Fix bisection yaml

.github/workflows/userbenchmark-a100-bisection.yml

@@ -23,7 +23,7 @@ jobs:
       CONDA_ENV: "bisection-ci-a100"
       PLATFORM_NAME: "gcp_a100"
       SETUP_SCRIPT: "/workspace/setup_instance.sh"
-      BISECT_WORKDIR: ".userbenchmark/${{ github.env.userbenchmark }}/bisection"
+      BISECT_WORKDIR: ".userbenchmark/${{ github.event.inputs.userbenchmark }}/bisection"
       AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
       AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
     if: ${{ github.repository_owner == 'pytorch' }}

userbenchmark/test_bench/run.py

@@ -7,6 +7,9 @@
 import json
 import os
 import shutil
+import yaml
+import re
+import ast
 import numpy
 
 from typing import List, Dict, Optional, Any, Union
@@ -25,6 +28,18 @@ def config_to_str(config: TorchBenchModelConfig) -> str:
         f" bs={config.batch_size}, extra_args={config.extra_args}"
     return metrics_base
 
+def str_to_config(metric_name: str) -> TorchBenchModelConfig:
+    regex = "model=(.*), test=(.*), device=(.*), bs=(.*), extra_args=(.*), metric=(.*)"
+    model, test, device, batch_size, extra_args, _metric = re.match(regex, metric_name).groups()
+    extra_args = ast.literal_eval(extra_args)
+    return TorchBenchModelConfig(
+        name=model,
+        test=test,
+        device=device,
+        batch_size=batch_size,
+        extra_args=extra_args,
+    )
+
 def generate_model_configs(devices: List[str], tests: List[str], batch_sizes: List[str], model_names: List[str], extra_args: List[str]) -> List[TorchBenchModelConfig]:
     """Use the default batch size and default mode."""
     if not model_names:
@@ -40,6 +55,12 @@ def generate_model_configs(devices: List[str], tests: List[str], batch_sizes: Li
     ) for device, test, batch_size, model_name in cfgs]
     return result
 
+def generate_model_configs_from_bisect_yaml(bisect_yaml: str) -> List[TorchBenchModelConfig]:
+    with open(bisect_yaml, "r") as fp:
+        bisect = yaml.safe_load(fp)
+    result = list(map(lambda x: str_to_config(x), bisect["details"].keys()))
+    return result
+
 def init_output_dir(configs: List[TorchBenchModelConfig], output_dir: pathlib.Path) -> List[TorchBenchModelConfig]:
     result = []
     for config in configs:
@@ -118,6 +139,7 @@ def parse_known_args(args):
     default_device = "cuda" if "cuda" in list_devices() else "cpu"
     parser.add_argument(
         "models",
+        nargs="*",
         help="Name of models to run, split by comma.",
     )
     parser.add_argument("--device", "-d", default=default_device, help="Devices to run, splited by comma.")
@@ -132,15 +154,18 @@ def parse_known_args(args):
 
 def run(args: List[str]):
     args, extra_args = parse_known_args(args)
-    # If not specified, use the entire model set
-    if not args.models:
-        args.models = list_models()
+    if args.run_bisect:
+        configs = generate_model_configs_from_bisect_yaml(args.run_bisect)
+    else:
+        # If not specified, use the entire model set
+        if not args.models:
+            args.models = list_models()
+        devices = validate(parse_str_to_list(args.device), list_devices())
+        tests = validate(parse_str_to_list(args.test), list_tests())
+        batch_sizes = parse_str_to_list(args.bs)
+        models = validate(parse_str_to_list(args.models), list_models())
+        configs = generate_model_configs(devices, tests, batch_sizes, model_names=models, extra_args=extra_args)
     debug_output_dir = get_default_debug_output_dir(args.output) if args.debug else None
-    devices = validate(parse_str_to_list(args.device), list_devices())
-    tests = validate(parse_str_to_list(args.test), list_tests())
-    batch_sizes = parse_str_to_list(args.bs)
-    models = validate(parse_str_to_list(args.models), list_models())
-    configs = generate_model_configs(devices, tests, batch_sizes, model_names=models, extra_args=extra_args)
     configs = init_output_dir(configs, debug_output_dir) if debug_output_dir else configs
     results = {}
     try: