wehub-resource-sync
705 lines
26 KiB
Python
705 lines
26 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2023 Baidu ErnieCode Authors and 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 random
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import unittest
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import numpy as np
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import paddle
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from parameterized import parameterized_class
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from paddlenlp.transformers import (
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ERNIECODE_PRETRAINED_MODEL_ARCHIVE_LIST,
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ErnieCodeConfig,
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ErnieCodeEncoderModel,
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ErnieCodeForConditionalGeneration,
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ErnieCodeModel,
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)
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from tests.testing_utils import slow
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from tests.transformers.test_generation_utils import GenerationTesterMixin
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from tests.transformers.test_modeling_common import ModelTesterMixin, ids_tensor
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def masked_fill(x, mask, value):
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y = paddle.full(x.shape, value, x.dtype)
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return paddle.where(mask, y, x)
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def make_model_instance(config: ErnieCodeConfig, model_class, base_model_class):
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if model_class == base_model_class:
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return model_class(config)
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else:
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return model_class(base_model_class(config))
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class ErnieCodeModelTester:
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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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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=8,
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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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):
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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.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.decoder_start_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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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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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.parent.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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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_pipeline_config(self) -> ErnieCodeConfig:
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return ErnieCodeConfig(
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vocab_size=66, # ErnieCode forces 0 extra tokens
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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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)
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def get_config(self) -> ErnieCodeConfig:
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return ErnieCodeConfig(
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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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)
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def check_prepare_lm_labels_via_shift_left(
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self,
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config: ErnieCodeConfig,
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input_ids,
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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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if not self.parent.use_labels:
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return
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model = ErnieCodeModel(config)
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model.eval()
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# make sure that lm_labels are correctly padded from the right
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lm_labels = masked_fill(lm_labels, (lm_labels == self.decoder_start_token_id), self.eos_token_id)
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# add casaul pad token mask
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triangular_mask = paddle.tril(paddle.ones(lm_labels.shape)).logical_not()
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lm_labels = masked_fill(lm_labels, triangular_mask, self.pad_token_id)
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decoder_input_ids = model._shift_right(lm_labels)
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for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
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# first item
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self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
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if i < decoder_input_ids_slice.shape[-1]:
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if i < decoder_input_ids.shape[-1] - 1:
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# items before diagonal
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self.parent.assertListEqual(
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decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
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)
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# pad items after diagonal
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if i < decoder_input_ids.shape[-1] - 2:
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self.parent.assertListEqual(
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decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
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)
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else:
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# all items after square
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self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
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def create_and_check_model(
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self,
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config: ErnieCodeConfig,
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input_ids,
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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 = ErnieCodeModel(config)
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model.eval()
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result = model(
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input_ids=input_ids,
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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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return_dict=self.parent.return_dict,
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)
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result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, return_dict=self.parent.return_dict)
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decoder_output = result[0]
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decoder_past = result[1]
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encoder_output = result[2]
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self.parent.assertEqual(encoder_output.shape, [self.batch_size, self.encoder_seq_length, self.hidden_size])
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self.parent.assertEqual(decoder_output.shape, [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: ErnieCodeConfig,
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input_ids,
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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 = ErnieCodeForConditionalGeneration(config)
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model.eval()
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outputs = model(
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input_ids=input_ids,
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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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return_dict=self.parent.return_dict,
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)
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self.parent.assertEqual(len(outputs), 4 if self.parent.use_labels else 3)
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if self.parent.use_labels:
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self.parent.assertEqual(outputs[1].shape, [self.batch_size, self.decoder_seq_length, self.vocab_size])
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self.parent.assertIsInstance(outputs[0].item(), float)
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else:
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self.parent.assertEqual(outputs[0].shape, [self.batch_size, self.decoder_seq_length, self.vocab_size])
