771 lines
31 KiB
Python
771 lines
31 KiB
Python
# coding=utf-8
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# Copyright 2020 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 os
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import tempfile
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import unittest
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from transformers import LxmertConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow
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from .test_configuration_common import ConfigTester
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from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
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if is_tf_available():
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import tensorflow as tf
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from transformers.models.lxmert.modeling_tf_lxmert import TFLxmertForPreTraining, TFLxmertModel
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class TFLxmertModelTester(object):
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def __init__(
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self,
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parent,
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vocab_size=300,
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hidden_size=28,
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num_attention_heads=2,
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num_labels=2,
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intermediate_size=64,
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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=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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num_qa_labels=30,
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num_object_labels=16,
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num_attr_labels=4,
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num_visual_features=10,
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l_layers=2,
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x_layers=1,
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r_layers=1,
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visual_feat_dim=128,
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visual_pos_dim=4,
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visual_loss_normalizer=6.67,
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seq_length=20,
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batch_size=8,
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is_training=True,
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task_matched=True,
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task_mask_lm=True,
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task_obj_predict=True,
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task_qa=True,
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visual_obj_loss=True,
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visual_attr_loss=True,
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visual_feat_loss=True,
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use_token_type_ids=True,
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use_lang_mask=True,
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output_attentions=False,
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output_hidden_states=False,
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scope=None,
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):
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self.parent = parent
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_labels = num_labels
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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.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.pad_token_id = pad_token_id
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self.num_qa_labels = num_qa_labels
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self.num_object_labels = num_object_labels
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self.num_attr_labels = num_attr_labels
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self.l_layers = l_layers
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self.x_layers = x_layers
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self.r_layers = r_layers
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self.visual_feat_dim = visual_feat_dim
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self.visual_pos_dim = visual_pos_dim
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self.visual_loss_normalizer = visual_loss_normalizer
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self.seq_length = seq_length
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self.batch_size = batch_size
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self.is_training = is_training
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self.use_lang_mask = use_lang_mask
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self.task_matched = task_matched
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self.task_mask_lm = task_mask_lm
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self.task_obj_predict = task_obj_predict
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self.task_qa = task_qa
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self.visual_obj_loss = visual_obj_loss
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self.visual_attr_loss = visual_attr_loss
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self.visual_feat_loss = visual_feat_loss
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self.num_visual_features = num_visual_features
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self.use_token_type_ids = use_token_type_ids
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self.output_attentions = output_attentions
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self.output_hidden_states = output_hidden_states
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self.scope = scope
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self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
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def prepare_config_and_inputs(self):
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output_attentions = self.output_attentions
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input_ids = ids_tensor([self.batch_size, self.seq_length], vocab_size=self.vocab_size)
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visual_feats = tf.random.uniform((self.batch_size, self.num_visual_features, self.visual_feat_dim))
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bounding_boxes = tf.random.uniform((self.batch_size, self.num_visual_features, 4))
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input_mask = None
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if self.use_lang_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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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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obj_labels = None
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if self.task_obj_predict:
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obj_labels = {}
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if self.visual_attr_loss and self.task_obj_predict:
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obj_labels["attr"] = (
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ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
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ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
