⚡️ Speed up method Qwen3VLBlockV1.run by 45% in PR #1968 (remote-exec-for-all-models)

[codeflash-ai]

⚡️ This pull request contains optimizations for PR #1968

If you approve this dependent PR, these changes will be merged into the original PR branch remote-exec-for-all-models.

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

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📄 45% (0.45x) speedup for Qwen3VLBlockV1.run in inference/core/workflows/core_steps/models/foundation/qwen3vl/v1.py

⏱️ Runtime : 5.64 milliseconds → 3.90 milliseconds (best of 71 runs)

📝 Explanation and details

The optimized code achieves a 44% speedup (5.64ms → 3.90ms) primarily by batching remote HTTP calls to reduce network overhead when processing multiple images.

Key Optimization: Request Batching in run_remotely()

What changed:

• Original: Looped through images and made one HTTP request per image via client.infer_lmm() (line profiler shows 89.2% of time spent in this loop)
• Optimized: Collects all base64_image values into a list and makes a single batched HTTP call for multiple images, with special handling for single-image cases

Why this is faster:

1. Network latency reduction: Each HTTP request incurs connection overhead, SSL handshake, and round-trip time. Batching N images into one request eliminates (N-1) network round-trips
2. Line profiler evidence: The original's client.infer_lmm calls consumed 68.7ms (89.2% of 77ms total), while the optimized version spends only 1.37ms (21.6% of 6.3ms total) on HTTP calls
3. The InferenceHTTPClient supports batch inputs: The infer_lmm method accepts List[ImagesReference] and returns List[dict], enabling efficient batch processing server-side

Secondary Optimization: Local Path Efficiency

What changed:

• Eliminated the prompts list construction: prompts = [combined_prompt] * len(inference_images)
• Now reuses the single combined_prompt string directly in the loop

Why this helps:

• Avoids allocating a list containing N duplicate string references
• Reduces memory allocations and list iteration overhead
• Line profiler shows minor improvement in local path (138.8ms → 137.8ms)

Test Results Analysis

The annotated tests show the optimization excels when:

• Multiple images processed remotely (test_large_scale_remote_many_images): The batching dramatically reduces overhead for 200 images
• Single remote image: Still benefits from cleaner code path (no loop overhead)
• Local inference: Minor gains from eliminated list allocation (2-5% improvement in local tests)

Impact Assessment

Without function_references, we cannot definitively determine hot path usage, but the optimization is universally beneficial for remote inference workloads:

• Any workflow processing multiple images remotely will see significant speedup
• No breaking changes to API or behavior (empty image list correctly returns [])
• Single-image cases maintain compatibility with separate code path

✅ Correctness verification report:

Test	Status
⏪ Replay Tests	🔘 None Found
⚙️ Existing Unit Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 12 Passed
📊 Tests Coverage	100.0%

🌀 Click to see Generated Regression Tests

from types import SimpleNamespace
from unittest.mock import patch

import inference_sdk  # to patch InferenceHTTPClient methods used in remote path
import numpy as np

# imports
import pytest  # used for our unit tests
from inference.core.managers.base import ModelManager
from inference.core.workflows.core_steps.common.entities import StepExecutionMode
from inference.core.workflows.core_steps.models.foundation.qwen3vl.v1 import (
    Qwen3VLBlockV1,
)
from inference.core.workflows.execution_engine.entities.base import WorkflowImageData
from inference_sdk import InferenceHTTPClient


# Helper to create a minimal WorkflowImageData with base64 content
def make_b64_image(b64: str):
    # parent_metadata is a typed hint in the real class but not required to be a real object here
    return WorkflowImageData(parent_metadata=None, base64_image=b64)


def make_image_reference(ref: str):
    return WorkflowImageData(parent_metadata=None, image_reference=ref)


def test_run_remote_single_image_with_prompts():
    # Create a real model manager (model_registry can be None; we'll not call local methods)
    mm = ModelManager(model_registry=None)
    # Create instance configured to run remotely
    block = Qwen3VLBlockV1(
        model_manager=mm,
        api_key="TEST_KEY",
        step_execution_mode=StepExecutionMode.REMOTE,
    )

    # Single image to send
    img = make_b64_image("base64-image-data")

    # Patch the network-facing method of InferenceHTTPClient so no external HTTP calls happen.
    # Ensure infer_lmm returns a dict with "response" key to match code path.
    with patch.object(
        inference_sdk.InferenceHTTPClient,
        "infer_lmm",
        return_value={"response": "a beautiful scenery"},
    ) as mock_infer:
        # Also patch select_api_v0 to avoid side effects regardless of environment target
        with patch.object(
            inference_sdk.InferenceHTTPClient, "select_api_v0", return_value=None
        ):
            # Call the method under test with explicit prompt and system_prompt
            codeflash_output = block.run(
                images=[img],
                model_version="qwen3vl-test",
                prompt="What is this?",
                system_prompt="You are helpful.",
            )
            out = codeflash_output
            # infer_lmm should have been invoked exactly once with the base64 content
            mock_infer.assert_called_once()
            called_args, called_kwargs = mock_infer.call_args


def test_run_remote_empty_images_returns_empty_list():
    mm = ModelManager(model_registry=None)
    block = Qwen3VLBlockV1(
        model_manager=mm, api_key=None, step_execution_mode=StepExecutionMode.REMOTE
    )

    # Patch infer_lmm to ensure it would not be called since images list is empty
    with patch.object(
        inference_sdk.InferenceHTTPClient,
        "infer_lmm",
        side_effect=AssertionError("Should not be called"),
    ):
        with patch.object(
            inference_sdk.InferenceHTTPClient, "select_api_v0", return_value=None
        ):
            codeflash_output = block.run(
                images=[], model_version="model-x", prompt=None, system_prompt=None
            )
            result = codeflash_output


def test_run_local_single_image_with_defaults():
    # Create a real ModelManager; we will monkeypatch its instance methods to avoid heavy operations
    mm = ModelManager(model_registry=None)
    block = Qwen3VLBlockV1(
        model_manager=mm,
        api_key="LOCAL_KEY",
        step_execution_mode=StepExecutionMode.LOCAL,
    )

    img = make_b64_image("local-b64")

    # Track calls and inspect the constructed request passed to infer_from_request_sync
    captured = {}

    def fake_add_model(model_id, api_key, *args, **kwargs):
        # record that add_model was called with expected args
        captured["add_model_called_with"] = (model_id, api_key)

    def fake_infer_from_request_sync(model_id, request, **kwargs):
        # ensure model_id matches and request has the expected attributes
        captured["infer_called_with_model_id"] = model_id
        # Return an object with a 'response' attribute (InferenceResponse-like)
        return SimpleNamespace(response="local-prediction")

    # Monkeypatch the instance methods directly (allowed; we're not stubbing the block under test)
    mm.add_model = fake_add_model
    mm.infer_from_request_sync = fake_infer_from_request_sync

    # Call run without providing prompts so defaults are used inside run_locally
    codeflash_output = block.run(
        images=[img], model_version="local-model", prompt=None, system_prompt=None
    )
    out = codeflash_output  # 34.1μs -> 32.9μs (3.65% faster)


def test_run_raises_for_unknown_execution_mode():
    mm = ModelManager(model_registry=None)
    # Provide something that is not StepExecutionMode.LOCAL or .REMOTE -> use None
    block = Qwen3VLBlockV1(model_manager=mm, api_key=None, step_execution_mode=None)
    with pytest.raises(ValueError) as exc:
        block.run(
            images=[make_b64_image("x")],
            model_version="m",
            prompt=None,
            system_prompt=None,
        )  # 3.12μs -> 2.96μs (5.09% faster)


def test_large_scale_remote_many_images():
    mm = ModelManager(model_registry=None)
    block = Qwen3VLBlockV1(
        model_manager=mm,
        api_key="KEY_LARGE",
        step_execution_mode=StepExecutionMode.REMOTE,
    )

    # Create a moderate-sized list of images (e.g., 200) to stay under the constraint of 1000
    count = 200
    images = [make_b64_image(f"img_{i}") for i in range(count)]

    # Patch infer_lmm to return a response based on the inference_input so we can verify mapping
    def infer_side_effect(inference_input, model_id, prompt=None):
        # Build response echoing the input - the code expects either dict with "response" or raw response
        return {"response": f"echo-{inference_input}"}

    with patch.object(
        inference_sdk.InferenceHTTPClient, "infer_lmm", side_effect=infer_side_effect
    ) as mocked:
        with patch.object(
            inference_sdk.InferenceHTTPClient, "select_api_v0", return_value=None
        ):
            codeflash_output = block.run(
                images=images,
                model_version="big-model",
                prompt="Describe",
                system_prompt=None,
            )
            out = codeflash_output


def test_large_scale_local_many_images_minimal_overhead():
    mm = ModelManager(model_registry=None)
    block = Qwen3VLBlockV1(
        model_manager=mm,
        api_key="KEY_LOCAL_BIG",
        step_execution_mode=StepExecutionMode.LOCAL,
    )

    count = 150  # keep safely under 1000
    images = [make_b64_image(f"limg_{i}") for i in range(count)]

    add_model_calls = []
    infer_calls = []

    def fake_add_model(model_id, api_key, *args, **kwargs):
        add_model_calls.append((model_id, api_key))

    def fake_infer_from_request_sync(model_id, request, **kwargs):
        # For scale tests we just echo back and record minimal info
        infer_calls.append((model_id, request.prompt))
        return SimpleNamespace(response=f"local-echo-{request.image}")

    # Patch the instance
    mm.add_model = fake_add_model
    mm.infer_from_request_sync = fake_infer_from_request_sync

    codeflash_output = block.run(
        images=images,
        model_version="local-big",
        prompt="Summarize",
        system_prompt="You are concise.",
    )
    out = codeflash_output  # 1.99ms -> 1.95ms (2.34% faster)


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-pr1968-2026-02-04T20.51.48 and push.