566 lines
25 KiB
Python
566 lines
25 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. 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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""" Testing suite for the TF Idefics model. """
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import os
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import tempfile
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import unittest
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from importlib import import_module
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from transformers import IdeficsConfig, is_tf_available, is_vision_available
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from transformers.testing_utils import TestCasePlus, is_pt_tf_cross_test, require_tf, require_vision, slow
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from transformers.utils import cached_property
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers import IdeficsProcessor, TFIdeficsForVisionText2Text, TFIdeficsModel
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from transformers.modeling_tf_utils import keras
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from transformers.models.idefics.configuration_idefics import IdeficsPerceiverConfig, IdeficsVisionConfig
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if is_vision_available():
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from PIL import Image
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IDEFICS_TINY_RANDOM_MODEL = "HuggingFaceM4/tiny-random-idefics"
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class IdeficsModelTester:
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def __init__(
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self,
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parent,
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batch_size=1,
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seq_length=7,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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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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scope=None,
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modality_type_vocab_size=2,
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vision_embed_dim=32,
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vision_patch_size=2,
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vision_image_size=30,
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vision_num_attention_heads=4,
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vision_num_hidden_layers=5,
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vision_intermediate_size=37,
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perceiver_qk_layer_norms_perceiver=False,
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perceiver_resampler_depth=2,
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perceiver_resampler_head_dim=8,
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perceiver_resampler_n_heads=2,
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perceiver_resampler_n_latents=16,
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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.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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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.scope = scope
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self.modality_type_vocab_size = modality_type_vocab_size
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self.vision_embed_dim = vision_embed_dim
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self.vision_patch_size = vision_patch_size
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self.vision_image_size = vision_image_size
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self.vision_num_attention_heads = vision_num_attention_heads
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self.vision_num_hidden_layers = vision_num_hidden_layers
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self.vision_intermediate_size = vision_intermediate_size
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self.vision_config = IdeficsVisionConfig(
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embed_dim=self.vision_embed_dim,
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patch_size=self.vision_patch_size,
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image_size=self.vision_image_size,
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num_attention_heads=self.vision_num_attention_heads,
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num_hidden_layers=self.vision_num_hidden_layers,
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intermediate_size=self.vision_intermediate_size,
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)
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self.perceiver_qk_layer_norms_perceiver = perceiver_qk_layer_norms_perceiver
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self.perceiver_resampler_depth = perceiver_resampler_depth
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self.perceiver_resampler_head_dim = perceiver_resampler_head_dim
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self.perceiver_resampler_n_heads = perceiver_resampler_n_heads
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self.perceiver_resampler_n_latents = perceiver_resampler_n_latents
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self.perceiver_config = IdeficsPerceiverConfig(
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qk_layer_norms_perceiver=self.perceiver_qk_layer_norms_perceiver,
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resampler_depth=self.perceiver_resampler_depth,
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resampler_head_dim=self.perceiver_resampler_head_dim,
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resampler_n_heads=self.perceiver_resampler_n_heads,
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resampler_n_latents=self.perceiver_resampler_n_latents,
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)
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# we set the expected sequence length (which is used in several tests)
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# this is equal to the seq length of the text tokens + number of image patches + 1 for the CLS token
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self.expected_seq_len = self.seq_length + (self.image_size // self.patch_size) ** 2 + 1
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def prepare_config_and_inputs(self, num_images=1, interpolate_pos_encoding=False, image_expansion=0):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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pixel_values = floats_tensor(
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[
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self.batch_size,
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num_images,
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self.num_channels,
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self.image_size + image_expansion,
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self.image_size + image_expansion,
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]
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)
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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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image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, num_images])
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config = self.get_config()
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return (config, input_ids, input_mask, pixel_values, image_attention_mask, interpolate_pos_encoding)
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def get_config(self):
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return IdeficsConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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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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is_decoder=False,
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initializer_range=self.initializer_range,
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num_labels=self.num_labels,
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modality_type_vocab_size=self.modality_type_vocab_size,
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vision_config=self.vision_config,
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)
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def create_and_check_model(
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self,
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config,
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input_ids,
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input_mask,
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pixel_values,
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image_attention_mask,
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interpolate_pos_encoding,
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):
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model = TFIdeficsModel(config=config)
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result = model(
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input_ids,
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attention_mask=input_mask,
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pixel_values=pixel_values,
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image_attention_mask=image_attention_mask,
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interpolate_pos_encoding=interpolate_pos_encoding,
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)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, input_ids.shape[1], self.hidden_size)
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)
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def create_and_check_model_gen(
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self,
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config,
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input_ids,
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input_mask,
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pixel_values,
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image_attention_mask,
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interpolate_pos_encoding,
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):
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model = TFIdeficsForVisionText2Text(config)
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model.generate(
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input_ids,
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attention_mask=input_mask,
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pixel_values=pixel_values,
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image_attention_mask=image_attention_mask,
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interpolate_pos_encoding=interpolate_pos_encoding,
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max_length=self.seq_length + 2,
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)
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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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pixel_values,
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image_attention_mask,
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interpolate_pos_encoding,
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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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"attention_mask": input_mask,
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"pixel_values": pixel_values,
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"image_attention_mask": image_attention_mask,
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"interpolate_pos_encoding": interpolate_pos_encoding,
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}
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return config, inputs_dict
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def prepare_pixel_values(self):
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return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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@require_tf
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class TFIdeficsModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (TFIdeficsModel, TFIdeficsForVisionText2Text) if is_tf_available() else ()
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pipeline_model_mapping = {"feature-extraction": TFIdeficsModel} if is_tf_available() else {}
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test_pruning = False
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test_headmasking = False
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test_onnx = False
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test_resize_embeddings = False
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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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# XXX: IdeficsForVisionText2TextTest has no MODEL_FOR group yet, but it should be the same
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# as MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, so for now manually changing to do the right thing
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# as super won't do it
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if return_labels:
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inputs_dict["labels"] = tf.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int64
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)
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return inputs_dict
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def test_model_outputs_equivalence(self):
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try:
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orig = self.all_model_classes
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# IdeficsModel.forward doesn't have labels input arg - only IdeficsForVisionText2Text does
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self.all_model_classes = (TFIdeficsForVisionText2Text,) if is_tf_available() else ()
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super().test_model_outputs_equivalence()
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finally:
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self.all_model_classes = orig
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def setUp(self):
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self.model_tester = IdeficsModelTester(self)
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self.config_tester = ConfigTester(self, config_class=IdeficsConfig, 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_model_single_image(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=1, interpolate_pos_encoding=False, image_expansion=0
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)
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_multiple_images(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=2, interpolate_pos_encoding=False, image_expansion=0
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)
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_with_image_pos_embeddings_interpolation_single_image(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=1, interpolate_pos_encoding=True, image_expansion=2
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)
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self.model_tester.create_and_check_model(*config_and_inputs)
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=1, interpolate_pos_encoding=True, image_expansion=0
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)
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_with_image_pos_embeddings_interpolation_multiple_images(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=2, interpolate_pos_encoding=True, image_expansion=2
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)
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self.model_tester.create_and_check_model(*config_and_inputs)
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=2, interpolate_pos_encoding=True, image_expansion=0
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)
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_generate_with_image_pos_embeddings_interpolation_single_image(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=1, interpolate_pos_encoding=True, image_expansion=2
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)
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self.model_tester.create_and_check_model_gen(*config_and_inputs)
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def test_generate_with_image_pos_embeddings_interpolation_multiple_images(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(
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num_images=2, interpolate_pos_encoding=True, image_expansion=2
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)
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self.model_tester.create_and_check_model_gen(*config_and_inputs)
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""")
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def test_retain_grad_hidden_states_attentions(self):
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return
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@unittest.skip(reason="IDEFICS uses out-of-bounds embeddings deliberately.")
