Added more fields to the schema of mlperf training summary results

mlperf_logging/result_summarizer/result_summarizer.py

@@ -7,18 +7,22 @@
 import os
 import re
 import sys
+import traceback
 import itertools
 import pandas as pd
 import yaml
+import numpy as np
 import hashlib
 import math
 import operator
 import uuid as uuidlib
+import copy
 
 from ..compliance_checker import mlp_compliance
 from ..compliance_checker.mlp_compliance import usage_choices, rule_choices
 from ..compliance_checker.mlp_parser import parse_file
 
+from ..rcp_checker import rcp_checker
 from ..benchmark_meta import get_allowed_benchmarks, get_result_file_counts
 
 
@@ -263,12 +267,23 @@ def _get_weak_scaling_metric_schema():
     }
 
 
-def _get_empty_summary(usage, ruleset, weak_scaling=False):
+def _get_strong_scaling_metric_schema():
+    return {
+        'time_to_train': float,
+        'Energy': float,
+        'GBS': float,
+        'epochs': float,
+        'RCP': str,
+        'rcp_scaling_factor': float,
+    }
+
+
+def _get_empty_summary(usage, ruleset, weak_scaling=False, detailed=False):
     return Summary(
-        _get_column_schema(usage, ruleset, weak_scaling=weak_scaling).keys())
+        _get_column_schema(usage, ruleset, weak_scaling=weak_scaling, detailed=detailed).keys())
 
 
-def _get_column_schema(usage, ruleset, weak_scaling=False):
+def _get_column_schema(usage, ruleset, weak_scaling=False, detailed=False):
     schema = {
         'division': str,
         'availability': str,
@@ -289,9 +304,17 @@ def _get_column_schema(usage, ruleset, weak_scaling=False):
             for metric, dtype in _get_weak_scaling_metric_schema().items():
                 schema['{}:{}'.format(benchmark, metric)] = dtype
     else:
-        schema.update(
-            {b: float
-             for b in get_allowed_benchmarks(usage, ruleset)})
+        if detailed:
+            benchmarks = get_allowed_benchmarks(usage, ruleset)
+            for benchmark in benchmarks:
+                for metric, dtype in _get_strong_scaling_metric_schema().items():
+                    schema['{}:{}'.format(benchmark, metric)] = dtype
+        else:
+            schema.update(
+                {
+                    b: float for b in get_allowed_benchmarks(usage, ruleset)
+                }
+            )
     schema.update({'details_url': str, 'code_url': str})
     return schema
 
@@ -404,8 +427,8 @@ def _compute_strong_score_standalone(
             power_score = olympic_avg
             power_score *= scaling_factor
     if return_full_scores:
-        return scores_track, power_scores_track, score, power_score
-    return score, power_score
+        return scores_track, power_scores_track, score, power_score, scaling_factor
+    return score, power_score, scaling_factor
 
 
 def _compute_weak_score_standalone(benchmark, system, has_power, benchmark_folder, usage, ruleset, desc = {"submitter": None}):
@@ -474,31 +497,106 @@ def _compute_weak_score_standalone(benchmark, system, has_power, benchmark_folde
 
 
 
