Switch Transformers: remove overwritten beam sample test (#25458)

tests/models/switch_transformers/test_modeling_switch_transformers.py

@@ -37,7 +37,6 @@ if is_torch_available():
         SwitchTransformersModel,
         SwitchTransformersTop1Router,
     )
-    from transformers.generation import BeamSampleDecoderOnlyOutput, BeamSampleEncoderDecoderOutput
     from transformers.models.switch_transformers.modeling_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
         load_balancing_loss_func,
@@ -613,101 +612,6 @@ class SwitchTransformersModelTest(ModelTesterMixin, GenerationTesterMixin, Pipel
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
 
-    @slow
-    def test_beam_sample_generate_dict_output(self):
-        r"""
-        This test needs to be overriden with a larger model since it fails for very small models due to precision issues.
-        """
-        for model_class in self.all_generative_model_classes:
-            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
-
-            # disable cache
-            config.use_cache = False
-
-            # It is important set set the eos_token_id to None to ensure that no sequences
-            # shorter than `max_length` can be generated which could lead to flaky circle ci
-            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
-            config.eos_token_id = None
-            config.forced_eos_token_id = None
-
-            model = model_class.from_pretrained("google/switch-base-8").to(torch_device).eval()
-            logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
-
-            num_return_sequences = 2
-            if model.config.is_encoder_decoder:
-                max_length = 4
-            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
-                input_ids.shape[0] * num_return_sequences, max_length
-            )
-            beam_kwargs["num_return_sequences"] = num_return_sequences
-
-            output_beam_sample, output_generate = self._beam_sample_generate(
-                model=model,
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                max_length=max_length,
-                num_return_sequences=num_return_sequences,
-                beam_scorer=beam_scorer,
-                beam_kwargs=beam_kwargs,
-                logits_warper=logits_warper,
-                logits_warper_kwargs=logits_warper_kwargs,
-                output_scores=True,
-                output_hidden_states=True,
-                output_attentions=True,
-                return_dict_in_generate=True,
-            )
-
-            if model.config.is_encoder_decoder:
-                self.assertIsInstance(output_beam_sample, BeamSampleEncoderDecoderOutput)
-                self.assertIsInstance(output_generate, BeamSampleEncoderDecoderOutput)
-            else:
-                self.assertIsInstance(output_beam_sample, BeamSampleDecoderOnlyOutput)
-                self.assertIsInstance(output_generate, BeamSampleDecoderOnlyOutput)
-
-            self.assertListEqual(output_generate.sequences.tolist(), output_beam_sample.sequences.tolist())
-
-    @slow
-    def test_beam_sample_generate(self):
-        r"""
-        This test needs to be overriden with a larger model since it fails for very small models due to precision issues.
-        """
-        for model_class in self.all_generative_model_classes:
-            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
-
-            # It is important set set the eos_token_id to None to ensure that no sequences
-            # shorter than `max_length` can be generated which could lead to flaky circle ci
-            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
-            config.eos_token_id = None
-            config.forced_eos_token_id = None
-
-            logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
-
-            model = model_class.from_pretrained("google/switch-base-8").to(torch_device).eval()
-
-            # check `generate()` and `beam_search()` are equal
-            # change `num_return_sequences = 2` but not for `beam_scorer`
-            num_return_sequences = 2
-            if model.config.is_encoder_decoder:
-                max_length = 4
-            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
-                input_ids.shape[0] * num_return_sequences, max_length
-            )
-            beam_kwargs["num_return_sequences"] = num_return_sequences
-
-            output_generate, output_beam_sample = self._beam_sample_generate(
-                model=model,
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                max_length=max_length,
-                num_return_sequences=num_return_sequences,
-                beam_scorer=beam_scorer,
-                beam_kwargs=beam_kwargs,
-                logits_warper=logits_warper,
-                logits_warper_kwargs=logits_warper_kwargs,
-            )
-
-            self.assertListEqual(output_generate.tolist(), output_beam_sample.tolist())
-
     def test_decoder_model_past_with_3d_attn_mask(self):
         (
             config,