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def create_and_check_decoder_model_past(
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self,
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config: ErnieCodeConfig,
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input_ids,
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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 = ErnieCodeModel(config).get_decoder()
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model.eval()
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# first forward pass
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outputs = model(input_ids, use_cache=True, return_dict=self.parent.return_dict)
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outputs_use_cache_conf = model(input_ids, return_dict=self.parent.return_dict)
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outputs_no_past = model(input_ids, use_cache=False, return_dict=self.parent.return_dict)
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf) + 1)
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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output, past_key_values = outputs[:2]
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor([self.batch_size, 1], config["vocab_size"])
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# append to next input_ids and
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next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
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output_from_no_past = model(next_input_ids, return_dict=self.parent.return_dict)[0]
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output_from_past = model(next_tokens, cache=past_key_values, return_dict=self.parent.return_dict)[0]
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# select random slice
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random_slice_idx = ids_tensor(
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[
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1,
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],
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output_from_past.shape[-1],
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).item()
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output_from_no_past_slice = output_from_no_past[:, -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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self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_decoder_model_attention_mask_past(
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self,
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config: ErnieCodeConfig,
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input_ids,
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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 = ErnieCodeModel(config).get_decoder()
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model.eval()
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# create attention mask
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attn_mask = paddle.ones(input_ids.shape, dtype="int64")
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half_seq_length = input_ids.shape[-1] // 2
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attn_mask[:, half_seq_length:] = 0
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# first forward pass
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output, past_key_values = model(
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input_ids, attention_mask=attn_mask, use_cache=True, return_dict=self.parent.return_dict
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)[:2]
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor([self.batch_size, 1], config["vocab_size"])
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# change a random masked slice from input_ids
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random_seq_idx_to_change = (
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ids_tensor(
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[
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1,
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],
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half_seq_length,
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).item()
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+ 1
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)
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random_other_next_tokens = ids_tensor([self.batch_size, 1], config["vocab_size"]).squeeze(-1)
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input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
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# append to next input_ids and attn_mask
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next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
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attn_mask = paddle.concat(
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[attn_mask, paddle.ones((attn_mask.shape[0], 1), dtype="int64")],
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axis=1,
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)
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# get two different outputs
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output_from_no_past = model(next_input_ids, attention_mask=attn_mask, return_dict=self.parent.return_dict)[0]
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output_from_past = model(
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next_tokens,
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cache=past_key_values,
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attention_mask=paddle.ones((attn_mask.shape[0], 1), dtype="int64"),
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return_dict=self.parent.return_dict,
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)[0]
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# select random slice
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random_slice_idx = ids_tensor(
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[
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1,
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],
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output_from_past.shape[-1],
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).item()
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output_from_no_past_slice = output_from_no_past[:, -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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self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config: ErnieCodeConfig,
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input_ids,
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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 = ErnieCodeModel(config).get_decoder()
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model.eval()
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, use_cache=True, return_dict=self.parent.return_dict)
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output, past_key_values = outputs[:2]
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor([self.batch_size, 3], config["vocab_size"])
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next_mask = ids_tensor([self.batch_size, 3], vocab_size=2)
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# append to next input_ids and
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next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
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next_attention_mask = paddle.concat([attention_mask, next_mask], axis=-1)
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output_from_no_past = model(
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next_input_ids, attention_mask=next_attention_mask, return_dict=self.parent.return_dict
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)[0]
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output_from_past = model(
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next_tokens, attention_mask=next_attention_mask, cache=past_key_values, return_dict=self.parent.return_dict
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)[0]
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# select random slice
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random_slice_idx = ids_tensor(
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[
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1,
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],
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output_from_past.shape[-1],
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).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_generate_with_past_key_values(
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self,
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config: ErnieCodeConfig,
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input_ids,
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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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paddle.seed(0)
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np.random.seed(0)
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random.seed(0)
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model = ErnieCodeForConditionalGeneration(config)
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model.eval()
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output_without_past_cache, _ = model.generate(