-def _compute_strong_scaling_scores(desc, system_folder, usage, ruleset):
+def _compute_strong_scaling_scores(desc, system_folder, usage, ruleset, division, rcp_bypass=False):
     # Collect scores for benchmarks.
     benchmark_scores = {}
-    benchmark_power_scores = {}
-    has_power = None
+    detailed_bechmark_scores = {}
     benchmark_folder_parent = os.path.join(
         system_folder, 'strong') if usage == 'hpc' else system_folder
     if not os.path.isdir(benchmark_folder_parent):
-        return benchmark_scores, benchmark_power_scores
+        return benchmark_scores, {}
     for benchmark_folder in _get_sub_folders(benchmark_folder_parent):
         folder_parts = benchmark_folder.split('/')
         # Check if this benchmark has power results
         has_power = _has_power(benchmark_folder)
         benchmark = _benchmark_alias(folder_parts[-1])
         system = folder_parts[-3] if usage == 'hpc' else folder_parts[-2]
-        # Read scores from result files.
-        score, power_score = _compute_strong_score_standalone(benchmark, system, has_power, benchmark_folder, usage, ruleset, desc)
+        # Compute base perf/power scores
+        score, power_score, rcp_scaling_factor = _compute_strong_score_standalone(
+            benchmark, system, has_power, benchmark_folder, usage, ruleset, desc
+        )
+
+        # RCP/GBS/Epochs additions for closed division
+        benchmark_gbs = None
+        benchmark_epochs = None
+        benchmark_rcp = None
+        if division == 'closed':
+            pattern = '{folder}/result_*.txt'.format(folder=benchmark_folder)
+            result_files = glob.glob(pattern, recursive=True)
+            try:
+                # RCP check
+                verbose = False
+                bert_train_samples = False
+                rcp_pass, rcp_msg, _ = rcp_checker.check_directory(
+                    benchmark_folder,
+                    usage,
+                    ruleset,
+                    verbose,
+                    bert_train_samples,
+                    rcp_file=None,
+                    rcp_pass='pruned_rcps',
+                    rcp_bypass=rcp_bypass,
+                    set_scaling=True,
+                )
+                if not rcp_pass:
+                    print(
+                        'ERROR: RCP Test Failed on {}/{}/{} with message: {}.'.format(
+                            desc['submitter'], system, benchmark, rcp_msg
+                        )
+                    )
+                    if rcp_msg == 'RCP found':
+                        benchmark_rcp = 'Fail'
+                    elif rcp_msg == 'RCP Interpolation':
+                        benchmark_rcp = 'Interp. Fail'
+                    elif 'Missing' in rcp_msg:
+                        benchmark_rcp = 'Missing'
+                    elif rcp_msg == 'Cannot find any RCPs':
+                        benchmark_rcp = 'No RCP'
+                    else:
+                        benchmark_rcp = 'Unknown state'
+                else:
+                    benchmark_rcp = 'Pass'
+
+                # GBS and epochs
+                benchmark_gbs, subm_epochs, _ = rcp_checker.get_submission_epochs(
+                    result_files, ruleset, bert_train_samples=False
+                )
+                subm_epochs.sort()
+                samples_rejected = 1
+                if len(subm_epochs) >= 2 * samples_rejected + 1:
+                    benchmark_epochs = float(
+                        np.mean(
+                            subm_epochs[
+                                samples_rejected : len(subm_epochs) - samples_rejected
+                            ]
+                        )
+                    )
+            except Exception as e:
+                print(
+                    f"WARNING: RCP/GBS computation failed for {benchmark_folder}: {e}"
+                )
+                traceback.print_exc()
+
+        # Map into metric-suffixed keys for schema
+        detailed_bechmark_scores[f"{benchmark}:rcp_scaling_factor"] = float(
+            rcp_scaling_factor
+        )
         if score is not None:
-            benchmark_scores[benchmark] = score
+            detailed_bechmark_scores[f"{benchmark}:time_to_train"] = score
+        if benchmark_gbs is not None:
+            detailed_bechmark_scores[f"{benchmark}:GBS"] = float(benchmark_gbs)
+        if benchmark_epochs is not None:
+            detailed_bechmark_scores[f"{benchmark}:samples_to_converge"] = float(benchmark_epochs)
+        if benchmark_rcp is not None:
+            detailed_bechmark_scores[f"{benchmark}:RCP"] = benchmark_rcp
         if power_score is not None:
-            benchmark_power_scores[benchmark] = power_score
-    _fill_empty_benchmark_scores(benchmark_scores, usage, ruleset)
-    if len(benchmark_power_scores) > 0:
-        _fill_empty_benchmark_scores(benchmark_power_scores, usage, ruleset)
-    return benchmark_scores, benchmark_power_scores
+            detailed_bechmark_scores[f"{benchmark}:Energy"] = power_score
+        benchmark_scores[f"{benchmark}"] = float(
+            rcp_scaling_factor
+        )
+    _fill_empty_benchmark_scores(benchmark_scores, usage, ruleset, detailed=False)
+    _fill_empty_benchmark_scores(detailed_bechmark_scores, usage, ruleset, detailed=True)
+    return benchmark_scores, detailed_bechmark_scores
 
