Repository: huggingface/transformers
File: tests/test_modeling_common.py (Python)

Code before:
        with _deepspeed_zero3(ds_config):
            self.test_resize_tokens_embeddings()

    @require_deepspeed
    @require_torch_multi_accelerator
    def test_resize_tokens_embeddings_with_deepspeed_multi_gpu(self):
        ds_config = {
            "zero_optimization": {
                "stage": 3,
            },
        }
        with _deepspeed_zero3(ds_config):
            self.test_resize_tokens_embeddings()

    def test_resize_embeddings_untied(self):
        if not self.test_resize_embeddings:
            self.skipTest(reason="test_resize_embeddings is set to `False`")

        original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        original_config.tie_word_embeddings = False
        try:
            original_config.get_text_config().tie_word_embeddings = False
        except Exception as e:
            model_type = getattr(original_config, "model_type", "unknown")
            # Config may not have a text config
            print(f"Could not set text config tie_word_embeddings: {e}, model type: {model_type}")
        inputs_dict.pop("labels", None)

        # if model cannot untied embeddings -> leave test
        if original_config.tie_word_embeddings:
            self.skipTest(reason="Model cannot untied embeddings")

        for model_class in self.all_model_classes:
            with self.subTest(model_class):
                config = copy.deepcopy(original_config)
                if is_deepspeed_zero3_enabled():
                    with deepspeed.zero.Init():
                        model = model_class(config)
                else:
                    model = model_class(config).to(torch_device)
                model.eval()

                # if no output embeddings -> leave test
                if model.get_output_embeddings() is None:
                    continue

                # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
                model_vocab_size = config.get_text_config().vocab_size
                model.resize_token_embeddings(model_vocab_size + 10)
                new_model_vocab_size = model.config.get_text_config().vocab_size
                self.assertEqual(new_model_vocab_size, model_vocab_size + 10)

Review comment (suggestion):
```suggestion
            print(f"Could not set text config's `tie_word_embeddings` for model type `{model_type}`: {e}")
```

Code after:
        with _deepspeed_zero3(ds_config):
            self.test_resize_tokens_embeddings()

    @require_deepspeed
    @require_torch_multi_accelerator
    def test_resize_tokens_embeddings_with_deepspeed_multi_gpu(self):
        ds_config = {
            "zero_optimization": {
                "stage": 3,
            },
        }
        with _deepspeed_zero3(ds_config):
            self.test_resize_tokens_embeddings()

    def test_resize_embeddings_untied(self):
        if not self.test_resize_embeddings:
            self.skipTest(reason="test_resize_embeddings is set to `False`")

        original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        original_config.tie_word_embeddings = False
        try:
            original_config.get_text_config().tie_word_embeddings = False
        except Exception as e:
            model_type = getattr(original_config, "model_type", "unknown")
            # Config may not have a text config
            print(f"Could not set text config's `tie_word_embeddings` for model type `{model_type}`: {e}")
        inputs_dict.pop("labels", None)

        # if model cannot untied embeddings -> leave test
        if original_config.tie_word_embeddings:
            self.skipTest(reason="Model cannot untied embeddings")

        for model_class in self.all_model_classes:
            with self.subTest(model_class):
                config = copy.deepcopy(original_config)
                if is_deepspeed_zero3_enabled():
                    with deepspeed.zero.Init():
                        model = model_class(config)
                else:
                    model = model_class(config).to(torch_device)
                model.eval()

                # if no output embeddings -> leave test
                if model.get_output_embeddings() is None:
                    continue

                # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
                model_vocab_size = config.get_text_config().vocab_size
                model.resize_token_embeddings(model_vocab_size + 10)
                new_model_vocab_size = model.config.get_text_config().vocab_size
                self.assertEqual(new_model_vocab_size, model_vocab_size + 10)