# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # 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 random import unittest import numpy as np import paddle from parameterized import parameterized_class from paddlenlp.transformers import ( CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST, AutoTokenizer, CodeGenConfig, CodeGenForCausalLM, CodeGenModel, ) from ...testing_utils import slow from ..test_generation_utils import GenerationTesterMixin from ..test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) class CodeGenModelTester: test_model_name_list = False def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, use_mc_token_ids=True, vocab_size=256, hidden_size=32, rotary_dim=4, num_hidden_layers=5, num_attention_heads=4, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, ): 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.use_mc_token_ids = use_mc_token_ids 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.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.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 paddle.seed(128) np.random.seed(128) random.seed(128) def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64") input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length], dtype="int64") mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length, dtype="int64") sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size, dtype="int64") token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels, dtype="int64") choice_labels = ids_tensor([self.batch_size], self.num_choices, dtype="int64") config = self.get_config() return ( config, input_ids, input_mask, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def get_config(self): return CodeGenConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, activation_function=self.hidden_act, resid_pdrop=self.hidden_dropout_prob, attn_pdrop=self.attention_probs_dropout_prob, n_positions=self.max_position_embeddings, n_ctx=self.max_position_embeddings, initializer_range=self.initializer_range, 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, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2, dtype="int64") return ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_codegen_model(self, config, input_ids, input_mask, *args): model = CodeGenModel(config) model.eval() result = model(input_ids, use_cache=True, return_dict=self.parent.return_dict) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertEqual(len(result[1]), config["n_layer"]) def create_and_check_codegen_model_past(self, config, input_ids, input_mask, *args): model = CodeGenModel(config) model.eval() # first forward pass outputs = model(input_ids, use_cache=True, return_dict=self.parent.return_dict) outputs_no_past = model(input_ids, use_cache=False, return_dict=self.parent.return_dict) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past = outputs[:2] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config["vocab_size"], dtype="int64") # append to next input_ids next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, return_dict=self.parent.return_dict)[0] output_from_past = model(next_tokens, cache=past, return_dict=self.parent.return_dict)[0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_codegen_model_attention_mask_past(self, config, input_ids, input_mask, *args): model = CodeGenModel(config) model.eval() # create attention mask attn_mask = paddle.ones(input_ids.shape, dtype="int64") half_seq_length = self.seq_length // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past = model(input_ids, attention_mask=attn_mask, use_cache=True, return_dict=self.parent.return_dict)[ :2 ] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config["vocab_size"], dtype="int64") # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length, dtype="int64").item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config["vocab_size"], dtype="int64").squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) attn_mask = paddle.concat( [attn_mask, paddle.ones((attn_mask.shape[0], 1), dtype="int64")], axis=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask, return_dict=self.parent.return_dict)[0] output_from_past = model( next_tokens, cache=past, attention_mask=attn_mask, return_dict=self.parent.return_dict )[0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_codegen_model_past_large_inputs(self, config, input_ids, input_mask, *args): model = CodeGenModel(config) model.eval() # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True, return_dict=self.parent.return_dict) output, past = outputs[:2] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config["vocab_size"], dtype="int64") next_mask = ids_tensor((self.batch_size, 3), vocab_size=2, dtype="int64") # append to next input_ids next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, return_dict=self.parent.return_dict )[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, cache=past, return_dict=self.parent.return_dict )[0] self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]) # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args): model = CodeGenForCausalLM(config) outputs = model( input_ids, labels=input_ids if self.parent.use_labels else None, return_dict=self.parent.return_dict ) if self.parent.use_labels: loss, logits = outputs[:2] self.parent.assertEqual(loss.shape, [1]) else: logits = outputs[0] self.parent.assertEqual(logits.shape, [self.batch_size, self.seq_length, self.vocab_size]) def create_and_check_forward_and_backwards(self, config, input_ids, input_mask, *args): model = CodeGenForCausalLM(config) loss, logits = model(input_ids, return_dict=self.parent.return_dict, labels=input_ids)[:2] self.parent.assertEqual(loss.shape, [1]) self.parent.assertEqual(logits.shape, [self.batch_size, self.seq_length, self.vocab_size]) loss.backward() def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids} return config, inputs_dict @parameterized_class( ("return_dict",), [ [False, False], [False, True], [True, False], [True, True], ], ) class CodeGenModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): base_model_class = CodeGenModel all_model_classes = (CodeGenModel, CodeGenForCausalLM) all_generative_model_classes = {CodeGenForCausalLM: (CodeGenModel, "transformer")} fx_compatible = False test_pruning = False test_missing_keys = False test_model_parallel = False test_head_masking = False use_test_model_name_list = False return_dict = False use_labels = False use_test_inputs_embeds = True # attention mask issue def _get_input_ids_and_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict[self.input_name] attention_mask = paddle.zeros_like(input_ids, dtype=paddle.float32) max_batch_size = 2 sequence_length = input_ids.shape[-1] // 2 input_ids = input_ids[:max_batch_size, :sequence_length] attention_mask = attention_mask[:max_batch_size, :sequence_length].unsqueeze([1, 2]) # generate max 3 tokens max_length = 3 if config.get("eos_token_id", None) is not None and config.get("pad_token_id", None) is None: # hack to allow generate for models such as GPT2 as is done in `generate()` config["pad_token_id"] = config["eos_token_id"] return config, input_ids, attention_mask, max_length # special case for DoubleHeads model def _prepare_for_class(self, inputs_dict, model_class): inputs_dict = super()._prepare_for_class(inputs_dict, model_class) return inputs_dict def setUp(self): self.model_tester = CodeGenModelTester(self) def test_codegen_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_codegen_model(*config_and_inputs) def test_codegen_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_codegen_model_past(*config_and_inputs) def test_codegen_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_codegen_model_attention_mask_past(*config_and_inputs) def test_codegen_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_codegen_model_past_large_inputs(*config_and_inputs) def test_codegen_lm_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head_model(*config_and_inputs) @slow def test_batch_generation(self): tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") model.eval() tokenizer.padding_side = "left" # Define PAD Token = EOS Token = 50256 tokenizer.pad_token = tokenizer.eos_token model.transformer.config["pad_token_id"] = model.transformer.config["eos_token_id"] # use different length sentences to test batching sentences = ["def hellow_world():", "def greet(name):"] inputs = tokenizer(sentences, return_tensors="pd", padding=True, return_attention_mask=True) input_ids = inputs["input_ids"] outputs, _ = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"], ) inputs_non_padded = tokenizer(sentences[0], return_tensors="pd")["input_ids"] output_non_padded, _ = model.generate(input_ids=inputs_non_padded) inputs_padded = tokenizer(sentences[1], return_tensors="pd")["input_ids"] output_padded, _ = model.generate(input_ids=inputs_padded) # batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) expected_output_sentence = [ '\n print("Hello World")\n\nhellow_world()\n\n#', '\n print(f"Hello {name}")\n\ngreet("Rolf")\n', ] # self.assertEqual(str(expected_output_sentence), str(batch_out_sentence)) self.assertEqual(str(expected_output_sentence), str([non_padded_sentence, padded_sentence])) @slow def test_model_from_pretrained(self): for model_name in CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = CodeGenModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip("Not implemented") def test_model_name_list(self): pass @slow def test_auto_tokenizer(self): for model_name in CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST: AutoTokenizer.from_pretrained(model_name) # assign a tokenizer but never use class CodeGenModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_codegen(self): tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") model.eval() inputs = tokenizer( "def hello_world():", return_tensors="pd", return_attention_mask=True, return_token_type_ids=False ) expected_output = '\n print("Hello World")\n\nhello_world()\n\n#' output_ids, _ = model.generate(**inputs, decode_strategy="sampling", top_k=1) output_str = tokenizer.batch_decode(output_ids)[0] self.assertEqual(output_str, expected_output) @slow def test_codegen_sample(self): tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") model.eval() tokenized = tokenizer( "def hello_world():", return_tensors="pd", return_token_type_ids=True, return_attention_mask=True ) input_ids = tokenized["input_ids"] output_ids, _ = model.generate(input_ids, decode_strategy="sampling", top_k=1) output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True) token_type_ids = tokenized.token_type_ids output_seq, _ = model.generate( input_ids=input_ids, decode_strategy="sampling", top_k=1, num_return_sequences=5 ) output_seq_tt, _ = model.generate( input_ids=input_ids, token_type_ids=token_type_ids, decode_strategy="sampling", top_k=1, num_return_sequences=5, ) output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True) output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True) EXPECTED_OUTPUT_STR = '\n print("Hello World")\n\nhello_world()\n\n#' self.assertEqual(output_str, EXPECTED_OUTPUT_STR) self.assertTrue( all([output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))]) ) # token_type_ids should change output