# Copyright 2021 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 unittest import numpy as np import timeout_decorator # noqa from transformers import MarianConfig, is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform" import jax import jax.numpy as jnp from transformers import MarianTokenizer from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right def prepare_marian_inputs_dict( config, input_ids, decoder_input_ids=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = np.where(input_ids != config.pad_token_id, 1, 0) if decoder_attention_mask is None: decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0) if head_mask is None: head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } class FlaxMarianModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=32, eos_token_id=2, pad_token_id=1, bos_token_id=0, initializer_range=0.02, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training 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.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.initializer_range = initializer_range def prepare_config_and_inputs(self): input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size) input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1) decoder_input_ids = shift_tokens_right(input_ids, 1, 2) config = MarianConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, initializer_range=self.initializer_range, use_cache=False, ) inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4") decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=outputs_cache.past_key_values, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) decoder_attention_mask_cache = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask_cache, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=decoder_attention_mask_cache, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class FlaxMarianModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin): is_encoder_decoder = True all_model_classes = (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else () all_generative_model_classes = (FlaxMarianMTModel,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxMarianModelTester(self) def test_use_cache_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(model_class, config, inputs_dict) def test_use_cache_forward_with_attn_mask(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict) def test_encode(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) @jax.jit def encode_jitted(input_ids, attention_mask=None, **kwargs): return model.encode(input_ids=input_ids, attention_mask=attention_mask) with self.subTest("JIT Enabled"): jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = encode_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) def test_decode(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): model = model_class(config) encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"]) prepared_inputs_dict = { "decoder_input_ids": inputs_dict["decoder_input_ids"], "decoder_attention_mask": inputs_dict["decoder_attention_mask"], "encoder_outputs": encoder_outputs, } @jax.jit def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs): return model.decode( decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, ) with self.subTest("JIT Enabled"): jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = decode_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de") # FlaxMarianForSequenceClassification expects eos token in input_ids input_ids = np.ones((1, 1)) * model.config.eos_token_id outputs = model(input_ids) self.assertIsNotNone(outputs) @require_flax @require_sentencepiece @require_tokenizers class MarianIntegrationTest(unittest.TestCase): src = None tgt = None @classmethod def setUpClass(cls) -> None: cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}" return cls @cached_property def tokenizer(self): return MarianTokenizer.from_pretrained(self.model_name) @property def eos_token_id(self) -> int: return self.tokenizer.eos_token_id @cached_property def model(self): model: FlaxMarianMTModel = FlaxMarianMTModel.from_pretrained(self.model_name) self.assertEqual(model.config.decoder_start_token_id, model.config.pad_token_id) return model def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs): generated_words = self.translate_src_text(**tokenizer_kwargs) self.assertListEqual(self.expected_text, generated_words) def translate_src_text(self, **tokenizer_kwargs): model_inputs = self.tokenizer(self.src_text, padding=True, return_tensors="np", **tokenizer_kwargs) generated_ids = self.model.generate( model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, max_length=128, ).sequences generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True) return generated_words @require_flax @require_sentencepiece @require_tokenizers class TestMarian_EN_FR(MarianIntegrationTest): src = "en" tgt = "fr" src_text = [ "I am a small frog.", "Now I can forget the 100 words of german that I know.", ] expected_text = [ "Je suis une petite grenouille.", "Maintenant, je peux oublier les 100 mots d'allemand que je connais.", ] @slow def test_batch_generation_en_fr(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_FR_EN(MarianIntegrationTest): src = "fr" tgt = "en" src_text = [ "Donnez moi le micro.", "Tom et Mary étaient assis à une table.", # Accents ] expected_text = [ "Give me the microphone.", "Tom and Mary were sitting at a table.", ] @slow def test_batch_generation_fr_en(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_MT_EN(MarianIntegrationTest): """Cover low resource/high perplexity setting. This breaks without adjust_logits_generation overwritten""" src = "mt" tgt = "en" src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."] expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."] @slow def test_batch_generation_mt_en(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_EN_DE(MarianIntegrationTest): src = "en" tgt = "de" src_text = [ "I am a small frog.", "Now I can forget the 100 words of german that I know.", "Tom asked his teacher for advice.", "That's how I would do it.", "Tom really admired Mary's courage.", "Turn around and close your eyes.", ] expected_text = [ "Ich bin ein kleiner Frosch.", "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.", "Tom bat seinen Lehrer um Rat.", "So würde ich das machen.", "Tom bewunderte Marias Mut wirklich.", "Drehen Sie sich um und schließen Sie die Augen.", ] @slow def test_batch_generation_en_de(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_en_zh(MarianIntegrationTest): src = "en" tgt = "zh" src_text = ["My name is Wolfgang and I live in Berlin"] expected_text = ["我叫沃尔夫冈 我住在柏林"] @slow def test_batch_generation_eng_zho(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_RU_FR(MarianIntegrationTest): src = "ru" tgt = "fr" src_text = ["Он показал мне рукопись своей новой пьесы."] expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."] @slow def test_batch_generation_ru_fr(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_en_ROMANCE(MarianIntegrationTest): """Multilingual on target side.""" src = "en" tgt = "ROMANCE" src_text = [ ">>fr<< Don't spend so much time watching TV.", ">>pt<< Your message has been sent.", ">>es<< He's two years older than me.", ] expected_text = [ "Ne passez pas autant de temps à regarder la télé.", "A sua mensagem foi enviada.", "Es dos años más viejo que yo.", ] @slow def test_batch_generation_en_ROMANCE_multi(self): self._assert_generated_batch_equal_expected()