⚡️ Speed up method InferencePipeline.init_with_workflow by 26% in PR #1759 (fix-modal-workspace-id-500)

[codeflash-ai]

⚡️ This pull request contains optimizations for PR #1759

If you approve this dependent PR, these changes will be merged into the original PR branch fix-modal-workspace-id-500.

This PR will be automatically closed if the original PR is merged.

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📄 26% (0.26x) speedup for InferencePipeline.init_with_workflow in inference/core/interfaces/stream/inference_pipeline.py

⏱️ Runtime : 82.3 microseconds → 65.1 microseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 26% speedup through several strategic micro-optimizations that reduce computational overhead in hot paths:

Key Optimizations

1. String validation optimization in get_workflow_specification()

• Replaced regex re.match(r"^[\w\-]+$", workflow_id) with a direct character set lookup using all(c in allowed for c in workflow_id)
• Character set operations are significantly faster than regex compilation and matching for simple validation patterns

2. File I/O optimization

• Changed from checking file existence with local_file_path.exists() followed by opening to a direct try/except approach when opening the file
• Eliminates redundant filesystem stat calls when the file doesn't exist, which is common in error paths

3. FPS extraction streamlining in WorkflowRunner.run_workflow()

• Simplified the FPS determination logic by directly checking measured_fps first, then falling back to fps
• Reduced multiple attribute accesses on the first video frame object

4. Conditional assignment optimization in init_with_custom_logic()

• Replaced nested conditional logic for desired_source_fps assignment with a direct conditional expression
• desired_source_fps = max_fps if ENABLE_FRAME_DROP_ON_VIDEO_FILE_RATE_LIMITING else None

Performance Impact Context

Based on the function references, InferencePipeline.init_with_workflow() is called during pipeline initialization in video processing applications. While initialization happens only once per pipeline, the optimization particularly benefits:

• Rapid pipeline restarts during development/testing
• Multiple concurrent pipeline creation in server environments
• Workflow validation during API calls where get_workflow_specification() is in the hot path

The test results show consistent 21-30% improvements across different initialization scenarios, with the string validation and file I/O optimizations providing the most substantial gains. These optimizations are especially effective for workflows using local file specifications where the validation and file access patterns are frequently exercised.

✅ Correctness verification report:

Test	Status
⏪ Replay Tests	🔘 None Found
⚙️ Existing Unit Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 25 Passed
📊 Tests Coverage	82.4%

🌀 Generated Regression Tests and Runtime

from unittest.mock import MagicMock, patch

# imports
import pytest
from inference.core.interfaces.stream.inference_pipeline import InferencePipeline


class DummyPipeline:
    def __init__(self, *args, **kwargs):
        self.args = args
        self.kwargs = kwargs


# Patch points for internal dependencies
# We'll patch InferencePipeline.init_with_custom_logic to return a DummyPipeline so we can inspect calls.


@pytest.fixture(autouse=True)
def patch_init_with_custom_logic(monkeypatch):
    # Patch to always return DummyPipeline and record calls
    pipelines = []

    def dummy_init_with_custom_logic(*args, **kwargs):
        pipelines.append((args, kwargs))
        return DummyPipeline(*args, **kwargs)

    monkeypatch.setattr(
        "inference.core.interfaces.stream.inference_pipeline.InferencePipeline.init_with_custom_logic",
        staticmethod(dummy_init_with_custom_logic),
    )
    yield pipelines
    pipelines.clear()


@pytest.fixture
def patch_get_workflow_specification(monkeypatch):
    # Patch get_workflow_specification to return a dummy workflow spec
    dummy_spec = {"id": "dummy", "version": "1.0.0"}
    monkeypatch.setattr(
        "inference.core.roboflow_api.get_workflow_specification",
        staticmethod(lambda **kwargs: dummy_spec),
    )
    return dummy_spec


@pytest.fixture
def patch_profiler(monkeypatch):
    # Patch BaseWorkflowsProfiler.init and NullWorkflowsProfiler.init
    monkeypatch.setattr(
        "inference.core.workflows.execution_engine.profiling.core.BaseWorkflowsProfiler.init",
        staticmethod(lambda **kwargs: "base_profiler"),
    )
    monkeypatch.setattr(
        "inference.core.workflows.execution_engine.profiling.core.NullWorkflowsProfiler.init",
        staticmethod(lambda **kwargs: "null_profiler"),
    )


