615 lines
24 KiB
Python
615 lines
24 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 copy
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import inspect
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import os
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import tempfile
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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, 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 (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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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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pixel_values = 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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pixel_values,
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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 prepare_config_and_inputs_for_decoder(self):
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(
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config,
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pixel_values,
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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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) = self.prepare_config_and_inputs()
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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pixel_values,
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input_mask,
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head_mask,
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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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def create_and_check_imagegpt_model(self, config, pixel_values, 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(pixel_values, token_type_ids=token_type_ids, head_mask=head_mask)
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result = model(pixel_values, token_type_ids=token_type_ids)
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result = model(pixel_values)
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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, pixel_values, 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(pixel_values, 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, pixel_values, 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(pixel_values, 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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pixel_values,
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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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"pixel_values": pixel_values,
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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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input_name = "pixel_values"
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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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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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def test_resize_tokens_embeddings(self):
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(
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original_config,
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inputs_dict,
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) = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.test_resize_embeddings:
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return
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for model_class in self.all_model_classes:
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config = copy.deepcopy(original_config)
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model = model_class(config)
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model.to(torch_device)
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if self.model_tester.is_training is False:
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model.eval()
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model_vocab_size = config.vocab_size
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# Retrieve the embeddings and clone theme
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model_embed = model.resize_token_embeddings(model_vocab_size)
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cloned_embeddings = model_embed.weight.clone()
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# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
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model_embed = model.resize_token_embeddings(model_vocab_size + 10)
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self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
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# Check that it actually resizes the embeddings matrix
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self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
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model_embed = model.resize_token_embeddings(model_vocab_size - 15)
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self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
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# Check that it actually resizes the embeddings matrix
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self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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# Input ids should be clamped to the maximum size of the vocabulary
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inputs_dict["pixel_values"].clamp_(max=model_vocab_size - 15 - 1)
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# Check that adding and removing tokens has not modified the first part of the embedding matrix.
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models_equal = True
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for p1, p2 in zip(cloned_embeddings, model_embed.weight):
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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def test_resize_embeddings_untied(self):
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(
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original_config,
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inputs_dict,
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) = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.test_resize_embeddings:
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return
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original_config.tie_word_embeddings = False
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# if model cannot untied embeddings -> leave test
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if original_config.tie_word_embeddings:
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return
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for model_class in self.all_model_classes:
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config = copy.deepcopy(original_config)
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model = model_class(config).to(torch_device)
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# if no output embeddings -> leave test
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if model.get_output_embeddings() is None:
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continue
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# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
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model_vocab_size = config.vocab_size
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model.resize_token_embeddings(model_vocab_size + 10)
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self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
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output_embeds = model.get_output_embeddings()
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self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
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# Check bias if present
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if output_embeds.bias is not None:
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self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
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model.resize_token_embeddings(model_vocab_size - 15)
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self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
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# Check that it actually resizes the embeddings matrix
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output_embeds = model.get_output_embeddings()
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self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
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# Check bias if present
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if output_embeds.bias is not None:
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self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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# Input ids should be clamped to the maximum size of the vocabulary
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inputs_dict["pixel_values"].clamp_(max=model_vocab_size - 15 - 1)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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def test_inputs_embeds(self):
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config, inputs_dict = 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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model.to(torch_device)
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model.eval()
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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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|
|
|
pixel_values = inputs["pixel_values"]
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|
del inputs["pixel_values"]
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|
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|
wte = model.get_input_embeddings()
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|
inputs["inputs_embeds"] = wte(pixel_values)
|
|
|
|
with torch.no_grad():
|
|
model(**inputs)[0]
|
|
|
|
# override because ImageGPT main input name is `pixel_values`
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# NOTE: in latest transformers this is deprecated, `input_ids` should be used. TODO
|
|
def test_inputs_embeds_matches_input_ids(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
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|
model.to(torch_device)
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|
model.eval()
|
|
|
|
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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|
with torch.no_grad():
|
|
out_ids = model(**inputs)[0]
|
|
|
|
pixel_values = inputs["pixel_values"]
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|
del inputs["pixel_values"]
|
|
|
|
wte = model.get_input_embeddings()
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|
inputs["inputs_embeds"] = wte(pixel_values)
|
|
|
|
with torch.no_grad():
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|
out_embeds = model(**inputs)[0]
|
|
|
|
self.assertTrue(torch.allclose(out_embeds, out_ids))
|
|
|
|
def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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|
configs_no_init.torchscript = True
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
try:
|
|
pixel_values = inputs["pixel_values"]
|
|
traced_model = torch.jit.trace(model, pixel_values)
|
|
except RuntimeError:
|
|
self.fail("Couldn't trace module.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
|
|
|
|
try:
|
|
torch.jit.save(traced_model, pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't save module.")
|
|
|
|
try:
|
|
loaded_model = torch.jit.load(pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't load module.")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
loaded_model.to(torch_device)
|
|
loaded_model.eval()
|
|
|
|
model_state_dict = model.state_dict()
|
|
loaded_model_state_dict = loaded_model.state_dict()
|
|
|
|
non_persistent_buffers = {}
|
|
for key in loaded_model_state_dict.keys():
|
|
if key not in model_state_dict.keys():
|
|
non_persistent_buffers[key] = loaded_model_state_dict[key]
|
|
|
|
loaded_model_state_dict = {
|
|
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
|
|
}
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
model_buffers = list(model.buffers())
|
|
for non_persistent_buffer in non_persistent_buffers.values():
|
|
found_buffer = False
|
|
for i, model_buffer in enumerate(model_buffers):
|
|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
|
|
break
|
|
|
|
self.assertTrue(found_buffer)
|
|
model_buffers.pop(i)
|
|
|
|
model_buffers = list(model.buffers())
|
|
for non_persistent_buffer in non_persistent_buffers.values():
|
|
found_buffer = False
|
|
for i, model_buffer in enumerate(model_buffers):
|
|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
|
|
break
|
|
|
|
self.assertTrue(found_buffer)
|
|
model_buffers.pop(i)
|
|
|
|
models_equal = True
|
|
for layer_name, p1 in model_state_dict.items():
|
|
if layer_name in loaded_model_state_dict:
|
|
p2 = loaded_model_state_dict[layer_name]
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
|
|
def test_left_padding_compatibility(self):
|
|
pass
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
return image
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class ImageGPTModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small") if is_vision_available() else None
|
|
|
|
@slow
|
|
def test_inference_causal_lm_head(self):
|
|
model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
|
|
|
|
image_processor = self.default_image_processor
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 1024, 512))
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[2.3445, 2.6889, 2.7313], [1.0530, 1.2416, 0.5699], [0.2205, 0.7749, 0.3953]]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))
|