 
 def _compute_weak_scaling_scores(desc, system_folder, usage, ruleset):
@@ -693,6 +791,7 @@ def _fill_empty_benchmark_scores(
     usage,
     ruleset,
     weak_scaling=False,
+    detailed=False,
 ):
     for benchmark in get_allowed_benchmarks(usage, ruleset):
         if weak_scaling:
@@ -702,8 +801,19 @@ def _fill_empty_benchmark_scores(
                     benchmark_scores[k] = None
 
         else:
-            if benchmark not in benchmark_scores:
-                benchmark_scores[benchmark] = None
+            if detailed:
+                strong_schema = _get_strong_scaling_metric_schema()
+                for metric, dtype in strong_schema.items():
+                    k = '{}:{}'.format(benchmark, metric)
+                    if dtype is str:
+                        if k not in benchmark_scores or benchmark_scores[k] is None:
+                            benchmark_scores[k] = ''
+                    else:
+                        if k not in benchmark_scores:
+                            benchmark_scores[k] = None
+            else:
+                if benchmark not in benchmark_scores:
+                    benchmark_scores[benchmark] = None
 
 
 def _get_id_from_sysinfo(summary):
@@ -841,7 +951,7 @@ def summarize_results(folder, usage, ruleset, csv_file=None, **kwargs):
     weak_scaling_summary = _get_empty_summary(usage,
                                               ruleset,
                                               weak_scaling=True)
-    power_summary = _get_empty_summary(usage, ruleset)
+    detailed_strong_scaling_summary = _get_empty_summary(usage, ruleset, detailed=True)
     power_weak_scaling_summary = _get_empty_summary(usage, ruleset, weak_scaling=True)
     for system_folder in _get_sub_folders(results_folder):
         folder_parts = system_folder.split('/')
@@ -924,8 +1034,8 @@ def _check_and_update_system_specs(desc_keys, column_name, query=None):
             continue
 
         # Compute the scores.
-        strong_scaling_scores, power_scores = _compute_strong_scaling_scores(
-            desc, system_folder, usage, ruleset)
+        strong_scaling_scores, detailed_strong_scaling_scores = _compute_strong_scaling_scores(
+            desc, system_folder, usage, ruleset, system_specs["division"], rcp_bypass=False)
         if usage == 'hpc':
             weak_scaling_scores, power_scores_weak_scaling = _compute_weak_scaling_scores(
                 desc, system_folder, usage, ruleset)
@@ -950,17 +1060,18 @@ def _check_and_update_system_specs(desc_keys, column_name, query=None):
                     urls.items(),
             ):
                 weak_scaling_summary.push(column_name, value)
-        if len(power_scores) > 0:
+        if len(detailed_strong_scaling_scores) > 0:
             for column_name, value in itertools.chain(
                     system_specs.items(),
-                    power_scores.items(),
+                    detailed_strong_scaling_scores.items(),
                     urls.items(),
             ):
-                power_summary.push(column_name, value)
-                if column_name in strong_scaling_scores:
-                    power_summary.push(column_name, strong_scaling_scores[column_name])
-                else:
-                    power_summary.push(column_name, value)
+                merged = (
+                    detailed_strong_scaling_scores[column_name]
+                    if column_name in detailed_strong_scaling_scores
+                    else value
+                )
+                detailed_strong_scaling_summary.push(column_name, merged)
         if usage == 'hpc' and len(power_scores_weak_scaling) > 0:
             for column_name, value in itertools.chain(
                     system_specs.items(),
@@ -975,13 +1086,13 @@ def _check_and_update_system_specs(desc_keys, column_name, query=None):
     if len(weak_scaling_summary) > 0:
         weak_scaling_summary = weak_scaling_summary.to_dataframe().sort_values(
             _get_sort_by_column_names()).reset_index(drop=True)
-    if len(power_summary) > 0:
-        power_summary = power_summary.to_dataframe().sort_values(
+    if len(detailed_strong_scaling_summary) > 0:
+        detailed_strong_scaling_summary = detailed_strong_scaling_summary.to_dataframe().sort_values(
             _get_sort_by_column_names()).reset_index(drop=True)
     if len(power_weak_scaling_summary) > 0:
         power_weak_scaling_summary = power_weak_scaling_summary.to_dataframe().sort_values(
             _get_sort_by_column_names()).reset_index(drop=True)
-    return strong_scaling_summary, weak_scaling_summary, power_summary, power_weak_scaling_summary
+    return strong_scaling_summary, weak_scaling_summary, detailed_strong_scaling_summary, power_weak_scaling_summary
 
