# Copyright 2022 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 OPTConfig, is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, slow 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 FlaxOPTForCausalLM, FlaxOPTModel, GPT2Tokenizer def prepare_opt_inputs_dict(config, input_ids, attention_mask=None, head_mask=None): if attention_mask is None: attention_mask = np.where(input_ids != config.pad_token_id, 1, 0) return { "input_ids": input_ids, "attention_mask": attention_mask, } @require_flax class FlaxOPTModelTester: 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=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, embed_dim=16, word_embed_proj_dim=16, 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.embed_dim = embed_dim self.word_embed_proj_dim = word_embed_proj_dim self.initializer_range = initializer_range self.is_encoder_decoder = False 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) config = OPTConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, 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, embed_dim=self.embed_dim, is_encoder_decoder=False, word_embed_proj_dim=self.word_embed_proj_dim, initializer_range=self.initializer_range, use_cache=False, ) inputs_dict = prepare_opt_inputs_dict(config, 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_length = 20 model = model_class_name(config) input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] past_key_values = model.init_cache(input_ids.shape[0], max_length) attention_mask = jnp.ones((input_ids.shape[0], max_length), dtype="i4") position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1), ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], attention_mask=attention_mask, past_key_values=outputs_cache.past_key_values, position_ids=position_ids, ) outputs = model(input_ids) 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_length = 20 model = model_class_name(config) input_ids, attention_mask = ( inputs_dict["input_ids"], inputs_dict["attention_mask"], ) attention_mask_cache = jnp.concatenate( [ attention_mask, jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(input_ids.shape[0], max_length) position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1), ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask_cache, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], past_key_values=outputs_cache.past_key_values, attention_mask=attention_mask_cache, position_ids=position_ids, ) outputs = model(input_ids, attention_mask=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 FlaxOPTModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin): all_model_classes = (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else () all_generative_model_classes = () if is_flax_available() else () def setUp(self): self.model_tester = FlaxOPTModelTester(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) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("facebook/opt-125m") input_ids = np.ones((1, 1)) * model.config.eos_token_id outputs = model(input_ids) self.assertIsNotNone(outputs) @require_sentencepiece @require_flax class FlaxOPTModelIntegrationTests(unittest.TestCase): @slow def test_inference_no_head(self): model = FlaxOPTModel.from_pretrained("facebook/opt-350m") input_ids = jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids=input_ids).last_hidden_state expected_shape = (1, 11, 512) self.assertEqual(output.shape, expected_shape) expected_slice = jnp.array( [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]] ) self.assertTrue(jnp.allclose(output[:, :3, :3], expected_slice, atol=4e-2)) @require_flax @slow class FlaxOPTEmbeddingsTest(unittest.TestCase): def setUp(self): super().setUp() self.path_model = "facebook/opt-350m" def test_logits(self): model = FlaxOPTForCausalLM.from_pretrained(self.path_model) tokenizer = GPT2Tokenizer.from_pretrained(self.path_model) prompts = [ "Today is a beautiful day and I want to", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False inputs = tokenizer(prompts, return_tensors="jax", padding=True, add_special_tokens=False) logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1) logits_meta = jnp.array( [ [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670], [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822], [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703], [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477], ] ) self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2)) model = jax.jit(model) logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1) self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2)) @require_flax @slow class FlaxOPTGenerationTest(unittest.TestCase): @property def prompts(self): return [ "Today is a beautiful day and I want", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] def test_generation_pre_attn_layer_norm(self): model_id = "facebook/opt-125m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of New York, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer = GPT2Tokenizer.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="jax").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_ids = generated_ids[0] generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) def test_generation_post_attn_layer_norm(self): model_id = "facebook/opt-350m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of San Francisco, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer = GPT2Tokenizer.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="jax").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_ids = generated_ids[0] generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) def test_jitted_batch_generation(self): model_id = "facebook/opt-125m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to thank", "In the city of Rome Canaver Canaver Canaver Canaver", ] model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer = GPT2Tokenizer.from_pretrained(model_id) inputs = tokenizer( [ "Today is a beautiful day and I want to", "In the city of", ], return_tensors="jax", padding=True, ) jit_generate = jax.jit(model.generate) output_sequences = jit_generate(inputs["input_ids"], attention_mask=inputs["attention_mask"]).sequences output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True) self.assertIsNotNone(output_string, EXPECTED_OUTPUTS) def test_batch_generation(self): model_id = "facebook/opt-350m" tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer.padding_side = "left" # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] inputs = tokenizer(sentences, return_tensors="jax", padding=True) input_ids = inputs["input_ids"] outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"], trace=False) inputs_non_padded = tokenizer(sentences[0], return_tensors="jax").input_ids output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum() inputs_padded = tokenizer(sentences[1], return_tensors="jax").input_ids output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) batch_out_sentence = tokenizer.batch_decode(outputs[0], skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0][0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0][0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little bit of a dork.\nI'm a little bit", "Today, I was in the middle of a conversation with a friend about the", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])