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input_ids[:1], top_k=1, max_length=5, decode_strategy="sampling", use_cache=False
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)
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paddle.seed(0)
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np.random.seed(0)
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random.seed(0)
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output_with_past_cache, _ = model.generate(input_ids[:1], top_k=1, max_length=5, decode_strategy="sampling")
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self.parent.assertTrue(paddle.all(output_with_past_cache == output_without_past_cache))
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def check_resize_embeddings_ErnieCode_v1_1(
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self,
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config: ErnieCodeConfig,
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):
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prev_vocab_size = config["vocab_size"]
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model = ErnieCodeForConditionalGeneration(config)
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model.eval()
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model.resize_token_embeddings(prev_vocab_size - 10)
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self.parent.assertEqual(model.get_input_embeddings().weight.shape[0], prev_vocab_size - 10)
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self.parent.assertEqual(model.get_output_embeddings().weight.shape[0], prev_vocab_size - 10)
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self.parent.assertEqual(model.ErnieCode.config["vocab_size"], prev_vocab_size - 10)
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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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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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"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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@parameterized_class(
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("return_dict", "use_labels"),
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[
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[False, False],
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[False, True],
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[True, False],
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[True, True],
|
|
],
|
|
)
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class ErnieCodeModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
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base_model_class = ErnieCodeModel
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return_dict: bool = False
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use_labels: bool = False
|
|
|
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all_model_classes = (ErnieCodeModel, ErnieCodeForConditionalGeneration)
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all_generative_model_classes = {ErnieCodeForConditionalGeneration: (ErnieCodeModel, "ErnieCode")}
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all_parallelizable_model_classes = (ErnieCodeModel, ErnieCodeForConditionalGeneration)
|
|
fx_compatible = True
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|
test_pruning = False
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|
test_resize_embeddings = True
|
|
test_model_parallel = True
|
|
use_test_inputs_embeds = True
|
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is_encoder_decoder = True
|
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use_test_model_name_list = False
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# The small ErnieCode model needs higher percentages for CPU/MP tests
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|
model_split_percents = [0.8, 0.9]
|
|
|
|
def setUp(self):
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self.model_tester = ErnieCodeModelTester(self)
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|
|
|
def test_shift_right(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)
|
|
|
|
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)
|
|
|
|
def test_model_v1_1(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
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# check that gated gelu feed forward and different word embeddings work
|
|
config = config_and_inputs[0]
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config["feed_forward_proj"] = "gated-gelu"
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|
self.model_tester.create_and_check_model(config, *config_and_inputs[1:])
|
|
|
|
def test_config_and_model_silu_gated(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
config = config_and_inputs[0]
|
|
config["feed_forward_proj"] = "gated-silu"
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
def test_with_lm_head(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_with_lm_head(*config_and_inputs)
|
|
|
|
def test_decoder_model_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
|
|
|
|
def test_decoder_model_past_with_attn_mask(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
|
|
|
|
def test_decoder_model_past_with_3d_attn_mask(self):
|
|
(
|
|
config,
|
|
input_ids,
|
|
decoder_input_ids,
|
|
attention_mask,
|
|
decoder_attention_mask,
|
|
lm_labels,
|
|
) = self.model_tester.prepare_config_and_inputs()
|
|
|
|
attention_mask = ids_tensor(
|
|
[self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length],
|
|
vocab_size=2,
|
|
)
|
|
decoder_attention_mask = ids_tensor(
|
|
[self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length],
|
|
vocab_size=2,
|
|
)
|
|
|
|
self.model_tester.create_and_check_decoder_model_attention_mask_past(
|
|
config,
|
|
input_ids,
|
|
decoder_input_ids,
|
|
attention_mask,
|
|
decoder_attention_mask,
|
|
lm_labels,
|
|
)
|
|
|
|
def test_decoder_model_past_with_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
|
|
|
def test_generate_with_past_key_values(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_generate_with_past_key_values(*config_and_inputs)
|
|
|
|
def test_v1_1_resize_embeddings(self):
|
|
config = self.model_tester.prepare_config_and_inputs()[0]
|
|
self.model_tester.check_resize_embeddings_ErnieCode_v1_1(config)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in ERNIECODE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = ErnieCodeModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
class ErnieCodeEncoderOnlyModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
vocab_size=99,
|
|
batch_size=13,
|
|
encoder_seq_length=7,
|
|
# For common tests
|
|
use_attention_mask=True,
|
|
hidden_size=32,
|
|
num_hidden_layers=5,
|
|
num_attention_heads=4,
|
|
d_ff=37,
|
|
relative_attention_num_buckets=8,
|
|
is_training=False,
|
|
dropout_rate=0.1,
|
|
initializer_factor=0.002,
|
|
is_encoder_decoder=False,
|
|
eos_token_id=1,
|
|
pad_token_id=0,
|
|
scope=None,
|
|
):
|
|
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.encoder_seq_length = encoder_seq_length
|
|
# For common tests
|
|
self.seq_length = self.encoder_seq_length
|
|
self.use_attention_mask = use_attention_mask
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.d_ff = d_ff
|
|
self.relative_attention_num_buckets = relative_attention_num_buckets
|
|
self.dropout_rate = dropout_rate
|
|
self.initializer_factor = initializer_factor
|
|
self.eos_token_id = eos_token_id
|
|
self.pad_token_id = pad_token_id
|
|
self.is_encoder_decoder = is_encoder_decoder
|
|
self.scope = None
|
|
self.is_training = is_training
|
|
|
|
def get_config(self):
|
|
config = ErnieCodeConfig(
|
|
vocab_size=self.vocab_size,
|
|
d_model=self.hidden_size,
|
|
d_ff=self.d_ff,
|
|
d_kv=self.hidden_size // self.num_attention_heads,
|
|
num_layers=self.num_hidden_layers,
|
|
num_heads=self.num_attention_heads,
|
|
relative_attention_num_buckets=self.relative_attention_num_buckets,
|
|
dropout_rate=self.dropout_rate,
|
|
initializer_factor=self.initializer_factor,
|
|
eos_token_id=self.eos_token_id,
|
|
bos_token_id=self.pad_token_id,
|
|
pad_token_id=self.pad_token_id,
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
)
|
|
return config
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
|
|
|
|
attention_mask = None
|
|
if self.use_attention_mask:
|
|
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
|
|
|
|
config = self.get_config()
|
|
return (
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
)
|
|
|
|
def create_and_check_model(
|
|
self,
|
|
config: ErnieCodeConfig,
|
|
input_ids,
|
|
attention_mask,
|
|
):
|
|
model = ErnieCodeEncoderModel(config)
|
|
model.eval()
|
|
result = model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
)
|
|
result = model(input_ids=input_ids)
|
|
encoder_output = result[0]
|
|
|
|
self.parent.assertEqual(encoder_output.shape, [self.batch_size, self.encoder_seq_length, self.hidden_size])
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
) = config_and_inputs
|
|
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
class ErnieCodeEncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (ErnieCodeEncoderModel,)
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_model_parallel = True
|
|
all_parallelizable_model_classes = (ErnieCodeEncoderModel,)
|
|
|
|
def _make_model_instance(self, config: ErnieCodeConfig, model_class):
|
|
return model_class(config)
|
|
|
|
def setUp(self):
|
|
self.model_tester = ErnieCodeEncoderOnlyModelTester(self)
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
def test_model_name_list(self):
|
|
pass
|
|
|
|
def test_save_load(self):
|
|
pass
|