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def test_embeddings_out_of_bounds_raise_exception(self):
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pass
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@unittest.skip(reason="IDEFICS attention weights are not extracted in scaled_dot_product_attention")
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def test_prepare_serving_output(self):
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pass
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def test_model_common_attributes(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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self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))
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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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config.return_dict = True
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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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config.return_dict = 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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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = 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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attentions = outputs.attentions
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# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
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self.assertTrue(attentions[0] is None)
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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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self.assertEqual(out_len + 1, len(outputs))
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self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
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self.assertTrue(self_attentions[0] is None)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, 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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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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seq_length = self.model_tester.seq_length
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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)
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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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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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@is_pt_tf_cross_test
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def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
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self.has_attentions = False
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super().test_pt_tf_model_equivalence(allow_missing_keys=allow_missing_keys)
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def test_keras_save_load(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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tf_main_layer_classes = {
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module_member
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for model_class in self.all_model_classes
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for module in (import_module(model_class.__module__),)
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for module_member_name in dir(module)
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if module_member_name.endswith("MainLayer")
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for module_member in (getattr(module, module_member_name),)
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if isinstance(module_member, type)
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and keras.layers.Layer in module_member.__bases__
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and getattr(module_member, "_keras_serializable", False)
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}
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for main_layer_class in tf_main_layer_classes:
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main_layer = main_layer_class(config)
|
|
|
|
symbolic_inputs = {
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|
name: keras.Input(tensor.shape[1:], dtype=tensor.dtype, batch_size=2)
|
|
for name, tensor in inputs_dict.items()
|
|
if tf.is_tensor(tensor)
|
|
}
|
|
model = keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
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|
outputs = model(inputs_dict)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
filepath = os.path.join(tmpdirname, "keras_model.h5")
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|
model.save(filepath)
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|
model = keras.models.load_model(filepath, custom_objects={main_layer_class.__name__: main_layer_class})
|
|
assert isinstance(model, keras.Model)
|
|
after_outputs = model(inputs_dict)
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|
self.assert_outputs_same(after_outputs, outputs)
|
|
|
|
@unittest.skip(reason="IDEFICS test_keras_fit testing done in TFIdeficsForVisionText2TextTest")
|
|
def test_keras_fit(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model = TFIdeficsModel.from_pretrained(IDEFICS_TINY_RANDOM_MODEL, from_pt=True)
|
|
self.assertIsNotNone(model)
|
|
|
|
@unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
|
|
def test_saved_model_creation(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="""IDEFICS loss computation not implemented yet""")
|
|
def test_loss_computation(self):
|
|
pass
|
|
|
|
|
|
@require_tf
|
|
class TFIdeficsForVisionText2TextTest(TFIdeficsModelTest, unittest.TestCase):
|
|
all_model_classes = (TFIdeficsForVisionText2Text,) if is_tf_available() else ()
|
|
test_resize_embeddings = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = IdeficsModelTester(
|
|
self,
|
|
modality_type_vocab_size=3,
|
|
)
|
|
self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37)
|
|
|
|
@unittest.skip("We only test the model that takes in multiple images")
|
|
def test_model(self):
|
|
pass
|
|
|
|
@unittest.skip("We only test the model that takes in multiple images")
|
|
def test_for_token_classification(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""")
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="""IDEFICS loss computation not implemented yet""")
|
|
def test_loss_computation(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_keras_fit(self):
|
|
super().test_keras_fit()
|
|
|
|
|
|
# Below is the expected output for the integration test TFIdeficsModelIntegrationTest.
|
|
# Since we are using tiny-random to be able to fit it on the CI GPU,it is better to assert on the
|
|
# ids because the generated text is gibberish
|
|
|
|
# fmt: off
|
|
EXPECTED_GENERATED_IDS = [[0, 0, 1, 4911, 29901, 32000, 32001, 32000, 20355, 915, 445, 1967, 29889, 13, 7900, 22137, 29901, 530, 1967, 310, 1023, 26361, 29889, 13, 2659, 29901, 32000, 32001, 32000, 20355, 915, 445, 1967, 29889, 13, 7900, 22137, 29901, 25519, 22326, 8071, 26357, 28004, 4428, 5916, 14383, 1033, 12358, 10536, 21834, 10447, 21201, 18102, 16886, 8875, 25388, 25914, 28304, 8558, 31048, 1322, 25952, 189, 31600, 3600, 12824, 7045, 28090, 20228, 32001, 5385, 29186, 2165, 11822, 13825, 23077, 7883, 22504, 2078, 18893, 2179, 10556, 9515, 7672, 3491, 12403, 5398, 27299, 6463, 16349, 23037, 28956, 16960, 22664, 7724, 17587, 17424, 10175, 17417, 5930, 30855, 17695, 16170, 14474, 29996, 313, 14502, 3241, 13618, 32001, 5385, 29186, 2165, 11822, 13825, 19934, 4875, 27142, 3230, 2709, 28054, 3270, 19148, 10917, 1060, 26443, 12259, 1347, 28482, 3830, 25519, 199, 12782, 9144, 12289, 1142, 18400, 21390, 19129, 7292, 28430, 24711, 5551, 30349, 30533, 13271, 17697, 4982, 8713, 5380, 17869, 12490, 5398, 27299, 11593, 19918, 15924, 29430, 10175, 17417, 5930, 30855, 17695, 16170, 14474, 19234],
|
|
[1, 4911, 29901, 32000, 32001, 32000, 20355, 915, 445, 1967, 29889, 13, 7900, 22137, 29901, 530, 1967, 310, 1023, 413, 986, 575, 29889, 13, 2659, 29901, 32000, 32001, 32000, 20355, 915, 445, 1967, 29889, 13, 7900, 22137, 29901, 25519, 22326, 8071, 26357, 28004, 4428, 17554, 20500, 21714, 27834, 4798, 12195, 30379, 5427, 20228, 10473, 14351, 8049, 15605, 14491, 212, 2711, 32000, 21714, 31259, 24368, 19036, 22970, 26083, 19394, 20372, 7672, 9939, 25388, 30533, 8200, 30271, 2114, 24749, 13224, 10603, 21118, 2179, 3759, 16515, 6587, 1287, 23998, 17793, 32001, 5385, 29186, 2165, 11822, 13825, 29732, 17503, 2729, 6722, 2943, 1221, 16043, 18244, 24965, 14383, 19840, 5980, 13488, 28531, 735, 26146, 22504, 2078, 18893, 20372, 7672, 32001, 5385, 29186, 2165, 11822, 13825, 29732, 17503, 2729, 6722, 19551, 220, 10528, 28940, 4453, 28266, 15416, 18693, 8199, 1153, 27706, 29231, 29186, 2165, 11822, 13825, 29732, 17503, 2729, 6722, 19551, 8231, 10739, 31992, 25906, 22254, 23127, 7689, 19614, 1149, 18844, 23037, 28956, 16960, 22664, 6975, 28938, 24002, 11026, 15020, 21964, 16307], ]
|
|
|
|
@require_tf
|
|
@require_vision
|
|
class TFIdeficsModelIntegrationTest(TestCasePlus):
|
|
@cached_property
|
|
def default_processor(self):
|
|
return IdeficsProcessor.from_pretrained(IDEFICS_TINY_RANDOM_MODEL) if is_vision_available() else None
|
|
|
|
@slow
|
|
def test_inference_natural_language_visual_reasoning(self):
|
|
cat_image_path = self.tests_dir / "fixtures/tests_samples/COCO/000000039769.png"
|
|
cats_image_obj = Image.open(cat_image_path) # 2 cats
|
|
dogs_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg"
|
|
|
|
prompts = [
|
|
[
|
|
"User:",
|
|
dogs_image_url,
|
|
"Describe this image.\nAssistant: An image of two dogs.\n",
|
|
"User:",
|
|
cats_image_obj,
|
|
"Describe this image.\nAssistant:",
|
|
],
|
|
[
|
|
"User:",
|
|
cats_image_obj,
|
|
"Describe this image.\nAssistant: An image of two kittens.\n",
|
|
"User:",
|
|
dogs_image_url,
|
|
"Describe this image.\nAssistant:",
|
|
],
|
|
]
|
|
|
|
model = TFIdeficsForVisionText2Text.from_pretrained(IDEFICS_TINY_RANDOM_MODEL, from_pt=True)
|
|
processor = self.default_processor
|
|
inputs = processor(prompts, return_tensors="tf")
|
|
generated_ids = model.generate(**inputs, max_length=100)
|
|
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
# keep for debugging
|
|
for i, t in enumerate(generated_text):
|
|
t = bytes(t, "utf-8").decode("unicode_escape")
|
|
print(f"{i}:\n{t}\n")
|
|
|
|
self.assertListEqual(EXPECTED_GENERATED_IDS[0], generated_ids[0].numpy().tolist())
|
|
self.assertListEqual(EXPECTED_GENERATED_IDS[1], generated_ids[1].numpy().tolist())
|