Add initial 5.0.0 files and configurations

mlperf_logging/benchmark_meta.py

@@ -131,6 +131,15 @@
         'stable_diffusion',
         'llama2_70b_lora',
         'gnn'
+    ],
+    '5.0': [
+        'bert',
+        'dlrm_dcnv2',
+        'gpt3',        
+        'retinanet',        
+        'stable_diffusion',
+        'llama2_70b_lora',
+        'gnn'
     ]    
     },
 

mlperf_logging/compliance_checker/README.md

@@ -10,7 +10,7 @@ To check a log file for compliance:
 
     python -m mlperf_logging.compliance_checker [--config YAML] [--usage training/hpc] [--ruleset MLPERF_EDITION] FILENAME
 
-By default, 4.1.0 training edition rules are used and the default config is set to `4.1.0/common.yaml`.
+By default, 5.0.0 training edition rules are used and the default config is set to `5.0.0/common.yaml`.
 This config will check all common keys and enqueue benchmark specific config to be checked as well.
 Old training editions, still supported are 4.0.0, 3.1.0, 3.0.0, 2.1.0, 2.0.0, 1.1.0, 1.0.0, 0.7.0 and 0.6.0
 
@@ -22,23 +22,23 @@ As log examples use [NVIDIA's training logs](https://github.com/mlperf/training_
 
 ### Existing config files for training submissions
 
-    4.1.0/common.yaml          - currently the default config file, checks common fields complience and equeues benchmark-specific config file
-    4.1.0/closed_common.yaml   - the common rules file for closed submissions. These rules apply to all benchmarks
-    4.1.0/open_common.yaml     - the common rules file for open submissions. These rules apply to all benchmarks
-    4.1.0/closed_ssd.yaml      - Per-benchmark rules, closed submissions.    
-    4.1.0/closed_bert.yaml
-    4.1.0/closed_dlrm_dcnv2.yaml
-    4.1.0/closed_gpt3.yaml
-    4.1.0/closed_gnn.yaml
-    4.1.0/closed_llama2_70b_lora.yaml
-    4.1.0/closed_stable_diffusion.yaml
-    4.1.0/open_ssd.yaml        - Per-benchmark rules, open submissions.    
-    4.1.0/open_bert.yaml
-    4.1.0/open_dlrm_dcnv2.yaml
-    4.1.0/open_gpt3.yaml
-    4.1.0/open_gnn.yaml
-    4.1.0/open_llama2_70b_lora.yaml
-    4.1.0/open_stable_diffusion.yaml
+    5.0.0/common.yaml          - currently the default config file, checks common fields complience and equeues benchmark-specific config file
+    5.0.0/closed_common.yaml   - the common rules file for closed submissions. These rules apply to all benchmarks
+    5.0.0/open_common.yaml     - the common rules file for open submissions. These rules apply to all benchmarks
+    5.0.0/closed_ssd.yaml      - Per-benchmark rules, closed submissions.    
+    5.0.0/closed_bert.yaml
+    5.0.0/closed_dlrm_dcnv2.yaml
+    5.0.0/closed_gpt3.yaml
+    5.0.0/closed_gnn.yaml
+    5.0.0/closed_llama2_70b_lora.yaml
+    5.0.0/closed_stable_diffusion.yaml
+    5.0.0/open_ssd.yaml        - Per-benchmark rules, open submissions.    
+    5.0.0/open_bert.yaml
+    5.0.0/open_dlrm_dcnv2.yaml
+    5.0.0/open_gpt3.yaml
+    5.0.0/open_gnn.yaml
+    5.0.0/open_llama2_70b_lora.yaml
+    5.0.0/open_stable_diffusion.yaml
 
 ### Existing config files for HPC submissions
 
@@ -173,7 +173,7 @@ Tested and confirmed working using the following software versions:
 - Python 2.7.12 + PyYAML 3.11
 - Python 3.6.8  + PyYAML 5.1
 - Python 2.9.2 + PyYAML 5.3.1
-- Python 3.9.10 + PyYAML 5.4.1
+- Python 3.9.10 + PyYAML 5.5.0
 
 ### How to install PyYaML
 

mlperf_logging/compliance_checker/mlp_compliance.py

@@ -315,7 +315,7 @@ def get_parser():
     parser.add_argument('--usage', type=str, default='training',
                     choices=usage_choices(),
                     help='what WG do the benchmarks come from')
-    parser.add_argument('--ruleset', type=str, default='4.1.0',
+    parser.add_argument('--ruleset', type=str, default='5.0.0',
                     choices=rule_choices(),
                     help='what version of rules to check the log against')
     parser.add_argument('--config',  type=str,

mlperf_logging/compliance_checker/mlp_parser/__init__.py

@@ -8,6 +8,7 @@
 from .ruleset_310 import parse_file as parse_file_310
 from .ruleset_400 import parse_file as parse_file_400
 from .ruleset_410 import parse_file as parse_file_410
+from .ruleset_410 import parse_file as parse_file_500
 
 
 def parse_file(filename, ruleset='0.6.0'):
@@ -31,5 +32,7 @@ def parse_file(filename, ruleset='0.6.0'):
         return parse_file_400(filename)    
     elif ruleset == '4.1.0':
         return parse_file_410(filename)
+    elif ruleset == '5.0.0':
+        return parse_file_500(filename)
     else:
         raise Exception(f'Ruleset "{ruleset}" is not supported')

