339 lines
12 KiB
Python
339 lines
12 KiB
Python
# coding=utf-8
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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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from transformers import BertGenerationConfig, is_torch_available
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from transformers.testing_utils import require_torch, slow, 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, floats_tensor, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import BertGenerationDecoder, BertGenerationEncoder
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class BertGenerationEncoderTester:
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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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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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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=50,
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initializer_range=0.02,
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use_labels=True,
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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.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.initializer_range = initializer_range
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self.use_labels = use_labels
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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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if self.use_labels:
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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config = self.get_config()
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return config, input_ids, input_mask, token_labels
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def get_config(self):
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return BertGenerationConfig(
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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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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_ids,
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input_mask,
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token_labels,
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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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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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input_mask,
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token_labels,
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**kwargs,
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):
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model = BertGenerationEncoder(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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**kwargs,
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):
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config.add_cross_attention = True
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model = BertGenerationEncoder(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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**kwargs,
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):
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config.is_decoder = True
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config.add_cross_attention = True
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model = BertGenerationDecoder(config=config).to(torch_device).eval()
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# first forward pass
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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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 = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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)["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_states"][0]
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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[:, -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(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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input_mask,
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token_labels,
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*args,
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):
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model = BertGenerationDecoder(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def prepare_config_and_inputs_for_common(self):
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config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class BertGenerationEncoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
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all_generative_model_classes = (BertGenerationDecoder,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
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if is_torch_available()
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else {}
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)
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def setUp(self):
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self.model_tester = BertGenerationEncoderTester(self)
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self.config_tester = ConfigTester(self, config_class=BertGenerationConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_as_bert(self):
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config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
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config.model_type = "bert"
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self.model_tester.create_and_check_model(config, input_ids, input_mask, token_labels)
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def test_model_as_decoder(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
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def test_decoder_model_past_with_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
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def test_model_as_decoder_with_default_input_mask(self):
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# This regression test was failing with PyTorch < 1.3
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(
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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) = self.model_tester.prepare_config_and_inputs_for_decoder()
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input_mask = None
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self.model_tester.create_and_check_model_as_decoder(
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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def test_for_causal_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
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self.assertIsNotNone(model)
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@require_torch
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class BertGenerationEncoderIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head_absolute_embedding(self):
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model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
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input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
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with torch.no_grad():
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output = model(input_ids)[0]
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expected_shape = torch.Size([1, 8, 1024])
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self.assertEqual(output.shape, expected_shape)
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expected_slice = torch.tensor(
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[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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@require_torch
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class BertGenerationDecoderIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head_absolute_embedding(self):
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model = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
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input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
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with torch.no_grad():
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output = model(input_ids)[0]
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expected_shape = torch.Size([1, 8, 50358])
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self.assertEqual(output.shape, expected_shape)
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expected_slice = torch.tensor(
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[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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