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)
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if self.visual_feat_loss and self.task_obj_predict:
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obj_labels["feat"] = (
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ids_tensor(
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[self.batch_size, self.num_visual_features, self.visual_feat_dim], self.num_visual_features
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),
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ids_tensor([self.batch_size, self.num_visual_features], self.num_visual_features),
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)
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if self.visual_obj_loss and self.task_obj_predict:
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obj_labels["obj"] = (
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ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
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ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
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)
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ans = None
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if self.task_qa:
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ans = ids_tensor([self.batch_size], self.num_qa_labels)
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masked_lm_labels = None
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if self.task_mask_lm:
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masked_lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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matched_label = None
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if self.task_matched:
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matched_label = ids_tensor([self.batch_size], self.num_labels)
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config = LxmertConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_attention_heads=self.num_attention_heads,
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num_labels=self.num_labels,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=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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layer_norm_eps=self.layer_norm_eps,
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pad_token_id=self.pad_token_id,
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num_qa_labels=self.num_qa_labels,
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num_object_labels=self.num_object_labels,
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num_attr_labels=self.num_attr_labels,
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l_layers=self.l_layers,
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x_layers=self.x_layers,
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r_layers=self.r_layers,
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visual_feat_dim=self.visual_feat_dim,
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visual_pos_dim=self.visual_pos_dim,
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visual_loss_normalizer=self.visual_loss_normalizer,
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task_matched=self.task_matched,
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task_mask_lm=self.task_mask_lm,
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task_obj_predict=self.task_obj_predict,
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task_qa=self.task_qa,
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visual_obj_loss=self.visual_obj_loss,
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visual_attr_loss=self.visual_attr_loss,
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visual_feat_loss=self.visual_feat_loss,
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output_attentions=self.output_attentions,
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output_hidden_states=self.output_hidden_states,
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)
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return (
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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)
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def create_and_check_lxmert_model(
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self,
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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):
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model = TFLxmertModel(config=config)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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output_attentions=output_attentions,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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output_attentions=not output_attentions,
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)
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result = model(input_ids, visual_feats, bounding_boxes, return_dict=False)
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result = model(input_ids, visual_feats, bounding_boxes, return_dict=True)
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self.parent.assertEqual(result.language_output.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(
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result.vision_output.shape, (self.batch_size, self.num_visual_features, self.hidden_size)
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)
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self.parent.assertEqual(result.pooled_output.shape, (self.batch_size, self.hidden_size))
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def prepare_config_and_inputs_for_common(self, return_obj_labels=False):
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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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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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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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"visual_feats": visual_feats,
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"visual_pos": bounding_boxes,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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if return_obj_labels:
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inputs_dict["obj_labels"] = obj_labels
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return config, inputs_dict
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def create_and_check_lxmert_for_pretraining(
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self,
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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):
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model = TFLxmertForPreTraining(config=config)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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obj_labels=obj_labels,
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matched_label=matched_label,
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ans=ans,
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output_attentions=output_attentions,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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output_attentions=not output_attentions,
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return_dict=False,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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obj_labels=obj_labels,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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matched_label=matched_label,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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ans=ans,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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obj_labels=obj_labels,
