# coding=utf-8 # Copyright 2020 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 copy import random import unittest from transformers import TransfoXLConfig, is_torch_available from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel from transformers.models.transfo_xl.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST class TransfoXLModelTester: def __init__( self, parent, batch_size=14, seq_length=7, mem_len=30, clamp_len=15, is_training=False, use_labels=True, vocab_size=99, cutoffs=[10, 50, 80], hidden_size=32, d_embed=32, num_attention_heads=4, d_head=8, d_inner=128, div_val=2, num_hidden_layers=2, scope=None, seed=1, eos_token_id=0, num_labels=3, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.mem_len = mem_len self.key_length = self.seq_length + self.mem_len self.clamp_len = clamp_len self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.cutoffs = cutoffs self.hidden_size = hidden_size self.d_embed = d_embed self.num_attention_heads = num_attention_heads self.d_head = d_head self.d_inner = d_inner self.div_val = div_val self.num_hidden_layers = num_hidden_layers self.scope = scope self.seed = seed self.eos_token_id = eos_token_id self.num_labels = num_labels self.pad_token_id = self.vocab_size - 1 def prepare_config_and_inputs(self): input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.get_config() return (config, input_ids_1, input_ids_2, lm_labels) def get_config(self): return TransfoXLConfig( vocab_size=self.vocab_size, mem_len=self.mem_len, clamp_len=self.clamp_len, cutoffs=self.cutoffs, d_model=self.hidden_size, d_embed=self.d_embed, n_head=self.num_attention_heads, d_head=self.d_head, d_inner=self.d_inner, div_val=self.div_val, n_layer=self.num_hidden_layers, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, ) def set_seed(self): random.seed(self.seed) torch.manual_seed(self.seed) def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels): model = TransfoXLModel(config) model.to(torch_device) model.eval() outputs1 = model(input_ids_1) outputs2 = model(input_ids_2, outputs1["mems"]) outputs = { "hidden_states_1": outputs1["last_hidden_state"], "mems_1": outputs1["mems"], "hidden_states_2": outputs2["last_hidden_state"], "mems_2": outputs2["mems"], } return outputs def check_transfo_xl_model_output(self, result): self.parent.assertEqual(result["hidden_states_1"].shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result["hidden_states_2"].shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertListEqual( [mem.shape for mem in result["mems_1"]], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) self.parent.assertListEqual( [mem.shape for mem in result["mems_2"]], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels): model = TransfoXLLMHeadModel(config) model.to(torch_device) model.eval() lm_logits_1 = model(input_ids_1)["prediction_scores"] outputs1 = model(input_ids_1, labels=lm_labels) lm_logits_2 = model(input_ids_2, mems=outputs1["mems"])["prediction_scores"] outputs2 = model(input_ids_2, labels=lm_labels, mems=outputs1["mems"]) outputs = { "loss_1": outputs1["loss"], "losses_1": outputs1["losses"], "mems_1": outputs1["mems"], "lm_logits_1": lm_logits_1, "loss_2": outputs2["loss"], "losses_2": outputs2["losses"], "mems_2": outputs2["mems"], "lm_logits_2": lm_logits_2, } return outputs def check_transfo_xl_lm_head_output(self, result): self.parent.assertEqual(result["loss_1"].shape, ()) self.parent.assertEqual(result["losses_1"].shape, (self.batch_size, self.seq_length - 1)) self.parent.assertEqual(result["lm_logits_1"].shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in result["mems_1"]], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) self.parent.assertEqual(result["loss_2"].shape, ()) self.parent.assertEqual(result["losses_2"].shape, (self.batch_size, self.seq_length - 1)) self.parent.assertEqual(result["lm_logits_2"].shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in result["mems_2"]], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_transfo_xl_lm_head_trainer_compatible_tuple(self, config, input_ids_1, input_ids_2, lm_labels): config.trainer_compatible = True model = TransfoXLLMHeadModel(config) model.to(torch_device) model.eval() lm_logits_1 = model(input_ids_1, return_dict=False)[0] outputs1 = model(input_ids_1, labels=lm_labels, return_dict=False) loss_1, _, losses_1, mems_1 = outputs1[:4] lm_logits_2 = model(input_ids_2, mems=mems_1, return_dict=False)[0] outputs2 = model(input_ids_2, labels=lm_labels, mems=mems_1, return_dict=False) loss_2, _, losses_2, mems_2 = outputs2[:4] outputs = { "losses_1": losses_1, "mems_1": mems_1, "lm_logits_1": lm_logits_1, "loss_1": loss_1, "losses_2": losses_2, "mems_2": mems_2, "lm_logits_2": lm_logits_2, "loss_2": loss_2, } config.trainer_compatible = None return outputs def create_transfo_xl_lm_head_trainer_incompatible_tuple(self, config, input_ids_1, input_ids_2, lm_labels): config.trainer_compatible = False model = TransfoXLLMHeadModel(config) model.to(torch_device) model.eval() lm_logits_1 = model(input_ids_1, return_dict=False)[0] outputs1 = model(input_ids_1, labels=lm_labels, return_dict=False) losses_1, _, mems_1 = outputs1[:3] loss_1 = outputs1[-1] lm_logits_2 = model(input_ids_2, mems=mems_1, return_dict=False)[0] outputs2 = model(input_ids_2, labels=lm_labels, mems=mems_1) losses_2, _, mems_2 = outputs2[:3] loss_2 = outputs2[-1] outputs = { "losses_1": losses_1, "mems_1": mems_1, "lm_logits_1": lm_logits_1, "loss_1": loss_1, "losses_2": losses_2, "mems_2": mems_2, "lm_logits_2": lm_logits_2, "loss_2": loss_2, } config.trainer_compatible = None return outputs def create_and_check_transfo_xl_for_sequence_classification(self, config, input_ids_1, input_ids_2, lm_labels): config.num_labels = self.num_labels model = TransfoXLForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids_1) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs inputs_dict = {"input_ids": input_ids_1} return config, inputs_dict @require_torch class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (TransfoXLModel, TransfoXLLMHeadModel, TransfoXLForSequenceClassification) if is_torch_available() else () ) all_generative_model_classes = (TransfoXLLMHeadModel,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": TransfoXLModel, "text-classification": TransfoXLForSequenceClassification, "text-generation": TransfoXLLMHeadModel, "zero-shot": TransfoXLForSequenceClassification, } if is_torch_available() else {} ) test_pruning = False test_resize_embeddings = True test_mismatched_shapes = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if pipeline_test_casse_name == "TextGenerationPipelineTests": # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`. # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple # tokenizer. return True return False def check_cutoffs_and_n_token( self, copied_cutoffs, layer, model_embed, model, model_class, resized_value, vocab_size ): # Check that the cutoffs were modified accordingly for i in range(len(copied_cutoffs)): if i < layer: self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i]) if model_class == TransfoXLLMHeadModel: self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i]) if i < len(model.config.cutoffs): self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i]) else: self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i] + resized_value) if model_class == TransfoXLLMHeadModel: self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i] + resized_value) if i < len(model.config.cutoffs): self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i] + resized_value) self.assertEqual(model_embed.n_token, vocab_size + resized_value) if model_class == TransfoXLLMHeadModel: self.assertEqual(model.crit.n_token, vocab_size + resized_value) def setUp(self): self.model_tester = TransfoXLModelTester(self) self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37) def test_config(self): self.config_tester.run_common_tests() def test_transfo_xl_model(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs) self.model_tester.check_transfo_xl_model_output(output_result) def test_transfo_xl_lm_head(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs) self.model_tester.check_transfo_xl_lm_head_output(output_result) output_result = self.model_tester.create_transfo_xl_lm_head_trainer_compatible_tuple(*config_and_inputs) self.model_tester.check_transfo_xl_lm_head_output(output_result) output_result = self.model_tester.create_transfo_xl_lm_head_trainer_incompatible_tuple(*config_and_inputs) self.model_tester.check_transfo_xl_lm_head_output(output_result) def test_transfo_xl_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # xlnet cannot keep gradients in attentions or hidden states return @require_torch_multi_gpu def test_multi_gpu_data_parallel_forward(self): # Opt-out of this test. pass @slow def test_model_from_pretrained(self): for model_name in TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TransfoXLModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_resize_tokens_embeddings(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = [emb.weight.clone() for emb in model_embed.emb_layers] # Retrieve the cutoffs and copy them copied_cutoffs = copy.copy(model_embed.cutoffs) test_layers = list(range(config.div_val)) for layer in test_layers: # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10, layer) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] + 10) # Check that the cutoffs were modified accordingly self.check_cutoffs_and_n_token( copied_cutoffs, layer, model_embed, model, model_class, 10, model_vocab_size ) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**inputs_dict) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 5, layer) self.assertEqual(model.config.vocab_size, model_vocab_size - 5) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] - 5) # Check that the cutoffs were modified accordingly self.check_cutoffs_and_n_token( copied_cutoffs, layer, model_embed, model, model_class, -5, model_vocab_size ) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 5 - 