⚡️ Speed up method BatchPredictionJob.submit by 40%

[codeflash-ai]

📄 40% (0.40x) speedup for BatchPredictionJob.submit in google/cloud/aiplatform/jobs.py

⏱️ Runtime : 140 microseconds → 100 microseconds (best of 10 runs)

📝 Explanation and details

The optimized code achieves a 39% speedup through several targeted micro-optimizations that reduce overhead without changing functionality:

1. Import Caching Strategy
The most significant optimization introduces a function-local cache (IMPORT_TYPE_CACHE) that stores expensive module imports based on the use_v1beta1 flag. This eliminates repeated import costs when the same code path is executed multiple times, which is common in batch processing scenarios or test suites.

2. Validation Reordering for Fast-Fail
Cheap validation checks (like boolean comparisons for source/destination conflicts) are moved before expensive operations like utils.validate_display_name() and utils.validate_labels(). This "fail-fast" approach means invalid inputs are caught earlier, avoiding unnecessary work.

3. Local Variable Optimization
Instead of repeatedly accessing object attributes, the code creates local variables (bqsrc, bqdest, uris) and then assigns them to object properties. This reduces attribute lookup overhead in Python.

4. Direct Parameter Forwarding
The submit() method now directly forwards all parameters to _submit_impl() without creating intermediate local variables, reducing function call overhead.

Performance Profile:

• Best for: High-frequency calls with repeated parameter patterns (like test suites showing 66-90% speedups on validation errors)
• Excellent for: Batch job submissions where the same import paths are used repeatedly
• Minimal impact: Single-execution scenarios still benefit from the validation reordering

The optimizations are particularly effective for the test cases shown, where validation errors are caught faster due to the reordered checks, and import caching provides substantial benefits in repeated execution contexts.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 22 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest
from aiplatform.jobs import BatchPredictionJob

# For test purposes, we will mock the BatchPredictionJob class and its dependencies.
# In real-world usage, these would be imported from the google.cloud.aiplatform library.
# Here, we only need to test the submit function logic and its validation, so we can
# stub/mock the rest.

class DummyModel:
    # Simulate a model object with a versioned_resource_name attribute
    def __init__(self, name):
        self.versioned_resource_name = name

class DummyBatchPredictionJob:
    # Simulate the return value of submit
    def __init__(self, **kwargs):
        self.kwargs = kwargs

class DummyUtils:
    # Simulate utils functions for validation
    @staticmethod
    def validate_display_name(display_name):
        if len(display_name) > 128:
            raise ValueError("Display name needs to be less than 128 characters.")

    @staticmethod
    def validate_labels(labels):
        for k, v in labels.items():
            if not isinstance(k, str) or not isinstance(v, str):
                raise ValueError(
                    "Expect labels to be a mapping of string key value pairs."
                )

    @staticmethod
    def full_resource_name(
        resource_name,
        resource_noun,
        parse_resource_name_method,
        format_resource_name_method,
        project=None,
        location=None,
        resource_id_validator=None,
    ):
        # For tests, just return the string
        return resource_name

class DummyPublisherModel:
    @staticmethod
    def _parse_resource_name(model_name):
        # Accept anything that starts with "publisher"
        return model_name.startswith("publisher")

class DummyConstants:
    BATCH_PREDICTION_INPUT_STORAGE_FORMATS = ["jsonl", "csv", "tf-record"]
    BATCH_PREDICTION_OUTPUT_STORAGE_FORMATS = ["jsonl", "csv", "tf-record", "bigquery"]

class DummyAiplatform:
    class Model:
        @staticmethod
        def _parse_resource_name(resource_name):
            # Accept anything that starts with "model"
            return resource_name.startswith("model")

        @staticmethod
        def _format_resource_name(**kwargs):
            # Return a formatted string for tests
            return f"projects/{kwargs.get('project')}/locations/{kwargs.get('location')}/models/{kwargs.get('models')}"

