Raise exceptions instead of using asserts in modeling_openai #12789 (#14386)

* Raise exceptions instead of using asserts for control flow in modeling_openai #12789

* reformatted file

src/transformers/models/openai/modeling_openai.py

@@ -83,13 +83,16 @@ def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
     # del init_params[1]
     init_params = [arr.squeeze() for arr in init_params]
 
-    try:
-        assert model.tokens_embed.weight.shape == init_params[1].shape
-        assert model.positions_embed.weight.shape == init_params[0].shape
-    except AssertionError as e:
-        e.args += (model.tokens_embed.weight.shape, init_params[1].shape)
-        e.args += (model.positions_embed.weight.shape, init_params[0].shape)
-        raise
+    # Check that the token and position embeddings weight dimensions map those of the init parameters.
+    if model.tokens_embed.weight.shape != init_params[1].shape:
+        raise ValueError(
+            f"tokens_embed.weight.shape: {model.tokens_embed.weight.shape} does not match init_param[1].shape: {init_params[1].shape}"
+        )
+
+    if model.positions_embed.weight.shape != init_params[0].shape:
+        raise ValueError(
+            f"positions_embed.weight.shape: {model.positions_embed.weight.shape} does not match init_param[0].shape: {init_params[0].shape}"
+        )
 
     model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
     model.positions_embed.weight.data = torch.from_numpy(init_params[0])
@@ -100,7 +103,8 @@ def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
 
     for name, array in zip(names, init_params):  # names[1:n_transfer], init_params[1:n_transfer]):
         name = name[6:]  # skip "model/"
-        assert name[-2:] == ":0"
+        if name[-2:] != ":0":
+            raise ValueError(f"Layer {name} does not end with :0")
         name = name[:-2]
         name = name.split("/")
         pointer = model
@@ -120,20 +124,11 @@ def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
             if len(scope_names) >= 2:
                 num = int(scope_names[1])
                 pointer = pointer[num]
-        try:
-            assert (
-                pointer.shape == array.shape
-            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
-        try:
-            assert (
-                pointer.shape == array.shape
-            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
+
+        # Ensure that the pointer and array have compatible shapes.
+        if pointer.shape != array.shape:
+            raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
+
         logger.info(f"Initialize PyTorch weight {name}")
         pointer.data = torch.from_numpy(array)
     return model
@@ -147,7 +142,8 @@ class Attention(nn.Module):
         super().__init__()
         n_state = nx  # in Attention: n_state=768 (nx=n_embd)
         # [switch nx => n_state from Block to Attention to keep identical to TF implementation]
-        assert n_state % config.n_head == 0
+        if n_state % config.n_head != 0:
+            raise ValueError(f"Attention n_state shape: {n_state} must be divisible by config.n_head {config.n_head}")
         self.register_buffer(
             "bias", torch.tril(torch.ones(n_positions, n_positions)).view(1, 1, n_positions, n_positions)
         )
@@ -804,9 +800,10 @@ class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
         else:
             batch_size, sequence_length = inputs_embeds.shape[:2]
 
-        assert (
-            self.config.pad_token_id is not None or batch_size == 1
-        ), "Cannot handle batch sizes > 1 if no padding token is defined."
+        # Ensure the batch size is > 1 if there is no padding.
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+
         if self.config.pad_token_id is None:
             sequence_lengths = -1
         else: