# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import tempfile import unittest from transformers import XLMRobertaTokenizer, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( DataCollatorWithFlattening, XmodConfig, XmodForCausalLM, XmodForMaskedLM, XmodForMultipleChoice, XmodForQuestionAnswering, XmodForSequenceClassification, XmodForTokenClassification, XmodModel, ) from transformers.models.xmod.modeling_xmod import XmodEmbeddings class XmodModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, 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_input_mask = use_input_mask 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) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) 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 = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return XmodConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=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, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, default_language="en_XX", ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = XmodModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = XmodModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = XmodForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) 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, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = XmodForCausalLM(config=config).to(torch_device).eval() # make sure that ids don't start with pad token mask = input_ids.ne(config.pad_token_id).long() input_ids = input_ids * mask # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) # make sure that ids don't start with pad token mask = next_tokens.ne(config.pad_token_id).long() next_tokens = next_tokens * mask next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # 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_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)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = XmodForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = XmodForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = XmodForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = XmodForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class XmodModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( XmodForCausalLM, XmodForMaskedLM, XmodModel, XmodForSequenceClassification, XmodForTokenClassification, XmodForMultipleChoice, XmodForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": XmodModel, "fill-mask": XmodForMaskedLM, "question-answering": XmodForQuestionAnswering, "text-classification": XmodForSequenceClassification, "text-generation": XmodForCausalLM, "token-classification": XmodForTokenClassification, "zero-shot": XmodForSequenceClassification, } if is_torch_available() else {} ) # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if pipeline_test_case_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"): return True return False # Overwriting to add `is_decoder` flag def prepare_config_and_inputs_for_generate(self, batch_size=2): config, inputs = super().prepare_config_and_inputs_for_generate(batch_size) config.is_decoder = True return config, inputs def setUp(self): self.model_tester = XmodModelTester(self) self.config_tester = ConfigTester(self, config_class=XmodConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_model_as_decoder_with_default_input_mask(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_create_position_ids_respects_padding_index(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is XmodEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = XmodEmbeddings(config=config) input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]]) expected_positions = torch.as_tensor( [[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]] ) position_ids = XmodEmbeddings.create_position_ids_from_input_ids(input_ids, model.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_create_position_ids_from_inputs_embeds(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is XmodEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = XmodEmbeddings(config=config) inputs_embeds = torch.empty(2, 4, 30) expected_single_positions = [ 0 + embeddings.padding_idx + 1, 1 + embeddings.padding_idx + 1, 2 + embeddings.padding_idx + 1, 3 + embeddings.padding_idx + 1, ] expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions]) position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds, embeddings.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_set_default_language(self): config = self.model_tester.prepare_config_and_inputs()[0] model = XmodForMaskedLM(config=config) model.set_default_language("en_XX") self.assertEqual(model.config.default_language, "en_XX") with self.assertRaises(ValueError): model.set_default_language("xx_XX") def test_freeze_embeddings_and_language_adapters(self): config = self.model_tester.prepare_config_and_inputs()[0] model = XmodForMaskedLM(config=config) num_trainable_params_before = sum(p.numel() for p in model.parameters() if p.requires_grad) model.freeze_embeddings_and_language_adapters() num_trainable_params_after = sum(p.numel() for p in model.parameters() if p.requires_grad) self.assertLess(num_trainable_params_after, num_trainable_params_before) def attention_mask_padding_matches_padding_free_with_position_ids( self, attn_implementation: str, fa_kwargs: bool = False ): """ Overwritten to account for the embeddings that rely on position ids. """ if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") max_new_tokens = 30 support_flag = { "sdpa": "_supports_sdpa", "flash_attention_2": "_supports_flash_attn", "flash_attention_3": "_supports_flash_attn", } for model_class in self.all_generative_model_classes: if attn_implementation != "eager" and not getattr(model_class, support_flag[attn_implementation]): self.skipTest(f"{model_class.__name__} does not support {attn_implementation}") # can't infer if new attn mask API is supported by assume that only model with attention backend support it if not model_class._supports_attention_backend: self.skipTest(f"{model_class.__name__} does not support new attention mask API") if model_class._is_stateful: # non-transformer models most probably have no packing support self.skipTest(f"{model_class.__name__} doesn't support packing!") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if config.is_encoder_decoder: self.skipTest("Model is an encoder-decoder") if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict: self.skipTest("Model dummy inputs should contain padding in their attention mask") if "input_ids" not in inputs_dict or inputs_dict["input_ids"].ndim != 2: self.skipTest("Model dummy inputs should contain text input ids") # make sure that all models have enough positions for generation dummy_input_ids = inputs_dict["input_ids"] if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input_ids.shape[1] + 1 model = model_class(config) if "position_ids" not in inspect.signature(model.forward).parameters: self.skipTest("Model does not support position_ids") if (not fa_kwargs) and "position_ids" not in inspect.signature(model.forward).parameters: continue # this model doesn't accept position ids as input with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # Drop all keys except for the minimal set. Hard to manipulate with multimodals etc inputs_dict = {k: v for k, v in inputs_dict.items() if k in ["input_ids", "attention_mask"]} # Ensure left padding, to adapt for some models if 0 in inputs_dict["attention_mask"][:, -1]: inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1) dummy_attention_mask = inputs_dict["attention_mask"] dummy_input_ids[~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id # Main difference to other models, we need to prepare position ids according to the attention mask # as we use it to extract embeddings that rely on the correct position - naively increasing sequences do # not suffice anymore atp. The solution here calculates an increasing sequences for all 1s and puts 0s else. inputs_dict["position_ids"] = ((inputs_dict["attention_mask"] == 1).long().cumsum(dim=1) - 1) * ( inputs_dict["attention_mask"] == 1 ).long() model = ( model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation=attn_implementation, ) .to(torch_device) .eval() ) if fa_kwargs: # flatten features = [ {"input_ids": i[a.bool()].tolist()} for i, a in zip(dummy_input_ids, dummy_attention_mask) ] # add position_ids + fa_kwargs data_collator = DataCollatorWithFlattening(return_tensors="pt", return_flash_attn_kwargs=True) batch = data_collator(features) padfree_inputs_dict = { k: t.to(torch_device) if torch.is_tensor(t) else t for k, t in batch.items() } else: # create packed position_ids position_ids = ( torch.cat([torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()]) .long() .unsqueeze(0) .to(torch_device) ) padfree_inputs_dict = { "input_ids": dummy_input_ids[dummy_attention_mask.bool()].unsqueeze(0), "position_ids": position_ids, } # We need to do simple forward without cache in order to trigger packed SDPA/flex/eager attention path res_padded = model(**inputs_dict, use_cache=False) res_padfree = model(**padfree_inputs_dict, use_cache=False) logits_padded = res_padded.logits[dummy_attention_mask.bool()] logits_padfree = res_padfree.logits[0] # acceptable numerical instability tol = torch.finfo(torch.bfloat16).eps torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol) @require_sentencepiece @require_tokenizers @require_torch class XmodModelIntegrationTest(unittest.TestCase): @slow def test_xmod_base(self): model = XmodModel.from_pretrained("facebook/xmod-base") # language en_XX model.set_default_language("en_XX") input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]) # The dog is cute and lives in the garden house expected_output_shape = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim expected_output_values_last_dim = torch.tensor( [[-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724]] ) output = model(input_ids)["last_hidden_state"].detach() self.assertEqual(output.shape, expected_output_shape) # compare the actual values for a slice of last dim torch.testing.assert_close(output[:, :, -1], expected_output_values_last_dim, rtol=1e-3, atol=1e-3) # language de_DE model.set_default_language("de_DE") input_ids = torch.tensor([[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2315, 58761, 18391, 5, 2]]) # Der Hund ist niedlich und wohnt in einem Gartenhaus. expected_output_shape = torch.Size((1, 16, 768)) # batch_size, sequence_length, embedding_vector_dim # fmt: off expected_output_values_last_dim = torch.tensor( [[0.0162, 0.0075, -0.1882, 0.2335, -0.0952, -0.3994, -0.0317, -0.1174, 0.0177, 0.4280, -0.0240, -0.2138, 0.0785, -0.1045, -0.2811, -0.3220]] ) # fmt: on output = model(input_ids)["last_hidden_state"].detach() self.assertEqual(output.shape, expected_output_shape) # compare the actual values for a slice of last dim torch.testing.assert_close(output[:, :, -1], expected_output_values_last_dim, rtol=1e-3, atol=1e-3) @slow def test_xmod_large_prenorm(self): model = XmodModel.from_pretrained("facebook/xmod-large-prenorm") # language en_XX model.set_default_language("en_XX") input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]) # The dog is cute and lives in the garden house expected_output_shape = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim # fmt: off expected_output_values_last_dim = torch.tensor( [[-0.0121, -0.0194, -0.0240, -0.0160, -0.0205, -0.0159, -0.0243, -0.0206, -0.0161, -0.0335, -0.0196, -0.0141]] ) # fmt: on output = model(input_ids)["last_hidden_state"].detach() self.assertEqual(output.shape, expected_output_shape) # compare the actual values for a slice of last dim torch.testing.assert_close(output[:, :, -1], expected_output_values_last_dim, rtol=1e-3, atol=1e-3) # language de_DE model.set_default_language("de_DE") input_ids = torch.tensor([[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2315, 58761, 18391, 5, 2]]) # Der Hund ist niedlich und wohnt in einem Gartenhaus. expected_output_shape = torch.Size((1, 16, 1024)) # batch_size, sequence_length, embedding_vector_dim # fmt: off expected_output_values_last_dim = torch.tensor( [[-0.0120, -0.0262, -0.0253, -0.0112, -0.0128, -0.0164, -0.0080, -0.0081, -0.0192, -0.0117, -0.0170, -0.0120, -0.0210, -0.0173, -0.0078, -0.0122]] ) # fmt: on output = model(input_ids)["last_hidden_state"].detach() self.assertEqual(output.shape, expected_output_shape) # compare the actual values for a slice of last dim torch.testing.assert_close(output[:, :, -1], expected_output_values_last_dim, rtol=1e-3, atol=1e-3) @slow def test_multilingual_batch(self): model = XmodModel.from_pretrained("facebook/xmod-base") # fmt: off input_ids = torch.tensor([ [0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2], [0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2], [0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2], [0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2], ]) # fmt: on lang_ids = torch.LongTensor([0, 8, 8, 0]) expected_output_shape = torch.Size((4, 12, 768)) # batch_size, sequence_length, embedding_vector_dim # fmt: off expected_output_values_last_dim = torch.tensor([ [-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724], [-0.2668, -0.0235, -0.1739, 0.2266, -0.0901, -0.3482, 0.0105, -0.1915, 0.0397, 0.3822, 0.1836, -0.3407], [-0.2668, -0.0235, -0.1739, 0.2266, -0.0901, -0.3482, 0.0105, -0.1915, 0.0397, 0.3822, 0.1836, -0.3407], [-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724], ]) # fmt: on output = model(input_ids, lang_ids=lang_ids)["last_hidden_state"].detach() self.assertEqual(output.shape, expected_output_shape) # compare the actual values for a slice of last dim torch.testing.assert_close(output[:, :, -1], expected_output_values_last_dim, rtol=1e-3, atol=1e-3) @slow def test_end_to_end_mask_fill(self): tokenizer = XLMRobertaTokenizer.from_pretrained("FacebookAI/xlm-roberta-base") model = XmodForMaskedLM.from_pretrained("facebook/xmod-base", default_language="en_XX") model.to(torch_device) sentences = [ "Hello, my dog is a little .", "Hi !", ] inputs = tokenizer(sentences, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) outputs = model( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), ) probs = outputs.logits.softmax(dim=-1) _, predictions = probs.topk(1) predictions = predictions.squeeze(-1) inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device) output_non_padded = model(input_ids=inputs_non_padded) probs_non_padded = output_non_padded.logits.softmax(dim=-1) _, predictions_non_padded = probs_non_padded.topk(1) predictions_non_padded = predictions_non_padded.squeeze(-1) inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device) output_padded = model(input_ids=inputs_padded) probs_padded = output_padded.logits.softmax(dim=-1) _, predictions_padded = probs_padded.topk(1) predictions_padded = predictions_padded.squeeze(-1) batch_out_sentence = tokenizer.batch_decode(predictions, skip_special_tokens=True) non_padded_sentence = tokenizer.decode(predictions_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(predictions_padded[0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little girl .", "Hi everyone !", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])