# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # Copyright 2023 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. """ Testing suite for the PyTorch MiniGPT4 model. """ import inspect import tempfile import unittest import numpy as np import paddle import paddle.nn as nn from paddlenlp.transformers import ( LlamaConfig, MiniGPT4Config, MiniGPT4ForConditionalGeneration, MiniGPT4QFormerConfig, MiniGPT4VisionConfig, MiniGPT4VisionModel, ) from paddlenlp.transformers.minigpt4.modeling import ( MiniGPT4_PRETRAINED_MODEL_ARCHIVE_LIST, ) from ...testing_utils import slow from ..test_configuration_common import ConfigTester from ..test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) class MiniGPT4VisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, projection_dim=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=1e-10, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return MiniGPT4VisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = MiniGPT4VisionModel(config=config) model.eval() with paddle.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, num_patches + 1, self.hidden_size]) self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size]) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict class MiniGPT4VisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as MiniGPT4's vision encoder does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (MiniGPT4VisionModel,) fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False use_test_model_name_list = False def setUp(self): self.model_tester = MiniGPT4VisionModelTester(self) self.config_tester = ConfigTester( self, config_class=MiniGPT4VisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="MiniGPT4's vision encoder does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Layer)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_save_load(out1, out2): # make sure we don't have nans out_2 = out2.numpy() out_2[np.isnan(out_2)] = 0 out_1 = out1.numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) for model_class in self.all_model_classes: model = self._make_model_instance(config, model_class) model.eval() with paddle.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained( tmpdirname, vit_dtype="float32", qformer_dtype="float32", llama_dtype="float32" ) model.eval() with paddle.no_grad(): second = model(**self._prepare_for_class(inputs_dict, model_class))[0] # support tuple of tensor if isinstance(first, tuple) and isinstance(second, tuple): for tensor1, tensor2 in zip(first, second): check_save_load(tensor1, tensor2) else: check_save_load(first, second) 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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="MiniGPT4VisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="MiniGPT4VisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in MiniGPT4_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = MiniGPT4VisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class MiniGPT4QFormerModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, num_hidden_layers=6, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, bos_token_id=0, 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 = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope self.bos_token_id = bos_token_id 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") if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return MiniGPT4QFormerConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, bos_token_id=self.bos_token_id, ) class MiniGPT4TextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=5, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=200, eos_token_id=2, pad_token_id=1, bos_token_id=0, embed_dim=16, num_labels=3, word_embed_proj_dim=16, type_sequence_label_size=2, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels 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.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.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.embed_dim = embed_dim self.num_labels = num_labels self.type_sequence_label_size = type_sequence_label_size self.word_embed_proj_dim = word_embed_proj_dim self.is_encoder_decoder = False def prepare_config_and_inputs(self): config = self.get_config() input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64").clip( 3, ) input_ids[:, -1] = self.eos_token_id # Eos Token attention_mask = input_ids.not_equal(paddle.to_tensor([self.pad_token_id], dtype="int64")).cast("int64") return config, input_ids, attention_mask def get_config(self): return LlamaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, is_encoder_decoder=False, ) class MiniGPT4ForConditionalGenerationModelTester: def __init__( self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10 ): if vision_kwargs is None: vision_kwargs = {} if qformer_kwargs is None: qformer_kwargs = {} if text_kwargs is None: text_kwargs = {} self.parent = parent self.vision_model_tester = MiniGPT4VisionModelTester(parent, **vision_kwargs) self.qformer_model_tester = MiniGPT4QFormerModelTester(parent, **qformer_kwargs) self.text_model_tester = MiniGPT4TextModelTester(parent, **text_kwargs) self.is_training = is_training self.num_query_tokens = num_query_tokens def prepare_config_and_inputs(self): _, pixel_values = self.vision_model_tester.prepare_config_and_inputs() _, first_input_ids, first_attention_mask = self.text_model_tester.prepare_config_and_inputs() _, second_input_ids, second_attention_mask = self.text_model_tester.prepare_config_and_inputs() config = self.get_config() return config, first_input_ids, first_attention_mask, second_input_ids, second_attention_mask, pixel_values def get_config(self): return MiniGPT4Config.from_vision_qformer_text_configs( vision_config=self.vision_model_tester.get_config(), qformer_config=self.qformer_model_tester.get_config(), text_config=self.text_model_tester.get_config(), num_query_tokens=self.num_query_tokens, ) def create_and_check_for_conditional_generation( self, config, first_input_ids, first_attention_mask, second_input_ids, second_attention_mask, pixel_values ): model = MiniGPT4ForConditionalGeneration(config) model.eval() with paddle.no_grad(): result = model( pixel_values, first_input_ids, first_attention_mask, second_input_ids, second_attention_mask, return_dict=True, ) expected_seq_length = first_input_ids.shape[1] + self.num_query_tokens + second_input_ids.shape[1] self.parent.assertEqual( result.logits.shape, [self.vision_model_tester.batch_size, expected_seq_length, self.text_model_tester.vocab_size], ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, first_input_ids, first_attention_mask, second_input_ids, second_attention_mask, pixel_values, ) = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "first_input_ids": first_input_ids, "first_attention_mask": first_attention_mask, "second_input_ids": second_input_ids, "second_attention_mask": second_attention_mask, "return_dict": True, } return config, inputs_dict class MiniGPT4ForConditionalGenerationTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (MiniGPT4ForConditionalGeneration,) fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False use_test_model_name_list = False def setUp(self): self.model_tester = MiniGPT4ForConditionalGenerationModelTester(self) def test_for_conditional_generation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="MiniGPT4Model does not have input/output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="There's no base MiniGPT4Model") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="There's no base MiniGPT4Model") def test_save_load_fast_init_to_base(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = self._make_model_instance(config, model_class) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values", "first_input_ids", "second_input_ids"] self.assertListEqual(arg_names[:3], expected_arg_names) def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_save_load(out1, out2): # make sure we don't have nans out_2 = out2.numpy() out_2[np.isnan(out_2)] = 0 out_1 = out1.numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) for model_class in self.all_model_classes: model = self._make_model_instance(config, model_class) model.eval() with paddle.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2 = model_class.from_pretrained( tmpdirname, llama_dtype="float32", vit_dtype="float32", qformer_dtype="float32" ) model2.eval() with paddle.no_grad(): second = model2(**self._prepare_for_class(inputs_dict, model_class))[0] # support tuple of tensor if isinstance(first, tuple) and isinstance(second, tuple): for tensor1, tensor2 in zip(first, second): check_save_load(tensor1, tensor2) else: check_save_load(first, second) def test_load_vision_qformer_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save MiniGPT4Config and check if we can load MiniGPT4VisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = MiniGPT4VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save MiniGPT4Config and check if we can load MiniGPT4QFormerConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) qformer_config = MiniGPT4QFormerConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in MiniGPT4_PRETRAINED_MODEL_ARCHIVE_LIST: model = MiniGPT4ForConditionalGeneration.from_pretrained(model_name) self.assertIsNotNone(model)