fixes issue with same timestamps messing up the ordering of events

pipit/readers/pytorch_reader.py

@@ -6,7 +6,7 @@
 import multiprocessing as mp
 
 
-# TODO: also, all the columns need to be made numeric, categorical, etc
+# TODO: need to handle duration events - B and E (look at chrome trace format documentation)
 # TODO: compare with HTA tool reader
 # TODO: add comments
 # TODO: unit tests
@@ -60,7 +60,20 @@ def events_reader(self, rank_size):
 
                 del df["dur"]
                 df["ph"].replace({"X": "Enter", "i": "Instant"}, inplace=True)
-                df = pd.concat([df, temp_df], ignore_index=True)
+
+                complete_events_indices = complete_events_df.index.values
+
+                new_df_index = np.full(len(df.index.values), 0)
+                new_df_index[complete_events_indices] = 1
+                new_df_index = np.roll(new_df_index.cumsum(), 1)
+                new_df_index[0] = 0
+
+                df.index =  df.index.values + new_df_index
+
+                temp_df.index = df.index.values[complete_events_indices] + 1
+
+                df = pd.concat([df, temp_df])
+                df.sort_index(inplace=True)
 
                 df["Rank"] = np.full(len(df), data["distributedInfo"]["rank"])
 
@@ -69,9 +82,6 @@ def events_reader(self, rank_size):
         df = pd.concat(dfs)
 
         df["ts"] *= 1000
-        # or do we want to leave these unchanged
-        # since this is technically changing the trace?
-        df["ts"] -= min(df["ts"])
 
         df.rename(
             columns={
@@ -84,7 +94,7 @@ def events_reader(self, rank_size):
             inplace=True,
         )
 
-        # is there a more performant way of doing this?
+        # TODO: is there a more performant way of doing this?
         attribute_cols = set(df.columns) - set(
             ["Event Type", "Name", "Rank", "Process", "Thread", "Timestamp (ns)"]
         )
@@ -120,21 +130,42 @@ def read(self):
         events_df = pd.concat(events_dfs)
         del events_dfs
 
-        events_df.sort_values(by="Timestamp (ns)", ignore_index=True, inplace=True)
+        # stable sorting so that order of events with same timestamps isn't "corrupted"
+        # TODO: this change needs to be made in all readers
+        events_df.sort_values(by="Timestamp (ns)", ignore_index=True, inplace=True, kind="stable")
 
         definitions_df = events_df.loc[events_df["Event Type"] == "M"]
         definitions_df.reset_index(inplace=True)
 
         events_df = events_df.loc[events_df["Event Type"] != "M"]
         events_df.reset_index(inplace=True, drop=True)
 
+        events_df = events_df.astype(
+            {
+                "Event Type": "category",
+                "Name": "category",
+                "Rank": "category",
+                "Process": "category",
+                "Thread": "category",
+            }
+        )
+
         definitions_df.rename(
             columns={"Name": "Definition Type", "args": "Attributes"}, inplace=True
         )
         definitions_df = definitions_df[
             ["Definition Type", "Rank", "Process", "Thread", "Attributes"]
         ]
 
+        definitions_df = definitions_df.astype(
+            {
+                "Definition Type": "category",
+                "Rank": "category",
+                "Process": "category",
+                "Thread": "category",
+            }
+        )
+
         trace = pipit.trace.Trace(definitions_df, events_df)
         if self.create_cct:
             trace.create_cct()