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matched_label=matched_label,
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ans=ans,
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output_attentions=not output_attentions,
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)
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self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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@require_tf
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class TFLxmertModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFLxmertModel, TFLxmertForPreTraining) if is_tf_available() else ()
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test_head_masking = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFLxmertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=LxmertConfig, hidden_size=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_lxmert_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_lxmert_model(*config_and_inputs)
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def test_lxmert_for_pretraining(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_lxmert_for_pretraining(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in ["unc-nlp/lxmert-base-uncased"]:
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model = TFLxmertModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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encoder_seq_length = (
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self.model_tester.encoder_seq_length
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if hasattr(self.model_tester, "encoder_seq_length")
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else self.model_tester.seq_length
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)
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encoder_key_length = (
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self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length
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)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
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self.assertEqual(model.config.output_hidden_states, False)
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self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
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self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
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self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
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attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
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attention_shapes = [
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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[
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self.model_tester.num_attention_heads,
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self.model_tester.num_visual_features,
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self.model_tester.num_visual_features,
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],
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[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
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]
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for attention, attention_shape in zip(attentions, attention_shapes):
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self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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# 2 hidden states were added
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self.assertEqual(out_len + 2, len(outputs))
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language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
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self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
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self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
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self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
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attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
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attention_shapes = [
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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[
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self.model_tester.num_attention_heads,
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self.model_tester.num_visual_features,
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self.model_tester.num_visual_features,
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],
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[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
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]
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for attention, attention_shape in zip(attentions, attention_shapes):
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self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
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def test_hidden_states_output(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def check_hidden_states_output(config, inputs_dict, model_class):
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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language_hidden_states, vision_hidden_states = outputs[-2], outputs[-1]
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self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
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self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
|
|
|
|
seq_length = self.model_tester.seq_length
|
|
num_visual_features = self.model_tester.num_visual_features
|
|
|
|
self.assertListEqual(
|
|
list(language_hidden_states[0].shape[-2:]),
|
|
[seq_length, self.model_tester.hidden_size],
|
|
)
|
|
self.assertListEqual(
|
|
list(vision_hidden_states[0].shape[-2:]),
|
|
[num_visual_features, self.model_tester.hidden_size],
|
|
)
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(config, inputs_dict, model_class)
|
|
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
check_hidden_states_output(config, inputs_dict, model_class)
|
|
|
|
def test_pt_tf_model_equivalence(self):
|
|
from transformers import is_torch_available
|
|
|
|
if not is_torch_available():
|
|
return
|
|
|
|
import torch
|
|
|
|
import transformers
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
|
|
return_obj_labels="PreTraining" in model_class.__name__
|
|
)
|
|
|
|
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning
|
|
pt_model_class = getattr(transformers, pt_model_class_name)
|
|
|
|
config.output_hidden_states = True
|
|
config.task_obj_predict = False
|
|
|
|
tf_model = model_class(config)
|
|
pt_model = pt_model_class(config)
|
|
|
|
# Check we can load pt model in tf and vice-versa with model => model functions
|
|
|
|
tf_model = transformers.load_pytorch_model_in_tf2_model(
|
|
tf_model, pt_model, tf_inputs=self._prepare_for_class(inputs_dict, model_class)
|
|
)
|
|
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
|
|
|
|
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
|
|
pt_model.eval()
|
|
|
|
# Delete obj labels as we want to compute the hidden states and not the loss
|
|
|
|
if "obj_labels" in inputs_dict:
|
|
del inputs_dict["obj_labels"]
|
|
|
|
def torch_type(key):
|
|
if key in ("visual_feats", "visual_pos"):
|
|
return torch.float32
|
|
else:
|
|
