - Union[BadputType, GoodputType], - float | dict[str, float], - ], - ]: - """Method to get the productive and non-productive time with breakdown of the job computed within an interval window.""" - try: - goodput, badput_breakdown, _, total_job_time, _ = ( - self.get_job_goodput_interval(interval_start, interval_end) - ) - productive_time = goodput * total_job_time / 100 - total_unproductive_time = {} - for badput_type, badput_value in badput_breakdown.items(): - total_unproductive_time[badput_type] = ( - badput_value * total_job_time / 100 - ) - total_productive_time = {GoodputType.TOTAL: productive_time} - - return { - 'goodput_time_dict': total_productive_time, - 'badput_time_dict': total_unproductive_time, - } - except ValueError as e: - logger.warning('Failed to get job goodput interval details: %s', e) - return { - 'goodput_time_dict': {}, - 'badput_time_dict': {}, - } diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py b/ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py deleted file mode 100644 index 673f5a3..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/src/goodput_cache.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Goodput Cache implementations.""" - -import datetime -from typing import Any - -from cloud_goodput.ml_goodput_measurement.src import goodput_utils - - -StepInfo = goodput_utils.StepInfo -GoodputInfo = goodput_utils.GoodputInfo -_TIME_ENTRY = 'time' -_JOB_START_TIME = 'job_start_time' -_JOB_END_TIME = 'job_end_time' -_STEP_START_TIME = 'step_start_time' - - -class GoodputCache: - """Goodput Cache.""" - - def __init__(self): - self._cached_entries = [] - self._step_entries = [] - self._goodput_info = None - self._last_entry_timestamp = None - self._job_start_time = None - self._job_end_time = None - self._step_info = None - - def update_step_info(self, step_info: StepInfo): - """Updates the step information.""" - self._step_info = step_info - - def update_cached_entries(self, entries: list[Any]): - """Updated the cached entries.""" - self._cached_entries.extend(entries) - self.update_last_entry_timestamp() - self.update_job_start_time() - self.update_job_end_time() - new_step_entries = [entry for entry in entries if _STEP_START_TIME in entry] - self._step_entries.extend(new_step_entries) - - def update_last_entry_timestamp(self): - """Helper function to store the timestamp of the last entry in the cache.""" - if self._cached_entries: - last_entry = self._cached_entries[-1] - last_entry_posix_time = [ - entry_value - for entry_label, entry_value in last_entry.items() - if _TIME_ENTRY in entry_label - ] - if last_entry_posix_time: - self._last_entry_timestamp = datetime.datetime.fromtimestamp( - last_entry_posix_time[0], tz=datetime.timezone.utc - ) - - def update_job_start_time(self): - """Updates the job start time.""" - # If the job start time is not set, try to find it in the cached entries. - if self._job_start_time is None and self._cached_entries: - for entry in self._cached_entries: - if _JOB_START_TIME in entry: - self._job_start_time = datetime.datetime.fromtimestamp( - entry[_JOB_START_TIME], tz=datetime.timezone.utc - ) - break - - def update_job_end_time(self): - """Updates the job end time.""" - # Overwrite the latest job end time if cached entries contain the job end - # time. - if self._job_end_time is None and self._cached_entries: - for entry in reversed(self._cached_entries): - if _JOB_END_TIME in entry: - self._job_end_time = datetime.datetime.fromtimestamp( - entry[_JOB_END_TIME], tz=datetime.timezone.utc - ) - break - - def update_goodput_info(self, goodput_info: GoodputInfo): - """Updates the last computed Goodput information.""" - self._goodput_info = goodput_info - - def get_cached_entries(self): - """Returns the cached entries.""" - return self._cached_entries - - def get_step_entries(self): - """Returns the step entries.""" - return self._step_entries - - def get_goodput_info(self): - """Returns the last computed Goodput information.""" - return self._goodput_info - - def get_job_start_time(self): - """Returns the job start time.""" - return self._job_start_time - - def get_job_end_time(self): - """Returns the job end time.""" - return self._job_end_time - - def get_last_entry_timestamp(self): - """Returns the timestamp of the last entry in the cache.""" - return self._last_entry_timestamp - - def get_step_info(self): - """Returns the step information.""" - return self._step_info - - def clear_cache(self): - """Clears the cache.""" - self._cached_entries = [] - self._goodput_info = None - self._last_entry_timestamp = None - - def is_cache_empty(self) -> bool: - """Checks if the cache is empty.""" - return not self._cached_entries diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py b/ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py deleted file mode 100644 index 9cd5e6c..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/src/goodput_utils.py +++ /dev/null @@ -1,258 +0,0 @@ -"""Goodput Utility Classes and Helpers.""" - -import dataclasses -import datetime -import enum -import logging -import math -from typing import Any, Optional - -import numpy as np -import requests -from scipy import stats -from urllib3.util import retry - - -Retry = retry.Retry -_TIME_ENTRY = 'time' -_METADATA_SERVER_URL = 'http://metadata.google.internal/computeMetadata/v1/' -_METADATA_HEADERS = {'Metadata-Flavor': 'Google'} - -MACHINE_TYPE_TO_ACCELERATOR_TYPE_MAPPING = { - 'ct6e': 'TPU-v6e', - 'ct5p': 'TPU-v5p', - 'ct5lp': 'TPU-v5e', - 'ct5l': 'TPU-v5e', - 'ct4p': 'TPU-v4p', - 'ct3p': 'TPU-v3', - 'ct3': 'TPU-v3', - 'tpu-v2': 'TPU-v2', - 'tpu': 'TPU', - 'a3-edgegpu': 'NVIDIA-H100', - 'a3-highgpu': 'NVIDIA-H100', - 'a3-megagpu': 'NVIDIA-H100', - 'a3-ultragpu': 'NVIDIA-H200', - 'a2': 'NVIDIA-A100', - 'gpu': 'GPU', -} - - -@dataclasses.dataclass -class GCPOptions: - project_id: Optional[str] = None - location: Optional[str] = None - replica_id: str = '0' - acc_type: Optional[str] = None - enable_gcp_goodput_metrics: bool = True - enable_gcp_step_deviation_metrics: bool = True - - -# Productive time is not broken down by activities yet. As such, we only have -# one type of Goodput which contributes to the total productive time. -class GoodputType(enum.Enum): - """The type of Goodput.""" - - TOTAL = 1 - - -class BadputType(enum.Enum): - """The type of Badput.""" - - TPU_INITIALIZATION = 1 - TRAINING_PREP = 2 - PROGRAM_STARTUP = 3 - DATA_LOADING_SYNC = 4 - DATA_LOADING_ASYNC = 5 - UNPRODUCTIVE_CHECKPOINT_SAVE_TIME = 6 - UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME = 7 - WASTED_PROGRESS_FROM_DISRUPTION = 8 - CUSTOM_BADPUT_EVENTS = 9 - OTHER = 10 - - -ACTIVITY_EXCLUSION_LIST = [ - # DATA_LOADING_ASYNC is not a non-productive activity as it is not - # blocking. Hence, we exclude it from calculating Goodput. - 'DATA_LOADING_ASYNC', -] - - -class GoodputInfo: - """Goodput Information.""" - - def __init__( - self, - total_productive_time: float = 0.0, - total_elapsed_time_since_start: float = 0.0, - total_unproductive_time: Optional[dict[BadputType, float]] = None, - last_recorded_step: int = 0, - last_updated_timestamp: datetime.datetime = datetime.datetime.now( - datetime.timezone.utc - ), - ): - self.total_productive_time = total_productive_time - self.total_elapsed_time_since_start = total_elapsed_time_since_start - - # We cannot use {} as the default argument directly because it's a mutable - # default argument. Mutable default arguments are shared between all - # instances of the class. If one instance modifies the default - # dictionary, it will affect all other instances. Instead, we use - # None as a sentinel value and create a new dictionary inside the - # __init__ method if no dictionary is provided. This ensures each - # instance gets its own dictionary. - self.total_unproductive_time = ( - total_unproductive_time or {} - ) - self.last_recorded_step = last_recorded_step - self.last_updated_timestamp = last_updated_timestamp - - -class StepInfo: - """Step Information.""" - - def __init__( - self, - ideal_step_time: float, - step_deviations: dict[int, float], - ): - self.ideal_step_time = ideal_step_time - self.step_deviations = step_deviations - - -def compute_ideal_step_time(step_times: list[float]) -> Optional[float]: - """Helper function to compute the ideal step time.""" - # Filter out step times that may be less than 1 second. - step_times = [step_time for step_time in step_times if step_time >= 1.0] - if not step_times: - return None - # Compute the median absolute deviation (MAD) and median of the step times - mad = stats.median_abs_deviation(step_times) - med = np.median(step_times) - - # Normalize the step times to the median + 3 * MAD. - normal_step_times = [ - step_time for step_time in step_times if step_time <= (med + mad * 3) - ] - return np.mean(normal_step_times) if normal_step_times else None - - -def get_anomalous_and_normal_step_times( - step_times: list[Any], -) -> tuple[list[Any], list[Any]]: - """Helper function to get anomalous and normal step times.""" - mad = stats.median_abs_deviation(step_times) - med = np.median(step_times) - - anomalous_step_times = [] - normal_step_times = [] - for step_time in step_times: - if step_time > (med + mad * 3): - anomalous_step_times.append(step_time) - else: - normal_step_times.append(step_time) - - return anomalous_step_times, normal_step_times - - -def get_extra_time_from_anomalous_steps(step_times: list[Any]) -> float: - anomalous_step_times, normal_step_times = get_anomalous_and_normal_step_times( - step_times - ) - normal_step_mean = np.mean(normal_step_times) - return sum(anomalous_step_times) - ( - len(anomalous_step_times) * normal_step_mean - ) - - -def get_timestamp_from_log_entry( - entry: dict[str, Any], -) -> Optional[datetime.datetime]: - """Helper function to get the timestamp from a log entry.""" - timestamp_posix_time = [ - entry_value - for entry_label, entry_value in entry.items() - if _TIME_ENTRY in entry_label - ] - if timestamp_posix_time: - return datetime.datetime.fromtimestamp( - timestamp_posix_time[0], datetime.timezone.utc - ) - return None - - -def get_gcp_metadata(category: str, attribute: str, timeout=5, retries=3): - """Fetch the specified attribute from GCP metadata server. - - Args: - category (str): The high-level metadata category (ex: 'instance', - 'project'). - attribute (str): The attribute to fetch under this category (ex: 'id', - 'zone'). - timeout (int): Timeout for the request in seconds. - retries (int): Number of retry attempts for transient failures. - - Returns: - str: The metadata value as a string, or None if the request fails. - """ - target_url = f'{_METADATA_SERVER_URL}{category}/{attribute}' - - session = requests.Session() - retry_strategy = Retry( - total=retries, - backoff_factor=0.5, - # Retry on the following status codes - status_forcelist=[429, 500, 502, 503, 504], - ) - adapter = requests.adapters.HTTPAdapter(max_retries=retry_strategy) - session.mount('http://', adapter) - - try: - response = session.get( - target_url, headers=_METADATA_HEADERS, timeout=timeout - ) - response.raise_for_status() - return response.text - except requests.exceptions.RequestException as e: - logging.warning( - 'Failed to retrieve metadata for %s/%s: %s', category, attribute, e - ) - return None - - -def get_gcp_project_id(): - """Returns the project id of the current GCP project.""" - return get_gcp_metadata('project', 'project-id') - - -def get_node_zone(): - """Returns the zone of the GCE instance.""" - zone_path = get_gcp_metadata('instance', 'zone') - # example zone_path: "projects/123456789/zones/us-central1-a" - return zone_path.rsplit('/', 1)[-1] if zone_path else None - - -def get_accelerator_type(): - """Retrieves the accelerator type from GCP metadata. - - For GKE TPU VMs, it extracts the type from the 'machine-type' metadata. - - Returns: - str: The accelerator type, or 'UNKNOWN' if not found. - """ - machine_type_url = get_gcp_metadata('instance', 'machine-type') - # example machine_type_url: "projects/123456789/machineTypes/a3-highgpu-8g" - machine_type_name = ( - machine_type_url.split('/')[-1] if machine_type_url else None - ) - - if not machine_type_name: - return 'UNKNOWN' - - for ( - prefix, - accelerator_type, - ) in MACHINE_TYPE_TO_ACCELERATOR_TYPE_MAPPING.items(): - if prefix.lower() in machine_type_name.lower(): - return accelerator_type - - return 'UNKNOWN' diff --git a/ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py b/ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py deleted file mode 100644 index bb8e0a9..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/src/monitoring.py +++ /dev/null @@ -1,638 +0,0 @@ -"""Goodput monitoring API. - -This file contains all the utilities to monitor and upload goodput data of a -user workload to Tensorboard asynchronously. -""" - -import datetime -import logging -import math -import os -import threading -import time - -from cloud_goodput.ml_goodput_measurement.src import gcp_metrics -from cloud_goodput.ml_goodput_measurement.src import goodput -from cloud_goodput.ml_goodput_measurement.src import goodput_utils -from tensorboardX import writer - -BadputType = goodput_utils.BadputType -GCPOptions = goodput_utils.GCPOptions -GCPMetrics = gcp_metrics.GCPMetrics -GoodputCalculator = goodput.GoodputCalculator -ValueType = gcp_metrics.ValueType - -ACTIVITY_EXCLUSION_LIST = goodput_utils.ACTIVITY_EXCLUSION_LIST -_TENSORBOARD_GCS_SUBDIR = 'goodput' -_TENSORBOARD_GOODPUT_LABEL = 'goodput' -_TENSORBOARD_BADPUT_LABEL = 'badput' -_TENSORBOARD_STEP_DEVIATION_LABEL = 'step_deviation' -_GOODPUT_DETAILS_KEY = 'goodput_time_dict' -_BADPUT_DETAILS_KEY = 'badput_time_dict' - -logger = logging.getLogger(__name__) - - -class GoodputMonitor: - """Queries and uploads goodput data to Tensorboard at a regular interval.""" - - def __init__( - self, - job_name: str, - logger_name: str, - tensorboard_dir: str, - upload_interval: int, - monitoring_enabled: bool = False, - pathway_enabled: bool = False, - include_badput_breakdown=False, - include_step_deviation=False, - configured_ideal_step_time=None, - step_deviation_interval_seconds=10, - gcp_options: GCPOptions = GCPOptions(), - ): - """Initializes the GoodputMonitor. - - Args: - job_name: The name of the job to monitor. - logger_name: The name of the Google Cloud Logging logger to use. - tensorboard_dir: The directory to write TensorBoard data to. - upload_interval: The interval to upload data to TensorBoard and GCP - Monitoring. - monitoring_enabled: Whether to enable monitoring. If the application is - interested in monitoring Goodput, it should set this value to True if - monitoring from TPU worker 0 andthe application's configurations - request Goodput monitoring. - pathway_enabled: Whether the application is using Pathways. - include_badput_breakdown: Whether to query and upload badput breakdown - data to Tensorboard. - include_step_deviation: Whether to query and upload step deviation data - to Tensorboard. - configured_ideal_step_time: The optional ideal step time configured by - the user. - step_deviation_interval_seconds: The interval to query step deviation - data. - gcp_options: The options for Google Cloud Monitoring. - """ - if not monitoring_enabled: - logger.info( - 'Monitoring is disabled. Returning without initializing' - ' GoodputMonitor.' - ) - return - - # Common configurations. - self._job_name = job_name - self._logger_name = logger_name - self._tensorboard_dir = os.path.join( - tensorboard_dir, _TENSORBOARD_GCS_SUBDIR - ) - # Goodput configurations. - self._upload_interval = upload_interval - self._include_badput_breakdown = include_badput_breakdown - - # Step deviation configurations. - self._include_step_deviation = include_step_deviation - self._step_deviation_interval_seconds = step_deviation_interval_seconds - self._configured_ideal_step_time = configured_ideal_step_time - - # Initialize the GoodputCalculator. - self._goodput_calculator = GoodputCalculator( - job_name=self._job_name, - logger_name=self._logger_name, - using_pathways=pathway_enabled, - ) - self._writer = writer.SummaryWriter(self._tensorboard_dir) - - # Goodput uploader flags to signal the daemon thread if it exists when to - # initate shutdown and wait for termination. - self._goodput_uploader_thread_running = False - self._goodput_upload_thread = None - self._termination_event = threading.Event() - self._termination_event.clear() - - # Step deviation threading flags. - self._step_deviation_uploader_thread_running = False - self._step_deviation_upload_thread = None - self._step_deviation_termination_event = threading.Event() - self._step_deviation_termination_event.clear() - - # Google Cloud Monitoring configurations. - self._gcp_options = gcp_options - self._metrics_sender = None - - # If step deviation is not included, disable GCP step deviation metrics. - if not self._include_step_deviation: - self._gcp_options.enable_gcp_step_deviation_metrics = False - - if ( - self._gcp_options.enable_gcp_goodput_metrics - or self._gcp_options.enable_gcp_step_deviation_metrics - ): - if not self._gcp_options.project_id: - self._gcp_options.project_id = goodput_utils.get_gcp_project_id() - if not self._gcp_options.location: - self._gcp_options.location = goodput_utils.get_node_zone() - if not self._gcp_options.acc_type: - self._gcp_options.acc_type = goodput_utils.get_accelerator_type() - if self._gcp_options.project_id and self._gcp_options.location: - self._metrics_sender = GCPMetrics( - project_id=self._gcp_options.project_id - ) - else: - self._gcp_options.enable_gcp_goodput_metrics = False - self._gcp_options.enable_gcp_step_deviation_metrics = False - logger.warning( - 'Project ID or location is not set. GCP Monitoring will not be' - ' enabled.' - ) - # Goodput interval uploader flags. - self._interval_uploader_thread_running = False - self._interval_goodput_upload_thread = None - self._interval_termination_event = threading.Event() - self._interval_termination_event.clear() - self._interval_window_size_seconds = 0 - - def __del__(self): - try: - self.flush_and_stop_goodput_uploader() - self.flush_and_stop_step_deviation_uploader() - self.flush_and_stop_interval_goodput_uploader() - - except Exception: # pylint: disable=broad-exception-caught - pass - - def _log_tensorboard_scalars( - self, - label_prefix: str, - data: dict[str, float | dict[str, float]], - step: int, - ): - """Logs scalar values (flat or nested) to TensorBoard under a label prefix.""" - if self._writer is None: - return - - for data_type, data_value in data.items(): - if isinstance(data_value, dict): - for subtype, subval in data_value.items(): - full_label = f'{label_prefix}/{data_type}/{subtype}'.lower() - self._writer.add_scalar( - full_label, float(subval), step, display_name=subtype.lower() - ) - else: - full_label = f'{label_prefix}/{data_type.lower()}' - self._writer.add_scalar( - full_label, float(data_value), step, display_name=data_type.lower() - ) - - self._writer.flush() - - def _write_goodput_and_badput_data_to_tensorboard( - self, - job_goodput: float, - badput_breakdown: dict[BadputType, float], - last_step: int, - ): - """Writes goodput and badput breakdown to Tensorboard.""" - self._write_goodput_to_tensorboard(job_goodput, last_step) - if self._include_badput_breakdown: - self._write_badput_to_tensorboard(badput_breakdown, last_step) - - def _write_goodput_to_tensorboard(self, job_goodput: float, last_step: int): - self._log_tensorboard_scalars( - _TENSORBOARD_GOODPUT_LABEL, - {_TENSORBOARD_GOODPUT_LABEL: job_goodput}, - last_step, - ) - - def _write_badput_to_tensorboard( - self, - job_badput_breakdown: dict[BadputType, float | dict[str, float]], - last_step: int, - ): - """Writes badput breakdown to TensorBoard.""" - flattened_badput: dict[str, float | dict[str, float]] = {} - - for badput_type, badput_value in job_badput_breakdown.items(): - if isinstance(badput_value, dict): - flattened_badput[badput_type.name.lower()] = { - subtype.lower(): value for subtype, value in badput_value.items() - } - else: - flattened_badput[badput_type.name.lower()] = badput_value - - self._log_tensorboard_scalars( - _TENSORBOARD_BADPUT_LABEL, - flattened_badput, - last_step, - ) - - def _query_and_upload_goodput_to_tensorboard(self): - """Queries and uploads goodput data to Tensorboard.""" - try: - job_goodput, job_badput_breakdown, last_step = ( - self._goodput_calculator.get_job_goodput( - include_badput_breakdown=self._include_badput_breakdown - ) - ) - self._write_goodput_and_badput_data_to_tensorboard( - job_goodput, job_badput_breakdown, last_step - ) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error( - 'Error while querying and uploading goodput to Tensorboard. This' - ' will not impact the workload. Error: %s', - e, - ) - - def _flatten_badput_dict( - self, - badput_time_dict: dict[BadputType, float | dict[str, float]], - ) -> list[tuple[str, float]]: - """Flattens nested badput types into (label, value) pairs for export.""" - flat_badput = [] - for badput_type, val in badput_time_dict.items(): - if isinstance(val, dict): - for subtype, subval in val.items(): - flat_badput.append((f'{badput_type.name}.{subtype.upper()}', subval)) - else: - flat_badput.append((badput_type.name, val)) - return flat_badput - - def _send_goodput_metrics_to_gcp(self, goodput_details): - """Sends goodput and badput metrics to GCP Monitoring.""" - try: - gcp_goodput_metrics = [] - - for goodput_type, time_value in goodput_details[ - _GOODPUT_DETAILS_KEY - ].items(): - if goodput_type.name in ACTIVITY_EXCLUSION_LIST: - continue - gcp_goodput_metrics.append({ - 'metric_type': 'compute.googleapis.com/workload/goodput_time', - 'value': time_value, - 'value_type': ValueType.DOUBLE, - 'metric_labels': { - 'goodput_source': goodput_type.name, - 'accelerator_type': self._gcp_options.acc_type, - }, - 'resource_type': 'compute.googleapis.com/Workload', - 'resource_labels': { - 'location': self._gcp_options.location, - 'workload_id': self._job_name, - 'replica_id': self._gcp_options.replica_id, - }, - }) - for badput_label, time_value in self._flatten_badput_dict( - goodput_details[_BADPUT_DETAILS_KEY] - ): - if badput_label in ACTIVITY_EXCLUSION_LIST: - continue - gcp_goodput_metrics.append({ - 'metric_type': 'compute.googleapis.com/workload/badput_time', - 'value': time_value, - 'value_type': ValueType.DOUBLE, - 'metric_labels': { - 'badput_source': badput_label, - 'accelerator_type': self._gcp_options.acc_type, - }, - 'resource_type': 'compute.googleapis.com/Workload', - 'resource_labels': { - 'location': self._gcp_options.location, - 'workload_id': self._job_name, - 'replica_id': self._gcp_options.replica_id, - }, - }) - if self._metrics_sender and gcp_goodput_metrics: - self._metrics_sender.send_metrics(gcp_goodput_metrics) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error( - 'Error while sending goodput metrics to GCP Monitoring. This' - ' will not impact the workload. Error: %s', - e, - ) - - def _query_and_upload_goodput(self): - """Queries and uploads goodput data to Tensorboard.""" - while not self._termination_event.is_set(): - time.sleep(self._upload_interval) - self._query_and_upload_goodput_to_tensorboard() - if self._gcp_options.enable_gcp_goodput_metrics: - self._send_goodput_metrics_to_gcp( - self._goodput_calculator.get_job_goodput_details() - ) - - def _final_goodput_query_and_upload(self): - """Performs final goodput query and uploads data to Tensorboard & GCM.""" - logger.info( - 'Final goodput query and upload for job: %s and logger: %s', - self._job_name, - self._logger_name, - ) - try: - job_goodput, job_badput_breakdown, last_step = ( - self._goodput_calculator.get_job_goodput( - include_badput_breakdown=self._include_badput_breakdown - ) - ) - self._write_goodput_and_badput_data_to_tensorboard( - job_goodput, job_badput_breakdown, last_step - ) - if self._gcp_options.enable_gcp_goodput_metrics: - self._send_goodput_metrics_to_gcp( - self._goodput_calculator.get_job_goodput_details() - ) - logger.info( - 'Final goodput query and upload for job: %s and logger: %s completed' - ' with total goodput: %.2f%%, last step: %d', - self._job_name, - self._logger_name, - job_goodput, - last_step, - ) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error( - 'Error while performing final goodput query and upload for job: %s' - ' and logger: %s. This will not impact the workload. Error: %s', - self._job_name, - self._logger_name, - e, - ) - - def flush_and_stop_goodput_uploader(self): - """Stops uploader and performs a final goodput upload.""" - if self._goodput_uploader_thread_running: - self.stop_goodput_uploader() - self._final_goodput_query_and_upload() - - def start_goodput_uploader(self): - """Starts the goodput uploader thread.""" - if self._goodput_uploader_thread_running: - raise RuntimeError('Goodput uploader thread is already running.') - - self._termination_event.clear() - self._goodput_upload_thread = threading.Thread( - target=self._query_and_upload_goodput, daemon=True - ) - logger.info( - 'Starting goodput query and uploader thread in the background for job:' - ' %s and logger: %s', - self._job_name, - self._logger_name, - ) - self._goodput_upload_thread.start() - self._goodput_uploader_thread_running = True - - def stop_goodput_uploader(self): - """Stops the goodput uploader thread.""" - if not self._goodput_uploader_thread_running: - raise RuntimeError('Goodput uploader thread is not running.') - - self._termination_event.set() - if self._goodput_upload_thread is not None: - logger.info('Waiting for goodput query and uploader thread to complete.') - self._goodput_upload_thread.join() - self._goodput_upload_thread = None - logger.info( - 'Goodput query and uploader thread stopped. No more goodput data will' - ' be uploaded to Tensorboard or GCP Monitoring.' - ) - self._goodput_uploader_thread_running = False - - def _write_step_deviation_to_tensorboard( - self, step_deviation: dict[int, float] - ): - if self._writer is not None: - for step_count, step_deviation in step_deviation.items(): - self._writer.add_scalar( - _TENSORBOARD_STEP_DEVIATION_LABEL, - float(step_deviation), - step_count, - ) - self._writer.flush() - - def _send_step_deviation_metric_to_gcp(self, step_deviations): - """Sends step deviation metric to GCP Monitoring.""" - try: - if not step_deviations: - logger.warning( - 'Step deviation is empty. This will not impact the workload.' - ) - return - avg_step_deviation = sum(step_deviations.values()) / len(step_deviations) - - if math.isnan(avg_step_deviation): - logger.warning( - 'Step deviation is NaN. This will not impact the workload.' - ) - return - - perf_metric = [{ - 'metric_type': 'compute.googleapis.com/workload/performance', - 'value': avg_step_deviation, - 'value_type': ValueType.DOUBLE, - 'resource_type': 'compute.googleapis.com/Workload', - 'resource_labels': { - 'location': self._gcp_options.location, - 'workload_id': self._job_name, - 'replica_id': self._gcp_options.replica_id, - }, - }] - if self._metrics_sender: - self._metrics_sender.send_metrics(perf_metric) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error( - 'Error while sending step deviation to GCP Monitoring.' - ' This will not impact the workload. Error: %s', - e, - ) - - def _query_and_upload_step_deviation_to_tensorboard_and_gcp(self): - """Queries and uploads step deviation data to Tensorboard and GCP Monitoring.""" - try: - step_deviation = self._goodput_calculator.get_step_deviation( - self._configured_ideal_step_time - ) - self._write_step_deviation_to_tensorboard(step_deviation) - if self._gcp_options.enable_gcp_step_deviation_metrics: - self._send_step_deviation_metric_to_gcp(step_deviation) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error( - 'Error while querying and uploading step deviation to Tensorboard.' - ' This will not impact the workload. Error: %s', - e, - ) - - def _query_and_upload_step_deviation(self): - """Queries and uploads step deviation data to Tensorboard.""" - while not self._step_deviation_termination_event.is_set(): - time.sleep(self._step_deviation_interval_seconds) - self._query_and_upload_step_deviation_to_tensorboard_and_gcp() - - def _final_step_deviation_query_and_upload(self): - """Performs final step deviation query and uploads data to Tensorboard & GCM.""" - logger.info( - 'Final step deviation query and upload for job: %s and logger: %s', - self._job_name, - self._logger_name, - ) - try: - step_deviation = self._goodput_calculator.get_step_deviation( - self._configured_ideal_step_time - ) - self._write_step_deviation_to_tensorboard(step_deviation) - if self._gcp_options.enable_gcp_step_deviation_metrics: - self._send_step_deviation_metric_to_gcp(step_deviation) - logger.info( - 'Final step deviation query and upload for job: %s and logger: %s' - ' completed', - self._job_name, - self._logger_name, - ) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error( - 'Error while performing final step deviation query and upload for' - ' job: %s and logger: %s. This will not impact the workload. Error:' - ' %s', - self._job_name, - self._logger_name, - e, - ) - - def flush_and_stop_step_deviation_uploader(self): - """Stops uploader and performs a final step deviation upload.""" - if self._step_deviation_uploader_thread_running: - self.stop_step_deviation_uploader() - self._final_step_deviation_query_and_upload() - - def start_step_deviation_uploader(self): - """Starts the step deviation uploader thread.""" - if not self._include_step_deviation: - logger.info( - 'Step deviation monitoring is disabled. Returning without' - ' initializing step deviation uploader thread.' - ) - return - - if self._step_deviation_uploader_thread_running: - raise RuntimeError('Step deviation uploader thread is already running.') - - self._step_deviation_termination_event.clear() - self._step_deviation_upload_thread = threading.Thread( - target=self._query_and_upload_step_deviation, daemon=True - ) - logger.info( - 'Starting step deviation query and uploader thread in the background' - ' for job: %s and logger: %s', - self._job_name, - self._logger_name, - ) - self._step_deviation_upload_thread.start() - self._step_deviation_uploader_thread_running = True - - def stop_step_deviation_uploader(self): - """Stops the step deviation uploader thread.""" - if not self._step_deviation_uploader_thread_running: - raise RuntimeError('Step deviation uploader thread is not running.') - - self._step_deviation_termination_event.set() - if self._step_deviation_upload_thread is not None: - logger.info( - 'Waiting for step deviation query and uploader thread to complete.' - ) - self._step_deviation_upload_thread.join() - logger.info( - 'Step deviation query and uploader thread stopped. No more step' - ' deviation data will be uploaded to Tensorboard or GCP Monitoring.' - ) - self._step_deviation_uploader_thread_running = False - - def _query_and_upload_interval_goodput(self): - """Queries and uploads goodput interval data to Tensorboard.""" - while not self._interval_termination_event.is_set(): - time.sleep(self._upload_interval) - if self._gcp_options.enable_gcp_goodput_metrics: - window_end = datetime.datetime.now(datetime.timezone.utc) - window_start = window_end - datetime.timedelta( - seconds=self._interval_window_size_seconds - ) - # Add timezone since deltatime removes it. - window_start = window_start.replace(tzinfo=datetime.timezone.utc) - self._send_goodput_metrics_to_gcp( - self._goodput_calculator.get_job_goodput_interval_details( - window_start, window_end - ) - ) - - def _final_interval_goodput_query_and_upload(self): - """Performs final interval goodput query and uploads data to GCM.""" - logger.info( - 'Final interval goodput query and upload for job: %s and logger: %s', - self._job_name, - self._logger_name, - ) - try: - window_end = datetime.datetime.now(datetime.timezone.utc) - window_start = window_end - datetime.timedelta( - seconds=self._interval_window_size_seconds - ) - # Add timezone since deltatime removes it. - window_start = window_start.replace(tzinfo=datetime.timezone.utc) - self._send_goodput_metrics_to_gcp( - self._goodput_calculator.get_job_goodput_interval_details( - window_start, window_end - ) - ) - except Exception as e: # pylint: disable=broad-exception-caught - logger.error( - 'Error while performing final interval goodput query and upload for' - ' job: %s and logger: %s. This will not impact the workload. Error:' - ' %s', - self._job_name, - self._logger_name, - e, - ) - - def flush_and_stop_interval_goodput_uploader(self): - """Stops uploader and performs a final interval goodput upload.""" - if self._interval_uploader_thread_running: - self.stop_goodput_interval_uploader() - self._final_interval_goodput_query_and_upload() - - def start_goodput_interval_uploader(self, window_size_seconds: float): - """Starts the goodput uploader thread for a user-specified interval window.""" - if self._interval_uploader_thread_running: - raise RuntimeError('Goodput interval uploader thread is already running.') - - self._interval_termination_event.clear() - self._interval_window_size_seconds = window_size_seconds - self._interval_goodput_upload_thread = threading.Thread( - target=self._query_and_upload_interval_goodput, - daemon=True, - ) - logger.info( - 'Starting goodput interval query and uploader thread in the background' - ' for job: %s and logger: %s', - self._job_name, - self._logger_name, - ) - self._interval_goodput_upload_thread.start() - self._interval_uploader_thread_running = True - - def stop_goodput_interval_uploader(self): - """Stops the goodput uploader thread.""" - if not self._interval_uploader_thread_running: - raise RuntimeError('Goodput intervaluploader thread is not running.') - - self._interval_termination_event.set() - if self._interval_goodput_upload_thread is not None: - logger.info( - 'Waiting for goodput interval query and uploader thread to complete.' - ) - self._interval_goodput_upload_thread.join() - self._interval_goodput_upload_thread = None - logger.info( - 'Goodput interval query and uploader thread stopped. No more goodput' - ' intervaldata will be uploaded to GCP Monitoring.' - ) - self._interval_uploader_thread_running = False diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py deleted file mode 100644 index 58b0d31..