diff --git a/src/transformers/models/plm/__init__.py b/src/transformers/models/plm/__init__.py index 1d25968f5d2..389e9e39abc 100644 --- a/src/transformers/models/plm/__init__.py +++ b/src/transformers/models/plm/__init__.py @@ -24,6 +24,4 @@ else: import sys _file = globals()["__file__"] - sys.modules[__name__] = _LazyModule( - __name__, _file, define_import_structure(_file), module_spec=__spec__ - ) + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/tests/models/plm/test_modeling_plm.py b/tests/models/plm/test_modeling_plm.py index 1cda4f240a3..81bc567c115 100644 --- a/tests/models/plm/test_modeling_plm.py +++ b/tests/models/plm/test_modeling_plm.py @@ -121,20 +121,14 @@ class PLMModelTester: 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 - ) + 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 - ) + 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 = self.get_config() @@ -239,9 +233,7 @@ class PLMModelTester: model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) - self.parent.assertEqual( - result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) - ) + self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, @@ -297,17 +289,13 @@ class PLMModelTester: # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() - output_from_no_past_slice = output_from_no_past[ - :, -3:, random_slice_idx - ].detach() + output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice - self.parent.assertTrue( - torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) - ) + self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() @@ -325,9 +313,7 @@ class PLMModelTester: @require_torch -class PLMModelTest( - ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase -): +class PLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): # breakpoint() all_model_classes = ( ( @@ -402,21 +388,15 @@ class PLMModelTest( def test_contrastive_generate_low_memory(self): pass - @unittest.skip( - "PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support." - ) + @unittest.skip("PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_with_static_cache(self): pass - @unittest.skip( - "PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support." - ) + @unittest.skip("PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_from_inputs_embeds_with_static_cache(self): pass - @unittest.skip( - "PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support." - ) + @unittest.skip("PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_continue_from_inputs_embeds(self): pass @@ -424,27 +404,19 @@ class PLMModelTest( def test_sdpa_equivalence(self): pass - @unittest.skip( - "PLM uses MLA so it is not compatible with the standard cache format" - ) + @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_beam_search_generate_dict_outputs_use_cache(self): pass - @unittest.skip( - "PLM uses MLA so it is not compatible with the standard cache format" - ) + @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_generate_compilation_all_outputs(self): pass - @unittest.skip( - "PLM uses MLA so it is not compatible with the standard cache format" - ) + @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_generate_compile_model_forward(self): pass - @unittest.skip( - "PLM uses MLA so it is not compatible with the standard cache format" - ) + @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_greedy_generate_dict_outputs_use_cache(self): pass @@ -576,9 +548,7 @@ class PLMIntegrationTest(unittest.TestCase): @classmethod def setUpClass(cls): if is_torch_available() and torch.cuda.is_available(): - cls.cuda_compute_capability_major_version = ( - torch.cuda.get_device_capability()[0] - ) + cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] @slow @require_torch_accelerator @@ -604,43 +574,29 @@ class PLMIntegrationTest(unittest.TestCase): "Simply put, the theory of relativity states that ", "My favorite all time favorite condiment is ketchup.", ] - tokenizer = AutoTokenizer.from_pretrained( - "PLM-Team/PLM-1.8B-Base", use_fast=False - ) + tokenizer = AutoTokenizer.from_pretrained("PLM-Team/PLM-1.8B-Base", use_fast=False) model = PLMForCausalLM.from_pretrained( "PLM-Team/PLM-1.8B-Base", device_map=torch_device, torch_dtype=torch.float16 ) inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) # Dynamic Cache - generated_ids = model.generate( - **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False - ) + generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False) dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text) # Static Cache generated_ids = model.generate( - **inputs, - max_new_tokens=NUM_TOKENS_TO_GENERATE, - do_sample=False, - cache_implementation="static" + **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text) # Static Cache + compile model._cache = None # clear cache object, initialized when we pass `cache_implementation="static"` - model.forward = torch.compile( - model.forward, mode="reduce-overhead", fullgraph=True - ) + model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) generated_ids = model.generate( - **inputs, - max_new_tokens=NUM_TOKENS_TO_GENERATE, - do_sample=False, - cache_implementation="static" - ) - static_compiled_text = tokenizer.batch_decode( - generated_ids, skip_special_tokens=True + **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) + static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)