return torch.long
|
|
|
|
def recursive_numpy_convert(iterable):
|
|
return_dict = {}
|
|
for key, value in iterable.items():
|
|
if isinstance(value, dict):
|
|
return_dict[key] = recursive_numpy_convert(value)
|
|
else:
|
|
if isinstance(value, (list, tuple)):
|
|
return_dict[key] = (
|
|
torch.from_numpy(iter_value.numpy()).to(torch_type(key)) for iter_value in value
|
|
)
|
|
else:
|
|
return_dict[key] = torch.from_numpy(value.numpy()).to(torch_type(key))
|
|
return return_dict
|
|
|
|
pt_inputs_dict = recursive_numpy_convert(self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# need to rename encoder-decoder "inputs" for PyTorch
|
|
if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
|
|
pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
|
|
|
|
with torch.no_grad():
|
|
pto = pt_model(**pt_inputs_dict)
|
|
tfo = tf_model(self._prepare_for_class(inputs_dict, model_class), training=False)
|
|
tf_hidden_states = tfo[0].numpy()
|
|
pt_hidden_states = pto[0].numpy()
|
|
|
|
import numpy as np
|
|
|
|
tf_nans = np.copy(np.isnan(tf_hidden_states))
|
|
pt_nans = np.copy(np.isnan(pt_hidden_states))
|
|
|
|
pt_hidden_states[tf_nans] = 0
|
|
tf_hidden_states[tf_nans] = 0
|
|
pt_hidden_states[pt_nans] = 0
|
|
tf_hidden_states[pt_nans] = 0
|
|
|
|
max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
|
|
# Debug info (remove when fixed)
|
|
if max_diff >= 2e-2:
|
|
print("===")
|
|
print(model_class)
|
|
print(config)
|
|
print(inputs_dict)
|
|
print(pt_inputs_dict)
|
|
self.assertLessEqual(max_diff, 6e-2)
|
|
|
|
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
import os
|
|
|
|
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
|
|
torch.save(pt_model.state_dict(), pt_checkpoint_path)
|
|
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
|
|
|
|
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
|
|
tf_model.save_weights(tf_checkpoint_path)
|
|
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
|
|
|
|
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
|
|
pt_model.eval()
|
|
pt_inputs_dict = dict(
|
|
(name, torch.from_numpy(key.numpy()).to(torch.long))
|
|
for name, key in self._prepare_for_class(inputs_dict, model_class).items()
|
|
)
|
|
|
|
for key, value in pt_inputs_dict.items():
|
|
if key in ("visual_feats", "visual_pos"):
|
|
pt_inputs_dict[key] = value.to(torch.float32)
|
|
else:
|
|
pt_inputs_dict[key] = value.to(torch.long)
|
|
|
|
with torch.no_grad():
|
|
pto = pt_model(**pt_inputs_dict)
|
|
tfo = tf_model(self._prepare_for_class(inputs_dict, model_class))
|
|
tfo = tfo[0].numpy()
|
|
pto = pto[0].numpy()
|
|
tf_nans = np.copy(np.isnan(tfo))
|
|
pt_nans = np.copy(np.isnan(pto))
|
|
|
|
pto[tf_nans] = 0
|
|
tfo[tf_nans] = 0
|
|
pto[pt_nans] = 0
|
|
tfo[pt_nans] = 0
|
|
|
|
max_diff = np.amax(np.abs(tfo - pto))
|
|
self.assertLessEqual(max_diff, 6e-2)
|
|
|
|
def test_save_load(self):
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
|
|
return_obj_labels="PreTraining" in model_class.__name__
|
|
)
|
|
|
|
model = model_class(config)
|
|
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model = model_class.from_pretrained(tmpdirname)
|
|
after_outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
self.assert_outputs_same(after_outputs, outputs)
|
|
|
|
def test_compile_tf_model(self):
|
|
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
|
|
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
|
|
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
|
|
return_obj_labels="PreTraining" in model_class.__name__
|
|
)
|
|
|
|
input_ids = tf.keras.Input(
|
|
batch_shape=(self.model_tester.batch_size, self.model_tester.seq_length),
|
|
name="input_ids",
|
|
dtype="int32",
|
|
)
|
|
visual_feats = tf.keras.Input(
|
|
batch_shape=(
|
|
self.model_tester.batch_size,
|
|
self.model_tester.num_visual_features,
|
|
self.model_tester.visual_feat_dim,
|
|
),
|
|
name="visual_feats",
|
|
dtype="int32",
|
|
)
|
|
visual_pos = tf.keras.Input(
|
|
batch_shape=(self.model_tester.batch_size, self.model_tester.num_visual_features, 4),
|
|
name="visual_pos",
|
|
dtype="int32",
|
|
)
|
|
|
|
# Prepare our model
|
|
model = model_class(config)
|
|
|
|
# Let's load it from the disk to be sure we can use pretrained weights
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
outputs = model(self._prepare_for_class(inputs_dict, model_class)) # build the model
|
|
model.save_pretrained(tmpdirname)
|
|
model = model_class.from_pretrained(tmpdirname)
|
|
|
|
outputs_dict = model(input_ids, visual_feats, visual_pos)
|
|
hidden_states = outputs_dict[0]
|
|
|
|
# Add a dense layer on top to test integration with other keras modules
|
|
outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
|
|
|
|
# Compile extended model
|
|
extended_model = tf.keras.Model(inputs=[input_ids, visual_feats, visual_pos], outputs=[outputs])
|
|
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
|
|
|
|
def test_model_common_attributes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
list_lm_models = [TFLxmertForPreTraining]
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
|
|
|
|
if model_class in list_lm_models:
|
|
x = model.get_output_embeddings()
|
|
assert isinstance(x, tf.keras.layers.Layer)
|
|
name = model.get_bias()
|
|
assert isinstance(name, dict)
|
|
for k, v in name.items():
|
|
assert isinstance(v, tf.Variable)
|
|
else:
|
|
x = model.get_output_embeddings()
|
|
assert x is None
|
|
name = model.get_bias()
|
|
assert name is None
|
|
|
|
def test_saved_model_creation(self):
|
|
# This test is too long (>30sec) and makes fail the CI
|
|
pass
|
|
|
|
@slow
|
|
def test_saved_model_creation_extended(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_hidden_states = True
|
|
config.output_attentions = True
|
|
|
|
if hasattr(config, "use_cache"):
|
|
config.use_cache = True
|
|
|
|
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
|
|
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
|
|
|
for model_class in self.all_model_classes:
|
|
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
|
model = model_class(config)
|
|
num_out = len(model(class_inputs_dict))
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname, saved_model=True)
|
|
saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
|
|
model = tf.keras.models.load_model(saved_model_dir)
|
|
outputs = model(class_inputs_dict)
|
|
language_hidden_states = outputs["language_hidden_states"]
|
|
vision_hidden_states = outputs["vision_hidden_states"]
|
|
language_attentions = outputs["language_attentions"]
|
|
vision_attentions = outputs["vision_attentions"]
|
|
cross_encoder_attentions = outputs["cross_encoder_attentions"]
|
|
|
|
self.assertEqual(len(outputs), num_out)
|
|
|
|
self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
|
|
self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
|
|
|
|
seq_length = self.model_tester.seq_length
|
|
num_visual_features = self.model_tester.num_visual_features
|
|
|
|
self.assertListEqual(
|
|
list(language_hidden_states[0].shape[-2:]),
|
|
[seq_length, self.model_tester.hidden_size],
|
|
)
|
|
self.assertListEqual(
|
|
list(vision_hidden_states[0].shape[-2:]),
|
|
[num_visual_features, self.model_tester.hidden_size],
|
|
)
|
|
|
|
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
|
|
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
|
|
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
|
|
|
|
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
|
|
attention_shapes = [
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
[
|
|
self.model_tester.num_attention_heads,
|
|
self.model_tester.num_visual_features,
|
|
self.model_tester.num_visual_features,
|
|
],
|
|
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
|
|
]
|
|
|
|
for attention, attention_shape in zip(attentions, attention_shapes):
|
|
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
|