578 lines
20 KiB
Python
578 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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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 unittest
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from huggingface_hub import hf_hub_download
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from transformers import UdopConfig, is_torch_available, is_vision_available
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from transformers.testing_utils import (
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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require_vision,
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slow,
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torch_device,
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)
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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_common import ModelTesterMixin, ids_tensor
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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 UdopEncoderModel, UdopForConditionalGeneration, UdopModel, UdopProcessor
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if is_vision_available():
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from PIL import Image
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class UdopModelTester:
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def __init__(
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self,
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parent,
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vocab_size=99,
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batch_size=13,
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encoder_seq_length=7,
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decoder_seq_length=9,
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# For common tests
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is_training=True,
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use_attention_mask=True,
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use_labels=True,
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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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d_ff=37,
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relative_attention_num_buckets=32,
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dropout_rate=0.1,
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initializer_factor=0.002,
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eos_token_id=1,
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pad_token_id=0,
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scope=None,
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decoder_layers=None,
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range_bbox=1000,
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decoder_start_token_id=0,
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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.encoder_seq_length = encoder_seq_length
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self.decoder_seq_length = decoder_seq_length
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# For common tests
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self.seq_length = self.decoder_seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_mask
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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.d_ff = d_ff
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.dropout_rate = dropout_rate
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self.initializer_factor = initializer_factor
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.scope = None
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self.decoder_layers = decoder_layers
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self.range_bbox = range_bbox
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self.decoder_start_token_id = decoder_start_token_id
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
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bbox = ids_tensor([self.batch_size, self.encoder_seq_length, 4], self.range_bbox).float()
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# Ensure that bbox is legal
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for i in range(bbox.shape[0]):
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for j in range(bbox.shape[1]):
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if bbox[i, j, 3] < bbox[i, j, 1]:
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t = bbox[i, j, 3]
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bbox[i, j, 3] = bbox[i, j, 1]
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bbox[i, j, 1] = t
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if bbox[i, j, 2] < bbox[i, j, 0]:
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t = bbox[i, j, 2]
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bbox[i, j, 2] = bbox[i, j, 0]
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bbox[i, j, 0] = t
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decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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attention_mask = None
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decoder_attention_mask = None
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if self.use_attention_mask:
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attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
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decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
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lm_labels = None
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if self.use_labels:
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lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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config = self.get_config()
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return (
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config,
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input_ids,
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bbox,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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)
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def get_config(self):
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return UdopConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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d_ff=self.d_ff,
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d_kv=self.hidden_size // self.num_attention_heads,
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num_layers=self.num_hidden_layers,
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num_decoder_layers=self.decoder_layers,
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num_heads=self.num_attention_heads,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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dropout_rate=self.dropout_rate,
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initializer_factor=self.initializer_factor,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.pad_token_id,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.decoder_start_token_id,
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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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bbox,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = UdopModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids=input_ids,
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bbox=bbox,
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decoder_input_ids=decoder_input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=decoder_attention_mask,
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)
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result = model(input_ids=input_ids, bbox=bbox, decoder_input_ids=decoder_input_ids)
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decoder_output = result.last_hidden_state
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decoder_past = result.past_key_values
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encoder_output = result.encoder_last_hidden_state
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self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
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self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size))
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# There should be `num_layers` key value embeddings stored in decoder_past
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self.parent.assertEqual(len(decoder_past), config.num_layers)
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# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
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self.parent.assertEqual(len(decoder_past[0]), 4)
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def create_and_check_with_lm_head(
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self,
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config,
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input_ids,
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bbox,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = UdopForConditionalGeneration(config=config).to(torch_device).eval()
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outputs = model(
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input_ids=input_ids,
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bbox=bbox,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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labels=lm_labels,
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)
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self.parent.assertEqual(len(outputs), 4)
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self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
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self.parent.assertEqual(outputs["loss"].size(), ())
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def create_and_check_generate_with_past_key_values(
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self,
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config,
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input_ids,
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bbox,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = UdopForConditionalGeneration(config=config).to(torch_device).eval()
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torch.manual_seed(0)
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output_without_past_cache = model.generate(
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input_ids[:1], bbox=bbox[:1, :, :], num_beams=2, max_length=5, do_sample=True, use_cache=False
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)
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torch.manual_seed(0)
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output_with_past_cache = model.generate(
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input_ids[:1], bbox=bbox[:1, :, :], num_beams=2, max_length=5, do_sample=True
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)
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self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
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def create_and_check_model_fp16_forward(
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self,
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config,
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input_ids,
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bbox,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = UdopForConditionalGeneration(config=config).to(torch_device).half().eval()
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output = model(input_ids, bbox=bbox, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids).logits
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self.parent.assertFalse(torch.isnan(output).any().item())
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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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bbox,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"bbox": bbox,
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"decoder_input_ids": decoder_input_ids,
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"decoder_attention_mask": decoder_attention_mask,
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"use_cache": False,
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}
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return config, inputs_dict
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@require_torch
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class UdopModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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UdopModel,
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UdopForConditionalGeneration,
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)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (UdopForConditionalGeneration,) if is_torch_available() else ()
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pipeline_model_mapping = {"feature-extraction": UdopModel} if is_torch_available() else {}
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fx_compatible = False
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test_pruning = False
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test_torchscript = False
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test_head_masking = False
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test_resize_embeddings = True
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test_model_parallel = False
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is_encoder_decoder = True
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test_cpu_offload = False
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# The small UDOP model needs higher percentages for CPU/MP tests
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model_split_percents = [0.8, 0.9]
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def setUp(self):
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self.model_tester = UdopModelTester(self)
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self.config_tester = ConfigTester(self, config_class=UdopConfig, d_model=37)
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = copy.deepcopy(inputs_dict)
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if model_class.__name__ == "UdopForConditionalGeneration":
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if return_labels:
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
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)
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return inputs_dict
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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(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_model(*config_and_inputs)
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def test_with_lm_head(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_with_lm_head(*config_and_inputs)
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def test_generate_with_past_key_values(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_generate_with_past_key_values(*config_and_inputs)
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@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
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def test_model_fp16_forward(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_model_fp16_forward(*config_and_inputs)
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@unittest.skip("Gradient checkpointing is not supported by this model")
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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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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 = sorted([*signature.parameters.keys()])
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expected_arg_names = [
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"attention_mask",
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"bbox",
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"cross_attn_head_mask",
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"decoder_attention_mask",
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"decoder_head_mask",
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"decoder_input_ids",
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"decoder_inputs_embeds",
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"encoder_outputs",
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"head_mask",
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"input_ids",
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"inputs_embeds",
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]
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if model_class in self.all_generative_model_classes:
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expected_arg_names.append(
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"labels",
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)
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expected_arg_names = sorted(expected_arg_names)
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self.assertListEqual(sorted(arg_names[: len(expected_arg_names)]), expected_arg_names)
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@unittest.skip(
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"Not currently compatible. Fails with - NotImplementedError: Cannot copy out of meta tensor; no data!"
