fixes

Author: sayakpaul

benchmarks/benchmarking_utils.py

@@ -112,8 +112,12 @@ def run_benchmark(self, scenario: BenchmarkScenario):
         logger.info(f"Running scenario: {scenario.name}.")
         try:
             model = model_init_fn(scenario.model_cls, **scenario.model_init_kwargs)
-            num_params = round(calculate_params(model) / 1e6, 2)
-            flops = round(calculate_flops(model, input_dict=scenario.get_model_input_dict()) / 1e6, 2)
+            num_params = round(calculate_params(model) / 1e9, 2)
+            try:
+                flops = round(calculate_flops(model, input_dict=scenario.get_model_input_dict()) / 1e9, 2)
+            except Exception as e:
+                logger.info(f"Problem in calculating FLOPs:\n{e}")
+                flops = None
             model.cpu()
             del model
         except Exception as e:
@@ -156,8 +160,8 @@ def run_benchmark(self, scenario: BenchmarkScenario):
         result = {
             "scenario": scenario.name,
             "model_cls": scenario.model_cls.__name__,
-            "num_params_M": num_params,
-            "flops_M": flops,
+            "num_params_B": num_params,
+            "flops_G": flops,
             "time_plain_s": plain["time"],
             "mem_plain_GB": plain["memory"],
             "time_compile_s": compiled["time"],

benchmarks/populate_into_db.py

@@ -60,8 +60,8 @@ def _cast_value(val, dtype: str):
         for _, row in df.iterrows():
             scenario = _cast_value(row.get("scenario"), "text")
             model_cls = _cast_value(row.get("model_cls"), "text")
-            num_params_M = _cast_value(row.get("num_params_M"), "float")
-            flops_M = _cast_value(row.get("flops_M"), "float")
+            num_params_B = _cast_value(row.get("num_params_B"), "float")
+            flops_G = _cast_value(row.get("flops_G"), "float")
             time_plain_s = _cast_value(row.get("time_plain_s"), "float")
             mem_plain_GB = _cast_value(row.get("mem_plain_GB"), "float")
             time_compile_s = _cast_value(row.get("time_compile_s"), "float")
@@ -84,8 +84,8 @@ def _cast_value(val, dtype: str):
                 "repository": "huggingface/diffusers",
                 "scenario": scenario,
                 "model_cls": model_cls,
-                "num_params_M": num_params_M,
-                "flops_M": flops_M,
+                "num_params_B": num_params_B,
+                "flops_G": flops_G,
                 "time_plain_s": time_plain_s,
                 "mem_plain_GB": mem_plain_GB,
                 "time_compile_s": time_compile_s,

benchmarks/push_results.py

@@ -1,3 +1,5 @@
+import os
+
 import pandas as pd
 from huggingface_hub import hf_hub_download, upload_file
 from huggingface_hub.utils import EntryNotFoundError
@@ -50,7 +52,7 @@ def push_to_hf_dataset():
 
             # combine
             current_results[column] = curr_str + append_str
-
+        os.remove(FINAL_CSV_FILENAME)
         current_results.to_csv(FINAL_CSV_FILENAME, index=False)
 
     commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"