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/tests/checkpoint_badput_calculator_test.py +++ /dev/null @@ -1,446 +0,0 @@ -"""Tests for checkpoint badput calculator.""" - -import dataclasses -from typing import Optional - -from absl.testing import absltest -from cloud_goodput.ml_goodput_measurement.src import checkpoint_badput_calculator -import google.cloud.logging as google_cloud_logging -import mock - - -_JOB_NAME = 'checkpoint_job' -_LOGGER_NAME = 'checkpoint_logger' - - -@dataclasses.dataclass -class MockSaveStepStatistics: - """Attributes for save step statistics. - - Attributes: - step: The step number. - event_type: The event type. - checkpoint_manager_blocking_start_time: The start time of checkpoint manager - blocking section. - directory: The directory of the checkpoint. - reached_preemption: Whether the event reached preemption. - preemption_received_at: The time when preemption was received. - wait_for_prev_start_time: The start time of waiting for previous checkpoint. - checkpointer_blocking_start_time: The start time of blocking time introduced - by checkpointer. - get_old_steps_start_time: The start time of getting old steps. - synchronous: Whether the event is synchronous. - wait_for_prev_duration_secs: The duration of waiting for previous - checkpoint. - checkpointer_blocking_duration_secs: The duration of blocking time - introduced by checkpointer. - get_old_steps_duration_secs: The duration of getting old steps. - checkpoint_manager_blocking_duration_secs: The duration of checkpoint - manager blocking section. - """ - - step: Optional[int] = None - event_type: Optional[str] = 'save' - directory: Optional[str] = None - reached_preemption: Optional[bool] = False - preemption_received_at: Optional[float] = None - synchronous: Optional[bool] = False - wait_for_prev_start_time: Optional[float] = None - wait_for_prev_duration_secs: Optional[float] = None - checkpointer_blocking_start_time: Optional[float] = None - checkpointer_blocking_duration_secs: Optional[float] = None - get_old_steps_start_time: Optional[float] = None - get_old_steps_duration_secs: Optional[float] = None - checkpoint_manager_blocking_start_time: Optional[float] = None - checkpoint_manager_blocking_duration_secs: Optional[float] = None - - -@dataclasses.dataclass -class MockRestoreStepStatistics: - """Attributes for restore step statistics. - - Attributes: - step: The step number. - event_type: The event type. - directory: The directory of the checkpoint. - checkpointer_start_time: The start time of restoring the checkpoint, while - using the checkpointer. - checkpointer_duration_secs: The total duration for restoring the checkpoint, - while using the checkpointer. - checkpoint_manager_start_time: The start time for restoring the checkpoint, - while using the checkpoint manager. - checkpoint_manager_duration_secs: The total duration for restoring the - checkpoint, while using the checkpoint manager. - """ - - step: Optional[int] = None - event_type: Optional[str] = 'restore' - directory: Optional[str] = None - checkpointer_start_time: Optional[float] = None - checkpointer_duration_secs: Optional[float] = None - checkpoint_manager_start_time: Optional[float] = None - checkpoint_manager_duration_secs: Optional[float] = None - - -@dataclasses.dataclass -class MockEmergencyRestoreStepStatistics: - """Attributes for emergency restore step statistics. - - Attributes: - step: The step number. - event_type: The event type. - checkpoint_manager_start_time: The start time of checkpoint manager - restore event. - directory: The directory of the checkpoint. - is_restoring_slice: Whether the event takes place on the slice responsible - for reading from the storage location. (Note that in_primary_slice=True - necessarily implies is_restoring_slice=True.) - in_primary_slice: Whether the event takes place on the slice designated as - primary (responsible for restoring from persistent storage). - checkpointer_start_time: The start time of restoring the checkpoint, while - using the checkpointer. - checkpointer_duration_secs: The total duration for restoring the checkpoint, - while using the checkpointer. - broadcast_start_time: The start time of broadcasting(Restore).The broadcast - operation performed by SingleReplicaArrayHandler won't be captured in this - context. - broadcast_duration_secs: The duration of broadcasting(Restore). - checkpoint_manager_duration_secs: The total duration of checkpoint - manager restore event. - """ - - step: Optional[int] = None - event_type: Optional[str] = 'emergency_restore' - checkpoint_manager_start_time: Optional[float] = None - directory: Optional[str] = None - is_restoring_slice: Optional[bool] = False - in_primary_slice: Optional[bool] = False - checkpointer_start_time: Optional[float] = None - checkpointer_duration_secs: Optional[float] = None - broadcast_start_time: Optional[float] = None - broadcast_duration_secs: Optional[float] = None - checkpoint_manager_duration_secs: Optional[float] = None - - -class CheckpointBadputCalculatorTest(absltest.TestCase): - - def setUp(self): - """Setup for the test.""" - super().setUp() - mock_gcloud_client = mock.create_autospec(google_cloud_logging.Client) - options = checkpoint_badput_calculator.CheckpointLoggerOptions( - job_name=_JOB_NAME, - logger_name=_LOGGER_NAME, - client=mock_gcloud_client, - use_goodput_logger=True, - ) - self.checkpoint_badput_calculator = ( - checkpoint_badput_calculator.CheckpointBadputCalculator(options) - ) - - def test_checkpoint_badput_calculator_persistent_save_operation(self): - """Test for persistent save operation.""" - step_count = 4 - default_cm_blocking_duration_secs = 4 - default_ckptr_blocking_duration_secs = 1 - default_gos_duration_secs = 1 - default_wfp_duration_secs = 2 - for i in range(1, step_count+1): - persistent_save_entry = dataclasses.asdict( - MockSaveStepStatistics( - step=i, - event_type='save', - directory='gs://bucket/path', - wait_for_prev_start_time=i * 10.0, - wait_for_prev_duration_secs=default_wfp_duration_secs, - checkpointer_blocking_start_time=i * 10.0 + 2, - checkpointer_blocking_duration_secs=default_ckptr_blocking_duration_secs, - get_old_steps_start_time=i * 10.0 + 3, - get_old_steps_duration_secs=default_gos_duration_secs, - checkpoint_manager_blocking_start_time=i * 10.0, - checkpoint_manager_blocking_duration_secs=default_cm_blocking_duration_secs, - reached_preemption=True, - preemption_received_at=i * 10.0, - synchronous=True, - ) - ) - self.checkpoint_badput_calculator.entries.append(persistent_save_entry) - - expected_breakdown = ( - checkpoint_badput_calculator.SaveCheckpointManagerVerticalStepStats() - ) - expected_breakdown.total_checkpoint_manager_blocking_time = ( - step_count * default_cm_blocking_duration_secs - ) - expected_breakdown.average_checkpoint_manager_blocking_time = ( - default_cm_blocking_duration_secs - ) - expected_breakdown.minimum_checkpoint_manager_blocking_time = ( - default_cm_blocking_duration_secs - ) - expected_breakdown.maximum_checkpoint_manager_blocking_time = ( - default_cm_blocking_duration_secs - ) - expected_breakdown.standard_deviation_checkpoint_manager_blocking_time = 0 - expected_breakdown.total_checkpointer_blocking_time = ( - step_count * default_ckptr_blocking_duration_secs - ) - expected_breakdown.average_checkpointer_blocking_time = ( - default_ckptr_blocking_duration_secs - ) - expected_breakdown.minimum_checkpointer_blocking_time = ( - default_ckptr_blocking_duration_secs - ) - expected_breakdown.maximum_checkpointer_blocking_time = ( - default_ckptr_blocking_duration_secs - ) - expected_breakdown.standard_deviation_checkpointer_blocking_time = 0 - expected_breakdown.total_wait_for_prev_time = ( - step_count * default_wfp_duration_secs - ) - expected_breakdown.average_wait_for_prev_time = default_wfp_duration_secs - expected_breakdown.minimum_wait_for_prev_time = default_wfp_duration_secs - expected_breakdown.maximum_wait_for_prev_time = default_wfp_duration_secs - expected_breakdown.standard_deviation_wait_for_prev_time = 0 - expected_breakdown.total_get_old_steps_time = ( - step_count * default_gos_duration_secs - ) - expected_breakdown.average_get_old_steps_time = default_gos_duration_secs - expected_breakdown.minimum_get_old_steps_time = default_gos_duration_secs - expected_breakdown.maximum_get_old_steps_time = default_gos_duration_secs - expected_breakdown.standard_deviation_get_old_steps_time = 0 - - cm_breakdown = ( - self.checkpoint_badput_calculator.calculate_save_operation_checkpoint_manager_blocking_time( - checkpoint_badput_calculator.OPERATION_TYPE_PERSISTENT - ) - ) - for field in dataclasses.fields(cm_breakdown): - value1 = getattr(cm_breakdown, field.name) - value2 = getattr(expected_breakdown, field.name) - if value1 != value2: - raise ValueError( - f"Mismatch in field '{field.name}':\n" - f" Actual: {value1}\n" - f" Expected: {value2}" - ) - - def test_checkpoint_badput_calculator_local_save_operation(self): - """Test for local save operation.""" - step_count = 4 - default_cm_blocking_duration_secs = 4 - default_ckptr_blocking_duration_secs = 1 - default_gos_duration_secs = 1 - default_wfp_duration_secs = 2 - for i in range(1, step_count+1): - local_save_entry = dataclasses.asdict( - MockSaveStepStatistics( - step=i, - event_type='save', - directory='local', - wait_for_prev_start_time=i * 10.0, - wait_for_prev_duration_secs=default_wfp_duration_secs, - checkpointer_blocking_start_time=i * 10.0 + 2, - checkpointer_blocking_duration_secs=default_ckptr_blocking_duration_secs, - get_old_steps_start_time=i * 10.0 + 3, - get_old_steps_duration_secs=default_gos_duration_secs, - checkpoint_manager_blocking_start_time=i * 10.0, - checkpoint_manager_blocking_duration_secs=default_cm_blocking_duration_secs, - reached_preemption=True, - preemption_received_at=i * 10.0, - synchronous=True, - ) - ) - self.checkpoint_badput_calculator.entries.append(local_save_entry) - - expected_breakdown = ( - checkpoint_badput_calculator.SaveCheckpointManagerVerticalStepStats() - ) - expected_breakdown.total_checkpoint_manager_blocking_time = ( - step_count * default_cm_blocking_duration_secs - ) - expected_breakdown.average_checkpoint_manager_blocking_time = ( - default_cm_blocking_duration_secs - ) - expected_breakdown.minimum_checkpoint_manager_blocking_time = ( - default_cm_blocking_duration_secs - ) - expected_breakdown.maximum_checkpoint_manager_blocking_time = ( - default_cm_blocking_duration_secs - ) - expected_breakdown.standard_deviation_checkpoint_manager_blocking_time = 0 - expected_breakdown.total_checkpointer_blocking_time = ( - step_count * default_ckptr_blocking_duration_secs - ) - expected_breakdown.average_checkpointer_blocking_time = ( - default_ckptr_blocking_duration_secs - ) - expected_breakdown.minimum_checkpointer_blocking_time = ( - default_ckptr_blocking_duration_secs - ) - expected_breakdown.maximum_checkpointer_blocking_time = ( - default_ckptr_blocking_duration_secs - ) - expected_breakdown.standard_deviation_checkpointer_blocking_time = 0 - expected_breakdown.total_wait_for_prev_time = ( - step_count * default_wfp_duration_secs - ) - expected_breakdown.average_wait_for_prev_time = default_wfp_duration_secs - expected_breakdown.minimum_wait_for_prev_time = default_wfp_duration_secs - expected_breakdown.maximum_wait_for_prev_time = default_wfp_duration_secs - expected_breakdown.standard_deviation_wait_for_prev_time = 0 - expected_breakdown.total_get_old_steps_time = ( - step_count * default_gos_duration_secs - ) - expected_breakdown.average_get_old_steps_time = default_gos_duration_secs - expected_breakdown.minimum_get_old_steps_time = default_gos_duration_secs - expected_breakdown.maximum_get_old_steps_time = default_gos_duration_secs - expected_breakdown.standard_deviation_get_old_steps_time = 0 - - cm_breakdown = ( - self.checkpoint_badput_calculator.calculate_save_operation_checkpoint_manager_blocking_time( - checkpoint_badput_calculator.OPERATION_TYPE_LOCAL - ) - ) - for field in dataclasses.fields(cm_breakdown): - value1 = getattr(cm_breakdown, field.name) - value2 = getattr(expected_breakdown, field.name) - if value1 != value2: - raise ValueError( - f"Mismatch in field '{field.name}':\n" - f" Actual: {value1}\n" - f" Expected: {value2}" - ) - - def test_checkpoint_badput_calculator_persistent_restore_operation(self): - """Test for persistent restore operation.""" - step_count = 4 - default_cm_duration_secs = 4 - default_ckptr_duration_secs = 1 - for i in range(1, step_count+1): - persitent_save_entry = dataclasses.asdict( - MockRestoreStepStatistics( - step=i, - event_type='restore', - directory='gs://bucket/path', - checkpointer_start_time=i * 10.0, - checkpointer_duration_secs=default_ckptr_duration_secs, - checkpoint_manager_start_time=i * 10.0 + 2, - checkpoint_manager_duration_secs=default_cm_duration_secs, - ) - ) - self.checkpoint_badput_calculator.entries.append(persitent_save_entry) - - expected_breakdown = ( - checkpoint_badput_calculator.RestoreCheckpointManagerVerticalStepStats() - ) - expected_breakdown.total_checkpoint_manager_time = ( - step_count * default_cm_duration_secs - ) - expected_breakdown.average_checkpoint_manager_time = ( - default_cm_duration_secs - ) - expected_breakdown.minimum_checkpoint_manager_time = ( - default_cm_duration_secs - ) - expected_breakdown.maximum_checkpoint_manager_time = ( - default_cm_duration_secs - ) - expected_breakdown.standard_deviation_checkpoint_manager_time = 0 - expected_breakdown.total_restore_time = ( - step_count * default_ckptr_duration_secs - ) - expected_breakdown.average_restore_time = default_ckptr_duration_secs - expected_breakdown.minimum_restore_time = default_ckptr_duration_secs - expected_breakdown.maximum_restore_time = default_ckptr_duration_secs - expected_breakdown.standard_deviation_restore_time = 0 - expected_breakdown.total_broadcast_time = 0 - expected_breakdown.average_broadcast_time = 0 - expected_breakdown.minimum_broadcast_time = 0 - expected_breakdown.maximum_broadcast_time = 0 - expected_breakdown.standard_deviation_broadcast_time = 0 - - cm_breakdown = ( - self.checkpoint_badput_calculator.calculate_restore_operation_checkpoint_manager_blocking_time( - checkpoint_badput_calculator.OPERATION_TYPE_PERSISTENT - ) - ) - for field in dataclasses.fields(cm_breakdown): - value1 = getattr(cm_breakdown, field.name) - value2 = getattr(expected_breakdown, field.name) - if value1 != value2: - raise ValueError( - f"Mismatch in field '{field.name}':\n" - f" Actual: {value1}\n" - f" Expected: {value2}" - ) - - def test_checkpoint_badput_calculator_local_restore_operation(self): - """Test for local restore operation.""" - step_count = 4 - default_cm_duration_secs = 4 - default_ckptr_duration_secs = 2 - default_broadcast_duration_secs = 2 - for i in range(1, step_count+1): - local_save_entry = dataclasses.asdict( - MockEmergencyRestoreStepStatistics( - step=i, - event_type='emergency_restore', - directory='local', - checkpointer_start_time=i * 10.0, - checkpointer_duration_secs=default_ckptr_duration_secs, - checkpoint_manager_start_time=i * 10.0 + 2, - checkpoint_manager_duration_secs=default_cm_duration_secs, - broadcast_start_time=i * 10.0 + 3, - broadcast_duration_secs=default_broadcast_duration_secs, - ) - ) - self.checkpoint_badput_calculator.entries.append(local_save_entry) - - expected_breakdown = ( - checkpoint_badput_calculator.RestoreCheckpointManagerVerticalStepStats() - ) - expected_breakdown.total_checkpoint_manager_time = ( - default_cm_duration_secs * step_count - ) - expected_breakdown.average_checkpoint_manager_time = ( - default_cm_duration_secs - ) - expected_breakdown.minimum_checkpoint_manager_time = ( - default_cm_duration_secs - ) - expected_breakdown.maximum_checkpoint_manager_time = ( - default_cm_duration_secs - ) - expected_breakdown.standard_deviation_checkpoint_manager_time = 0 - expected_breakdown.total_restore_time = ( - step_count * default_ckptr_duration_secs - ) - expected_breakdown.average_restore_time = default_ckptr_duration_secs - expected_breakdown.minimum_restore_time = default_ckptr_duration_secs - expected_breakdown.maximum_restore_time = default_ckptr_duration_secs - expected_breakdown.standard_deviation_restore_time = 0 - expected_breakdown.total_broadcast_time = ( - step_count * default_broadcast_duration_secs - ) - expected_breakdown.average_broadcast_time = default_broadcast_duration_secs - expected_breakdown.minimum_broadcast_time = default_broadcast_duration_secs - expected_breakdown.maximum_broadcast_time = default_broadcast_duration_secs - expected_breakdown.standard_deviation_broadcast_time = 0 - - cm_breakdown = ( - self.checkpoint_badput_calculator.calculate_restore_operation_checkpoint_manager_blocking_time( - checkpoint_badput_calculator.OPERATION_TYPE_LOCAL - ) - ) - for field in dataclasses.fields(cm_breakdown): - value1 = getattr(cm_breakdown, field.name) - value2 = getattr(expected_breakdown, field.name) - if value1 != value2: - raise ValueError( - f"Mismatch in field '{field.name}':\n" - f" Actual: {value1}\n" - f" Expected: {value2}" - ) -if __name__ == '__main__': - absltest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py deleted file mode 100644 index 4981a71..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/tests/gcp_metrics_test.py +++ /dev/null @@ -1,150 +0,0 @@ -"""Tests for GCP metrics.""" - -from unittest import mock - -from absl.testing import absltest -from cloud_goodput.ml_goodput_measurement.src import gcp_metrics -from google.api_core import exceptions -from google.cloud import monitoring_v3 - - -ValueType = gcp_metrics.ValueType -GCPMetrics = gcp_metrics.GCPMetrics -patch = mock.patch -GoogleAPIError = exceptions.GoogleAPIError - - -class GCPMetricsTest(absltest.TestCase): - - @patch("google.cloud.monitoring_v3.MetricServiceClient") - def setUp(self, mock_client): - super().setUp() - self.mock_client = mock_client.return_value - self.project_id = "test-project" - self.metrics_sender = GCPMetrics(self.project_id) - - def test_create_time_series(self): - metric_type = "compute.googleapis.com/workload/goodput_time" - value = 123.45 - value_type = ValueType.DOUBLE - metric_labels = { - "goodput_source": "TOTAL", - "accelerator_type": "tpu-v5p", - } - resource_type = "compute.googleapis.com/Workload" - resource_labels = { - "location": "us-central1", - "workload_id": "test-workload", - "replica_id": "0", - } - seconds = 1677347200 - nanos = 123456789 - - time_series = self.metrics_sender.create_time_series( - metric_type, - value, - value_type, - metric_labels, - resource_type, - resource_labels, - seconds, - nanos, - ) - - # Assertions to check if the TimeSeries object is created correctly - self.assertIsInstance(time_series, monitoring_v3.TimeSeries) - self.assertEqual(time_series.metric.type, metric_type) - self.assertEqual(time_series.resource.type, resource_type) - self.assertEqual(time_series.resource.labels, resource_labels) - self.assertEqual(time_series.metric.labels, metric_labels) - - # Correctly check the value based on value_type - if value_type == ValueType.BOOL: - self.assertEqual(time_series.points[0].value.bool_value, value) - elif value_type == ValueType.INT: - self.assertEqual(time_series.points[0].value.int64_value, value) - elif value_type == ValueType.DOUBLE: - self.assertEqual(time_series.points[0].value.double_value, value) - elif value_type == ValueType.STRING: - self.assertEqual(time_series.points[0].value.string_value, value) - elif value_type == ValueType.DISTRIBUTION: - self.assertEqual( - time_series.points[0].value.distribution_value, value - ) - - @patch("time.time") - def test_send_metrics(self, mock_time): - # Set a fixed return value for the mocked time.time() - mock_time.return_value = 1677347200.5 - - metrics_to_send = [ - { - "metric_type": "compute.googleapis.com/workload/goodput_time", - "value": 42.0, - "value_type": ValueType.DOUBLE, - "resource_type": "test_resource", - "resource_labels": {"loc": "us"}, - }, - { - "metric_type": "compute.googleapis.com/workload/badput_time", - "value": 10, - "value_type": ValueType.INT, - "metric_labels": {"source": "test2"}, - "resource_type": "test_resource", - "resource_labels": {"loc": "eu"}, - }, - ] - - self.metrics_sender.send_metrics(metrics_to_send) - - # Verify that create_time_series was called with the correct arguments - expected_name = f"projects/{self.project_id}" - expected_calls = [] - for metric in metrics_to_send: - metric_labels = metric.get("metric_labels", {}) - series = self.metrics_sender.create_time_series( - metric["metric_type"], - metric["value"], - metric["value_type"], - metric_labels, - metric["resource_type"], - metric["resource_labels"], - 1677347200, # seconds - 500000000, # nanos - ) - expected_calls.append(series) - - self.mock_client.create_time_series.assert_called_once() - _, kwargs = self.mock_client.create_time_series.call_args - self.assertEqual(kwargs["name"], expected_name) - # Check time series - actual_series = kwargs["time_series"] - self.assertEqual(len(actual_series), len(expected_calls)) - for actual, expected in zip(actual_series, expected_calls): - self.assertEqual(actual.metric.type, expected.metric.type) - self.assertEqual(actual.resource.type, expected.resource.type) - self.assertEqual(actual.resource.labels, expected.resource.labels) - self.assertEqual(actual.metric.labels, expected.metric.labels) - - @patch("cloud_goodput.ml_goodput_measurement.src.gcp_metrics.logger.error") - def test_send_metrics_failure(self, mock_logging_error): - - self.mock_client.create_time_series.side_effect = GoogleAPIError( - "Test Error" - ) - - metrics_to_send = [ - { - "metric_type": "compute.googleapis.com/workload/goodput_time", - "value": 42.0, - "value_type": ValueType.DOUBLE, - "resource_type": "test_resource", - "resource_labels": {"loc": "us"}, - } - ] - - self.metrics_sender.send_metrics(metrics_to_send) - mock_logging_error.assert_called_once() - -if __name__ == "__main__": - absltest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py deleted file mode 100644 index 0d0a690..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_cache_test.py +++ /dev/null @@ -1,141 +0,0 @@ -"""Tests to unit test GoodputCache class.""" - -import datetime -from unittest import mock - -from cloud_goodput.ml_goodput_measurement.src import goodput_cache -from cloud_goodput.ml_goodput_measurement.src import goodput_utils -from cloud_goodput.ml_goodput_measurement.src.goodput_utils import BadputType, GoodputInfo - -from google3.testing.pybase import googletest - - -class GoodputCacheTest(googletest.TestCase): - - def setUp(self): - super().setUp() - self.goodput_cache = goodput_cache.GoodputCache() - - def test_update_cached_entries(self): - mock_entries = [ - {'time': 1, 'step': 1}, - {'time': 2, 'step': 2}, - {'time': 3, 'step': 3}, - ] - self.goodput_cache.update_cached_entries(mock_entries) - self.assertFalse(self.goodput_cache.is_cache_empty()) - self.assertEqual(self.goodput_cache.get_cached_entries(), mock_entries) - - def test_update_goodput_info(self): - goodput_info = GoodputInfo( - total_productive_time=100, - total_elapsed_time_since_start=200, - total_unproductive_time={ - BadputType.TPU_INITIALIZATION: 10, - BadputType.TRAINING_PREP: 10, - BadputType.DATA_LOADING_SYNC: 30, - BadputType.PROGRAM_STARTUP: 10, - BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME: 20, - BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME: 10, - BadputType.WASTED_PROGRESS_FROM_DISRUPTION: 10, - BadputType.OTHER: 10, - }, - last_recorded_step=3, - ) - self.goodput_cache.update_goodput_info(goodput_info) - self.assertEqual(self.goodput_cache._goodput_info, goodput_info) - - def test_clear_cache(self): - mock_entries = [ - {'time': 1, 'step': 1}, - {'time': 2, 'step': 2}, - {'time': 3, 'step': 3}, - ] - self.goodput_cache.update_cached_entries(mock_entries) - self.goodput_cache.update_goodput_info( - GoodputInfo( - total_productive_time=100, - total_elapsed_time_since_start=200, - total_unproductive_time={ - BadputType.TPU_INITIALIZATION: 10, - BadputType.TRAINING_PREP: 10, - BadputType.DATA_LOADING_SYNC: 30, - BadputType.PROGRAM_STARTUP: 10, - BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME: 20, - BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME: 10, - BadputType.WASTED_PROGRESS_FROM_DISRUPTION: 10, - BadputType.OTHER: 10, - }, - last_recorded_step=3, - ) - ) - self.goodput_cache.clear_cache() - self.assertEqual(self.goodput_cache.get_cached_entries(), []) - self.assertIsNone(self.goodput_cache._goodput_info) - self.assertIsNone(self.goodput_cache._last_entry_timestamp) - - def test_is_cache_empty(self): - self.assertTrue(self.goodput_cache.is_cache_empty()) - self.goodput_cache.update_cached_entries([ - {'time': 1, 'step': 1}, - {'time': 2, 'step': 2}, - {'time': 3, 'step': 3}, - ]) - self.assertFalse(self.goodput_cache.is_cache_empty()) - - def test_get_last_entry_timestamp(self): - self.assertIsNone(self.goodput_cache._last_entry_timestamp) - self.goodput_cache.update_cached_entries([ - {'time': 1, 'step': 1}, - {'time': 2, 'step': 2}, - {'time': 3, 'step': 3}, - ]) - self.assertFalse(self.goodput_cache.is_cache_empty()) - self.assertEqual( - self.goodput_cache._last_entry_timestamp, - datetime.datetime.fromtimestamp(3, tz=datetime.timezone.utc), - ) - - def test_get_step_info(self): - step_info = goodput_utils.StepInfo( - step_deviations={1: 1.0, 2: 2.0}, - ideal_step_time=1.0, - ) - self.goodput_cache.update_step_info(step_info) - self.assertEqual(self.goodput_cache._step_info, step_info) - - def test_update_job_start_time(self): - self.assertIsNone(self.goodput_cache._job_start_time) - self.goodput_cache.update_cached_entries([ - {'step_start_time': 2, 'step': 1}, - {'step_start_time': 3, 'step': 2}, - {'job_end_time': 4}, - ]) - self.assertIsNone(self.goodput_cache._job_start_time) - self.goodput_cache.update_cached_entries([ - {'job_start_time': 1}, - {'job_start_time': 9}, - {'step_start_time': 2, 'step': 1}, - {'step_start_time': 3, 'step': 2}, - {'job_end_time': 4}, - ]) - self.assertEqual( - self.goodput_cache._job_start_time, - datetime.datetime.fromtimestamp(1, tz=datetime.timezone.utc), - ) - - def test_update_job_end_time(self): - self.assertIsNone(self.goodput_cache._job_end_time) - self.goodput_cache.update_cached_entries([ - {'job_end_time': 1}, - {'job_end_time': 2}, - {'job_end_time': 3}, - ]) - self.assertEqual( - self.goodput_cache._job_end_time, - datetime.datetime.fromtimestamp(3, tz=datetime.timezone.utc), - ) - - -if __name__ == '__main__': - googletest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py deleted file mode 100644 index 78515e6..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/tests/goodput_test.py +++ /dev/null @@ -1,2102 +0,0 @@ -"""Goodput tests to validate Recorder, Calculator and Logger classes.""" - -import dataclasses -from dataclasses import asdict -import datetime -import random -import time -import threading -from typing import Optional - -from cloud_goodput.ml_goodput_measurement.src import goodput -from cloud_goodput.ml_goodput_measurement.src.goodput_utils import BadputType -from cloud_goodput.ml_goodput_measurement.src.goodput_utils import compute_ideal_step_time, get_timestamp_from_log_entry - -from google3.testing.pybase import googletest - - -# Fake job timeline information for test purposes. -_TEST_JOB_START_TIME = datetime.datetime( - year=2024, - month=1, - day=1, - hour=1, - minute=0, - second=0, - microsecond=0, - tzinfo=datetime.timezone.utc, -) -_TEST_PROGRAM_STARTUP_TIME = datetime.timedelta(seconds=5) -_TEST_TPU_INIT_TIME = datetime.timedelta(seconds=1) -_TEST_TRAINING_PREPARATION_TIME = datetime.timedelta(seconds=2) -_TEST_DATA_LOADING_TIME = datetime.timedelta(seconds=2) -_TEST_STEP_START_TIME = _TEST_JOB_START_TIME + _TEST_PROGRAM_STARTUP_TIME -_TEST_TOTAL_STEPS = 5 -_TEST_STEP_TIME = datetime.timedelta(seconds=3) -_TEST_JOB_END_TIME = _TEST_STEP_START_TIME + _TEST_STEP_TIME * _TEST_TOTAL_STEPS -# Badput time included in the first step time after start and restart. -_TEST_FIRST_STEP_EXTRA_TIME = datetime.timedelta(seconds=5) -# Anomalous large step times -_TEST_ANOMALOUS_STEP_TIME = datetime.timedelta(seconds=30) -# Custom badput event (overlapped with training) time -_TEST_CUSTOM_BADPUT_TIME = datetime.timedelta(seconds=10) - - -class MockCloudLogger: - - def __init__(self, job_name, logger_name): - self.job_name = job_name - self.logger_name = logger_name - self.entries = [] - - def write_cloud_logging_entry(self, entry): - timestamp = get_timestamp_from_log_entry(entry) - if timestamp is not None: - self.entries.append((timestamp, entry)) - - def read_cloud_logging_entries(self, start_time=None, end_time=None): - - def to_aware(dt): - return ( - dt.replace(tzinfo=datetime.timezone.utc) - if dt is not None and dt.tzinfo is None - else dt - ) - - start_time = to_aware(start_time) - end_time = to_aware(end_time) - return [ - entry - for timestamp, entry in self.entries - if (start_time is None or to_aware(timestamp) > start_time) - and (end_time is None or to_aware(timestamp) <= end_time) - ] - - -@dataclasses.dataclass -class MockSaveStepStatistics: - """Attributes for save step statistics. - - Attributes: - step: The step number. - event_type: The event type. - checkpoint_manager_blocking_start_time: The start time of checkpoint manager - blocking section. - directory: The directory of the checkpoint. - reached_preemption: Whether the event reached preemption. - preemption_received_at: The time when preemption was received. - wait_for_prev_start_time: The start time of waiting for previous checkpoint. - checkpointer_blocking_start_time: The start time of blocking time introduced - by checkpointer. - get_old_steps_start_time: The start time of getting old steps. - synchronous: Whether the event is synchronous. - wait_for_prev_duration_secs: The duration of waiting for previous - checkpoint. - checkpointer_blocking_duration_secs: The duration of blocking time - introduced by checkpointer. - get_old_steps_duration_secs: The duration of getting old steps. - checkpoint_manager_blocking_duration_secs: The duration of checkpoint - manager blocking section. - """ - - step: Optional[int] = None - event_type: Optional[str] = 'save' - directory: Optional[str] = None - reached_preemption: Optional[bool] = False - preemption_received_at: Optional[float] = None - synchronous: Optional[bool] = False - wait_for_prev_start_time: Optional[float] = None - wait_for_prev_duration_secs: Optional[float] = None - checkpointer_blocking_start_time: Optional[float] = None - checkpointer_blocking_duration_secs: Optional[float] = None - get_old_steps_start_time: Optional[float] = None - get_old_steps_duration_secs: Optional[float] = None - checkpoint_manager_blocking_start_time: Optional[float] = None - checkpoint_manager_blocking_duration_secs: Optional[float] = None - - -@dataclasses.dataclass -class MockRestoreStepStatistics: - """Attributes for restore step statistics. - - Attributes: - step: The step number. - event_type: The event type. - directory: The directory of the checkpoint. - checkpointer_start_time: The start time of restoring the checkpoint, while - using the checkpointer. - checkpointer_duration_secs: The total duration for restoring the checkpoint, - while using the checkpointer. - checkpoint_manager_start_time: The start time for restoring the checkpoint, - while using the checkpoint manager. - checkpoint_manager_duration_secs: The total duration for restoring the - checkpoint, while using the checkpoint manager. - """ - - step: Optional[int] = None - event_type: Optional[str] = 'restore' - directory: Optional[str] = None - checkpointer_start_time: Optional[float] = None - checkpointer_duration_secs: Optional[float] = None - checkpoint_manager_start_time: Optional[float] = None - checkpoint_manager_duration_secs: Optional[float] = None - - -class GoodputTest(googletest.TestCase): - - def setUp(self): - super().setUp() - self.job_name = 'test-run' - self.logger_name = 'test-log' - self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) - self.goodput_recorder = goodput.GoodputRecorder( - self.job_name, - self.logger_name, - True, - self.mock_cloud_logger, - ) - self.goodput_calculator = goodput.GoodputCalculator( - self.job_name, self.logger_name, self.mock_cloud_logger - ) - - def _mock_sample_program(self): - # Record job start time of the job: use a fake timestamp - self.goodput_recorder.record_job_start_time(_TEST_JOB_START_TIME) - - # Mock _TEST_TOTAL_STEPS steps of training - step_start_time = _TEST_STEP_START_TIME - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Record job end time - self.goodput_recorder.record_job_end_time(_TEST_JOB_END_TIME) - - def _mock_sample_program_with_badput(self): - mock_current_time = _TEST_JOB_START_TIME - delay = datetime.timedelta(seconds=1) - - # Record job start time of the job: use a fake timestamp - self.goodput_recorder.record_job_start_time(mock_current_time) - - # Mock TPU initialization time - mock_current_time += delay - self.goodput_recorder.record_tpu_init_start_time(mock_current_time) - mock_current_time += _TEST_TPU_INIT_TIME - self.goodput_recorder.record_tpu_init_end_time(mock_current_time) - - # Mock training preparation time - mock_current_time += delay - self.goodput_recorder.record_training_preparation_start_time( - mock_current_time - ) - mock_current_time += _TEST_TRAINING_PREPARATION_TIME - self.goodput_recorder.record_training_preparation_end_time( - mock_current_time - ) - - # Mock data loading time - mock_current_time += delay - self.goodput_recorder.record_data_loading_start_time(mock_current_time) - mock_current_time += _TEST_DATA_LOADING_TIME - self.goodput_recorder.record_data_loading_end_time(mock_current_time) - - # Mock _TEST_TOTAL_STEPS steps of training - mock_current_time += delay - custom_badput_event_frequency = 3 - for step in range(_TEST_TOTAL_STEPS): - step_start_time = mock_current_time - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - # Record synchronous data loading time - self.goodput_recorder.record_data_loading_start_time(mock_current_time) - mock_current_time += _TEST_DATA_LOADING_TIME - self.goodput_recorder.record_data_loading_end_time(mock_current_time) - # Record custom badput event time - if step % custom_badput_event_frequency == 0: - self.goodput_recorder.record_custom_badput_event_start_time( - mock_current_time, 'test_sync' - ) - mock_current_time += _TEST_CUSTOM_BADPUT_TIME - self.goodput_recorder.record_custom_badput_event_end_time( - mock_current_time, 'test_sync' - ) - mock_current_time += _TEST_STEP_TIME - # Record job end time - self.goodput_recorder.record_job_end_time(mock_current_time) - - def test_goodput_recorder(self): - """Test function to validate goodput recorder and logger.""" - # Emulate job run timeline. - self._mock_sample_program() - - # Ensure read returns the right number of entries. - validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() - # There should be one entry for each of the 5 steps, one job start - # and one job end entry. - self.assertLen(validate_entries, _TEST_TOTAL_STEPS + 2) - # Ensure payload contains the expected information. - for entry_payload in validate_entries: - self.assertIn(goodput._JOB_NAME, entry_payload) - self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) - if goodput._JOB_START_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._JOB_START_TIME], - _TEST_JOB_START_TIME.timestamp(), - ) - if goodput._JOB_END_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._JOB_END_TIME], - _TEST_JOB_END_TIME.timestamp(), - ) - if goodput._STEP_START_TIME in entry_payload: - step_count = entry_payload[goodput._STEP_COUNT] - expected_start_start_time = ( - _TEST_STEP_START_TIME + _TEST_STEP_TIME * step_count - ) - self.assertEqual( - entry_payload[goodput._STEP_START_TIME], - expected_start_start_time.timestamp(), - ) - - def test_goodput_recorder_badput(self): - """Test function to validate goodput recorder and logger.""" - # Emulate job run timeline. - self._mock_sample_program_with_badput() - - validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() - - # Ensure payload contains the required information. - expected_keys = { - goodput._JOB_NAME, - goodput._STEP_COUNT, - goodput._STEP_START_TIME, - goodput._JOB_START_TIME, - goodput._JOB_END_TIME, - goodput._TPU_INIT_START_TIME, - goodput._TPU_INIT_END_TIME, - goodput._TRAINING_PREPARATION_START_TIME, - goodput._TRAINING_PREPARATION_END_TIME, - goodput._DATA_LOADING_START_TIME, - goodput._DATA_LOADING_END_TIME, - goodput._CUSTOM_BADPUT_EVENT_TYPE, - goodput._CUSTOM_BADPUT_EVENT_START_TIME, - goodput._CUSTOM_BADPUT_EVENT_END_TIME, - } - # Ensure right number of entries are written. - found_keys = set() - for entry_payload in validate_entries: - self.assertIn(goodput._JOB_NAME, entry_payload) - self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) - found_keys.update(entry_payload.keys() & expected_keys) - - self.assertEqual(found_keys, expected_keys) - - def test_goodput_calculator(self): - """Test function to validate goodput calculator.""" - # Emulate job run timeline. - self._mock_sample_program() - # Get the computed Goodput from the library and compare with expected - # result. - computed_goodput, _, total_steps = self.goodput_calculator.get_job_goodput() - expected_goodput = ( - (_TEST_STEP_TIME * _TEST_TOTAL_STEPS) - / (_TEST_JOB_END_TIME - _TEST_JOB_START_TIME) - * 100 - ) - self.assertEqual(computed_goodput, expected_goodput) - self.assertEqual(total_steps, _TEST_TOTAL_STEPS - 1) - - def test_goodput_with_startup_badput(self): - """Test function to validate goodput with startup badput.