wehub-resource-sync
423 lines
15 KiB
Python
423 lines
15 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. 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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from __future__ import annotations
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import tempfile
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import unittest
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from typing import List
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import numpy as np
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import paddle
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from paddlenlp.transformers import (
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DebertaConfig,
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DebertaForMultipleChoice,
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DebertaForQuestionAnswering,
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DebertaForSequenceClassification,
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DebertaForTokenClassification,
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DebertaModel,
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)
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from paddlenlp.transformers.model_utils import PretrainedModel
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from ...testing_utils import require_package
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from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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class DebertaCompatibilityTest(unittest.TestCase):
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test_model_id = "hf-internal-testing/tiny-random-DebertaModel"
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@classmethod
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@require_package("transformers", "torch")
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def setUpClass(cls) -> None:
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from transformers import DebertaModel
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# when python application is done, `TemporaryDirectory` will be free
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cls.torch_model_path = tempfile.TemporaryDirectory().name
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model = DebertaModel.from_pretrained(cls.test_model_id)
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model.save_pretrained(cls.torch_model_path)
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def test_model_config_mapping(self):
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config = DebertaConfig(num_labels=22, hidden_dropout_prob=0.99)
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self.assertEqual(config.hidden_dropout_prob, 0.99)
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self.assertEqual(config.num_labels, 22)
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def setUp(self) -> None:
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self.tempdirs: List[tempfile.TemporaryDirectory] = []
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def tearDown(self) -> None:
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for tempdir in self.tempdirs:
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tempdir.cleanup()
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def get_tempdir(self) -> str:
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tempdir = tempfile.TemporaryDirectory()
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self.tempdirs.append(tempdir)
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return tempdir.name
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def compare_two_model(self, first_model: PretrainedModel, second_model: PretrainedModel):
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first_weight_name = "encoder.layer.3.attention.self.in_proj.weight"
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second_weight_name = "encoder.layer.3.attention.self.in_proj.weight"
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first_tensor = first_model.state_dict()[first_weight_name]
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second_tensor = second_model.state_dict()[second_weight_name]
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self.compare_two_weight(first_tensor, second_tensor)
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def compare_two_weight(self, first_tensor, second_tensor):
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diff = paddle.sum(first_tensor - second_tensor).numpy().item()
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self.assertEqual(diff, 0.0)
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@require_package("transformers", "torch")
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def test_deberta_converter(self):
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create common input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the paddle model
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from paddlenlp.transformers.deberta.modeling import DebertaModel
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paddle_model = DebertaModel.from_pretrained(
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"hf-internal-testing/tiny-random-DebertaModel", from_hf_hub=True, cache_dir=tempdir
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)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
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# 3. forward the torch model
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import torch
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from transformers import DebertaModel
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torch_model = DebertaModel.from_pretrained(
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"hf-internal-testing/tiny-random-DebertaModel", cache_dir=tempdir
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)
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torch_model.eval()
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torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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rtol=1e-4,
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)
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)
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@require_package("transformers", "torch")
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def test_deberta_converter_from_local_dir(self):
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create common input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the torch model
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import torch
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from transformers import DebertaModel
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torch_model = DebertaModel.from_pretrained("hf-internal-testing/tiny-random-DebertaModel")
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torch_model.eval()
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torch_model.save_pretrained(tempdir)
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torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
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# 2. forward the paddle model
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from paddlenlp.transformers.deberta.modeling import DebertaModel
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paddle_model = DebertaModel.from_pretrained(tempdir, convert_from_torch=True)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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rtol=1e-4,
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)
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)
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class DebertaModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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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=0,
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initializer_range=0.02,
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pad_token_id=0,
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type_sequence_label_size=2,
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use_relative_position=True,
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num_labels=3,
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num_choices=4,
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num_classes=3,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.pad_token_id = pad_token_id
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self.type_sequence_label_size = type_sequence_label_size
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self.use_relative_position = use_relative_position
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self.num_classes = num_classes
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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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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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.parent.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return DebertaConfig(
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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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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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use_relative_position=self.use_relative_position,
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num_class=self.num_classes,
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num_labels=self.num_labels,
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num_choices=self.num_choices,
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pooler_hidden_size=self.hidden_size,
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pooler_dropout=self.hidden_dropout_prob,
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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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = DebertaModel(config)
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model.eval()
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result = model(
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input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, return_dict=self.parent.return_dict
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)
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result = model(input_ids, return_dict=self.parent.return_dict)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
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def create_and_check_for_multiple_choice(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = DebertaForMultipleChoice(config)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
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result = model(
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multiple_choice_inputs_ids,
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labels=choice_labels,
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return_dict=self.parent.return_dict,
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)
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if choice_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_choices])
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def create_and_check_for_question_answering(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = DebertaForQuestionAnswering(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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return_dict=self.parent.return_dict,
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)
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if sequence_labels is not None:
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start_logits, end_logits = result[1], result[2]
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else:
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start_logits, end_logits = result[0], result[1]
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self.parent.assertEqual(start_logits.shape, [self.batch_size, self.seq_length])
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self.parent.assertEqual(end_logits.shape, [self.batch_size, self.seq_length])
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def create_and_check_for_sequence_classification(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = DebertaForSequenceClassification(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=sequence_labels,
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return_dict=self.parent.return_dict,
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)
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if sequence_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_classes])
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def create_and_check_for_token_classification(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = DebertaForTokenClassification(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=token_labels,
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return_dict=self.parent.return_dict,
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)
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.num_classes])
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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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = DebertaModel
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return_dict: bool = False
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use_labels: bool = False
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use_test_inputs_embeds: bool = False
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all_model_classes = (
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DebertaModel,
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DebertaForMultipleChoice,
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DebertaForQuestionAnswering,
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DebertaForSequenceClassification,
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DebertaForTokenClassification,
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)
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def setUp(self):
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self.model_tester = DebertaModelTester(self)
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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_for_multiple_choice(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_for_multiple_choice(*config_and_inputs)
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def test_for_question_answering(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_for_question_answering(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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if __name__ == "__main__":
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unittest.main()
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