add arithmetic intensity

app.py

@@ -57,7 +57,7 @@ subtotal_operations = [
 
 
 
-col1, col2, col3, col4, col5 = st.columns([1,1.5,2,2,2])
+col1, col2, col3, col4, col5 = st.columns([1,1.5,2.3,2.3,0.1])
 
 inference_config = {}
 parameter_count = {}
@@ -144,15 +144,19 @@ with col3: # Prefilling
 
     operation_items = {key: "{:,}".format(int(prefilling_operation_count[key])) for key in prefilling_operation_count if key not in subtotal_operations}
     subtotal_operation_items = {key: "{:,}".format(int(prefilling_operation_count[key])) for key in prefilling_operation_count if key in subtotal_operations}
+    prefilling_arithmetic_intensity = {key: "{:.3f}".format(prefilling_operation_count[key]/prefilling_activation_memory_count[key]) for key in prefilling_activation_memory_count}
     prefilling_activation_memory_count = {key: "{:,}".format(int(value)) for key, value in prefilling_activation_memory_count.items()}
 
+
     ## Convert dictionaries to pandas dataframes for table display
     df_operation_count = pd.DataFrame(list(operation_items.items()), columns=["Operation", "FLOPS"])
     df_subtotal_operation_count = pd.DataFrame(list(subtotal_operation_items.items()), columns=["Operation", "FLOPS"])
     
     df_operation_count["Activation (Byte)"] = df_operation_count["Operation"].map(prefilling_activation_memory_count)
+    df_operation_count["Arithmetic Intensity"] = df_operation_count["Operation"].map(prefilling_arithmetic_intensity)
     df_subtotal_operation_count["Activation (Byte)"] = df_subtotal_operation_count["Operation"].map(prefilling_activation_memory_count)
-
+    df_subtotal_operation_count["Arithmetic Intensity"] = df_subtotal_operation_count["Operation"].map(prefilling_arithmetic_intensity)
+    
     header4("Inference Ops: Prefilling")
     st.markdown(create_table(df_operation_count))
 

render_util.py

@@ -4,7 +4,7 @@ def create_table(df):
     # Table header based on df columns
     header = "| " + " | ".join(df.columns) + " |"
     # Number of columns in df to set table divider accordingly
-    divider = "|:---" * len(df.columns[:-1]) + "|-----:|"
+    divider = "|:---|" + "|-----:|" * len(df.columns[:-1])
     rows = [header, divider]
 
     for _, row in df.iterrows():