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"""Profile GmNet-S3 inference without training or dataset access."""
from __future__ import annotations
import argparse
import json
import os
import platform
import sys
import time
from pathlib import Path
from typing import Any
import torch
REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from gmnet.analysis import load_model_checkpoint
from gmnet.analysis.profiling import benchmark_model
from gmnet.analysis.profiling import percentile
DEFAULT_CHECKPOINT = Path("/nfs/ywang29/GmNet/gmnet_s3.npy")
DEFAULT_OUTPUT_DIR = Path("/tmp/gmnet_runs/e12_profile")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--checkpoint", type=Path, default=DEFAULT_CHECKPOINT)
parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR)
parser.add_argument("--input-size", type=int, default=224)
parser.add_argument("--cuda-device", default="cuda:0")
parser.add_argument("--cuda-batches", type=int, nargs="+", default=[1, 32])
parser.add_argument(
"--cuda-precisions",
nargs="+",
choices=("fp32", "bf16"),
default=["fp32", "bf16"],
)
parser.add_argument("--cuda-warmup", type=int, default=30)
parser.add_argument("--cuda-iterations", type=int, default=100)
parser.add_argument("--skip-cuda", action="store_true")
parser.add_argument("--cpu", action="store_true")
parser.add_argument("--cpu-batches", type=int, nargs="+", default=[1])
parser.add_argument("--cpu-precision", choices=("fp32", "bf16"), default="fp32")
parser.add_argument("--cpu-warmup", type=int, default=5)
parser.add_argument("--cpu-iterations", type=int, default=20)
parser.add_argument("--cpu-threads", type=int, default=min(os.cpu_count() or 1, 16))
parser.add_argument("--onnx", action="store_true")
parser.add_argument("--onnx-runtime", action="store_true")
parser.add_argument("--onnx-opset", type=int, default=18)
parser.add_argument("--onnx-warmup", type=int, default=10)
parser.add_argument("--onnx-iterations", type=int, default=50)
return parser.parse_args()
def export_and_check_onnx(
model: torch.nn.Module,
*,
destination: Path,
input_size: int,
opset: int,
) -> dict[str, Any]:
import onnx
destination.parent.mkdir(parents=True, exist_ok=True)
model.eval().cpu()
example = torch.randn(1, 3, input_size, input_size)
torch.onnx.export(
model,
example,
destination,
input_names=["images"],
output_names=["logits"],
dynamic_axes={"images": {0: "batch"}, "logits": {0: "batch"}},
opset_version=opset,
do_constant_folding=True,
dynamo=False,
)
graph = onnx.load(destination)
onnx.checker.check_model(graph)
return {
"status": "checked",
"path": str(destination),
"size_bytes": destination.stat().st_size,
"onnx_version": onnx.__version__,
"opset": opset,
"dynamic_batch": True,
"checker": "passed",
}
def benchmark_onnxruntime(
path: Path,
*,
model: torch.nn.Module,
input_size: int,
warmup_iterations: int,
measured_iterations: int,
threads: int,
) -> tuple[dict[str, Any], dict[str, Any]]:
import numpy as np
import onnxruntime as ort
options = ort.SessionOptions()
options.intra_op_num_threads = threads
options.inter_op_num_threads = 1
session = ort.InferenceSession(
str(path),
sess_options=options,
providers=["CPUExecutionProvider"],
)
input_name = session.get_inputs()[0].name
inputs = np.random.default_rng(20260712).standard_normal(
(1, 3, input_size, input_size), dtype=np.float32
)
with torch.inference_mode():
torch_output = model(torch.from_numpy(inputs)).detach().cpu().numpy()
ort_output = session.run(None, {input_name: inputs})[0]
absolute_error = np.abs(torch_output - ort_output)
for _ in range(warmup_iterations):
session.run(None, {input_name: inputs})
timings = []
for _ in range(measured_iterations):
started = time.perf_counter()
outputs = session.run(None, {input_name: inputs})
timings.append((time.perf_counter() - started) * 1_000.0)
if not np.isfinite(outputs[0]).all():
raise ValueError("ONNX Runtime produced non-finite output")
mean_ms = sum(timings) / len(timings)
measurement = {
"device": "onnxruntime-cpu",
"precision": "fp32",
"batch_size": 1,
"input_size": input_size,
"warmup_iterations": warmup_iterations,
"measured_iterations": measured_iterations,
"latency_mean_ms": mean_ms,
"latency_p50_ms": percentile(timings, 0.50),
"latency_p95_ms": percentile(timings, 0.95),
"throughput_mean_images_per_second": 1_000.0 / mean_ms,
"throughput_at_p50_images_per_second": (
1_000.0 / percentile(timings, 0.50)
),
"peak_cuda_memory_mb": None,
"current_cuda_memory_mb": None,
}
runtime = {
"status": "completed",
"onnxruntime_version": ort.__version__,
"providers": session.get_providers(),
"intra_op_threads": threads,
"inter_op_threads": 1,
"numerical_parity": {
"max_absolute_error": float(absolute_error.max()),
"mean_absolute_error": float(absolute_error.mean()),
"top1_equal": bool(
np.array_equal(torch_output.argmax(1), ort_output.argmax(1))
),
},
}
return measurement, runtime
def render_markdown(result: dict[str, Any]) -> str:
rows = []
for measurement in result["measurements"]:
peak = measurement["peak_cuda_memory_mb"]
rows.append(
f"| {measurement['device']} | {measurement['precision']} | "
f"{measurement['batch_size']} | {measurement['latency_p50_ms']:.3f} | "
f"{measurement['latency_p95_ms']:.3f} | "
f"{measurement['throughput_mean_images_per_second']:.2f} | "
f"{peak:.2f} |" if peak is not None else
f"| {measurement['device']} | {measurement['precision']} | "
f"{measurement['batch_size']} | {measurement['latency_p50_ms']:.3f} | "
f"{measurement['latency_p95_ms']:.3f} | "
f"{measurement['throughput_mean_images_per_second']:.2f} | n/a |"
)
checkpoint = result["checkpoint"]
onnx_audit = result["onnx"]
runtime_audit = result["onnxruntime"]
onnx_lines = []
if onnx_audit["status"] == "checked":
onnx_lines.append(
f"- ONNX: checker passed at opset {onnx_audit['opset']}; artifact "
f"size is {onnx_audit['size_bytes']} bytes."
