# 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 tempfile import unittest import numpy as np import transformers from transformers import GPT2Tokenizer, GPTJConfig, is_flax_available, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow from .test_generation_flax_utils import FlaxGenerationTesterMixin from .test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax import jax.numpy as jnp from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel if is_torch_available(): import torch class FlaxGPTJModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, rotary_dim=4, num_hidden_layers=4, 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, initializer_range=0.02, ): 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.rotary_dim = rotary_dim 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.initializer_range = initializer_range self.scope = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 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]) config = GPTJConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, n_positions=self.max_position_embeddings, use_cache=False, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, rotary_dim=self.rotary_dim, ) 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_flax class FlaxGPTJModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase): all_model_classes = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else () all_generative_model_classes = (FlaxGPTJForCausalLM,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxGPTJModelTester(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 ) @tooslow def test_batch_generation(self): tokenizer = GPT2Tokenizer.from_pretrained("gpt2", pad_token="<|endoftext|>", padding_side="left") inputs = tokenizer(["Hello this is a long string", "Hey"], return_tensors="np", padding=True, truncation=True) model = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gptj-6B") model.do_sample = False model.config.pad_token_id = model.config.eos_token_id jit_generate = jax.jit(model.generate) output_sequences = jit_generate( inputs["input_ids"], attention_mask=inputs["attention_mask"], pad_token_id=tokenizer.pad_token_id ).sequences output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True) expected_string = [ "Hello this is a long string of text.\n\nI'm trying to get the text of the", "Hey, I'm a little late to the party. I'm going to", ] self.assertListEqual(output_string, expected_string) # overwrite from common since `attention_mask` in combination # with `causal_mask` behaves slighly differently @is_pt_flax_cross_test def test_equivalence_pt_to_flax(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__): # prepare inputs prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()} # load corresponding PyTorch class pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) batch_size, seq_length = pt_inputs["input_ids"].shape rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): pt_inputs["attention_mask"][batch_idx, :start_index] = 0 pt_inputs["attention_mask"][batch_idx, start_index:] = 1 prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0 prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1 pt_model = pt_model_class(config).eval() fx_model = model_class(config, dtype=jnp.float32) fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() fx_outputs = fx_model(**prepared_inputs_dict).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs, pt_outputs): self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple() self.assertEqual( len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch" ) for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs): self.assert_almost_equals(fx_output_loaded[:, -1], pt_output[:, -1].numpy(), 4e-2) # overwrite from common since `attention_mask` in combination # with `causal_mask` behaves slighly differently @is_pt_flax_cross_test def test_equivalence_flax_to_pt(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__): # prepare inputs prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()} # load corresponding PyTorch class pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) pt_model = pt_model_class(config).eval() fx_model = model_class(config, dtype=jnp.float32) pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) batch_size, seq_length = pt_inputs["input_ids"].shape rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): pt_inputs["attention_mask"][batch_idx, :start_index] = 0 pt_inputs["attention_mask"][batch_idx, start_index:] = 1 prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0 prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1 # make sure weights are tied in PyTorch pt_model.tie_weights() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() fx_outputs = fx_model(**prepared_inputs_dict).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs, pt_outputs): self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2) with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True) with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple() self.assertEqual( len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch" ) for fx_output, pt_output in zip(fx_outputs, pt_outputs_loaded): self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2) @tooslow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("EleutherAI/gptj-6B") outputs = model(np.ones((1, 1))) self.assertIsNotNone(outputs)