From fbe3b7818279208ff7b6b5161d3c95658b2cf620 Mon Sep 17 00:00:00 2001 From: DesmonDay <908660116@qq.com> Date: Thu, 29 Feb 2024 15:44:31 +0800 Subject: [PATCH] add unittest --- paddlenlp/transformers/mixtral/modeling.py | 2 - tests/transformers/mixtral/__init__.py | 13 + tests/transformers/mixtral/test_modeling.py | 318 ++++++++++++++++++++ 3 files changed, 331 insertions(+), 2 deletions(-) create mode 100644 tests/transformers/mixtral/__init__.py create mode 100644 tests/transformers/mixtral/test_modeling.py diff --git a/paddlenlp/transformers/mixtral/modeling.py b/paddlenlp/transformers/mixtral/modeling.py index 75c73e0c8963..e591e30a9b91 100644 --- a/paddlenlp/transformers/mixtral/modeling.py +++ b/paddlenlp/transformers/mixtral/modeling.py @@ -1070,8 +1070,6 @@ def __init__(self, config: MixtralConfig): ) self.norm = MixtralRMSNorm(config) - self.gradient_checkpointing = False - def get_input_embeddings(self): return self.embed_tokens diff --git a/tests/transformers/mixtral/__init__.py b/tests/transformers/mixtral/__init__.py new file mode 100644 index 000000000000..595add0aed9e --- /dev/null +++ b/tests/transformers/mixtral/__init__.py @@ -0,0 +1,13 @@ +# 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. diff --git a/tests/transformers/mixtral/test_modeling.py b/tests/transformers/mixtral/test_modeling.py new file mode 100644 index 000000000000..2ae98a6fb326 --- /dev/null +++ b/tests/transformers/mixtral/test_modeling.py @@ -0,0 +1,318 @@ +# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. +# Copyright 2020 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. +from __future__ import annotations + +import unittest + +import paddle + +from paddlenlp.transformers import MixtralConfig, MixtralForCausalLM, MixtralModel +from tests.transformers.test_configuration_common import ConfigTester +from tests.transformers.test_generation_utils import GenerationTesterMixin +from tests.transformers.test_modeling_common import ( + ModelTesterMixin, + ids_tensor, + random_attention_mask, +) + + +class MixtralModelTester: + def __init__( + self, + parent, + vocab_size=32000, + hidden_size=64, + num_hidden_layers=2, + num_attention_heads=8, + masked_softmax_fusion=True, + layer_norm_epsilon=1e-5, + initializer_range=0.02, + is_training=True, + use_cache=False, + bos_token_id=1, + eos_token_id=2, + apply_residual_connection_post_layernorm=False, + hidden_dropout=0.0, + attention_dropout=0.0, + attention_softmax_in_fp32=True, + pretraining_tp=1, # TP rank used when training with megatron + dtype="bfloat16", + slow_but_exact=False, + batch_size: int = 2, + seq_length: int = 10, + type_sequence_label_size=2, + activation_function="gelu", + num_labels=3, + num_choices=4, + scope=None, + dropout=0.56, + use_input_mask: bool = False, + use_labels: bool = False, + return_dict=False, + ): + self.parent: MixtralModelTest = parent + 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.masked_softmax_fusion = masked_softmax_fusion + self.layer_norm_epsilon = layer_norm_epsilon + self.initializer_range = initializer_range + self.is_training = is_training + self.use_cache = use_cache + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.attention_softmax_in_fp32 = attention_softmax_in_fp32 + self.pretraining_tp = pretraining_tp + self.dtype = dtype + self.slow_but_exact = slow_but_exact + + self.batch_size = batch_size + self.seq_length = seq_length + self.type_sequence_label_size = type_sequence_label_size + self.activation_function = activation_function + self.num_labels = num_labels + self.num_choices = num_choices + self.scope = scope + self.dropout = dropout + + self.use_input_mask = use_input_mask + self.use_labels = use_labels + self.return_dict = return_dict + + def prepare_config_and_inputs(self): + input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype=paddle.int64) + + input_mask = None + if self.use_input_mask: + input_mask = random_attention_mask([self.batch_size, self.seq_length]) + + 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) + token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) + choice_labels = ids_tensor([self.batch_size], self.num_choices) + + config = self.get_config() + return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels + + def get_config(self) -> MixtralConfig: + return MixtralConfig( + vocab_size=self.vocab_size, + hidden_size=self.hidden_size, + num_hidden_layers=self.num_hidden_layers, + num_attention_heads=self.num_attention_heads, + masked_softmax_fusion=self.masked_softmax_fusion, + layer_norm_epsilon=self.layer_norm_epsilon, + initializer_range=self.initializer_range, + use_cache=self.use_cache, + bos_token_id=self.bos_token_id, + eos_token_id=self.eos_token_id, + apply_residual_connection_post_layernorm=self.apply_residual_connection_post_layernorm, + hidden_dropout=self.hidden_dropout, + attention_dropout=self.attention_dropout, + attention_softmax_in_fp32=self.attention_softmax_in_fp32, + pretraining_tp=self.pretraining_tp, + dtype=self.dtype, + slow_but_exact=self.slow_but_exact, + activation_function=self.activation_function, + ) + + def