mlperf_logging/compliance_checker/mlp_parser/ruleset_500.py

@@ -0,0 +1,105 @@
+'''
+Parses a text MLPerf log into a structured format.
+'''
+
+from __future__ import print_function
+
+import collections
+import json
+import re
+import sys
+from dataclasses import dataclass
+
+from io import open
+
+@dataclass
+class LogLine:
+    """Class for keeping track of an item in inventory."""
+    full_string: str
+    timestamp: float
+    key: str
+    value: str
+    lineno: int
+
+TOKEN = ':::MLLOG '
+
+
+def parse_line(line):
+    if not line.startswith(TOKEN):
+        return None
+
+    return json.loads(line[len(TOKEN):])
+
+
+def string_to_logline(lineno, string):
+    ''' Returns a LogLine or raises a ValueError '''
+    m = parse_line(string)
+
+    if m is None:
+        raise ValueError('does not match regex')
+
+    args = []
+    args.append(string) # full string
+
+    ts = float(m['time_ms']) # may raise error, e.g. "1.2.3"
+    # TODO check for weird values
+    args.append(ts)
+
+    args.append(m['key']) # key
+
+    j = { 'value': m['value'], 'metadata': m['metadata'] }
+    args.append(j)
+
+    args.append(lineno)
+    return LogLine(*args)
+
+
+def parse_file(filename):
+    ''' Reads a file by name and returns list of loglines and list of errors'''
+    with open(filename, encoding='latin-1') as f:
+        return parse_generator(f)
+
+
+def strip_and_dedup(gen):
+    lines = []
+    for l in gen:
+        if TOKEN not in l:
+            continue
+        lines.append(re.sub(".*"+TOKEN, TOKEN, l))
+    return lines
+
+
+
+def parse_generator(gen):
+    ''' Reads a generator of lines and returns (loglines, errors)
+    The list of errors are any parsing issues as a tuple (str_line, error_msg)
+    '''
+    loglines = []
+    failed = []
+    for lineno, line in enumerate(strip_and_dedup(gen)):
+        line = line.strip()
+        try:
+            ll = string_to_logline(lineno, line)
+            loglines.append(ll)
+        except ValueError as e:
+            failed.append((line, str(e)))
+    return loglines, failed
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 2:
+        print('usage: mlp_parser.py FILENAME')
+        print('       tests parsing on the file.')
+        sys.exit(1)
+
+    filename = sys.argv[1]
+    lines, errors = parse_file(filename)
+
+    print('Parsed {} log lines with {} errors.'.format(len(lines), len(errors)))
+
+    if len(errors) > 0:
+        print('Lines which failed to parse:')
+        for line, error in errors:
+            print('  Following line failed: {}'.format(error))
+            print(line)
+

mlperf_logging/compliance_checker/training_5.0.0/closed_bert.yaml

@@ -0,0 +1,48 @@
+- KEY:
+    NAME:  global_batch_size
+    REQ:   EXACTLY_ONE
+    POST: >
+        s['global_batch_size'] = v['value']
+
+- KEY:
+    NAME:  opt_base_learning_rate
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_lamb_epsilon
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_training_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_warmup_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  num_warmup_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  start_warmup_step
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_lamb_beta_1
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_lamb_beta_2
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_lamb_weight_decay_rate
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  eval_accuracy
+    REQ:   AT_LEAST_ONE
+    CHECK:
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "(v['value'] >= 0.720) and v['value'] < 1.0"

mlperf_logging/compliance_checker/training_5.0.0/closed_common.yaml

@@ -0,0 +1,11 @@
+
+- KEY:
+    NAME:  submission_benchmark
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] in ['resnet', 'ssd', 'stable_diffusion', 'maskrcnn', 'gpt3', 'dlrm_dcnv2', 'bert', 'rnnt', 'unet3d', 'gnn', 'llama2_70b_lora'] "
+    POST:  " enqueue_config('training_4.1.0/closed_{}.yaml'.format(v['value'])) "
+
+- KEY:
+    NAME: gradient_accumulation_steps
+    REQ: EXACTLY_ONE
+    CHECK: " v['value'] > 0 "

mlperf_logging/compliance_checker/training_5.0.0/closed_dlrm_dcnv2.yaml

@@ -0,0 +1,59 @@
+- KEY:
+    NAME:  global_batch_size
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_name
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 'adagrad' "
+
+- KEY:
+    NAME:  opt_base_learning_rate
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_adagrad_learning_rate_decay
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
+- KEY:
+    NAME:  opt_weight_decay
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
+- KEY:
+    NAME:  opt_adagrad_initial_accumulator_value
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
+- KEY:
+    NAME:  opt_adagrad_epsilon
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 1e-8 "
+
+- KEY:
+    NAME:  opt_learning_rate_warmup_steps
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
+- KEY:
+    NAME:  opt_learning_rate_decay_start_step
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
+- KEY:
+    NAME:  opt_learning_rate_decay_steps
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
+- KEY:
+    NAME:  eval_accuracy
+    REQ:   AT_LEAST_ONE
+    CHECK:
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "v['value'] >= 0.80275 and v['value'] <= 1.0"
+
+- KEY:
+    NAME:  eval_samples
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 89137319 "

mlperf_logging/compliance_checker/training_5.0.0/closed_gnn.yaml

@@ -0,0 +1,21 @@
+- KEY:
+    NAME:  global_batch_size
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] > 0"
+
+- KEY:
+    NAME:  opt_name
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 'adam' "
+
+- KEY:
+    NAME:  opt_base_learning_rate
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] >= 0.0"
+
+- KEY:
+    NAME:  eval_accuracy
+    REQ:   AT_LEAST_ONE
+    CHECK:
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "v['value'] >= 0.72 and v['value'] < 1.0"