| |
| |
| """ttnn-visualizer memory + graph report of ONE eager frame of the served graph (``sparse_<bucket>`` then ``dense``; |
| default precision policy, ETH dispatch, the device grid). Never use it for timing: graph capture and detailed buffer |
| tracing synchronise after every op. |
| |
| ROOT=/home/ubuntu/experiments/tt-models; cd $ROOT/bundles/bevfusion-p150 |
| $ROOT/bin/devrun -t 2400 -- env PYTHONPATH=$PWD/code:$PYTHONPATH \\ |
| python code/scripts/visualizer_report.py $ROOT/generated/ttnn_visualizer/bevfusion_baseline |
| ttnn-visualizer --profiler-path $ROOT/generated/ttnn_visualizer/bevfusion_baseline \\ |
| --performance-path $ROOT/generated/profiler/bevfusion_baseline/reports/<ts> |
| |
| What the pytest fixture of ttnn does (ttnn/graph_report.py ``_run_pytest_graph_report_fixture``), in a script: the |
| model is built without capturing traces (``warmup_variants="none"``), a warm-up eager frame (JIT, weight upload), |
| then the second eager frame (``TraceRunner.run_eager`` of both segments) inside ``ttnn.graph.full_graph_capture`` |
| (Python I/O, stack traces, detailed buffer tracing), ``ttnn.graph_report.import_report`` -> ``db.sqlite``, and |
| ``config.json`` (the settings the capture ran with). The intermediate graph JSON is deleted after the import. |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import time |
| from pathlib import Path |
|
|
| SAMPLE = Path(__file__).resolve().parents[1] / "tt_bevfusion" / "samples" / "test_pcd.npz" |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser(description=__doc__.split("\n")[0]) |
| ap.add_argument("out_dir") |
| ap.add_argument("--input", default=str(SAMPLE)) |
| ap.add_argument("--weights-dir", default=None) |
| a = ap.parse_args() |
| out_dir = Path(a.out_dir).resolve() |
| out_dir.mkdir(parents=True, exist_ok=True) |
| import ttnn |
|
|
| from tt_bevfusion import BEVFusion |
| from tt_bevfusion.tt.model import sparse_variant |
|
|
| json_path = out_dir / "graph_capture.json" |
| with BEVFusion.from_pretrained(weights_dir=a.weights_dir, warmup_variants="none") as model: |
| print("device:", json.dumps(model.device_info), flush=True) |
| tr, dev, g = model.runner, model.device, model.graph |
| prepared = model._prepare(points=a.input) |
| plan = prepared.plan |
| sv = sparse_variant(plan.bucket) |
| inputs = g.frame_inputs(prepared.vfe, plan, plan.bucket) |
|
|
| def frame(): |
| tr.run_eager(sv, inputs=inputs) |
| tr.run_eager("dense") |
| ttnn.synchronize_device(dev) |
|
|
| t0 = time.perf_counter() |
| frame() |
| print(f"warm-up frame {time.perf_counter() - t0:.1f} s (bucket {plan.bucket})", flush=True) |
| t0 = time.perf_counter() |
| with ttnn.graph.full_graph_capture(str(json_path)): |
| frame() |
| print(f"captured frame {time.perf_counter() - t0:.1f} s -> {json_path} " |
| f"({json_path.stat().st_size / 1e6:.1f} MB)", flush=True) |
| t0 = time.perf_counter() |
| db = ttnn.graph_report.import_report(json_path, out_dir) |
| ttnn.save_config_to_json_file(out_dir / "config.json") |
| cfg = json.loads((out_dir / "config.json").read_text()) |
| cfg.update({"enable_fast_runtime_mode": False, "enable_graph_report": True, "enable_graph_python_stack_traces": True, |
| "enable_detailed_buffer_report": True, "root_report_path": str(out_dir.parent), |
| "report_name": out_dir.name}) |
| (out_dir / "config.json").write_text(json.dumps(cfg, indent=4) + "\n") |
| print(f"imported in {time.perf_counter() - t0:.1f} s -> {db} ({Path(db).stat().st_size / 1e6:.1f} MB)", flush=True) |
| json_path.unlink(missing_ok=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