create_and_check_model( + self, config: MixtralConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels + ): + model = MixtralModel(config) + model.eval() + result = model(input_ids) + self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) + + def create_and_check_model_attention_mask( + self, config: MixtralConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels + ): + model = MixtralModel(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()) + + def create_and_check_model_past_large_inputs( + self, + config: MixtralConfig, + input_ids, + input_mask, + sequence_labels, + token_labels, + choice_labels, + ): + model = MixtralModel(config) + model.eval() + + # first forward pass + outputs = model(input_ids, attention_mask=input_mask, use_cache=True, return_dict=self.return_dict) + past_key_values = outputs.past_key_values if self.return_dict else outputs[2] + + # create hypothetical multiple next token and extent to next_input_ids + next_tokens = ids_tensor((self.batch_size, 3), self.vocab_size) + next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) + + # append to next input_ids and + next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) + next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1) + + outputs = model( + next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, return_dict=self.return_dict + ) + + output_from_no_past = outputs[2][0] + + outputs = model( + next_tokens, + attention_mask=next_attention_mask, + past_key_values=past_key_values, + output_hidden_states=True, + return_dict=self.return_dict, + ) + + output_from_past = outputs[2][0] + + # select random slice + random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).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, + input_mask, + sequence_labels, + token_labels, + choice_labels, + ) = config_and_inputs + inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} + return config, inputs_dict + + def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args): + model = MixtralForCausalLM(config) + model.eval() + + result = model( + input_ids, + use_cache=True, + labels=input_ids if self.parent.use_labels else None, + return_dict=self.parent.return_dict, + ) + if self.parent.use_labels: + self.parent.assertIsInstance(result[0].item(), float) + self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size]) + else: + self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size]) + + def check_model_position_ids(self, config, input_ids, input_mask, *args): + model = MixtralForCausalLM(config) + model.eval() + + result_no_position_id = model( + input_ids, + labels=input_ids if self.parent.use_labels else None, + return_dict=self.parent.return_dict, + ) + batch_size, seq_len = input_ids.shape + position_ids = paddle.arange(seq_len).expand((batch_size, seq_len)) + result_position_id = model( + input_ids, + position_ids, + labels=input_ids if self.parent.use_labels else None, + return_dict=self.parent.return_dict, + ) + if self.parent.use_labels: + self.parent.assertTrue((result_position_id[1] == result_no_position_id[1]).all()) + else: + self.parent.assertTrue((result_position_id[0] == result_no_position_id[0]).all()) + + +class MixtralModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): + base_model_class = MixtralModel + return_dict = False + use_labels = False + use_test_model_name_list = False + + all_model_classes = (MixtralModel, MixtralForCausalLM) + all_generative_model_classes = {MixtralForCausalLM: (MixtralModel, "mixtral")} + + def setUp(self): + super().setUp() + + self.model_tester = MixtralModelTester(self) + self.config_tester = ConfigTester(self, config_class=MixtralConfig, vocab_size=256, hidden_size=24) + + 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.ones_like(input_ids, dtype=paddle.int64) + + 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] + 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_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) + + def test_model_position_ids(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.check_model_position_ids(*config_and_inputs) + + def test_generate_without_input_ids(self): + # this requires 4-D attention mask logic, which is not supported yet + pass + + def test_mixtral_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) + + +if __name__ == "__main__": + unittest.main()