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)
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def test_save_load_low_cpu_mem_usage(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 = "microsoft/udop-large"
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model = UdopForConditionalGeneration.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class UdopEncoderOnlyModelTester:
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def __init__(
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self,
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parent,
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vocab_size=99,
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batch_size=13,
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seq_length=7,
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# For common tests
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is_training=False,
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use_attention_mask=True,
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hidden_size=32,
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num_hidden_layers=5,
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decoder_layers=2,
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num_attention_heads=4,
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d_ff=37,
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relative_attention_num_buckets=32,
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dropout_rate=0.1,
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initializer_factor=0.002,
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eos_token_id=1,
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pad_token_id=0,
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scope=None,
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range_bbox=1000,
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):
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self.parent = parent
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self.batch_size = batch_size
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# For common tests
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_mask
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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.decoder_layers = decoder_layers
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self.num_attention_heads = num_attention_heads
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self.d_ff = d_ff
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.dropout_rate = dropout_rate
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self.initializer_factor = initializer_factor
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.scope = None
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self.range_bbox = range_bbox
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def get_config(self):
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return UdopConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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d_ff=self.d_ff,
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d_kv=self.hidden_size // self.num_attention_heads,
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num_layers=self.num_hidden_layers,
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num_decoder_layers=self.decoder_layers,
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num_heads=self.num_attention_heads,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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dropout_rate=self.dropout_rate,
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initializer_factor=self.initializer_factor,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.pad_token_id,
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pad_token_id=self.pad_token_id,
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is_encoder_decoder=False,
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)
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox).float()
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# Ensure that bbox is legal
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for i in range(bbox.shape[0]):
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for j in range(bbox.shape[1]):
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if bbox[i, j, 3] < bbox[i, j, 1]:
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t = bbox[i, j, 3]
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bbox[i, j, 3] = bbox[i, j, 1]
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bbox[i, j, 1] = t
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if bbox[i, j, 2] < bbox[i, j, 0]:
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t = bbox[i, j, 2]
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bbox[i, j, 2] = bbox[i, j, 0]
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bbox[i, j, 0] = t
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attention_mask = None
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if self.use_attention_mask:
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attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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config = self.get_config()
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return (
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config,
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input_ids,
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bbox,
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attention_mask,
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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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bbox,
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attention_mask,
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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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"bbox": bbox,
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"attention_mask": attention_mask,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
def create_and_check_model(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
bbox,
|
|
attention_mask,
|
|
):
|
|
model = UdopEncoderModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(
|
|
input_ids=input_ids,
|
|
bbox=bbox,
|
|
attention_mask=attention_mask,
|
|
)
|
|
encoder_output = result.last_hidden_state
|
|
|
|
self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.seq_length, self.hidden_size))
|
|
|
|
def create_and_check_model_fp16_forward(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
bbox,
|
|
attention_mask,
|
|
):
|
|
model = UdopEncoderModel(config=config).to(torch_device).half().eval()
|
|
output = model(input_ids, bbox=bbox, attention_mask=attention_mask)["last_hidden_state"]
|
|
self.parent.assertFalse(torch.isnan(output).any().item())
|
|
|
|
|
|
class UdopEncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (UdopEncoderModel,) if is_torch_available() else ()
|
|
test_pruning = False
|
|
test_torchscript = False
|
|
test_head_masking = False
|
|
test_resize_embeddings = False
|
|
test_model_parallel = False
|
|
all_parallelizable_model_classes = (UdopEncoderModel,) if is_torch_available() else ()
|
|
|
|
def setUp(self):
|
|
self.model_tester = UdopEncoderOnlyModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=UdopConfig, d_model=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
@unittest.skip(
|
|
"Not currently compatible. Fails with - NotImplementedError: Cannot copy out of meta tensor; no data!"
|
|
)
|
|
def test_save_load_low_cpu_mem_usage(self):
|
|
pass
|
|
|
|
|
|
@require_torch
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
@require_vision
|
|
@slow
|
|
class UdopModelIntegrationTests(unittest.TestCase):
|
|
@cached_property
|
|
def image(self):
|
|
filepath = hf_hub_download(
|
|
repo_id="hf-internal-testing/fixtures_docvqa", filename="document_2.png", repo_type="dataset"
|
|
)
|
|
image = Image.open(filepath).convert("RGB")
|
|
|
|
return image
|
|
|
|
@cached_property
|
|
def processor(self):
|
|
return UdopProcessor.from_pretrained("microsoft/udop-large")
|
|
|
|
@cached_property
|
|
def model(self):
|
|
return UdopForConditionalGeneration.from_pretrained("microsoft/udop-large").to(torch_device)
|
|
|
|
def test_conditional_generation(self):
|
|
processor = self.processor
|
|
model = self.model
|
|
|
|
prompt = "Question answering. In which year is the report made?"
|
|
encoding = processor(images=self.image, text=prompt, return_tensors="pt").to(torch_device)
|
|
|
|
predicted_ids = model.generate(**encoding)
|
|
|
|
predicted_text = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
|
|
self.assertEqual(predicted_text, "2013")
|