add benchmark comparison utils

benchmarks/benchmark_tensorrt.py

@@ -73,6 +73,8 @@ def tensorrt_any(
     image_height: int,
     image_width: int,
 ) -> BenchmarkResults:
+    import torch
+
     trt_path = prepare_tensorrt()
     diffusion_dir = trt_path / "demo" / "Diffusion"
     if str(diffusion_dir) not in sys.path:
@@ -102,7 +104,7 @@ def tensorrt_any(
 
         pipeline = StableDiffusionPipeline(**options)
         pipeline.loadEngines(
-            engine_dir=f"engine-{model_version}",
+            engine_dir=f"engine-{model_version}-{torch.cuda.get_device_name(0)}",
             framework_model_dir="pytorch_model",
             onnx_dir=f"onnx-{model_version}",
             onnx_opset=18,
@@ -116,7 +118,7 @@ def tensorrt_any(
             force_optimize=False,
             static_batch=True,
             static_shape=True,
-            timing_cache=f"cache-{model_version}",
+            timing_cache=f"cache-{model_version}-{torch.cuda.get_device_name(0)}",
         )
 
         # Load resources

benchmarks/compare_table.py

@@ -0,0 +1,72 @@
+import json
+import statistics
+from argparse import ArgumentParser
+from collections import defaultdict
+from pathlib import Path
+
+from rich.console import Console
+from rich.table import Table
+
+README_PATH = Path(__file__).parent.parent / "README.md"
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument("results_files", type=Path, nargs="+")
+
+    options = parser.parse_args()
+    results = {
+        result_file.stem: json.loads(result_file.read_text())
+        for result_file in options.results_files
+    }
+
+    benchmarks = defaultdict(dict)
+    for result_name, result_values in results.items():
+        for timing in result_values["timings"]:
+            benchmarks[(timing["category"], timing["name"])][result_name] = timing[
+                "timings"
+            ]
+
+    with Console() as console:
+        table = Table()
+        table.add_column("Benchmark")
+        for result_name in results.keys():
+            table.add_column(" ".join(map(str.title, result_name.split("-"))))
+
+        for benchmark_key, benchmark_results in sorted(
+            benchmarks.items(),
+            key=lambda kv: kv[0],
+        ):
+            if "CPU" in benchmark_key[0]:
+                continue
+
+            row = [f"{benchmark_key[0].split(' ')[0]:5} {benchmark_key[1]}"]
+            raw_values = []
+            for result_name in results.keys():
+                if result_name in benchmark_results:
+                    raw_values.append(
+                        (
+                            statistics.mean(benchmark_results[result_name]),
+                            statistics.stdev(benchmark_results[result_name]),
+                        )
+                    )
+                else:
+                    raw_values.append((float("nan"), float("nan")))
+
+            # Bold the best result
+            best_index = raw_values.index(min(raw_values))
+            for i, (mean, std) in enumerate(raw_values):
+                if i == best_index:
+                    row.append(f"[bold][green]{mean:.2f}s[/green][/bold] ± {std:.2f}s")
+                elif mean is not float("nan"):
+                    row.append(f"{mean:.2f}s ± {std:.2f}s")
+                else:
+                    row.append("N/A")
+
+            table.add_row(*row)
+
+        console.print(table)
+
+
+if __name__ == "__main__":
+    main()