wehub-resource-sync
205 lines
7.6 KiB
Python
205 lines
7.6 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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from paddlenlp.transformers import (
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ProphetNetConfig,
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ProphetNetForConditionalGeneration,
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ProphetNetModel,
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ProphetNetPretrainedModel,
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)
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from ...testing_utils import slow
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from ..test_modeling_common import ModelTesterMixin, ids_tensor
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class ProphetNetModelTester:
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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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tgt_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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bos_token_id=1,
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pad_token_id=0,
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eos_token_id=2,
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decoder_start_token_id=1,
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hidden_size=32,
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relative_max_distance=32,
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ngram=2,
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num_buckets=8,
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num_encoder_layers=2,
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num_decoder_layers=4,
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num_encoder_attention_heads=4,
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num_decoder_attention_heads=4,
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encoder_ffn_dim=64,
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decoder_ffn_dim=64,
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dropout=0.1,
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activation_function="gelu",
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attention_dropout=0.1,
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activation_dropout=0.1,
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max_position_embeddings=128,
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init_std=0.02,
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eps=0.1,
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add_cross_attention=True,
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disable_ngram_loss=False,
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):
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self.parent = parent
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self.vocab_size = vocab_size
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.tgt_seq_length = tgt_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.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.decoder_start_token_id = decoder_start_token_id
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self.hidden_size = hidden_size
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self.num_encoder_layers = num_encoder_layers
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self.num_encoder_attention_heads = num_encoder_attention_heads
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self.num_decoder_layers = num_decoder_layers
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self.num_decoder_attention_heads = num_decoder_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.encoder_ffn_dim = encoder_ffn_dim
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self.add_cross_attention = add_cross_attention
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self.decoder_ffn_dim = decoder_ffn_dim
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self.dropout = dropout
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self.activation_function = activation_function
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self.activation_dropout = activation_dropout
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self.init_std = init_std
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self.disable_ngram_loss = disable_ngram_loss
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self.pad_token_id = pad_token_id
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self.attention_dropout = attention_dropout
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self.eps = eps
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self.ngram = ngram
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self.relative_max_distance = relative_max_distance
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self.num_buckets = num_buckets
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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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decoder_input_ids = ids_tensor([self.batch_size, self.tgt_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 = paddle.ones([self.batch_size, self.seq_length], dtype="float32")
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decoder_input_mask = None
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if self.use_input_mask:
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decoder_input_mask = paddle.ones([self.batch_size, self.tgt_seq_length], dtype="float32")
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config = self.get_config()
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return config, input_ids, input_mask, decoder_input_ids, decoder_input_mask
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def get_config(self):
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return ProphetNetConfig(
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vocab_size=self.vocab_size,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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eos_token_id=self.eos_token_id,
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hidden_size=self.hidden_size,
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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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activation_function=self.activation_function,
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activation_dropout=self.activation_dropout,
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dropout=self.dropout,
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relative_max_distance=self.relative_max_distance,
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ngram=self.ngram,
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num_buckets=self.num_buckets,
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encoder_ffn_dim=self.encoder_ffn_dim,
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num_encoder_attention_heads=self.num_encoder_attention_heads,
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num_encoder_layers=self.num_encoder_layers,
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decoder_ffn_dim=self.decoder_ffn_dim,
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num_decoder_attention_heads=self.num_decoder_attention_heads,
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num_decoder_layers=self.num_decoder_layers,
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attention_dropout=self.attention_dropout,
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init_std=self.init_std,
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eps=self.eps,
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add_cross_attention=self.add_cross_attention,
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disable_ngram_loss=self.disable_ngram_loss,
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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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(config, input_ids, input_mask, decoder_input_ids, decoder_input_mask) = 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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"decoder_input_ids": decoder_input_ids,
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"decoder_attention_mask": decoder_input_mask,
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}
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return config, inputs_dict
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def create_and_check_model(self, config, input_ids, input_mask, decoder_input_ids, decoder_attention_mask):
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model = ProphetNetModel(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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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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)
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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_conditional_generation_model(
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self, config, input_ids, input_mask, decoder_input_ids, decoder_attention_mask
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):
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model = ProphetNetForConditionalGeneration(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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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size])
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class ProphetNetModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = ProphetNetModel
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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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ProphetNetModel,
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ProphetNetForConditionalGeneration,
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)
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def setUp(self):
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self.model_tester = ProphetNetModelTester(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_conditional_generation_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_conditional_generation_model(*config_and_inputs)
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@slow
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@unittest.skip("Skip for missing model weight.")
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def test_model_from_pretrained(self):
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for model_name in list(ProphetNetPretrainedModel.pretrained_init_configuration)[:1]:
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model = ProphetNetModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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