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock _TEST_TOTAL_STEPS steps of training - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - - # All steps but first progress with average step time. - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Introduce startup badput during the first step - if step == 0: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - total_time = ( - _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + _TEST_FIRST_STEP_EXTRA_TIME - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Get the computed Goodput from the library and compare with expected - # result. - - computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() - expected_goodput = ( - (_TEST_TOTAL_STEPS * _TEST_STEP_TIME.total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - - -class GoodputDisruptionCompleteRestartTest(googletest.TestCase): - - def setUp(self): - super().setUp() - self.job_name = 'test-run' - self.logger_name = 'test-log' - self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) - self.goodput_recorder = goodput.GoodputRecorder( - self.job_name, - self.logger_name, - True, - self.mock_cloud_logger, - ) - self.goodput_calculator = goodput.GoodputCalculator( - self.job_name, self.logger_name, self.mock_cloud_logger - ) - - def test_goodput_calculator(self): - """Test function to validate goodput calculator.""" - # It is not ideal to use non-deterministic timestamps in unit tests, but - # testing this complex scenario using deterministic timestamps is not - # straightforward. - # TODO(xfgu): Refactor this test. - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock _TEST_TOTAL_STEPS steps of training - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Simulate a disruption. - disruption_time = datetime.timedelta(seconds=5) - job_start_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_start_time) - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - - steps_before_query = _TEST_TOTAL_STEPS - 2 - for step in range(steps_before_query): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Get the computed Goodput from the library and compare with expected - # result. - - # The time from when the job first started to when the last step start was - # logged. - total_time = ( - _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + disruption_time - + _TEST_PROGRAM_STARTUP_TIME - + (steps_before_query - 1) * _TEST_STEP_TIME - ) - seconds_before_query = 2 - query_time = total_time.total_seconds() + seconds_before_query - - time.sleep(query_time) - computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() - expected_goodput = ( - ( - (steps_before_query - 1) * _TEST_STEP_TIME.total_seconds() - ) - / query_time - * 100 - ) - - self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - - -class GoodputDisruptionPartialRestartTest(googletest.TestCase): - - def setUp(self): - super().setUp() - self.job_name = 'test-run' - self.logger_name = 'test-log' - self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) - self.goodput_recorder = goodput.GoodputRecorder( - self.job_name, - self.logger_name, - True, - self.mock_cloud_logger, - ) - self.goodput_calculator = goodput.GoodputCalculator( - self.job_name, self.logger_name, self.mock_cloud_logger - ) - - def test_goodput_calculator(self): - """Test function to validate goodput calculator.""" - # It is not ideal to use non-deterministic timestamps in unit tests, but - # testing this complex scenario using deterministic timestamps is not - # straightforward. - # TODO(xfgu): Refactor this test. - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock _TEST_TOTAL_STEPS steps of training - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Simulate a disruption. - disruption_time = datetime.timedelta(seconds=5) - job_start_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_start_time) - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - - restart_from_step = 2 - for step in range(restart_from_step, _TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Get the computed Goodput from the library and compare with expected - # result. - - # The time from when the job first started to when the last step start was - # logged. - total_time = ( - _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + disruption_time - + _TEST_PROGRAM_STARTUP_TIME - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - seconds_before_query = 2 - query_time = total_time.total_seconds() + seconds_before_query - - time.sleep(query_time) - computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() - expected_goodput = ( - ((_TEST_TOTAL_STEPS - 1) * _TEST_STEP_TIME.total_seconds()) - / query_time - * 100 - ) - # Validate that the cache is updated correctly. - cached_goodput_info = ( - self.goodput_calculator._goodput_cache.get_goodput_info() - ) - expected_productive_time = ( - _TEST_TOTAL_STEPS - 1 - ) * _TEST_STEP_TIME.total_seconds() - self.assertAlmostEqual( - cached_goodput_info.total_productive_time, - expected_productive_time, - delta=0.1, - ) - - self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - - def test_goodput_with_startup_badput(self): - """Test function to validate goodput with startup badput.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock _TEST_TOTAL_STEPS steps of training - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - - # All steps but first progress with average step time. - for step in range(0, _TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Introduce startup badput during the first step - if step == 0: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - # Simulate a disruption. - disruption_time = datetime.timedelta(seconds=5) - job_start_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_start_time) - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - - restart_from_step = 2 - # All steps but first progress with average step time. - for step in range(restart_from_step, _TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Introduce badput during the first step after restart - if step == restart_from_step: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - # Get the computed Goodput from the library and compare with expected - # result. - - # The time from when the job first started to when the last step start was - # logged. - total_time = ( - _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + _TEST_FIRST_STEP_EXTRA_TIME - + disruption_time - + _TEST_PROGRAM_STARTUP_TIME - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - ) - seconds_before_query = 2 - query_time = total_time.total_seconds() + seconds_before_query - - time.sleep(query_time) - computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() - expected_goodput = ( - ((_TEST_TOTAL_STEPS - 1) * _TEST_STEP_TIME.total_seconds()) - / query_time - * 100 - ) - - self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - - -class GoodputPathwaysTest(googletest.TestCase): - - def setUp(self): - super().setUp() - self.job_name = 'test-run' - self.logger_name = 'test-log' - self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) - self.goodput_recorder = goodput.GoodputRecorder( - self.job_name, - self.logger_name, - True, - self.mock_cloud_logger, - ) - self.goodput_calculator = goodput.GoodputCalculator( - self.job_name, self.logger_name, self.mock_cloud_logger, True - ) - - def test_goodput_with_anomalous_steps_single_disruption(self): - """Test function to validate goodput with anomalous step times due to a single disruption.""" - # This test simulates _TEST_TOTAL_STEPS training steps and a single - # disruption during the job's run time as follows: - # [0, 1, 2, Handled Disruption, 3, 4] - # The handled disruption will manifest as anomalously large step times. - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock some program startup time before the training steps - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - - # First few steps progress with normal step time. - for step in range(_TEST_TOTAL_STEPS - 3): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Introduce an anomalously large step time due to a disruption. - self.goodput_recorder.record_step_start_time( - _TEST_TOTAL_STEPS - 3, step_start_time - ) - step_start_time += _TEST_ANOMALOUS_STEP_TIME + _TEST_STEP_TIME - - # Remaining steps progress with normal step time. - for step in range(_TEST_TOTAL_STEPS - 2, _TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - job_end_time = ( - job_start_time - + _TEST_PROGRAM_STARTUP_TIME - + _TEST_ANOMALOUS_STEP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - ) - self.goodput_recorder.record_job_end_time(job_end_time) - - # The time from when the job first started to when the last step start was - # logged. - total_time = ( - _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + _TEST_ANOMALOUS_STEP_TIME - ) - - computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() - expected_goodput = ( - (_TEST_TOTAL_STEPS * _TEST_STEP_TIME.total_seconds()) - / total_time.total_seconds() - * 100 - ) - # TODO(b/400837154): Add this back once the bug is fixed. - # self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - - def test_goodput_with_anomalous_steps_multiple_disruptions(self): - """Test function to validate goodput with anomalous step times due to multiple disruptions.""" - - # This test simulates _TEST_TOTAL_STEPS * 2 training steps and multiple - # disruptions during the job's run time as follows: - # [0, 1, 2, Handled Disruption, 3, 4, 5, 6, 7 Handled Disruption, 8, 9] - # The handled disruptions will manifest as anomalously large step times. - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock some program startup time before the training steps - step_start_time = job_start_time + _TEST_PROGRAM_STARTUP_TIME - - # First few steps progress with normal step time. - for step in range(_TEST_TOTAL_STEPS - 3): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Introduce an anomalously large step time due to a disruption. - self.goodput_recorder.record_step_start_time( - _TEST_TOTAL_STEPS - 3, step_start_time - ) - step_start_time += _TEST_ANOMALOUS_STEP_TIME + _TEST_STEP_TIME - - # A few more steps progress with normal step time. - for step in range(_TEST_TOTAL_STEPS - 2, _TEST_TOTAL_STEPS + 2): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - # Introduce an anomalously large step time due to a second disruption. - self.goodput_recorder.record_step_start_time( - _TEST_TOTAL_STEPS + 2, step_start_time - ) - step_start_time += _TEST_ANOMALOUS_STEP_TIME + _TEST_STEP_TIME - - # Remaining steps progress with normal step time. - for step in range(_TEST_TOTAL_STEPS + 3, _TEST_TOTAL_STEPS * 2): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - job_end_time = ( - job_start_time - + _TEST_PROGRAM_STARTUP_TIME - + _TEST_ANOMALOUS_STEP_TIME * 2 - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS * 2 - ) - self.goodput_recorder.record_job_end_time(job_end_time) - - # The time from when the job first started to when the last step start was - # logged. - total_time = ( - _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS * 2 - + _TEST_ANOMALOUS_STEP_TIME * 2 - ) - - computed_goodput, _, _ = self.goodput_calculator.get_job_goodput() - expected_goodput = ( - (2 * _TEST_TOTAL_STEPS * _TEST_STEP_TIME.total_seconds()) - / total_time.total_seconds() - * 100 - ) - # TODO(b/400837154): Add this back once the bug is fixed. - # self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - - -class BadputTest(googletest.TestCase): - - def setUp(self): - super().setUp() - self.job_name = 'test-run' - self.logger_name = 'test-log' - self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) - self.goodput_recorder = goodput.GoodputRecorder( - self.job_name, - self.logger_name, - True, - self.mock_cloud_logger, - ) - self.goodput_calculator = goodput.GoodputCalculator( - self.job_name, self.logger_name, self.mock_cloud_logger - ) - - def test_tpu_init_recorder(self): - """Test function to validate goodput recorder for TPU init.""" - # Record TPU init - self.goodput_recorder.record_tpu_init_start_time(_TEST_JOB_START_TIME) - self.goodput_recorder.record_tpu_init_end_time( - _TEST_JOB_START_TIME + _TEST_TPU_INIT_TIME - ) - - # Ensure read returns the right number of entries. - validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() - self.assertLen(validate_entries, 2) - # Ensure payload contains the expected information. - for entry_payload in validate_entries: - self.assertIn(goodput._JOB_NAME, entry_payload) - self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) - if goodput._TPU_INIT_START_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._TPU_INIT_START_TIME], - _TEST_JOB_START_TIME.timestamp(), - ) - if goodput._TPU_INIT_END_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._TPU_INIT_END_TIME], - (_TEST_JOB_START_TIME + _TEST_TPU_INIT_TIME).timestamp(), - ) - - def test_training_prep_recorder(self): - """Test function to validate goodput recorder for training preparation.""" - # Record training preparation time. - training_prep_start_time = _TEST_JOB_START_TIME + _TEST_TPU_INIT_TIME - training_prep_end_time = ( - _TEST_JOB_START_TIME - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_training_preparation_start_time( - training_prep_start_time - ) - self.goodput_recorder.record_training_preparation_end_time( - training_prep_end_time - ) - - # Ensure read returns the right number of entries. - validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() - self.assertLen(validate_entries, 2) - # Ensure payload contains the expected information. - for entry_payload in validate_entries: - self.assertIn(goodput._JOB_NAME, entry_payload) - self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) - if goodput._TRAINING_PREPARATION_START_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._TRAINING_PREPARATION_START_TIME], - training_prep_start_time.timestamp(), - ) - if goodput._TRAINING_PREPARATION_END_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._TRAINING_PREPARATION_END_TIME], - training_prep_end_time.timestamp(), - ) - - def test_training_prep_recorder_no_timestamps(self): - """Test function to validate goodput recorder for training preparation with no timestamps.""" - # Record training preparation time. - expected_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_training_preparation_start_time(None) - time.sleep(_TEST_TRAINING_PREPARATION_TIME.total_seconds()) - expected_end_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_training_preparation_end_time(None) - - # Ensure read returns the right number of entries. - validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() - self.assertLen(validate_entries, 2) - # Ensure payload contains the expected information. - for entry_payload in validate_entries: - self.assertIn(goodput._JOB_NAME, entry_payload) - self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) - if goodput._TRAINING_PREPARATION_START_TIME in entry_payload: - self.assertAlmostEqual( - entry_payload[goodput._TRAINING_PREPARATION_START_TIME], - expected_start_time.timestamp(), - delta=0.1, - ) - - if goodput._TRAINING_PREPARATION_END_TIME in entry_payload: - self.assertAlmostEqual( - entry_payload[goodput._TRAINING_PREPARATION_END_TIME], - expected_end_time.timestamp(), - delta=0.1, - ) - - def test_data_loading_recorder(self): - """Test function to validate goodput recorder for data loading.""" - # Record data loading time. - data_loading_start_time = ( - _TEST_JOB_START_TIME - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - ) - data_loading_end_time = ( - _TEST_JOB_START_TIME - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - self.goodput_recorder.record_data_loading_start_time( - data_loading_start_time - ) - self.goodput_recorder.record_data_loading_end_time(data_loading_end_time) - - # Ensure read returns the right number of entries. - validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() - self.assertLen(validate_entries, 2) - # Ensure payload contains the expected information. - for entry_payload in validate_entries: - self.assertIn(goodput._JOB_NAME, entry_payload) - self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) - if goodput._DATA_LOADING_START_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._DATA_LOADING_START_TIME], - data_loading_start_time.timestamp(), - ) - if goodput._DATA_LOADING_END_TIME in entry_payload: - self.assertEqual( - entry_payload[goodput._DATA_LOADING_END_TIME], - data_loading_end_time.timestamp(), - ) - - def test_data_loading_recorder_no_timestamps(self): - """Test function to validate goodput recorder for data loading.""" - # Record data loading time. - expected_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_data_loading_start_time(None) - time.sleep(_TEST_DATA_LOADING_TIME.total_seconds()) - expected_end_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_data_loading_end_time(None) - - # Ensure read returns the right number of entries. - validate_entries = self.mock_cloud_logger.read_cloud_logging_entries() - self.assertLen(validate_entries, 2) - # Ensure payload contains the expected information. - for entry_payload in validate_entries: - self.assertIn(goodput._JOB_NAME, entry_payload) - self.assertEqual(entry_payload[goodput._JOB_NAME], self.job_name) - if goodput._DATA_LOADING_START_TIME in entry_payload: - self.assertAlmostEqual( - entry_payload[goodput._DATA_LOADING_START_TIME], - expected_start_time.timestamp(), - delta=0.1, - ) - if goodput._DATA_LOADING_END_TIME in entry_payload: - self.assertAlmostEqual( - entry_payload[goodput._DATA_LOADING_END_TIME], - expected_end_time.timestamp(), - delta=0.1, - ) - - def test_badput_calculator_tpu_initialization(self): - """Test function to validate computation of badput due to TPU initialization.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - - # Mock _TEST_TOTAL_STEPS steps of training with built-in badput - # due to program startup. - step_start_time = ( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_PROGRAM_STARTUP_TIME - ) - for step in range(_TEST_TOTAL_STEPS): - # Record step time. - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - expected_badput_due_to_tpu_initialization = ( - (_TEST_TPU_INIT_TIME.total_seconds()) / total_time.total_seconds() * 100 - ) - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn(BadputType.TPU_INITIALIZATION, computed_badput_breakdown) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.TPU_INITIALIZATION], - expected_badput_due_to_tpu_initialization, - delta=0.1, - ) - - def test_badput_calculator_training_preparation(self): - """Test function to validate computation of badput due to training preparation.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - for step in range(_TEST_TOTAL_STEPS): - # Record step time. - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Badput with selection. - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - expected_badput_due_to_training_preparation = ( - (_TEST_TRAINING_PREPARATION_TIME.total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn(BadputType.TRAINING_PREP, computed_badput_breakdown) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.TRAINING_PREP], - expected_badput_due_to_training_preparation, - delta=0.1, - ) - - def test_badput_calculator_sync_data_loading(self): - """Test function to validate computation of badput due to data loading.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - # Mock data loading. - self.goodput_recorder.record_data_loading_start_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - for step in range(_TEST_TOTAL_STEPS): - # Record step time. - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Badput with selection. - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - expected_badput_due_to_sync_data_loading = ( - (_TEST_DATA_LOADING_TIME.total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn(BadputType.DATA_LOADING_SYNC, computed_badput_breakdown) - self.assertIn(BadputType.DATA_LOADING_ASYNC, computed_badput_breakdown) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.DATA_LOADING_SYNC], - expected_badput_due_to_sync_data_loading, - delta=0.1, - ) - - def test_badput_calculator_async_data_loading(self): - """Test function to validate computation of badput due to data loading.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - # Mock syncdata loading. - self.goodput_recorder.record_data_loading_start_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - for step in range(_TEST_TOTAL_STEPS): - # Record step time. - self.goodput_recorder.record_step_start_time(step, step_start_time) - # Record async (overlapped) data loading. - self.goodput_recorder.record_data_loading_start_time( - step_start_time + _TEST_STEP_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - step_start_time + _TEST_STEP_TIME - + _TEST_DATA_LOADING_TIME - ) - step_start_time += (_TEST_STEP_TIME + _TEST_DATA_LOADING_TIME) - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + (_TEST_STEP_TIME + _TEST_DATA_LOADING_TIME) * _TEST_TOTAL_STEPS - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Badput with selection. - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - - # Every step has overloaded (async) data loading. - expected_badput_due_to_async_data_loading = ( - ((_TEST_DATA_LOADING_TIME * _TEST_TOTAL_STEPS).total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn(BadputType.DATA_LOADING_ASYNC, computed_badput_breakdown) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.DATA_LOADING_ASYNC], - expected_badput_due_to_async_data_loading, - delta=0.1, - ) - - def test_badput_calculator_program_startup(self): - """Test function to validate computation of badput due to program startup.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - # Mock data loading. - self.goodput_recorder.record_data_loading_start_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - # All steps but first progress with average step time. - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Add startup badput during the first step - if step == 0: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Badput. - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - expected_badput_due_to_program_startup = ( - (_TEST_FIRST_STEP_EXTRA_TIME.total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn(BadputType.PROGRAM_STARTUP, computed_badput_breakdown) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.PROGRAM_STARTUP], - expected_badput_due_to_program_startup, - delta=0.1, - ) - - def test_badput_calculator_program_startup_with_disruptions(self): - """Validate computation of badput due to program startup after a disruption.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - # Mock data loading. - self.goodput_recorder.record_data_loading_start_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - # All steps but first progress with average step time. - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Add startup badput during the first step - if step == 0: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - # Simulate a disruption. - disruption_time = datetime.timedelta(seconds=5) - job_restart_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_restart_time) - step_start_time = ( - job_restart_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - restart_from_step = 2 - # All steps but first progress with average step time. - for step in range(restart_from_step, _TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - if step == restart_from_step: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + disruption_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Badput. - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - expected_badput_due_to_program_startup = ( - ((_TEST_FIRST_STEP_EXTRA_TIME * 2).total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn(BadputType.PROGRAM_STARTUP, computed_badput_breakdown) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.PROGRAM_STARTUP], - expected_badput_due_to_program_startup, - delta=0.1, - ) - - def test_badput_calculator_wasted_progress_and_disruptions(self): - """Validate computation of badput due to wasted progress and disruptions.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - # Mock data loading. - self.goodput_recorder.record_data_loading_start_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - # All steps but first progress with average step time. - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Add startup badput during the first step - if step == 0: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - # Simulate a disruption. - disruption_time = datetime.timedelta(seconds=5) - job_restart_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_restart_time) - step_start_time = ( - job_restart_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - restart_from_step = 2 - # All steps but first progress with average step time. - for step in range(restart_from_step, _TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - if step == restart_from_step: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + disruption_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Badput. - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - wasted_progress_and_disruption_time = ( - disruption_time - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - expected_badput_due_to_disruptions = ( - (wasted_progress_and_disruption_time.total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn( - BadputType.WASTED_PROGRESS_FROM_DISRUPTION, - computed_badput_breakdown, - ) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], - expected_badput_due_to_disruptions, - delta=0.1, - ) - - def test_badput_calculator_unknown_badput(self): - """Test function to validate unknown badput bucket.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - - # Mock _TEST_TOTAL_STEPS steps of training with built-in badput - # due to program startup. - step_start_time = ( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_PROGRAM_STARTUP_TIME - ) - for step in range(_TEST_TOTAL_STEPS): - # Record step time. - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - unknown_badput_time = datetime.timedelta(seconds=5) - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + unknown_badput_time - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn(BadputType.OTHER, computed_badput_breakdown) - - expected_badput_due_to_unknown = ( - (unknown_badput_time.total_seconds()) / total_time.total_seconds() * 100 - ) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.OTHER], - expected_badput_due_to_unknown, - delta=0.1, - ) - # Make sure this data is cached correctly. - cached_goodput_info = ( - self.goodput_calculator._goodput_cache.get_goodput_info() - ) - self.assertNotEmpty(cached_goodput_info.total_unproductive_time) - self.assertIn(BadputType.OTHER, cached_goodput_info.total_unproductive_time) - self.assertAlmostEqual( - cached_goodput_info.total_unproductive_time[BadputType.OTHER], - unknown_badput_time.total_seconds(), - delta=0.1, - ) - - def test_badput_calculator_checkpoint_badput(self): - """Validate computation of badput due to checkpoint manager time.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - # Mock data loading. - self.goodput_recorder.record_data_loading_start_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - # All steps but first progress with average step time. - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Add startup badput during the first step - if step == 0: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - # Mock a save operation. - save_stats = MockSaveStepStatistics( - step=1, - event_type='save', - directory='gs://bucket/path', - wait_for_prev_start_time=10.0, - wait_for_prev_duration_secs=1.0, - checkpointer_blocking_start_time=12.0, - checkpointer_blocking_duration_secs=2.0, - get_old_steps_start_time=13.0, - get_old_steps_duration_secs=3.0, - checkpoint_manager_blocking_start_time=10.0, - checkpoint_manager_blocking_duration_secs=6.0, - reached_preemption=True, - preemption_received_at=10.0, - synchronous=True, - ) - self.mock_cloud_logger.write_cloud_logging_entry(asdict(save_stats)) - - # Simulate a disruption. - disruption_time = datetime.timedelta(seconds=5) - job_restart_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_restart_time) - step_start_time = ( - job_restart_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - restart_from_step = 2 - # All steps but first progress with average step time. - for step in range(restart_from_step, _TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - if step == restart_from_step: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + disruption_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - restore_stats = MockRestoreStepStatistics( - step=1, - event_type='restore', - directory='gs://bucket/path', - checkpointer_start_time=10.0, - checkpointer_duration_secs=2.0, - checkpoint_manager_start_time=10.0, - checkpoint_manager_duration_secs=2.0, - ) - self.mock_cloud_logger.write_cloud_logging_entry(asdict(restore_stats)) - - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Badput. - _, computed_badput_breakdown, _ = self.goodput_calculator.get_job_goodput( - include_badput_breakdown=True - ) - wasted_progress_and_disruption_time = ( - disruption_time - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - expected_badput_due_to_disruptions = ( - (wasted_progress_and_disruption_time.total_seconds()) - / total_time.total_seconds() - * 100 - ) - - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn( - BadputType.WASTED_PROGRESS_FROM_DISRUPTION, - computed_badput_breakdown, - ) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], - expected_badput_due_to_disruptions, - delta=0.1, - ) - self.assertIn( - BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME, computed_badput_breakdown - ) - - self.assertIn( - BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME, - computed_badput_breakdown, - ) - - expect_badput_due_to_checkpointing_save = ( - (save_stats.checkpoint_manager_blocking_duration_secs) - / total_time.total_seconds() - * 100 - ) - - expect_badput_due_to_checkpointing_restore = ( - (restore_stats.checkpoint_manager_duration_secs) - / total_time.total_seconds() - * 100 - ) - - self.assertEqual( - computed_badput_breakdown[BadputType.UNPRODUCTIVE_CHECKPOINT_SAVE_TIME], - expect_badput_due_to_checkpointing_save, - ) - - self.assertEqual( - computed_badput_breakdown[ - BadputType.UNPRODUCTIVE_CHECKPOINT_RESTORE_TIME - ], - expect_badput_due_to_checkpointing_restore, - ) - - def test_goodput_badput_with_interval_query(self): - """Validate computation of goodput and badput with interval query.""" - - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - # Mock training preparation. - self.goodput_recorder.record_training_preparation_start_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - self.goodput_recorder.record_training_preparation_end_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - # Mock data loading. - self.goodput_recorder.record_data_loading_start_time( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_TRAINING_PREPARATION_TIME - ) - self.goodput_recorder.record_data_loading_end_time( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - # Mock training. - step_start_time = ( - job_start_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - # All steps but first progress with average step time. - for step in range(_TEST_TOTAL_STEPS): - # Record step time - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - # Add startup badput during the first step - if step == 0: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - intermediate_job_end_time = step_start_time - - # Simulate a disruption. - disruption_time = datetime.timedelta(seconds=5) - job_restart_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_restart_time) - step_start_time = ( - job_restart_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - ) - - restart_from_step = 2 - # All steps but first progress with average step time. - for step in range(restart_from_step, _TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - if step == restart_from_step: - step_start_time += _TEST_FIRST_STEP_EXTRA_TIME - - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS - + disruption_time - + _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - # Compute Goodput and Badput with the interval query