# Now, we define a minimal submit function with the same validation logic
def submit(
    *,
    job_display_name=None,
    model_name=None,
    instances_format="jsonl",
    predictions_format="jsonl",
    gcs_source=None,
    bigquery_source=None,
    gcs_destination_prefix=None,
    bigquery_destination_prefix=None,
    model_parameters=None,
    machine_type=None,
    accelerator_type=None,
    accelerator_count=None,
    starting_replica_count=None,
    max_replica_count=None,
    generate_explanation=False,
    explanation_metadata=None,
    explanation_parameters=None,
    labels=None,
    project=None,
    location=None,
    credentials=None,
    encryption_spec_key_name=None,
    create_request_timeout=None,
    batch_size=None,
    model_monitoring_objective_config=None,
    model_monitoring_alert_config=None,
    analysis_instance_schema_uri=None,
    service_account=None,
):
    # Validate display name
    if not job_display_name:
        job_display_name = "BatchPredictionJob"
    DummyUtils.validate_display_name(job_display_name)

    # Validate labels
    if labels:
        DummyUtils.validate_labels(labels)

    # Validate model_name
    if isinstance(model_name, str):
        try:
            model_name = DummyUtils.full_resource_name(
                resource_name=model_name,
                resource_noun="models",
                parse_resource_name_method=DummyAiplatform.Model._parse_resource_name,
                format_resource_name_method=DummyAiplatform.Model._format_resource_name,
                project=project,
                location=location,
                resource_id_validator=None,
            )
        except ValueError:
            if not DummyPublisherModel._parse_resource_name(model_name):
                raise

    # Validate sources
    if bool(gcs_source) == bool(bigquery_source):
        raise ValueError(
            "Please provide either a gcs_source or bigquery_source, but not both."
        )

    # Validate destinations
    if bool(gcs_destination_prefix) == bool(bigquery_destination_prefix):
        raise ValueError(
            "Please provide either a gcs_destination_prefix or bigquery_destination_prefix, but not both."
        )

    # Validate instance format
    if instances_format not in DummyConstants.BATCH_PREDICTION_INPUT_STORAGE_FORMATS:
        raise ValueError(
            f"{instances_format} is not an accepted instances format type. Please choose from: {DummyConstants.BATCH_PREDICTION_INPUT_STORAGE_FORMATS}"
        )

    # Validate prediction format
    if predictions_format not in DummyConstants.BATCH_PREDICTION_OUTPUT_STORAGE_FORMATS:
        raise ValueError(
            f"{predictions_format} is not an accepted prediction format type. Please choose from: {DummyConstants.BATCH_PREDICTION_OUTPUT_STORAGE_FORMATS}"
        )

    # If all validations pass, return a DummyBatchPredictionJob object
    return DummyBatchPredictionJob(
        job_display_name=job_display_name,
        model_name=model_name,
        instances_format=instances_format,
        predictions_format=predictions_format,
        gcs_source=gcs_source,
        bigquery_source=bigquery_source,
        gcs_destination_prefix=gcs_destination_prefix,
        bigquery_destination_prefix=bigquery_destination_prefix,
        model_parameters=model_parameters,
        machine_type=machine_type,
        accelerator_type=accelerator_type,
        accelerator_count=accelerator_count,
        starting_replica_count=starting_replica_count,
        max_replica_count=max_replica_count,
        generate_explanation=generate_explanation,
        explanation_metadata=explanation_metadata,
        explanation_parameters=explanation_parameters,
        labels=labels,
        project=project,
        location=location,
        credentials=credentials,
        encryption_spec_key_name=encryption_spec_key_name,
        create_request_timeout=create_request_timeout,
        batch_size=batch_size,
        model_monitoring_objective_config=model_monitoring_objective_config,
        model_monitoring_alert_config=model_monitoring_alert_config,
        analysis_instance_schema_uri=analysis_instance_schema_uri,
        service_account=service_account,
    )

