Minor fixes for 4.1.0 submission

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, 3.1.0 training edition rules are used and the default config is set to `4.1.0/common.yaml`.
+By default, 4.1.0 training edition rules are used and the default config is set to `4.1.0/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
 
@@ -25,14 +25,14 @@ As log examples use [NVIDIA's training logs](https://github.com/mlperf/training_
     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_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, closed submissions.    
+    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

mlperf_logging/compliance_checker/training_4.1.0/closed_common.yaml

@@ -2,7 +2,7 @@
 - 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'] "
+    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:

mlperf_logging/compliance_checker/training_4.1.0/closed_llama2_70b_lora.yaml

@@ -7,10 +7,7 @@
 - KEY:
     NAME:  opt_base_learning_rate
     REQ:   EXACTLY_ONE
-
-- KEY:
-    NAME:  lora_alpha
-    REQ:   EXACTLY_ONE        
+
 
 - KEY:
     NAME:  opt_learning_rate_training_steps

mlperf_logging/rcp_checker/rcp_checker.py

@@ -162,7 +162,7 @@ class RCP_Checker:
 
     def __init__(self, usage, ruleset, benchmark, verbose, rcp_file=None):
         if ruleset not in {'1.0.0', "1.1.0", "2.0.0", "2.1.0", "3.0.0", "3.1.0", "4.0.0", "4.1.0"}:
-            raise Exception('RCP Checker only supported in 1.0.0, 1.1.0, 2.0.0, 2.1.0, 3.0.0, 3.1.0, 4.0.0, and "4.1.0"')
+            raise Exception('RCP Checker only supported in 1.0.0, 1.1.0, 2.0.0, 2.1.0, 3.0.0, 3.1.0, 4.0.0, and 4.1.0')
         self.usage = usage
         self.ruleset = ruleset
         self.benchmark = benchmark

mlperf_logging/result_summarizer/config.yaml

@@ -75,7 +75,7 @@ columns:
       llama2_70b_lora: ["Benchmark results (minutes)", "LLM-Finetune", "SCROLSS Gov Report", "LLama2-70B-LoRA"]
       gnn: ["Benchmark results (minutes)", "Graph node classification", "IGBH-Full", "R-GAT"]
       default: [" ", " ", " "]
-    "4.0.1":
+    "4.1.0":
       bert: ["Benchmark results (minutes)", "NLP", "Wikipedia", "BERT"]
       gpt3: ["Benchmark results (minutes)", "LLM", "C4", "GPT3"]
       dlrm_dcnv2: ["Benchmark results (minutes)", "Recommendation", "1TB Multihot Clickthrough", "DLRM DCNv2"]