API. - ( - computed_goodput, - computed_badput_breakdown, - last_step, - total_job_time, - number_of_disruptions, - ) = self.goodput_calculator.get_job_goodput_interval( - job_start_time - datetime.timedelta(microseconds=1), job_end_time - ) - - productive_time = _TEST_STEP_TIME * _TEST_TOTAL_STEPS - expected_goodput = ( - (productive_time.total_seconds()) / total_time.total_seconds() * 100 - ) - wasted_progress_and_disruption_time = ( - disruption_time - + (_TEST_TOTAL_STEPS - restart_from_step) * _TEST_STEP_TIME - ) - expected_badput_due_to_disruptions = ( - (wasted_progress_and_disruption_time.total_seconds()) - / total_time.total_seconds() - * 100 - ) - - # Validate last step - self.assertEqual(last_step, _TEST_TOTAL_STEPS - 1) - # Validate total job time - self.assertEqual(total_job_time, total_time.total_seconds()) - # Validate number of disruptions - self.assertEqual(number_of_disruptions, 1) - # Validate Goodput - self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - # Validate Badput - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn( - BadputType.WASTED_PROGRESS_FROM_DISRUPTION, - computed_badput_breakdown, - ) - self.assertAlmostEqual( - computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], - expected_badput_due_to_disruptions, - delta=0.1, - ) - - # Update the interval to exclude the disruption and validate new values. - ( - computed_goodput, - computed_badput_breakdown, - last_step, - total_job_time, - number_of_disruptions, - ) = self.goodput_calculator.get_job_goodput_interval( - job_start_time - datetime.timedelta(microseconds=1), intermediate_job_end_time - ) - - productive_time = _TEST_STEP_TIME * (_TEST_TOTAL_STEPS - 1) - expected_intermediate_total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_TRAINING_PREPARATION_TIME - + _TEST_DATA_LOADING_TIME - + _TEST_FIRST_STEP_EXTRA_TIME - + _TEST_STEP_TIME * (_TEST_TOTAL_STEPS - 1) - ) - expected_goodput = ( - (productive_time.total_seconds()) - / expected_intermediate_total_time.total_seconds() - * 100 - ) - - # Validate last step - self.assertEqual(last_step, _TEST_TOTAL_STEPS - 1) - # Validate total job time - self.assertEqual( - total_job_time, expected_intermediate_total_time.total_seconds() - ) - # There should be no disruptions in the interval. - self.assertEqual(number_of_disruptions, 0) - # Validate Goodput - self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - # Validate Badput - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn( - BadputType.WASTED_PROGRESS_FROM_DISRUPTION, - computed_badput_breakdown, - ) - self.assertEqual( - computed_badput_breakdown[BadputType.WASTED_PROGRESS_FROM_DISRUPTION], 0 - ) - - def _generate_step_start_times(self, number_of_steps: int, start_time): - """Generate a list of n non-decreasing datetime objects.""" - max_step_seconds = 600 - step_start_times = [start_time] - for _ in range(1, number_of_steps): - increment = random.randint(1, max_step_seconds) - new_time = step_start_times[-1] + datetime.timedelta(seconds=increment) - step_start_times.append(new_time) - return step_start_times - - def test_get_step_deviation(self): - """Test function to validate step deviation computation.""" - job_start_time = datetime.datetime.now(datetime.timezone.utc) - self.goodput_recorder.record_job_start_time(job_start_time) - # Generate a list of 100 step start times with random step times. - step_count = 0 - max_steps = 100 - test_step_start_times = self._generate_step_start_times( - number_of_steps=max_steps, start_time=job_start_time - ) - - # Record step start times. - for step_start_time in test_step_start_times: - self.goodput_recorder.record_step_start_time(step_count, step_start_time) - step_count += 1 - - job_end_time = test_step_start_times[-1] + datetime.timedelta(seconds=10) - self.goodput_recorder.record_job_end_time(job_end_time) - - step_times = self.goodput_calculator._get_step_times(self.mock_cloud_logger.entries) - ideal_step_time = compute_ideal_step_time( - step_times=list(step_times.values()) - ) - computed_step_deviations = self.goodput_calculator.get_step_deviation() - expected_step_deviations = { - step_count: abs(step_time - ideal_step_time) - for step_count, step_time in step_times.items() - } - for step_count, expected_deviation in expected_step_deviations.items(): - computed_deviation = computed_step_deviations[step_count] - self.assertAlmostEqual( - expected_deviation, - computed_deviation, - delta=0.1, - ) - - def test_badput_calculator_custom_sync_badput(self): - """Test function to validate unknown badput bucket.""" - - job_start_time = _TEST_JOB_START_TIME - self.goodput_recorder.record_job_start_time(job_start_time) - - # Mock TPU initialization. - self.goodput_recorder.record_tpu_init_start_time(job_start_time) - self.goodput_recorder.record_tpu_init_end_time( - job_start_time + _TEST_TPU_INIT_TIME - ) - - # Mock _TEST_TOTAL_STEPS steps of training with built-in badput - # due to program startup. - step_start_time = ( - job_start_time + _TEST_TPU_INIT_TIME + _TEST_PROGRAM_STARTUP_TIME - ) - for step in range(_TEST_TOTAL_STEPS): - # Record step time. - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - eval_sync_badput_time = datetime.timedelta(seconds=5) - self.goodput_recorder.record_custom_badput_event_start_time( - step_start_time, 'eval_step' - ) - self.goodput_recorder.record_custom_badput_event_end_time( - step_start_time + eval_sync_badput_time, 'eval_step' - ) - step_start_time += eval_sync_badput_time - - # Continue training for _TEST_TOTAL_STEPS more steps. - for step in range(_TEST_TOTAL_STEPS, _TEST_TOTAL_STEPS * 2): - # Record step time. - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - total_time = ( - _TEST_TPU_INIT_TIME - + _TEST_PROGRAM_STARTUP_TIME - + _TEST_STEP_TIME * _TEST_TOTAL_STEPS * 2 - + eval_sync_badput_time - ) - job_end_time = job_start_time + total_time - self.goodput_recorder.record_job_end_time(job_end_time) - - computed_goodput, computed_badput_breakdown, _ = ( - self.goodput_calculator.get_job_goodput(include_badput_breakdown=True) - ) - # Validate Badput breakdown. - self.assertNotEmpty(computed_badput_breakdown) - self.assertIn( - BadputType.CUSTOM_BADPUT_EVENTS, computed_badput_breakdown - ) - self.assertIn( - 'EVAL_STEP', - computed_badput_breakdown[BadputType.CUSTOM_BADPUT_EVENTS], - ) - computed_badput_due_to_custom_sync = computed_badput_breakdown[ - BadputType.CUSTOM_BADPUT_EVENTS - ]['EVAL_STEP'] - - expected_badput_due_to_custom_sync = ( - (eval_sync_badput_time.total_seconds()) - / total_time.total_seconds() - * 100 - ) - self.assertAlmostEqual( - computed_badput_due_to_custom_sync, - expected_badput_due_to_custom_sync, - delta=0.1, - ) - # Validate Goodput. - expected_goodput = ( - (_TEST_STEP_TIME * (_TEST_TOTAL_STEPS * 2)).total_seconds() - / total_time.total_seconds() - * 100 - ) - self.assertAlmostEqual(computed_goodput, expected_goodput, delta=0.1) - # Make sure this data is cached correctly. - cached_goodput_info = ( - self.goodput_calculator._goodput_cache.get_goodput_info() - ) - self.assertNotEmpty(cached_goodput_info.total_unproductive_time) - self.assertIn( - BadputType.CUSTOM_BADPUT_EVENTS, - cached_goodput_info.total_unproductive_time, - ) - self.assertAlmostEqual( - cached_goodput_info.total_unproductive_time[ - BadputType.CUSTOM_BADPUT_EVENTS - ]['EVAL_STEP'], - eval_sync_badput_time.total_seconds(), - delta=0.1, - ) - - def test_goodput_with_disruption_and_caching(self): - """Test function to validate goodput with disruption and caching. - - Verifies that productive time is correctly computed when a disruption is - detected after the last cache update, and previous cached data is stale. - - Scenario: - - Initial productive steps (0-4) are cached before disruption. - - A disruption occurs and the job restarts from step 3. - - Delta between cached and new logs show steps 3-4 (latent disruption). - - Final computed and cached productive time should be correct at each query. - """ - job_start_time = _TEST_JOB_START_TIME - self.goodput_recorder.record_job_start_time(job_start_time) - - step_start_time = job_start_time - for step in range(_TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - disruption_time = datetime.timedelta(seconds=5) - job_start_time = step_start_time + disruption_time - self.goodput_recorder.record_job_start_time(job_start_time) - - # Query after restart but before any steps (emulate above scenario). - _, _, _ = self.goodput_calculator.get_job_goodput() - # Validate productive in the cache. - cached_goodput_info = ( - self.goodput_calculator._goodput_cache.get_goodput_info() - ) - self.assertAlmostEqual( - cached_goodput_info.total_productive_time, - (_TEST_STEP_TIME * (_TEST_TOTAL_STEPS - 1)).total_seconds(), - delta=0.1, - ) - - step_start_time = job_start_time - repeat_steps = 2 - restart_step = _TEST_TOTAL_STEPS - repeat_steps - for step in range(restart_step, _TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_start_time) - step_start_time += _TEST_STEP_TIME - - total_time = ( - +_TEST_STEP_TIME * _TEST_TOTAL_STEPS - + disruption_time - + (restart_step - 1) * _TEST_STEP_TIME - ) - self.goodput_recorder.record_job_end_time(_TEST_JOB_START_TIME + total_time) - # Compute Goodput and Badput. - _, _, _ = self.goodput_calculator.get_job_goodput() - - # Validate that the cache is updated correctly. - cached_goodput_info = ( - self.goodput_calculator._goodput_cache.get_goodput_info() - ) - # Validate productive time, - expected_productive_time = _TEST_STEP_TIME * _TEST_TOTAL_STEPS - self.assertAlmostEqual( - cached_goodput_info.total_productive_time, - expected_productive_time.total_seconds(), - ) - # Validate that previous progress is now unproductive and marked as - # wasted progress). - self.assertNotEmpty(cached_goodput_info.total_unproductive_time) - self.assertIn( - BadputType.WASTED_PROGRESS_FROM_DISRUPTION, - cached_goodput_info.total_unproductive_time, - ) - expected_unproductive_time = ( - total_time.total_seconds() - expected_productive_time.total_seconds() - ) - cached_unproductive_time = sum( - value if isinstance(value, float) else sum(value.values()) - for badput_type, value in cached_goodput_info.total_unproductive_time.items() - if badput_type != BadputType.DATA_LOADING_ASYNC - ) - - self.assertAlmostEqual( - cached_unproductive_time, - expected_unproductive_time, - delta=0.1, - ) - expected_wasted_progress_from_disruption = ( - disruption_time + (restart_step - 2) * _TEST_STEP_TIME - ) - self.assertAlmostEqual( - cached_goodput_info.total_unproductive_time[ - BadputType.WASTED_PROGRESS_FROM_DISRUPTION - ], - expected_wasted_progress_from_disruption.total_seconds(), - delta=0.1, - ) - - -class GoodputStepDeviationConcurrencyTest(googletest.TestCase): - - def setUp(self): - super().setUp() - self.job_name = 'test-concurrent-run' - self.logger_name = 'test-concurrent-log' - self.mock_cloud_logger = MockCloudLogger(self.job_name, self.logger_name) - self.goodput_recorder = goodput.GoodputRecorder( - self.job_name, - self.logger_name, - True, - self.mock_cloud_logger, - ) - self.goodput_calculator = goodput.GoodputCalculator( - self.job_name, self.logger_name, self.mock_cloud_logger - ) - self._mock_sample_program() - - def _mock_sample_program(self): - self.goodput_recorder.record_job_start_time(_TEST_JOB_START_TIME) - step_time = _TEST_STEP_START_TIME - for step in range(_TEST_TOTAL_STEPS): - self.goodput_recorder.record_step_start_time(step, step_time) - step_time += _TEST_STEP_TIME - self.goodput_recorder.record_job_end_time(_TEST_JOB_END_TIME) - - def test_concurrent_goodput_and_step_deviation(self): - """Test concurrent access to Goodput and Step Deviation calculations.""" - errors = [] - - def compute_goodput(): - try: - for _ in range(10): - self.goodput_calculator.get_job_goodput() - except ( - ValueError, - TypeError, - KeyError, - ) as e: - errors.append(f'Goodput thread error: {e}') - - def compute_step_deviation(): - try: - for _ in range(10): - self.goodput_calculator.get_step_deviation() - except ( - ValueError, - TypeError, - ) as e: - errors.append(f'Step deviation thread error: {e}') - - threads = [] - thread_count = 5 - for _ in range(thread_count): - threads.append(threading.Thread(target=compute_goodput)) - threads.append(threading.Thread(target=compute_step_deviation)) - - for t in threads: - t.start() - for t in threads: - t.join() - self.assertEmpty(errors, msg=f'Errors occurred in concurrent threads: {errors}') - -if __name__ == '__main__': - googletest.main() diff --git a/ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py b/ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py deleted file mode 100644 index a97fcfc..0000000 --- a/ml-goodput-measurement/ml_goodput_measurement/tests/monitoring_test.py +++ /dev/null @@ -1,794 +0,0 @@ -"""Tests to validate the monitoring module. - -This module tests the GoodputMonitor class and its functionality, specifically -the uploading of step deviation, goodput and badput data to Tensorboard. -""" - -from unittest import mock - -from absl.testing import absltest -from cloud_goodput.ml_goodput_measurement.src import gcp_metrics -from cloud_goodput.ml_goodput_measurement.src import goodput_utils -from cloud_goodput.ml_goodput_measurement.src import monitoring - -from google.cloud import monitoring_v3 - -BadputType = goodput_utils.BadputType -GCPOptions = goodput_utils.GCPOptions -GoodputMonitor = monitoring.GoodputMonitor -GoodputType = goodput_utils.GoodputType -MagicMock = mock.MagicMock -ValueType = gcp_metrics.ValueType - -patch = mock.patch -_TEST_UPLOAD_INTERVAL = 1 - - -class GoodputMonitorTests(absltest.TestCase): - """Tests for the GoodputMonitor class.""" - - def setUp(self): - super().setUp() - self.job_name = 'test-run' - self.logger_name = 'test-logger' - self.tensorboard_dir = 'test-dir' - - def _create_timeseries( - self, metric_type: str, labels: dict, value: float - ) -> monitoring_v3.TimeSeries: - ts = monitoring_v3.TimeSeries() - ts.metric.type = metric_type - ts.metric.labels.update(labels) - ts.resource.type = 'compute.googleapis.com/Workload' - ts.resource.labels.update({ - 'location': 'test-location', - 'workload_id': 'test-run', - 'replica_id': 'test-replica-id', - }) - ts.points.append( - monitoring_v3.Point( - value=monitoring_v3.TypedValue(double_value=value), - ) - ) - return ts - - def _compare_calls_ignore_time_series( - self, expected_call, actual_call - ) -> bool: - if ( - expected_call.args != actual_call.args - or expected_call.kwargs.keys() != actual_call.kwargs.keys() - ): - return False - - for key, expected_value in expected_call.kwargs.items(): - actual_value = actual_call.kwargs[key] - if key == 'time_series': - continue - if expected_value != actual_value: - return False - - return True - - def _setup_mock_goodput_monitor( - self, mock_logging_client, mock_summary_writer, mock_metric_service_client - ) -> GoodputMonitor: - mock_client = MagicMock() - mock_metric_service_client.return_value = mock_client - mock_logging_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - - gcp_options = GCPOptions( - enable_gcp_goodput_metrics=True, - project_id='test-project', - location='test-location', - acc_type='test-acc-type', - replica_id='test-replica-id', - ) - - return GoodputMonitor( - job_name='test-run', - logger_name='test-logger', - tensorboard_dir='/tmp', - upload_interval=1, - monitoring_enabled=True, - gcp_options=gcp_options, - ) - - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - def test_goodput_monitor_init(self, mock_logger_client, mock_summary_writer): - mock_summary_writer.return_value = MagicMock() - mock_logger_client.return_value = MagicMock() - goodput_monitor = GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - ) - # Objects should be initialized correctly. - self.assertIsNotNone(goodput_monitor) - self.assertIs(goodput_monitor._writer, mock_summary_writer.return_value) - self.assertIsNotNone(goodput_monitor._goodput_calculator) - - # Thread events should be initialized correctly. - self.assertIsNotNone(goodput_monitor._step_deviation_termination_event) - self.assertFalse(goodput_monitor._step_deviation_termination_event.is_set()) - self.assertFalse(goodput_monitor._step_deviation_uploader_thread_running) - self.assertIsNotNone(goodput_monitor._termination_event) - self.assertFalse(goodput_monitor._termination_event.is_set()) - self.assertFalse(goodput_monitor._goodput_uploader_thread_running) - - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_goodput_to_tensorboard' - ) - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - async def test_goodput_monitor_start_goodput_uploader_success( - self, mock_logger_client, mock_summary_writer, mock_goodput_to_tensorboard - ): - mock_summary_writer.return_value = MagicMock() - mock_goodput_to_tensorboard.return_value = MagicMock() - mock_logger_client.return_value = MagicMock() - goodput_monitor = monitoring.GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - ) - goodput_monitor.start_goodput_uploader() - self.assertTrue(goodput_monitor._uploader_thread_running) - self.assertIsNotNone(goodput_monitor._goodput_upload_thread) - self.assertFalse(goodput_monitor._termination_event.is_set()) - mock_goodput_to_tensorboard.assert_called_once() - mock_summary_writer.return_value.add_scalar.assert_called_once() - goodput_monitor.stop_goodput_uploader() - self.assertFalse(goodput_monitor._uploader_thread_running) - self.assertIsNone(goodput_monitor._goodput_upload_thread) - self.assertTrue(goodput_monitor._termination_event.is_set()) - - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_goodput_to_tensorboard' - ) - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - async def test_goodput_monitor_start_goodput_uploader_failure( - self, mock_logger_client, mock_summary_writer, mock_goodput_to_tensorboard - ): - mock_logger_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - mock_goodput_to_tensorboard.side_effect = ValueError('Test Error') - goodput_monitor = monitoring.GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - ) - goodput_monitor.start_goodput_uploader() - self.assertTrue(goodput_monitor._uploader_thread_running) - self.assertIsNotNone(goodput_monitor._goodput_upload_thread) - self.assertFalse(goodput_monitor._termination_event.is_set()) - mock_goodput_to_tensorboard.assert_called_once() - with self.assertRaisesRegex(ValueError, 'Test Error'): - goodput_monitor._query_and_upload_goodput() - mock_summary_writer.return_value.add_scalar.assert_not_called() - goodput_monitor.stop_goodput_uploader() - self.assertFalse(goodput_monitor._uploader_thread_running) - self.assertIsNone(goodput_monitor._goodput_upload_thread) - self.assertTrue(goodput_monitor._termination_event.is_set()) - - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_badput_to_tensorboard' - ) - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - async def test_goodput_monitor_start_badput_uploader_success( - self, mock_logger_client, mock_summary_writer, mock_badput_to_tensorboard - ): - mock_summary_writer.return_value = MagicMock() - mock_badput_to_tensorboard.return_value = MagicMock() - mock_logger_client.return_value = MagicMock() - goodput_monitor = monitoring.GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - include_badput_breakdown=True, - ) - - goodput_monitor.start_goodput_uploader() - self.assertTrue(goodput_monitor._uploader_thread_running) - self.assertIsNotNone(goodput_monitor._goodput_upload_thread) - self.assertFalse(goodput_monitor._termination_event.is_set()) - self.assertTrue(goodput_monitor._include_badput_breakdown) - - mock_badput_to_tensorboard.assert_called_once() - mock_summary_writer.return_value.add_scalar.assert_called_once() - - goodput_monitor.stop_goodput_uploader() - self.assertFalse(goodput_monitor._uploader_thread_running) - self.assertIsNone(goodput_monitor._goodput_upload_thread) - self.assertTrue(goodput_monitor._termination_event.is_set()) - - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_step_deviation_to_tensorboard' - ) - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - async def test_goodput_monitor_start_step_deviation_uploader_success( - self, - mock_logger_client, - mock_summary_writer, - mock_step_deviation_to_tensorboard, - ): - mock_logger_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - mock_step_deviation_to_tensorboard.return_value = MagicMock() - goodput_monitor = monitoring.GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - include_step_deviation=True, - ) - goodput_monitor.start_step_deviation_uploader() - self.assertTrue(goodput_monitor._step_deviation_uploader_thread_running) - self.assertIsNotNone(goodput_monitor._step_deviation_upload_thread) - self.assertFalse(goodput_monitor._step_deviation_termination_event.is_set()) - mock_step_deviation_to_tensorboard.assert_called_once() - mock_summary_writer.return_value.add_scalar.assert_called_once() - goodput_monitor.stop_step_deviation_uploader() - self.assertFalse(goodput_monitor._step_deviation_uploader_thread_running) - self.assertIsNone(goodput_monitor._step_deviation_upload_thread) - self.assertTrue(goodput_monitor._step_deviation_termination_event.is_set()) - - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._write_step_deviation_to_tensorboard' - ) - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - async def test_goodput_monitor_start_step_deviation_uploader_failure( - self, - mock_logger_client, - mock_summary_writer, - mock_query_and_upload_step_deviation, - ): - mock_logger_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - mock_query_and_upload_step_deviation.side_effect = ValueError('Test Error') - goodput_monitor = monitoring.GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - include_step_deviation=True, - ) - goodput_monitor.start_step_deviation_uploader() - self.assertTrue(goodput_monitor._step_deviation_uploader_thread_running) - self.assertIsNotNone(goodput_monitor._step_deviation_upload_thread) - self.assertFalse(goodput_monitor._step_deviation_termination_event.is_set()) - mock_query_and_upload_step_deviation.assert_called_once() - with self.assertRaisesRegex(ValueError, 'Test Error'): - goodput_monitor._query_and_upload_step_deviation() - mock_summary_writer.return_value.add_scalar.assert_not_called() - goodput_monitor.stop_step_deviation_uploader() - self.assertFalse(goodput_monitor._step_deviation_uploader_thread_running) - self.assertIsNone(goodput_monitor._step_deviation_upload_thread) - self.assertTrue(goodput_monitor._step_deviation_termination_event.is_set()) - - @patch('google.cloud.monitoring_v3.MetricServiceClient') - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - def test_send_goodput_metrics_to_gcp_success( - self, - mock_logging_client, - mock_summary_writer, - mock_metric_service_client, - ): - mock_client = MagicMock() - mock_metric_service_client.return_value = mock_client - mock_logging_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - - gcp_options = GCPOptions( - enable_gcp_goodput_metrics=True, - project_id='test-project', - location='test-location', - acc_type='test-acc-type', - replica_id='test-replica-id', - ) - - goodput_monitor = GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - gcp_options=gcp_options, - ) - - # Mock the get_job_goodput_details to return test data - goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( - return_value={ - 'goodput_time_dict': { - GoodputType.TOTAL: 10.0, - }, - 'badput_time_dict': { - BadputType.TPU_INITIALIZATION: 2.0, - BadputType.DATA_LOADING_SYNC: 1.0, - }, - } - ) - - goodput_monitor._send_goodput_metrics_to_gcp( - goodput_monitor._goodput_calculator.get_job_goodput_details() - ) - - expected_calls = [ - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/goodput_time', - { - 'goodput_source': 'TOTAL', - 'accelerator_type': 'test-acc-type', - }, - 10.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'TPU_INITIALIZATION', - 'accelerator_type': 'test-acc-type', - }, - 2.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'DATA_LOADING_SYNC', - 'accelerator_type': 'test-acc-type', - }, - 1.0, - ) - ], - ), - ] - - actual_calls = mock_client.create_time_series.call_args_list - - # Verify each call individually - for expected_call in expected_calls: - self.assertTrue( - any( - self._compare_calls_ignore_time_series(expected_call, actual) - for actual in actual_calls - ), - f'Expected call not found: {expected_call}', - ) - - @patch('google.cloud.monitoring_v3.MetricServiceClient') - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - def test_send_goodput_metrics_to_gcp_exception( - self, - mock_logging_client, - mock_summary_writer, - mock_metric_service_client, - ): - mock_client = MagicMock() - mock_client.create_time_series.side_effect = Exception('Test Exception') - mock_metric_service_client.return_value = mock_client - mock_logging_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - - gcp_options = GCPOptions( - enable_gcp_goodput_metrics=True, - project_id='test-project', - location='test-location', - acc_type='test-acc-type', - replica_id='test-replica-id', - ) - - goodput_monitor = GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - gcp_options=gcp_options, - ) - - # Mock the get_job_goodput_details to return test data - goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( - return_value={ - 'goodput_time_dict': { - GoodputType.TOTAL: 10.0, - }, - 'badput_time_dict': { - BadputType.DATA_LOADING_SYNC: 2.0, - }, - } - ) - - goodput_monitor._send_goodput_metrics_to_gcp( - goodput_monitor._goodput_calculator.get_job_goodput_details() - ) - - # Verify that create_time_series was called, even if it raised an exception - mock_client.create_time_series.assert_called_once() - - @patch('google.cloud.monitoring_v3.MetricServiceClient') - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - def test_send_goodput_metrics_to_gcp_exclusion( - self, - mock_logging_client, - mock_summary_writer, - mock_metric_service_client - ): - mock_client = MagicMock() - mock_metric_service_client.return_value = mock_client - mock_logging_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - - gcp_options = GCPOptions( - enable_gcp_goodput_metrics=True, - project_id='test-project', - location='test-location', - acc_type='test-acc-type', - replica_id='test-replica-id', - ) - - goodput_monitor = GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - gcp_options=gcp_options, - ) - - # Mock the get_job_goodput_details to return test data, including an - # excluded type - goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( - return_value={ - 'goodput_time_dict': { - GoodputType.TOTAL: 10.0, - }, - 'badput_time_dict': { - BadputType.TPU_INITIALIZATION: 2.0, - BadputType.DATA_LOADING_SYNC: 1.0, - BadputType.DATA_LOADING_ASYNC: ( - 3.0 - ), # DATA_LOADING_ASYNC is in ACTIVITY_EXCLUSION_LIST - }, - } - ) - - goodput_monitor._send_goodput_metrics_to_gcp( - goodput_monitor._goodput_calculator.get_job_goodput_details() - ) - - # Verify that create_time_series was called with the correct data, - # excluding DATA_LOADING_ASYNC - expected_calls = [ - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/goodput_time', - { - 'goodput_source': 'TOTAL', - 'accelerator_type': 'test-acc-type', - }, - 10.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'TPU_INITIALIZATION', - 'accelerator_type': 'test-acc-type', - }, - 2.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'DATA_LOADING_SYNC', - 'accelerator_type': 'test-acc-type', - }, - 1.0, - ) - ], - ), - ] - - actual_calls = mock_client.create_time_series.call_args_list - - # Verify each call individually - for expected_call in expected_calls: - self.assertTrue( - any( - self._compare_calls_ignore_time_series(expected_call, actual) - for actual in actual_calls - ), - f'Expected call not found: {expected_call}', - ) - # Verify unexpected calls are not made - for actual_call in actual_calls: - for ts in actual_call.kwargs.get('time_series', []): - if ( - 'badput_source' in ts.metric.labels - and ts.metric.labels['badput_source'] == 'DATA_LOADING_ASYNC' - ): - self.fail(f'Unexpected call found: {ts}') - - @patch('google.cloud.monitoring_v3.MetricServiceClient') - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - def test_send_interval_goodput_metrics_to_gcp( - self, - mock_logging_client, - mock_summary_writer, - mock_metric_service_client, - ): - mock_client = MagicMock() - mock_metric_service_client.return_value = mock_client - mock_logging_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - - gcp_options = GCPOptions( - enable_gcp_goodput_metrics=True, - project_id='test-project', - location='test-location', - acc_type='test-acc-type', - replica_id='test-replica-id', - ) - - goodput_monitor = GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - gcp_options=gcp_options, - ) - - # Mock the get_job_goodput_details to return test data - goodput_monitor._goodput_calculator.get_job_goodput_interval_details = ( - MagicMock( - return_value={ - 'goodput_time_dict': { - GoodputType.TOTAL: 10.0, - }, - 'badput_time_dict': { - BadputType.TPU_INITIALIZATION: 2.0, - BadputType.DATA_LOADING_SYNC: 1.0, - }, - } - ) - ) - - goodput_monitor._send_goodput_metrics_to_gcp( - goodput_monitor._goodput_calculator.get_job_goodput_interval_details() - ) - - expected_calls = [ - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/goodput_time', - { - 'goodput_source': 'TOTAL', - 'accelerator_type': 'test-acc-type', - }, - 10.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'TPU_INITIALIZATION', - 'accelerator_type': 'test-acc-type', - }, - 2.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'DATA_LOADING_SYNC', - 'accelerator_type': 'test-acc-type', - }, - 1.0, - ) - ], - ), - ] - - actual_calls = mock_client.create_time_series.call_args_list - - # Verify each call individually - for expected_call in expected_calls: - self.assertTrue( - any( - self._compare_calls_ignore_time_series(expected_call, actual) - for actual in actual_calls - ), - f'Expected call not found: {expected_call}', - ) - - @patch('google.cloud.monitoring_v3.MetricServiceClient') - @patch('tensorboardX.writer.SummaryWriter') - @patch('google.cloud.logging.Client') - def test_send_goodput_metrics_custom_sync_events( - self, mock_logging_client, mock_summary_writer, mock_metric_service_client - ): - mock_client = MagicMock() - mock_metric_service_client.return_value = mock_client - mock_logging_client.return_value = MagicMock() - mock_summary_writer.return_value = MagicMock() - - gcp_options = GCPOptions( - enable_gcp_goodput_metrics=True, - project_id='test-project', - location='test-location', - acc_type='test-acc-type', - replica_id='test-replica-id', - ) - - goodput_monitor = GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - gcp_options=gcp_options, - ) - - # Mock the get_job_goodput_details to return test data, including an - # excluded type - goodput_monitor._goodput_calculator.get_job_goodput_details = MagicMock( - return_value={ - 'goodput_time_dict': { - GoodputType.TOTAL: 10.0, - }, - 'badput_time_dict': { - BadputType.TPU_INITIALIZATION: 2.0, - BadputType.DATA_LOADING_SYNC: 1.0, - BadputType.CUSTOM_BADPUT_EVENTS: { - 'EVAL_STEP': 3.0, - 'SDC_COMPILATION': 4.0, - }, - }, - } - ) - - goodput_monitor._send_goodput_metrics_to_gcp( - goodput_monitor._goodput_calculator.get_job_goodput_details() - ) - - expected_calls = [ - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/goodput_time', - { - 'goodput_source': 'TOTAL', - 'accelerator_type': 'test-acc-type', - }, - 10.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'TPU_INITIALIZATION', - 'accelerator_type': 'test-acc-type', - }, - 2.0, - ) - ], - ), - mock.call.create_time_series( - name='projects/test-project', - time_series=[ - self._create_timeseries( - 'compute.googleapis.com/workload/badput_time', - { - 'badput_source': 'DATA_LOADING_SYNC', - 'accelerator_type': 'test-acc-type', - }, - 1.0, - ) - ], - ), - ] - - actual_calls = mock_client.create_time_series.call_args_list - - # Verify each call individually - for expected_call in expected_calls: - self.assertTrue( - any( - self._compare_calls_ignore_time_series(expected_call, actual_call) - for actual_call in actual_calls - ), - f'Expected call not found: {expected_call}', - ) - - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._final_interval_goodput_query_and_upload' - ) - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._final_step_deviation_query_and_upload' - ) - @patch( - 'cloud_goodput.ml_goodput_measurement.src.monitoring.GoodputMonitor._final_goodput_query_and_upload' - ) - async def test_goodput_monitor_final_query_and_upload( - self, - mock_final_goodput_query_and_upload, - mock_final_step_deviation_query_and_upload, - mock_final_interval_goodput_query_and_upload, - ): - mock_final_goodput_query_and_upload.return_value = MagicMock() - mock_final_step_deviation_query_and_upload.return_value = MagicMock() - mock_final_interval_goodput_query_and_upload.return_value = MagicMock() - goodput_monitor = monitoring.GoodputMonitor( - self.job_name, - self.logger_name, - self.tensorboard_dir, - upload_interval=_TEST_UPLOAD_INTERVAL, - monitoring_enabled=True, - ) - goodput_monitor.__del__() - mock_final_goodput_query_and_upload.assert_called_once() - mock_final_step_deviation_query_and_upload.assert_called_once() - mock_final_interval_goodput_query_and_upload.assert_called_once() - - -if __name__ == '__main__': - absltest.main() diff --git a/ml-goodput-measurement/pyproject.toml b/ml-goodput-measurement/pyproject.toml deleted file mode 100644 index ad696f4..0000000 --- a/ml-goodput-measurement/pyproject.toml +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -[project] -name = "ml_goodput_measurement" -version = "0.0.10" -authors = [ - { name="Cloud TPU Team", email="cloud-tpu-eng@google.com" }, -] -description = "Package to monitor Goodput, Badput and other metrics of ML workloads." -readme = "README.md" -requires-python = ">=3.8" -license = {text = "Apache-2.0"} -classifiers = [ - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", -] -keywords = [] - -# pip dependencies installed with `pip install -e .` -dependencies = [ - "google-api-core>=2.24.1", - "google-cloud-logging>=3.5.0", - "google-cloud-monitoring>=2.20.0", - "numpy", - "requests", - "scipy", - "tensorboardx", - "urllib3", -] - -[project.urls] -"Homepage" = "https://github.com/AI-Hypercomputer/ml-goodput-measurement" -"Bug Tracker" = "https://github.com/AI-Hypercomputer/ml-goodput-measurement/issues" - -[build-system] -# Build system specify which backend is used to build/install the project -requires = ["flit_core >=3.8,<4"] -build-backend = "flit_core.buildapi" - -[tool.flit.sdist] -# Flit specific options (files to exclude from the PyPI package) -exclude = [ - # Do not release tests files on PyPI - "tests/*_test.py", -] \ No newline at end of file