# Copyright (c) 2023 PaddlePaddle Authors. 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 paddle from parameterized import parameterized_class from paddlenlp.transformers import ChatGLMv2Config, ChatGLMv2ForCausalLM, ChatGLMv2Model from tests.transformers.test_generation_utils import GenerationTesterMixin from tests.transformers.test_modeling_common import ( GenerationD2STestMixin, ModelTesterMixin, ids_tensor, random_attention_mask, ) class ChatGLMv2Tester: def __init__( self, parent, is_training=True, num_hidden_layers=3, seq_length=10, batch_size=2, vocab_size=123, kv_channels=4, hidden_size=8, ffn_hidden_size=8, num_attention_heads=2, rmsnorm=True, use_cache=True, ): self.parent = parent self.is_training = is_training self.num_hidden_layers = num_hidden_layers self.vocab_size = vocab_size self.kv_channels = kv_channels self.seq_length = seq_length self.batch_size = batch_size self.hidden_size = hidden_size self.ffn_hidden_size = ffn_hidden_size self.num_attention_heads = num_attention_heads self.rmsnorm = rmsnorm self.use_cache = use_cache def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64") labels = None context_length = self.seq_length // 2 if self.parent.use_labels: labels = paddle.ones([self.batch_size, self.seq_length], dtype=input_ids.dtype) * -100 labels[:, context_length:] = input_ids[:, context_length:] config = self.get_config() return config, input_ids, labels def get_config(self): return ChatGLMv2Config( vocab_size=self.vocab_size, num_hidden_layers=self.num_hidden_layers, hidden_size=self.hidden_size, ffn_hidden_size=self.ffn_hidden_size, num_attention_heads=self.num_attention_heads, kv_channels=self.kv_channels, use_cache=self.use_cache, rmsnorm=self.rmsnorm, ) def create_and_check_model(self, config, input_ids, labels): model = ChatGLMv2Model(config) model.eval() result = model(input_ids) self.parent.assertEqual(result[0].shape, [self.seq_length, self.batch_size, self.hidden_size]) def create_and_check_model_past_large_inputs(self, config, input_ids, labels): model = ChatGLMv2Model(config) model.eval() outputs = model(input_ids, return_dict=self.parent.return_dict) past_key_values = outputs.past_key_values[0] if self.parent.return_dict else outputs[1][0] next_tokens = ids_tensor([self.batch_size, 3], self.vocab_size, dtype="int64") next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) next_attention_mask = model.get_masks(next_input_ids) outputs = model(next_input_ids, attention_mask=next_attention_mask, return_dict=self.parent.return_dict) output_from_no_past = outputs.past_key_values[0] if self.parent.return_dict else outputs[1][0] outputs = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, return_dict=self.parent.return_dict, ) output_from_past = outputs.past_key_values[0] if self.parent.return_dict else outputs[1][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[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # 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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, labels = config_and_inputs inputs_dict = {"input_ids": input_ids} return config, inputs_dict def create_and_check_lm_head_model(self, config, input_ids, labels, *args): model = ChatGLMv2ForCausalLM(config) model.eval() result = model( input_ids, labels=labels if self.parent.use_labels else None, return_dict=self.parent.return_dict, ) if self.parent.use_labels: loss = result.loss if self.parent.return_dict else result[0] self.parent.assertIsNotNone(loss) logits = result.logits if self.parent.return_dict else result[1] past_key_values = result.past_key_values[0] if self.parent.return_dict else result[2][0] else: loss = result.loss if self.parent.return_dict else None self.parent.assertIsNone(loss) logits = result.logits if self.parent.return_dict else result[0] past_key_values = result.past_key_values[0] if self.parent.return_dict else result[1][0] self.parent.assertEqual(logits.shape, [self.batch_size, self.seq_length, self.vocab_size]) if config.use_cache: self.parent.assertTrue(isinstance(past_key_values, tuple)) self.parent.assertEqual( past_key_values[0].shape, [self.seq_length, self.batch_size, config.multi_query_group_num, config.kv_channels], ) else: self.parent.assertTrue(past_key_values is None) def create_and_check_model_attention_mask(self, config: ChatGLMv2Config, input_ids, labels): model = ChatGLMv2ForCausalLM(config) model.eval() attn_mask_2d = random_attention_mask([self.batch_size, self.seq_length]) result_2d = model(input_ids, attention_mask=attn_mask_2d)[0] batch, seq_length = input_ids.shape causal_mask = paddle.tril(paddle.ones((batch, seq_length, seq_length), dtype=attn_mask_2d.dtype)) attn_mask_3d = causal_mask & attn_mask_2d.unsqueeze(-1) result_3d = model(input_ids, attention_mask=attn_mask_3d)[0] attn_mask_4d = attn_mask_3d.unsqueeze(1) result_4d = model(input_ids, attention_mask=attn_mask_4d)[0] result_no_attention_mask = model(input_ids, attention_mask=None)[0] # Assert non-padding tokens have the same logits with different attention_mask shape self.parent.assertTrue((result_2d[attn_mask_2d] == result_3d[attn_mask_2d]).all()) self.parent.assertTrue((result_2d[attn_mask_2d] == result_4d[attn_mask_2d]).all()) self.parent.assertTrue((result_2d[attn_mask_2d] == result_no_attention_mask[attn_mask_2d]).all()) @parameterized_class( ("return_dict", "use_labels"), [ [False, True], [True, False], ], ) class ChatGLMv2Test(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): base_model_class = ChatGLMv2Model return_dict: bool = True use_labels: bool = False use_test_model_name_list = False all_model_classes = (ChatGLMv2Model, ChatGLMv2ForCausalLM) all_generative_model_classes = {ChatGLMv2ForCausalLM: (ChatGLMv2Model, "chatglm_v2")} def setUp(self): self.model_tester = ChatGLMv2Tester(self) 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] print(input_ids) attention_mask = paddle.ones_like(input_ids) 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] # generate max 3 tokens max_length = 3 return config, input_ids, attention_mask, max_length def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_ChatGLMv2_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) def test_model_attention_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_attention_mask(*config_and_inputs) class ChatGLMV2GenerationD2STest(GenerationD2STestMixin, unittest.TestCase): internal_testing_model = "__internal_testing__/tiny-random-chatglm2" if __name__ == "__main__": unittest.main()