various bugfixes and new primary metric

-remove some models which do not work
-process the JSON generated from pytorch that will make your eyes bleed
-add latency primary metric

multiplex.json

@@ -7,7 +7,7 @@
       "args" : [
         "model"
       ],
-      "vals" : "BERT_pytorch|fastNLP_Bert|hf_Bert_large|hf_BigBird|hf_DistilBert|hf_GPT2|hf_GPT2_large|hf_Longformer|hf_Roberta_base|hf_Reformer|llama|llama_v2_7b_16h|llava|hf_Whisper|hf_T5_large|hf_T5|nanogpt|timm_vision_transformer|timm_vision_transformer_large"
+      "vals" : "^BERT_pytorch$|^fastNLP_Bert$|^hf_Bert_large$|^hf_BigBird$|^hf_DistilBert$|^hf_GPT2$|^hf_GPT2_large$|^hf_Longformer$|^hf_Roberta_base$|^hf_Reformer$|^llama$|^llama_v2_7b_16h$|^llava$|^hf_Whisper$|^hf_T5_large$|^hf_T5$|^nanogpt$|^timm_vision_transformer$|^timm_vision_transformer_large$"
     }
   }
 }

pytorch-post-process

@@ -93,17 +93,21 @@ def main():
         #  }
         # }
 
-        print(d)
-
         # Now create the primary metric and the primary-period
-        iter_sample['primary-metric'] = 'elapsed-time-milliseconds'
+        iter_sample['primary-metric'] = 'latency-milliseconds'
         period = { 'name': 'measurement', 'metric-files': [] }
         file_id = 'measurement'
         desc = {'source' : 'pytorch', 'class': 'count', 'type': 'elapsed-time-milliseconds'}
         names = {}
         sample = {'begin': begin, 'end': end, 'value': end - begin}
         log_sample(file_id, desc, names, sample)
 
+        for key in d['metrics'].keys():
+            if (re.search("metric=latencies$", key)):
+                desc = {'source' : 'pytorch', 'class': 'count', 'type': 'latency-milliseconds'}
+                sample = {'begin': begin, 'end': end, 'value': d['metrics'][key]}
+                log_sample(file_id, desc, names, sample)
+
         metric_file_name = finish_samples()
         period['metric-files'].append(metric_file_name)
         iter_sample['periods'].append(period)