[cleanup] Hoist ModelTester objects to top level (#4939)

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

tests/test_modeling_openai.py

@@ -34,6 +34,139 @@ if is_torch_available():
     )
 
 
+class OpenAIGPTModelTester:
+    def __init__(
+        self, parent,
+    ):
+        self.parent = parent
+        self.batch_size = 13
+        self.seq_length = 7
+        self.is_training = True
+        self.use_token_type_ids = True
+        self.use_labels = True
+        self.vocab_size = 99
+        self.hidden_size = 32
+        self.num_hidden_layers = 5
+        self.num_attention_heads = 4
+        self.intermediate_size = 37
+        self.hidden_act = "gelu"
+        self.hidden_dropout_prob = 0.1
+        self.attention_probs_dropout_prob = 0.1
+        self.max_position_embeddings = 512
+        self.type_vocab_size = 16
+        self.type_sequence_label_size = 2
+        self.initializer_range = 0.02
+        self.num_labels = 3
+        self.num_choices = 4
+        self.scope = None
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = OpenAIGPTConfig(
+            vocab_size=self.vocab_size,
+            n_embd=self.hidden_size,
+            n_layer=self.num_hidden_layers,
+            n_head=self.num_attention_heads,
+            # intermediate_size=self.intermediate_size,
+            # hidden_act=self.hidden_act,
+            # hidden_dropout_prob=self.hidden_dropout_prob,
+            # attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            n_positions=self.max_position_embeddings,
+            n_ctx=self.max_position_embeddings
+            # type_vocab_size=self.type_vocab_size,
+            # initializer_range=self.initializer_range
+        )
+
+        head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
+
+        return (
+            config,
+            input_ids,
+            head_mask,
+            token_type_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        )
+
+    def check_loss_output(self, result):
+        self.parent.assertListEqual(list(result["loss"].size()), [])
+
+    def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
+        model = OpenAIGPTModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
+        model(input_ids, token_type_ids=token_type_ids)
+        (sequence_output,) = model(input_ids)
+
+        result = {"sequence_output": sequence_output}
+        self.parent.assertListEqual(
+            list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size],
+        )
+
+    def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
+        model = OpenAIGPTLMHeadModel(config)
+        model.to(torch_device)
+        model.eval()
+
+        loss, lm_logits = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
+
+        result = {"loss": loss, "lm_logits": lm_logits}
+
+        self.parent.assertListEqual(list(result["loss"].size()), [])
+        self.parent.assertListEqual(
+            list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
+        )
+
+    def create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
+        model = OpenAIGPTDoubleHeadsModel(config)
+        model.to(torch_device)
+        model.eval()
+
+        loss, lm_logits, mc_logits = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
+
+        result = {"loss": loss, "lm_logits": lm_logits}
+
+        self.parent.assertListEqual(list(result["loss"].size()), [])
+        self.parent.assertListEqual(
+            list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            head_mask,
+            token_type_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "head_mask": head_mask,
+        }
+
+        return config, inputs_dict
+
+
 @require_torch
 class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):
 
@@ -44,161 +177,8 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):
         (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 
-    class OpenAIGPTModelTester(object):
-        def __init__(
-            self,
-            parent,
-            batch_size=13,
-            seq_length=7,
-            is_training=True,
-            use_token_type_ids=True,
-            use_labels=True,
-            vocab_size=99,
-            hidden_size=32,
-            num_hidden_layers=5,
-            num_attention_heads=4,
-            intermediate_size=37,
-            hidden_act="gelu",
-            hidden_dropout_prob=0.1,
-            attention_probs_dropout_prob=0.1,
-            max_position_embeddings=512,
-            type_vocab_size=16,
-            type_sequence_label_size=2,
-            initializer_range=0.02,
-            num_labels=3,
-            num_choices=4,
-            scope=None,
-        ):
-            self.parent = parent
-            self.batch_size = batch_size
-            self.seq_length = seq_length
-            self.is_training = is_training
-            self.use_token_type_ids = use_token_type_ids
-            self.use_labels = use_labels
-            self.vocab_size = vocab_size
-            self.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.intermediate_size = intermediate_size
-            self.hidden_act = hidden_act
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.type_vocab_size = type_vocab_size
-            self.type_sequence_label_size = type_sequence_label_size
-            self.initializer_range = initializer_range
-            self.num_labels = num_labels
-            self.num_choices = num_choices
-            self.scope = scope
-
-        def prepare_config_and_inputs(self):
-            input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-
-            token_type_ids = None
-            if self.use_token_type_ids:
-                token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
-
-            sequence_labels = None
-            token_labels = None
-            choice_labels = None
-            if self.use_labels:
-                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
-                token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
-                choice_labels = ids_tensor([self.batch_size], self.num_choices)
-
-            config = OpenAIGPTConfig(
-                vocab_size=self.vocab_size,
-                n_embd=self.hidden_size,
-                n_layer=self.num_hidden_layers,
-                n_head=self.num_attention_heads,
-                # intermediate_size=self.intermediate_size,
-                # hidden_act=self.hidden_act,
-                # hidden_dropout_prob=self.hidden_dropout_prob,
-                # attention_probs_dropout_prob=self.attention_probs_dropout_prob,
-                n_positions=self.max_position_embeddings,
-                n_ctx=self.max_position_embeddings
-                # type_vocab_size=self.type_vocab_size,
-                # initializer_range=self.initializer_range
-            )
-
-            head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
-
-            return (
-                config,
-                input_ids,
-                head_mask,
-                token_type_ids,
-                sequence_labels,
-                token_labels,
-                choice_labels,
-            )
-
-        def check_loss_output(self, result):
-            self.parent.assertListEqual(list(result["loss"].size()), [])
-
-        def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
-            model = OpenAIGPTModel(config=config)
-            model.to(torch_device)
-            model.eval()
-
-            model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
-            model(input_ids, token_type_ids=token_type_ids)
-            (sequence_output,) = model(input_ids)
-
-            result = {"sequence_output": sequence_output}
-            self.parent.assertListEqual(
-                list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size],
-            )
-
-        def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
-            model = OpenAIGPTLMHeadModel(config)
-            model.to(torch_device)
-            model.eval()
-
-            loss, lm_logits = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
-
-            result = {"loss": loss, "lm_logits": lm_logits}
-
-            self.parent.assertListEqual(list(result["loss"].size()), [])
-            self.parent.assertListEqual(
-                list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
-            )
-
-        def create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
-            model = OpenAIGPTDoubleHeadsModel(config)
-            model.to(torch_device)
-            model.eval()
-
-            loss, lm_logits, mc_logits = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
-
-            result = {"loss": loss, "lm_logits": lm_logits}
-
-            self.parent.assertListEqual(list(result["loss"].size()), [])
-            self.parent.assertListEqual(
-                list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
-            )
-
-        def prepare_config_and_inputs_for_common(self):
-            config_and_inputs = self.prepare_config_and_inputs()
-            (
-                config,
-                input_ids,
-                head_mask,
-                token_type_ids,
-                sequence_labels,
-                token_labels,
-                choice_labels,
-            ) = config_and_inputs
-            inputs_dict = {
-                "input_ids": input_ids,
-                "token_type_ids": token_type_ids,
-                "head_mask": head_mask,
-            }
-
-            return config, inputs_dict
-
     def setUp(self):
-        self.model_tester = OpenAIGPTModelTest.OpenAIGPTModelTester(self)
+        self.model_tester = OpenAIGPTModelTester(self)
         self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
 
     def test_config(self):