mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
b912f5ee43
* use torch.testing.assertclose instead to get more details about error in cis * fix * style * test_all * revert for I bert * fixes and updates * more image processing fixes * more image processors * fix mamba and co * style * less strick * ok I won't be strict * skip and be done * up
358 lines
14 KiB
Python
358 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import unittest
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from transformers import ImageGPTConfig
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from transformers.testing_utils import require_torch, require_vision, run_test_using_subprocess, slow, torch_device
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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ImageGPTForCausalImageModeling,
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ImageGPTForImageClassification,
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ImageGPTModel,
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)
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if is_vision_available():
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from PIL import Image
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from transformers import ImageGPTImageProcessor
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class ImageGPTModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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seq_length=7,
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is_training=True,
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use_token_type_ids=True,
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use_input_mask=True,
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use_labels=True,
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use_mc_token_ids=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_token_type_ids = use_token_type_ids
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.use_mc_token_ids = use_mc_token_ids
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = None
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def get_large_model_config(self):
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return ImageGPTConfig.from_pretrained("imagegpt")
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def prepare_config_and_inputs(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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mc_token_ids = None
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if self.use_mc_token_ids:
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mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config(
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gradient_checkpointing=gradient_checkpointing,
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scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
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reorder_and_upcast_attn=reorder_and_upcast_attn,
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def get_config(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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return ImageGPTConfig(
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vocab_size=self.vocab_size,
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n_embd=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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n_inner=self.intermediate_size,
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activation_function=self.hidden_act,
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resid_pdrop=self.hidden_dropout_prob,
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attn_pdrop=self.attention_probs_dropout_prob,
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n_positions=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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use_cache=True,
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gradient_checkpointing=gradient_checkpointing,
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scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
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reorder_and_upcast_attn=reorder_and_upcast_attn,
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)
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def get_pipeline_config(self):
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config = self.get_config()
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config.vocab_size = 513
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config.max_position_embeddings = 1024
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return config
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def create_and_check_imagegpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = ImageGPTModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(len(result.past_key_values), config.n_layer)
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def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = ImageGPTForCausalImageModeling(config)
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model.to(torch_device)
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model.eval()
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labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
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result = model(input_ids, token_type_ids=token_type_ids, labels=labels)
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self.parent.assertEqual(result.loss.shape, ())
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# ImageGPTForCausalImageModeling doens't have tied input- and output embeddings
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
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def create_and_check_imagegpt_for_image_classification(
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self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
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):
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config.num_labels = self.num_labels
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model = ImageGPTForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"head_mask": head_mask,
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}
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return config, inputs_dict
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@require_torch
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class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
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)
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all_generative_model_classes = (ImageGPTForCausalImageModeling,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
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if is_torch_available()
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else {}
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)
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test_missing_keys = False
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# as ImageGPTForImageClassification isn't included in any auto mapping, we add labels here
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "ImageGPTForImageClassification":
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inputs_dict["labels"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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return inputs_dict
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# we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
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def _check_scores(self, batch_size, scores, length, config):
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expected_shape = (batch_size, config.vocab_size - 1)
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self.assertIsInstance(scores, tuple)
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self.assertEqual(len(scores), length)
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self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
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@run_test_using_subprocess
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def test_beam_search_generate_dict_outputs_use_cache(self):
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super().test_beam_search_generate_dict_outputs_use_cache()
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def setUp(self):
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self.model_tester = ImageGPTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ImageGPTConfig, n_embd=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_imagegpt_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_imagegpt_model(*config_and_inputs)
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def test_imagegpt_causal_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
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def test_imagegpt_image_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_imagegpt_for_image_classification(*config_and_inputs)
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "openai/imagegpt-small"
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model = ImageGPTModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["input_ids"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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@unittest.skip(reason="The model doesn't support left padding") # and it's not used enough to be worth fixing :)
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def test_left_padding_compatibility(self):
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pass
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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@require_vision
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class ImageGPTModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small") if is_vision_available() else None
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@slow
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def test_inference_causal_lm_head(self):
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model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
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image_processor = self.default_image_processor
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(**inputs)
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# verify the logits
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expected_shape = torch.Size((1, 1024, 512))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor(
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[[2.3445, 2.6889, 2.7313], [1.0530, 1.2416, 0.5699], [0.2205, 0.7749, 0.3953]]
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).to(torch_device)
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torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
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