# =========================
# Unit tests for submit()
# =========================

# --------- Basic Test Cases ---------




















#------------------------------------------------
import pytest  # used for our unit tests
from aiplatform.jobs import BatchPredictionJob

# function to test
# (The full implementation of BatchPredictionJob.submit is above.)

# --- UNIT TESTS FOR BatchPredictionJob.submit ---

class DummyModel:
    # Dummy model to simulate aiplatform.Model
    def __init__(self, versioned_resource_name="projects/test/locations/us-central1/models/12345"):
        self.versioned_resource_name = versioned_resource_name

class DummyObjectiveConfig:
    # Dummy ObjectiveConfig for model_monitoring_objective_config
    def as_proto(self):
        return "objective_config_proto"

class DummyAlertConfig:
    # Dummy AlertConfig for model_monitoring_alert_config
    def as_proto(self):
        return "alert_config_proto"

@pytest.fixture
def default_args():
    # Returns a dict of default valid arguments for submit
    return dict(
        job_display_name="unit-test-job",
        model_name="projects/test/locations/us-central1/models/12345",
        instances_format="jsonl",
        predictions_format="jsonl",
        gcs_source="gs://bucket/input.jsonl",
        gcs_destination_prefix="gs://bucket/output/",
        project="test",
        location="us-central1",
    )

# 1. BASIC TEST CASES









def test_submit_both_gcs_and_bigquery_source(default_args):
    # Edge: both gcs_source and bigquery_source provided
    args = default_args.copy()
    args["bigquery_source"] = "bq://project.dataset.table"
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 20.2μs -> 11.1μs (82.5% faster)

def test_submit_neither_gcs_nor_bigquery_source(default_args):
    # Edge: neither gcs_source nor bigquery_source provided
    args = default_args.copy()
    args.pop("gcs_source")
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 16.3μs -> 8.55μs (90.3% faster)

def test_submit_both_gcs_and_bigquery_destination(default_args):
    # Edge: both gcs_destination_prefix and bigquery_destination_prefix provided
    args = default_args.copy()
    args["bigquery_destination_prefix"] = "bq://project.dataset.output"
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 14.8μs -> 8.01μs (85.1% faster)

def test_submit_neither_gcs_nor_bigquery_destination(default_args):
    # Edge: neither gcs_destination_prefix nor bigquery_destination_prefix provided
    args = default_args.copy()
    args.pop("gcs_destination_prefix")
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 14.4μs -> 7.98μs (81.0% faster)

def test_submit_invalid_instances_format(default_args):
    # Edge: unsupported instances_format
    args = default_args.copy()
    args["instances_format"] = "invalid_format"
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 16.9μs -> 10.2μs (66.3% faster)

def test_submit_invalid_predictions_format(default_args):
    # Edge: unsupported predictions_format
    args = default_args.copy()
    args["predictions_format"] = "invalid_format"
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 16.1μs -> 9.02μs (78.0% faster)

def test_submit_invalid_display_name_too_long(default_args):
    # Edge: display name too long
    args = default_args.copy()
    args["job_display_name"] = "a" * 129
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 7.60μs -> 9.11μs (16.7% slower)

def test_submit_invalid_labels_type(default_args):
    # Edge: labels not str->str
    args = default_args.copy()
    args["labels"] = {1: "val", "key": 2}
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 11.2μs -> 12.5μs (10.3% slower)

def test_submit_model_name_invalid_resource_id(default_args):
    # Edge: model_name is invalid resource id
    args = default_args.copy()
    args["model_name"] = "invalid resource id!"
    with pytest.raises(ValueError) as e:
        BatchPredictionJob.submit(**args) # 22.9μs -> 24.0μs (4.31% slower)

To edit these changes git checkout codeflash/optimize-BatchPredictionJob.submit-mgley9qd and push.