199 lines
7.2 KiB
Python
199 lines
7.2 KiB
Python
# coding=utf-8
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# Copyright 2021 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 PyTorch TrOCR model. """
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import unittest
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from transformers import TrOCRConfig
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from transformers.testing_utils import is_torch_available, require_torch, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor
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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.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
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@require_torch
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class TrOCRStandaloneDecoderModelTester:
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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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d_model=16,
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decoder_seq_length=7,
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is_training=True,
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is_decoder=True,
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use_attention_mask=True,
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use_cache=False,
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use_labels=True,
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decoder_start_token_id=2,
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decoder_ffn_dim=32,
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decoder_layers=2,
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decoder_attention_heads=4,
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max_position_embeddings=30,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.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.d_model = d_model
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self.hidden_size = d_model
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self.num_hidden_layers = decoder_layers
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self.decoder_layers = decoder_layers
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self.decoder_ffn_dim = decoder_ffn_dim
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self.decoder_attention_heads = decoder_attention_heads
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self.num_attention_heads = decoder_attention_heads
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self.eos_token_id = eos_token_id
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self.bos_token_id = bos_token_id
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self.pad_token_id = pad_token_id
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self.decoder_start_token_id = decoder_start_token_id
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self.use_cache = use_cache
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self.max_position_embeddings = max_position_embeddings
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self.scope = None
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self.decoder_key_length = decoder_seq_length
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self.base_model_out_len = 2
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self.decoder_attention_idx = 1
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def prepare_config_and_inputs(self):
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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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if self.use_attention_mask:
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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 = TrOCRConfig(
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vocab_size=self.vocab_size,
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d_model=self.d_model,
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decoder_layers=self.decoder_layers,
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decoder_ffn_dim=self.decoder_ffn_dim,
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decoder_attention_heads=self.decoder_attention_heads,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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use_cache=self.use_cache,
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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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max_position_embeddings=self.max_position_embeddings,
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)
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return (config, input_ids, attention_mask, lm_labels)
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def create_and_check_decoder_model_past(
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self,
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config,
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input_ids,
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attention_mask,
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lm_labels,
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):
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config.use_cache = True
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model = TrOCRDecoder(config=config).to(torch_device).eval()
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input_ids = input_ids[:2]
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input_ids[input_ids == 0] += 1
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# first forward pass
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outputs = model(input_ids, use_cache=True)
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outputs_use_cache_conf = model(input_ids)
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outputs_no_past = model(input_ids, use_cache=False)
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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past_key_values = outputs["past_key_values"]
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((2, 1), config.vocab_size - 1) + 1
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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output_from_no_past = model(next_input_ids)["last_hidden_state"]
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output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
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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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config, input_ids, attention_mask, lm_labels = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
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return config, inputs_dict
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@require_torch
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class TrOCRStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (TrOCRForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
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fx_compatible = True
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test_pruning = False
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def setUp(self):
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self.model_tester = TrOCRStandaloneDecoderModelTester(self, is_training=False)
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self.config_tester = ConfigTester(self, config_class=TrOCRConfig)
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# not implemented currently
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def test_inputs_embeds(self):
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pass
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# trocr has no base model
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def test_save_load_fast_init_from_base(self):
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pass
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# trocr has no base model
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def test_save_load_fast_init_to_base(self):
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pass
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_decoder_model_past(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_decoder_model_past(*config_and_inputs)
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# decoder cannot keep gradients
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def test_retain_grad_hidden_states_attentions(self):
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return
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@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
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def test_left_padding_compatibility(self):
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pass
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