1) model(**inputs_dict) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings[layer], model_embed.emb_layers[layer].weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) # Reset model embeddings to original size model.resize_token_embeddings(model_vocab_size, layer) self.assertEqual(model_vocab_size, model.config.vocab_size) self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0]) def test_resize_embeddings_untied(self): # transfo-xl requires special resize for lm-head return def _check_attentions_for_generate( self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(attentions, tuple) self.assertListEqual( [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions) ) self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups) for idx, iter_attentions in enumerate(attentions): tgt_len = min_length if idx == 0 else (min_length - 2) src_len = (min_length + config.mem_len) if idx == 0 else (min_length + config.mem_len - 2) expected_shape = ( batch_size * num_beam_groups, config.num_attention_heads, tgt_len, src_len, ) # check attn size self.assertListEqual( [layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions) ) def _check_hidden_states_for_generate( self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(hidden_states, tuple) self.assertListEqual( [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states], [True] * len(hidden_states), ) self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups) for idx, iter_hidden_states in enumerate(hidden_states): seq_len = min_length if idx == 0 else min_length - 2 expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size) # check hidden size self.assertListEqual( [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states], [expected_shape] * len(iter_hidden_states), ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "cluster_weight") and module.cluster_weight is not None: module.cluster_weight.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "cluster_bias") and module.cluster_bias is not None: module.cluster_bias.data.fill_(3) if hasattr(module, "emb_projs"): for i in range(len(module.emb_projs)): if module.emb_projs[i] is not None: nn.init.constant_(module.emb_projs[i], 0.0003) if hasattr(module, "out_projs"): for i in range(len(module.out_projs)): if module.out_projs[i] is not None: nn.init.constant_(module.out_projs[i], 0.0003) for param in ["r_emb", "r_w_bias", "r_r_bias", "r_bias"]: if hasattr(module, param) and getattr(module, param) is not None: weight = getattr(module, param) weight.data.fill_(3) @unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :) def test_left_padding_compatibility(self): pass @require_torch class TransfoXLModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_transfo_xl_wt103(self): model = TransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103") model.to(torch_device) # fmt: off input_ids = torch.tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]],dtype=torch.long,device=torch_device) # noqa: E231 # fmt: on # In 1991 , the remains of Russian Tsar Nicholas II and his family # ( except for Alexei and Maria ) are discovered . # The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the # remainder of the story . 1883 Western Siberia , # a young Grigori Rasputin is asked by his father and a group of men to perform magic . # Rasputin has a vision and denounces one of the men as a horse thief . Although his # father initially slaps him for making such an accusation , Rasputin watches as the # man is chased outside and beaten . Twenty years later , Rasputin sees a vision of # the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous , # with people , even a bishop , begging for his blessing . # fmt: off expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,142,1298,188,2,29546,113,8,3654,4,1,1109,7136,833,3,13,1645,4,29546,11,104,7,1,1109,532,7129,2,10,83507,2,1162,1123,2,6,7245,10,2,5,11,104,7,1,1109,532,7129,2,10,24,24,10,22,10,13,770,5863,4,7245,10] # noqa: E231 # fmt: on # In 1991, the remains of Russian Tsar Nicholas II and his family ( except for # Alexei and Maria ) are discovered. The voice of young son, Tsarevich Alexei # Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, a young # Grigori Rasputin is asked by his father and a group of men to perform magic. # Rasputin has a vision and denounces one of the men as a horse thief. Although # his father initially slaps him for making such an accusation, Rasputin watches # as the man is chased outside and beaten. Twenty years later, Rasputin sees a # vision of the Virgin Mary, prompting him to become a priest. Rasputin quickly # becomes famous, with people, even a bishop, begging for his blessing. In the # early 20th century, Rasputin became a symbol of the Russian Orthodox Church. # The image of Rasputin was used in the Russian national anthem, " Nearer, My God, # to Heaven ", and was used in the Russian national anthem, " " ( " The Great Spirit # of Heaven " output_ids = model.generate(input_ids, max_length=200, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids)