Split models list up into groups, pipe through CLI option

Author: eagarvey-amd

models/turbine_models/custom_models/torchbench/cmd_opts.py

@@ -43,6 +43,13 @@ def is_valid_file(arg):
     help="model ID as it appears in the torchbench models text file lists, or 'all' for batch export",
     default="all",
 )
+p.add_argument(
+    "--model_lists",
+    type=Path,
+    nargs="*"
+    help="path to a JSON list of models to benchmark. One or more paths.",
+    default=["torchbench_models.json", "timm_models.json", "torchvision_models.json"],
+)
 p.add_argument(
     "--external_weights_dir",
     type=str,

models/turbine_models/custom_models/torchbench/export.py

@@ -33,7 +33,7 @@
 
 import csv
 
-torchbench_models_dict = {
+torchbench_models_all = {
     # "BERT_pytorch": {
     #     "dim": 128,
     # }, # Dynamo Export Issue
@@ -420,10 +420,17 @@ def run_main(model_id, args, tb_dir, tb_args):
 
 if __name__ == "__main__":
     from turbine_models.custom_models.torchbench.cmd_opts import args, unknown
-
-    tb_dir = setup_torchbench_cwd()
-    if args.model_id.lower() == "all":
-        for name in torchbench_models_dict.keys():
-            run_main(name, args, tb_dir, unknown)
-    else:
-        run_main(args.model_id, args, tb_dir, unknown)
+    import json
+
+    torchbench_models_dict = json.load(args.model_list_json
+    for list in args.model_lists:
+        torchbench_models_dict = json.load(list)
+        with open(args.models_json, "r") as f:
+            torchbench_models_dict = json.load(file)
+
+        tb_dir = setup_torchbench_cwd()
+        if args.model_id.lower() == "all":
+            for name in torchbench_models_dict.keys():
+                run_main(name, args, tb_dir, unknown)
+        else:
+            run_main(args.model_id, args, tb_dir, unknown)

models/turbine_models/custom_models/torchbench/timm_models.json

@@ -0,0 +1,14 @@
+{
+    "timm_efficientnet": {
+        "dim": 128
+    },
+    "timm_regnet": {
+        "dim": 128
+    },
+    "timm_resnest": {
+        "dim": 256
+    },
+    "timm_vovnet": {
+        "dim": 128
+    }
+}

models/turbine_models/custom_models/torchbench/torchbench_models.json

@@ -0,0 +1,5 @@
+{
+    "pytorch_unet": {
+        "dim": 8
+    }
+}

models/turbine_models/custom_models/torchbench/torchvision_models.json

@@ -0,0 +1,35 @@
+{
+    "LearningToPaint": {
+        "dim": 1024
+    },
+    "alexnet": {
+        "dim": 1024
+    },
+    "densenet121": {
+        "dim": 64
+    },
+    "mnasnet1_0": {
+        "dim": 256
+    },
+    "mobilenet_v2": {
+        "dim": 128
+    },
+    "mobilenet_v3_large": {
+        "dim": 256
+    },
+    "resnet18": {
+        "dim": 512
+    },
+    "resnet50": {
+        "dim": 128
+    },
+    "resnext50_32x4d": {
+        "dim": 128
+    },
+    "shufflenet_v2_x1_0": {
+        "dim": 512
+    },
+    "squeezenet1_1": {
+        "dim": 512
+    }
+}