Michael Luciuk
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Documentation and formatting updates: - Updates to project README. - Adding project health files (`LICENSE` and `SECURITY.md`) - A few minor formatting changes throughout - A few typo fixes, removal of unused code, cleanup of shadowed variables, and fixed import ordering with isort. **Note:** These changes have not been tested. Co-authored-by: Michael Luciuk <michael.luciuk@gmail.com> Co-authored-by: Liyu Xiao <liyu@qoherent.ai> Reviewed-on: https://git.riahub.ai/qoherent/modrec-workflow/pulls/1 Reviewed-by: Liyux <liyux@noreply.localhost> Co-authored-by: Michael Luciuk <michael@qoherent.ai> Co-committed-by: Michael Luciuk <michael@qoherent.ai>
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
import json
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import os
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import time
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import numpy as np
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import onnxruntime as ort
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def profile_onnx_model(
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path_to_onnx: str, num_runs: int = 100, warmup_runs: int = 5
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) -> None:
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"""
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Profiles an ONNX model by running inference multiple times and collecting performance data.
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Prints session initialization time, provider used, average inference time (excluding warm-up),
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and parses the ONNX Runtime JSON trace to show the most expensive operation.
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Parameters:
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path_to_onnx (str): Path to the ONNX model file.
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num_runs (int): Number of inference runs (including warm-ups).
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warmup_runs (int): Number of warm-up runs to skip from timing.
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"""
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# Session setup
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options = ort.SessionOptions()
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options.enable_profiling = True
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options.add_session_config_entry("session.enable_quant_qdq_cleanup", "1")
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options.add_session_config_entry("ep.dynamic.workload_type", "Efficient")
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# Try GPU, then fallback to CPU
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try:
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start_time = time.time()
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session = ort.InferenceSession(
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path_to_onnx, sess_options=options, providers=["CUDAExecutionProvider"]
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)
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print("Running on the GPU")
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except Exception as e:
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session = ort.InferenceSession(
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path_to_onnx, sess_options=options, providers=["CPUExecutionProvider"]
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)
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print("Could not find GPU, running on CPU")
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end_time = time.time()
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print(f"[Timing] Model load + session init time: {end_time - start_time:.4f} sec")
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print("Session providers:", session.get_providers())
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# Prepare dummy input
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input_name = session.get_inputs()[0].name
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input_shape = [
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dim if isinstance(dim, int) and dim > 0 else 1
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for dim in session.get_inputs()[0].shape
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]
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input_data = np.random.randn(*input_shape).astype(np.float32)
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# Time multiple inferences (skip warm-up)
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times = []
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for i in range(num_runs):
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t0 = time.time()
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session.run(None, {input_name: input_data})
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t1 = time.time()
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if i >= warmup_runs:
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times.append(t1 - t0)
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avg_time = sum(times) / len(times)
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print(
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f"[Timing] Avg inference time (excluding {warmup_runs} warm-ups): {avg_time:.6f} sec"
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)
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# End profiling & parse JSON
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profile_file = session.end_profiling()
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print(f"[Output] Profiling trace saved to: {profile_file}")
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try:
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with open(profile_file, "r") as f:
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trace = json.load(f)
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nodes = [e for e in trace if e.get("cat") == "Node"]
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print(f"[Profile] Number of nodes executed: {len(nodes)}")
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if nodes:
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top = max(nodes, key=lambda x: x.get("dur", 0))
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print(
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f"[Profile] Most expensive op: {top['name']} — {top['dur'] / 1e6:.3f} ms"
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)
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except Exception as e:
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print(f"[Warning] Failed to parse profiling JSON: {e}")
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if __name__ == "__main__":
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output_path = os.path.join("onnx_files", "inference_recognition_model.onnx")
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profile_onnx_model(output_path)
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