remove memroy from composite

app.py

@@ -52,7 +52,7 @@ COLUMNS_DATATYPES = [
     "number",
     "str",
 ]
-# SORTING_COLUMN = ["Average Open LLM Score ⬆️", "Throughput (tokens/s) ⬆️"]
+SORTING_COLUMN = ["Composite Score ⬆️"]
 
 
 llm_perf_dataset_repo = load_dataset_repo(LLM_PERF_DATASET_REPO, OPTIMUM_TOKEN)
@@ -86,21 +86,24 @@ def get_benchmark_df(benchmark="1xA100-80GB"):
         axis=1,
     )
 
+    # create composite score
+    normalized_score = bench_df["score"] / 100
+    normalized_latency = (
+        bench_df["generate.latency(s)"] / bench_df["generate.latency(s)"].max()
+    )
+    # normalized_memory = (bench_df["forward.peak_memory(MB)"].max()-bench_df["forward.peak_memory(MB)"])/(bench_df["forward.peak_memory(MB)"].max()-bench_df["forward.peak_memory(MB)"].min())
+    bench_df["composite_score"] = normalized_score - normalized_latency
+
     return bench_df
 
 
 def get_benchmark_table(bench_df):
-    # sort based on composite score made by adding score, -latency, -memory each normalized to values between 0 and 1
-    normalized_score =(bench_df["score"]-bench_df["score"].min())/(bench_df["score"].max()-bench_df["score"].min())
-    normalized_latency = (bench_df["generate.latency(s)"].max()-bench_df["generate.latency(s)"])/(bench_df["generate.latency(s)"].max()-bench_df["generate.latency(s)"].min())
-    normalized_memory = (bench_df["forward.peak_memory(MB)"].max()-bench_df["forward.peak_memory(MB)"])/(bench_df["forward.peak_memory(MB)"].max()-bench_df["forward.peak_memory(MB)"].min())
-    bench_df["composite_score"] = normalized_score + normalized_latency + normalized_memory
-    bench_df.sort_values(by=["composite_score"], ascending=False, inplace=True)
-
     # filter
     bench_df = bench_df[list(COLUMNS_MAPPING.keys())]
     # rename
     bench_df.rename(columns=COLUMNS_MAPPING, inplace=True)
+    # sort
+    bench_df.sort_values(by=SORTING_COLUMN, ascending=False, inplace=True)
 
     # transform
     bench_df["Model 🤗"] = bench_df["Model 🤗"].apply(make_clickable_model)