[nsys-jax] Add ratio of hidden communication time to total communicat…

…ion time (#1241)

.github/container/nsys_jax/nsys_jax/analyses/communication.py

@@ -1,38 +1,21 @@
 #!/usr/bin/env python
 import argparse
+import csv
 from collections import defaultdict
+
 from nsys_jax import (
     align_profiler_data_timestamps,
     apply_warmup_heuristics,
     ensure_compiled_protos_are_importable,
     load_profiler_data,
 )
 from math import sqrt
+from prettytable import PrettyTable
 import pathlib
 from uncertainties import ufloat  # type: ignore
 
 
-def main():
-    parser = argparse.ArgumentParser(
-        description="Summarise communication in an nsys-jax report"
-    )
-    parser.add_argument("prefix", type=pathlib.Path)
-    args = parser.parse_args()
-    # Make sure that the .proto files under protos/ have been compiled to .py, and
-    # that those generated .py files are importable.
-    ensure_compiled_protos_are_importable(prefix=args.prefix)
-    # Load the profiler data; the compilation part is needed for the warmup heuristics
-    all_data = load_profiler_data(args.prefix, frames={"communication", "compile"})
-    # Align timestamps
-    all_data, alignment_metadata = align_profiler_data_timestamps(all_data)
-    # TODO: make this pretty
-    # print(alignment_metadata)
-    # Partition the profile data into initialisation and steady-state running
-    _, steady_state = apply_warmup_heuristics(all_data)
-    assert len(steady_state.communication), (
-        "Communication summary was requested but no steady-state communication was "
-        "identified."
-    )
+def process_communication_data(steady_state):
     collective_types = set()
     summary_data = defaultdict(dict)
     for (collective, message_size), df in steady_state.communication.groupby(
@@ -52,7 +35,10 @@ def main():
         summary_data[message_size][collective] = ufloat(
             bandwidth.mean(), bandwidth.std() / sqrt(len(bandwidth))
         )
-    collective_types = sorted(collective_types)
+    return sorted(collective_types), summary_data
+
+
+def print_bandwidth_table(collective_types, summary_data):
     collective_widths = {
         collective: max(
             len(collective),
@@ -96,5 +82,137 @@ def format_bandwidth(data, collective):
         )
 
 
+def process_hidden_ms_to_total_ms(steady_state):
+    if steady_state.communication["ProjDurHiddenMs"].sum() == 0:
+        return None, None
+
+    collective_types = set()
+    summary_data = defaultdict(dict)
+    for collective, df in steady_state.communication.groupby(["Collective"]):
+        collective_types.add(collective)
+        mean_dur_hidden_ms_to_total_ms = (
+            df["ProjDurHiddenMs"] / (df["ProjDurMs"] + df["ProjDurHiddenMs"])
+        ).mean()
+        summary_data[collective] = mean_dur_hidden_ms_to_total_ms
+    return collective_types, summary_data
+
+
+def print_hidden_ms_to_total_ms_table(
+    collective_types, summary_data, overall_hidden_ms_to_total_ms
+):
+    table = PrettyTable()
+    table.field_names = ["Collective", "Mean HiddenToTotalMs"]
+
+    for collective in collective_types:
+        mean_value = summary_data[collective]
+        table.add_row([collective[0], mean_value])
+
+    print(table)
+    print("Overall HiddenMs to TotalMs:", overall_hidden_ms_to_total_ms)
+
+
+def calculate_overall_hidden_ms_to_total_ms(steady_state):
+    if steady_state.communication["ProjDurHiddenMs"].sum() == 0:
+        return None
+
+    overall_hidden_ms_to_total_ms = (
+        steady_state.communication["ProjDurHiddenMs"].sum()
+        / (
+            steady_state.communication["ProjDurMs"]
+            + steady_state.communication["ProjDurHiddenMs"]
+        ).sum()
+    )
+    return overall_hidden_ms_to_total_ms
+
+
+def write_to_csv(
+    collective_types,
+    bandwidth_summary,
+    hidden_to_total_summary,
+    overall_hidden_ms_to_total_ms,
+    output_file,
+):
+    with open(output_file, "w", newline="") as csvfile:
+        writer = csv.writer(csvfile)
+
+        # Write bandwidth table
+        writer.writerow(["Bandwidth Table"])
+        writer.writerow(["Size [B]"] + list(collective_types))
+        for message_size in sorted(bandwidth_summary.keys()):
+            row = [message_size]
+            for collective in collective_types:
+                if collective in bandwidth_summary[message_size]:
+                    row.append(f"{bandwidth_summary[message_size][collective]:S}")
+                else:
+                    row.append("-")
+            writer.writerow(row)
+
+        writer.writerow([])  # Empty row for separation
+
+        # Write hidden to total table if data is available
+        if hidden_to_total_summary is not None:
+            writer.writerow(["HiddenMs to TotalMs Table"])
+            writer.writerow(["Collective", "Mean HiddenToTotalMs"])
+            for collective in hidden_to_total_summary:
+                writer.writerow([collective[0], hidden_to_total_summary[collective]])
+
+            writer.writerow([])  # Empty row for separation
+
+            if overall_hidden_ms_to_total_ms is not None:
+                writer.writerow(
+                    ["Overall HiddenMs to TotalMs", overall_hidden_ms_to_total_ms]
+                )
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Summarise communication in an nsys-jax report"
+    )
+    parser.add_argument("prefix", type=pathlib.Path)
+    args = parser.parse_args()
+
+    # Make sure that the .proto files under protos/ have been compiled to .py, and
+    # that those generated .py files are importable.
+    ensure_compiled_protos_are_importable(prefix=args.prefix)
+    # Load the profiler data; the compilation part is needed for the warmup heuristics
+    all_data = load_profiler_data(args.prefix, frames={"communication", "compile"})
+    # Align timestamps
+    all_data, alignment_metadata = align_profiler_data_timestamps(all_data)
+    # TODO: make this pretty
+    # print(alignment_metadata)
+    # Partition the profile data into initialisation and steady-state running
+    _, steady_state = apply_warmup_heuristics(all_data)
+
+    assert len(steady_state.communication), (
+        "Communication summary was requested but no steady-state communication was "
+        "identified."
+    )
+
+    collective_types, bandwidth_summary = process_communication_data(steady_state)
+    print_bandwidth_table(collective_types, bandwidth_summary)
+
+    hidden_to_total_collective_types, hidden_to_total_summary = (
+        process_hidden_ms_to_total_ms(steady_state)
+    )
+    if hidden_to_total_summary is not None:
+        overall_hidden_ms_to_total_ms = calculate_overall_hidden_ms_to_total_ms(
+            steady_state
+        )
+        print_hidden_ms_to_total_ms_table(
+            hidden_to_total_collective_types,
+            hidden_to_total_summary,
+            overall_hidden_ms_to_total_ms,
+        )
+
+    # Write all tables to a single CSV file
+    write_to_csv(
+        collective_types,
+        bandwidth_summary,
+        hidden_to_total_summary,
+        overall_hidden_ms_to_total_ms,
+        "communication_summary.csv",
+    )
+
+
 if __name__ == "__main__":
     main()

.github/container/nsys_jax/pyproject.toml

@@ -9,6 +9,7 @@ dependencies = [
   "pyarrow",
   "requests", # for install-protoc
   "uncertainties", # communication analysis recipe
+  "prettytable",
 ]
 requires-python = ">= 3.10"