update QA models tests + run_generation

examples/run_generation.py

@@ -131,8 +131,10 @@ def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--model_name', type=str, default=None, required=True,
-                        help="GPT, GPT-2, Transformer-XL or XLNet pre-trained model selected in the list: " + ", ".join(ALL_MODELS))
+    parser.add_argument("--model_type", default=None, type=str, required=True,
+                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
+    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
+                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
     parser.add_argument("--prompt", type=str, default="")
     parser.add_argument("--padding_text", type=str, default="")
     parser.add_argument("--length", type=int, default=20)
@@ -150,15 +152,10 @@ def main():
 
     set_seed(args)
 
-    args.model_type = ""
-    for key in MODEL_CLASSES:
-        if key in args.model_name.lower():
-            args.model_type = key  # take the first match in model types
-            break
-
+    args.model_type = args.model_type.lower()
     model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    tokenizer = tokenizer_class.from_pretrained(args.model_name)
-    model = model_class.from_pretrained(args.model_name)
+    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
+    model = model_class.from_pretrained(args.model_name_or_path)
     model.to(args.device)
     model.eval()
 

examples/test_examples.py

@@ -101,7 +101,8 @@ class ExamplesTests(unittest.TestCase):
                     "--prompt=Hello",
                     "--length=10",
                     "--seed=42"]
-        model_name = "--model_name=openai-gpt"
+        model_type, model_name = ("--model_type=openai-gpt",
+                                  "--model_name_or_path=openai-gpt")
         with patch.object(sys, 'argv', testargs + [model_name]):
             result = run_generation.main()
             self.assertGreaterEqual(len(result), 10)

pytorch_transformers/tests/modeling_xlm_test.py

@@ -191,17 +191,19 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
                                          cls_index=sequence_labels,
                                          is_impossible=is_impossible_labels)
 
-            total_loss, start_logits, end_logits, cls_logits = outputs
+            (total_loss,) = outputs
 
             outputs = model(input_ids, start_positions=sequence_labels,
                                          end_positions=sequence_labels)
 
-            total_loss, start_logits, end_logits = outputs
+            (total_loss,) = outputs
 
             result = {
                 "loss": total_loss,
-                "start_logits": start_logits,
-                "end_logits": end_logits,
+                "start_top_log_probs": start_top_log_probs,
+                "start_top_index": start_top_index,
+                "end_top_log_probs": end_top_log_probs,
+                "end_top_index": end_top_index,
                 "cls_logits": cls_logits,
             }
 
@@ -209,11 +211,17 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
                 list(result["loss"].size()),
                 [])
             self.parent.assertListEqual(
-                list(result["start_logits"].size()),
-                [self.batch_size, self.seq_length])
+                list(result["start_top_log_probs"].size()),
+                [self.batch_size, model.config.start_n_top])
             self.parent.assertListEqual(
-                list(result["end_logits"].size()),
-                [self.batch_size, self.seq_length])
+                list(result["start_top_index"].size()),
+                [self.batch_size, model.config.start_n_top])
+            self.parent.assertListEqual(
+                list(result["end_top_log_probs"].size()),
+                [self.batch_size, model.config.start_n_top * model.config.end_n_top])
+            self.parent.assertListEqual(
+                list(result["end_top_index"].size()),
+                [self.batch_size, model.config.start_n_top * model.config.end_n_top])
             self.parent.assertListEqual(
                 list(result["cls_logits"].size()),
                 [self.batch_size])

pytorch_transformers/tests/modeling_xlnet_test.py

@@ -210,17 +210,19 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
                                          cls_index=sequence_labels,
                                          is_impossible=is_impossible_labels)
 
-            total_loss, start_logits, end_logits, cls_logits, mems = outputs
+            total_loss, mems = outputs
 
             outputs = model(input_ids_1, start_positions=sequence_labels,
                                          end_positions=sequence_labels)
 
-            total_loss, start_logits, end_logits, mems = outputs
+            total_loss, mems = outputs
 
             result = {
                 "loss": total_loss,
-                "start_logits": start_logits,
-                "end_logits": end_logits,
+                "start_top_log_probs": start_top_log_probs,
+                "start_top_index": start_top_index,
+                "end_top_log_probs": end_top_log_probs,
+                "end_top_index": end_top_index,
                 "cls_logits": cls_logits,
                 "mems": mems,
             }
@@ -229,11 +231,17 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
                 list(result["loss"].size()),
                 [])
             self.parent.assertListEqual(
-                list(result["start_logits"].size()),
-                [self.batch_size, self.seq_length])
+                list(result["start_top_log_probs"].size()),
+                [self.batch_size, model.config.start_n_top])
             self.parent.assertListEqual(
-                list(result["end_logits"].size()),
-                [self.batch_size, self.seq_length])
+                list(result["start_top_index"].size()),
+                [self.batch_size, model.config.start_n_top])
+            self.parent.assertListEqual(
+                list(result["end_top_log_probs"].size()),
+                [self.batch_size, model.config.start_n_top * model.config.end_n_top])
+            self.parent.assertListEqual(
+                list(result["end_top_index"].size()),
+                [self.batch_size, model.config.start_n_top * model.config.end_n_top])
             self.parent.assertListEqual(
                 list(result["cls_logits"].size()),
                 [self.batch_size])