# Copyright 2021 The HuggingFace Inc. 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 from transformers import XGLMConfig, XGLMTokenizer, is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax import jax.numpy as jnp import numpy as np from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel @require_flax class FlaxXGLMModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, d_model=32, num_hidden_layers=2, num_attention_heads=4, ffn_dim=37, activation_function="gelu", activation_dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, 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_labels = use_labels self.vocab_size = vocab_size self.hidden_size = d_model self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.ffn_dim = ffn_dim self.activation_function = activation_function self.activation_dropout = activation_dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = None self.bos_token_id = 0 self.eos_token_id = 2 self.pad_token_id = 1 def prepare_config_and_inputs(self): input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 3, self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) config = XGLMConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, ) return (config, input_ids, input_mask) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask} return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length) attention_mask = jnp.ones((input_ids.shape[0], max_decoder_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, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) attention_mask_cache = jnp.concatenate( [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))], axis=-1, ) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_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_sentencepiece @require_flax class FlaxXGLMModelTest(FlaxModelTesterMixin, unittest.TestCase): all_model_classes = (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else () def setUp(self): self.model_tester = FlaxXGLMModelTester(self) def test_use_cache_forward(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask) def test_use_cache_forward_with_attn_mask(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward_with_attn_mask( model_class_name, config, input_ids, attention_mask ) @slow def test_batch_generation(self): tokenizer = XGLMTokenizer.from_pretrained("XGLM", padding_side="left") inputs = tokenizer(["Hello this is a long string", "Hey"], return_tensors="np", padding=True, truncation=True) model = FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M") model.config.num_beams = 1 model.config.do_sample = False 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) expected_string = [ "Hello this is a long string of questions, but I'm not sure if I'm", "Hey, I'm a newbie to the forum and I'", ] self.assertListEqual(output_string, expected_string) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("facebook/xglm-564M") outputs = model(np.ones((1, 1))) self.assertIsNotNone(outputs)