)
if runtime_audit["status"] == "completed":
parity = runtime_audit["numerical_parity"]
onnx_lines.append(
"- ONNX Runtime parity: max absolute error "
f"{parity['max_absolute_error']:.6g}, top-1 equal "
f"{parity['top1_equal']}."
)
return "\n".join(
[
"# E12 Local Inference Profile",
"",
f"- Checkpoint: `{checkpoint['path']}` (`{checkpoint['sha256']}`).",
f"- Topology: `{checkpoint['selected_topology']}`.",
"- Runtime: eager PyTorch, inference mode, random normalized-shape input.",
"- CUDA measurements use CUDA events after warmup; CPU uses perf_counter.",
*onnx_lines,
"- INT8 is blocked: no validated full-model calibration/quantization "
"pipeline is available.",
"",
"| Device | Precision | Batch | p50 (ms) | p95 (ms) | "
"Mean throughput (image/s) | Peak CUDA memory (MiB) |",
"|---|---|---:|---:|---:|---:|---:|",
*rows,
"",
]
)
def main() -> int:
args = parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
measurements = []
checkpoint_audit: dict[str, Any] | None = None
cpu_model: torch.nn.Module | None = None
if not args.skip_cuda:
if not torch.cuda.is_available():
raise RuntimeError("CUDA profiling requested but CUDA is unavailable")
torch.backends.cudnn.benchmark = True
model, checkpoint_audit = load_model_checkpoint(
args.checkpoint, device=args.cuda_device
)
for precision in args.cuda_precisions:
for batch_size in args.cuda_batches:
measurements.append(
benchmark_model(
model,
device=args.cuda_device,
batch_size=batch_size,
input_size=args.input_size,
precision=precision,
warmup_iterations=args.cuda_warmup,
measured_iterations=args.cuda_iterations,
)
)
del model
torch.cuda.empty_cache()
if args.cpu:
torch.set_num_threads(args.cpu_threads)
cpu_model, cpu_audit = load_model_checkpoint(args.checkpoint, device="cpu")
checkpoint_audit = checkpoint_audit or cpu_audit
for batch_size in args.cpu_batches:
measurements.append(
benchmark_model(
cpu_model,
device="cpu",
batch_size=batch_size,
input_size=args.input_size,
precision=args.cpu_precision,
warmup_iterations=args.cpu_warmup,
measured_iterations=args.cpu_iterations,
)
)
onnx_audit: dict[str, Any] = {"status": "not_requested"}
onnxruntime_audit: dict[str, Any] = {"status": "not_requested"}
if args.onnx or args.onnx_runtime:
if cpu_model is None:
cpu_model, cpu_audit = load_model_checkpoint(
args.checkpoint, device="cpu"
)
checkpoint_audit = checkpoint_audit or cpu_audit
onnx_path = args.output_dir / "gmnet_s3.onnx"
onnx_audit = export_and_check_onnx(
cpu_model,
destination=onnx_path,
input_size=args.input_size,
opset=args.onnx_opset,
)
if args.onnx_runtime:
measurement, onnxruntime_audit = benchmark_onnxruntime(
onnx_path,
model=cpu_model,
input_size=args.input_size,
warmup_iterations=args.onnx_warmup,
measured_iterations=args.onnx_iterations,
threads=args.cpu_threads,
)
measurements.append(measurement)
if not measurements or checkpoint_audit is None:
raise ValueError("no profiling target selected; enable CUDA or --cpu")
result = {
"schema_version": 1,
"experiment": "E12",
"status": "completed",
"method": "eager_inference_cuda_events_or_cpu_perf_counter",
"torch_version": torch.__version__,
"cuda_version": torch.version.cuda,
"cudnn_version": torch.backends.cudnn.version(),
"python_version": platform.python_version(),
"platform": platform.platform(),
"cpu_count": os.cpu_count(),
"cpu_threads_used": args.cpu_threads if args.cpu else None,
"checkpoint": checkpoint_audit,
"onnx": onnx_audit,
"onnxruntime": onnxruntime_audit,
"int8": {
"status": "blocked",
"reason": (
"No validated full-model INT8 calibration and quantization "
"pipeline is available. Linear-only dynamic quantization is not "
"reported as whole-model INT8."
),
},
"measurements": measurements,
}
json_path = args.output_dir / "results.json"
markdown_path = args.output_dir / "RESULTS.md"
json_path.write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8")
markdown_path.write_text(render_markdown(result), encoding="utf-8")
print(json.dumps({"results": str(json_path), "markdown": str(markdown_path)}))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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