transpose energy

app.py

@@ -37,7 +37,7 @@ ALL_COLUMNS_MAPPING = {
     #
     "generate.peak_memory(MB)": "Memory (MB) ⬇️",
     "generate.throughput(tokens/s)": "Throughput (tokens/s) ⬆️",
-    "generate.energy_consumption(kWh/token)": "Energy (kWh/token) ⬇️",
+    "generate.energy_consumption(tokens/kWh)": "Energy (tokens/kWh) ⬇️",
     "best_score": "Best Score (%) ⬆️",
     #
     "best_scored_model": "Best Scored LLM 🏆",
@@ -84,8 +84,12 @@ def get_benchmark_df(benchmark="Succeeded-1xA100-80GB"):
     merged_df = benchmark_df.merge(
         clusters_df, left_on="model", right_on="best_scored_model"
     )
-    # fix energy consumption nans
-    merged_df["generate.energy_consumption(kWh/token)"].fillna("N/A", inplace=True)
+    # transpose energy consumption
+    merged_df["generate.energy_consumption(tokens/kWh)"] = 1 / merged_df[
+        "generate.energy_consumption(kWh/token)"
+    ].fillna(1)
+    # fix nan values
+    merged_df[merged_df["generate.energy_consumption(tokens/kWh)"] == 1] = "N/A"
 
     # add optimizations
     merged_df["optimizations"] = merged_df["backend.bettertransformer"].apply(
@@ -95,6 +99,7 @@ def get_benchmark_df(benchmark="Succeeded-1xA100-80GB"):
     merged_df["quantization"] = merged_df["backend.quantization_strategy"].apply(
         lambda x: "BnB.4bit" if x == "bnb" else ("GPTQ.4bit" if x == "gptq" else "None")
     )
+
     # # distance to 100% score
     # score_distance = 100 - merged_df["best_score"]
     # # distance to 0s latency