 
 
@@ -1039,23 +1150,23 @@ def main():
 
     strong_scaling_summaries = []
     weak_scaling_summaries = []
-    power_summaries = []
+    detailed_strong_scaling_summaries = []
     power_weak_scaling_summaries = []
 
     def _update_summaries(folder):
         if args.usage == "Training":
             config_path = os.path.join(os.path.dirname(__file__), "config.yaml")
             with open(config_path, "r") as f:
                 config = yaml.safe_load(f)
-            strong_scaling_summary, weak_scaling_summary, power_summary, power_weak_scaling_summary = summarize_results(
+            strong_scaling_summary, weak_scaling_summary, detailed_strong_scaling_summary, power_weak_scaling_summary = summarize_results(
                 folder,
                 args.usage,
                 args.ruleset,
                 availability = config["availability"],
                 generate_private_ids = args.generate_private_ids,
             )
         else:
-            strong_scaling_summary, weak_scaling_summary, power_summary, power_weak_scaling_summary = summarize_results(
+            strong_scaling_summary, weak_scaling_summary, detailed_strong_scaling_summary, power_weak_scaling_summary = summarize_results(
                 folder,
                 args.usage,
                 args.ruleset,
@@ -1064,8 +1175,8 @@ def _update_summaries(folder):
         strong_scaling_summaries.append(strong_scaling_summary)
         if len(weak_scaling_summary) > 0:
             weak_scaling_summaries.append(weak_scaling_summary)
-        if len(power_summary) > 0:
-            power_summaries.append(power_summary)
+        if len(detailed_strong_scaling_summary) > 0:
+            detailed_strong_scaling_summaries.append(detailed_strong_scaling_summary)
         if len(power_weak_scaling_summary) > 0:
             power_weak_scaling_summaries.append(power_weak_scaling_summary)
 
@@ -1180,13 +1291,14 @@ def _summaries_to_xlsx(summaries: pd.DataFrame, path, version):
 
         writer.save()
     # Print and write back results.
-    def _print_and_write(summaries, weak_scaling=False, mode='w', power = False):
+    def _print_and_write(summaries, weak_scaling=False, mode='w', power = False, detailed = False):
         if len(summaries) > 0:
             summaries = pd.concat(summaries).astype(
                 _get_column_schema(
                     args.usage,
                     args.ruleset,
                     weak_scaling=weak_scaling,
+                    detailed=detailed
                 )
             )
             if weak_scaling:
@@ -1208,6 +1320,9 @@ def _print_and_write(summaries, weak_scaling=False, mode='w', power = False):
                     specs_and_notes = [c for c in summaries.columns if c not in benchmarks]
                     csv = csv.replace(".csv", "_power.csv")
                     summaries.groupby(specs_and_notes).apply(lambda x: agg_columns_fn(x, benchmarks)).to_csv(csv, mode=mode)
+                elif detailed:
+                    csv = csv.replace(".csv", "_detailed.csv")
+                    summaries.to_csv(csv, index=False, mode=mode)
                 else:
                     summaries.to_csv(csv, index=False, mode=mode)
             json_path = "summary.json" if args.csv is None else f"""{csv.replace(".csv", ".json")}"""
@@ -1224,7 +1339,7 @@ def _print_and_write(summaries, weak_scaling=False, mode='w', power = False):
                            None, 'display.max_colwidth', None):
         _print_and_write(strong_scaling_summaries)
         _print_and_write(weak_scaling_summaries, weak_scaling=True, mode='a')
-        _print_and_write(power_summaries, mode='a', power=True)
+        _print_and_write(detailed_strong_scaling_summaries, mode='a', detailed=True)
         _print_and_write(power_weak_scaling_summaries, weak_scaling=True, mode='a', power=True)