@pytest.fixture
def patch_env(monkeypatch):
    # Patch all relevant env variables
    monkeypatch.setattr("inference.core.env.ENABLE_WORKFLOWS_PROFILING", False)
    monkeypatch.setattr("inference.core.env.API_KEY", "dummy_api_key")
    monkeypatch.setattr("inference.core.env.WORKFLOWS_PROFILER_BUFFER_SIZE", 32)
    monkeypatch.setattr("inference.core.env.MAX_ACTIVE_MODELS", 2)
    monkeypatch.setattr("inference.core.env.PREDICTIONS_QUEUE_SIZE", 512)
    monkeypatch.setattr("inference.core.env.DEFAULT_BUFFER_SIZE", 16)


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


def test_init_with_workflow_missing_all_required_args_raises(patch_profiler, patch_env):
    with pytest.raises(ValueError) as e:
        InferencePipeline.init_with_workflow(
            video_reference="video.mp4"
        )  # 18.5μs -> 14.4μs (28.4% faster)


def test_init_with_workflow_missing_api_key_raises(
    patch_profiler, patch_env, monkeypatch
):

    # Remove API_KEY from env
    monkeypatch.setattr("inference.core.env.API_KEY", None)
    with pytest.raises(Exception) as e:
        InferencePipeline.init_with_workflow(
            video_reference="video.mp4",
            workspace_name="ws",
            workflow_id="wid",
            api_key=None,
        )  # 21.8μs -> 16.7μs (30.8% faster)

from typing import Any, Dict, List, Optional, Union

# imports
import pytest
from inference.core.interfaces.stream.inference_pipeline import InferencePipeline


# Minimal stubs for dependencies (since we cannot import the full real modules in this test context)
class DummyVideoSource:
    def __init__(self, reference):
        self.reference = reference


class DummyWatchdog:
    def __init__(self):
        self.registered_sources = []
        self.status_updates = []

    def register_video_sources(self, video_sources):
        self.registered_sources.extend(video_sources)

    def on_status_update(self, status_update):
        self.status_updates.append(status_update)


class DummyProfiler:
    def __init__(self):
        self.traces = []

    @classmethod
    def init(cls, **kwargs):
        return cls()

    def profile_execution_phase(self, name, categories=None, metadata=None):
        class DummyContext:
            def __enter__(self):
                return None

            def __exit__(self, exc_type, exc_val, exc_tb):
                return False

        return DummyContext()

    def export_trace(self):
        return self.traces


class DummyExecutionEngine:
    def __init__(self, workflow_definition, **kwargs):
        self.workflow_definition = workflow_definition
        self.kwargs = kwargs

    @classmethod
    def init(cls, workflow_definition, **kwargs):
        return cls(workflow_definition, **kwargs)

    def run(
        self, runtime_parameters, fps=0, _is_preview=False, serialize_results=False
    ):
        # Just echo the parameters for test purposes
        return [
            {
                "runtime_parameters": runtime_parameters,
                "fps": fps,
                "serialize_results": serialize_results,
            }
        ]


class DummyWorkflowRunner:
    def run_workflow(
        self,
        video_frames,
        workflows_parameters,
        execution_engine,
        image_input_name,
        video_metadata_input_name,
        serialize_results=False,
    ):
        # Just echo the parameters for test purposes
        return [
            {
                "video_frames": video_frames,
                "workflows_parameters": workflows_parameters,
                "serialize_results": serialize_results,
            }
        ]


class DummyModelManager:
    pass


from inference.core.interfaces.stream.inference_pipeline import InferencePipeline

# ===========================
# UNIT TESTS
# ===========================

# ----------- BASIC TEST CASES ------------


def test_init_with_workflow_missing_all_arguments():
    """Test error when neither workflow_specification nor workspace_name/workflow_id provided"""
    with pytest.raises(ValueError):
        InferencePipeline.init_with_workflow(
            video_reference="video.mp4"
        )  # 20.5μs -> 16.3μs (25.3% faster)


def test_init_with_workflow_missing_api_key():
    """Test error when fetching workflow_specification but api_key is missing"""
    with pytest.raises(Exception) as excinfo:
        InferencePipeline.init_with_workflow(
            video_reference="video.mp4",
            workspace_name="workspace",
            workflow_id="workflow123",
        )  # 21.5μs -> 17.7μs (21.5% faster)

To edit these changes git checkout codeflash/optimize-pr1759-2025-11-28T15.54.14 and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #1759 by digaobarbosa was closed.