745 lines
29 KiB
Python
745 lines
29 KiB
Python
# coding=utf-8
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# Copyright 2023 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 MegaConfig, is_torch_available
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from transformers.testing_utils import (
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TestCasePlus,
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is_flaky,
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require_torch,
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require_torch_fp16,
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slow,
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torch_device,
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)
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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 (
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MegaForCausalLM,
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MegaForMaskedLM,
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MegaForMultipleChoice,
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MegaForQuestionAnswering,
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MegaForSequenceClassification,
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MegaForTokenClassification,
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MegaModel,
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)
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class MegaModelTester:
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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_labels=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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intermediate_size=37,
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_positions=1024,
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bidirectional=False, # needed for decoding, and can't modify common generation tests; test separately by overriding
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ema_projection_size=16,
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shared_representation_size=64,
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use_chunking=False,
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chunk_size=32,
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attention_activation="softmax",
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use_normalized_ffn=True,
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nffn_hidden_size=24,
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add_token_type_embeddings=True,
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type_vocab_size=2,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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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.add_token_type_embeddings = add_token_type_embeddings
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self.use_labels = use_labels
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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.intermediate_size = intermediate_size
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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_positions = max_positions
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self.bidirectional = bidirectional
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self.ema_projection_size = ema_projection_size
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self.shared_representation_size = shared_representation_size
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self.use_chunking = use_chunking
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self.chunk_size = chunk_size
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self.attention_activation = attention_activation
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self.use_normalized_ffn = use_normalized_ffn
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self.nffn_hidden_size = nffn_hidden_size
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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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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self.num_attention_heads = 1
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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.add_token_type_embeddings:
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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.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 MegaConfig(
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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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intermediate_size=self.intermediate_size,
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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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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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# added args
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add_token_type_embeddings=self.add_token_type_embeddings,
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max_positions=self.max_positions,
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bidirectional=self.bidirectional,
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ema_projection_size=self.ema_projection_size,
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shared_representation_size=self.shared_representation_size,
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use_chunking=self.use_chunking,
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chunk_size=self.chunk_size,
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attention_activation=self.attention_activation,
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use_normalized_ffn=self.use_normalized_ffn,
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nffn_hidden_size=self.nffn_hidden_size,
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)
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def get_pipeline_config(self):
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config = self.get_config()
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config.vocab_size = 300
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return config
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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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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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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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config.bidirectional = False
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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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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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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, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = MegaModel(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, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, 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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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = MegaModel(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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token_type_ids=token_type_ids,
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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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token_type_ids=token_type_ids,
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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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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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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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = MegaForCausalLM(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, token_type_ids=token_type_ids, 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 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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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.is_decoder = True
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config.bidirectional = False
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config.add_cross_attention = True
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model = MegaForCausalLM(config=config).to(torch_device).eval()
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# make sure that ids don't start with pad token
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mask = input_ids.ne(config.pad_token_id).long()
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input_ids = input_ids * mask
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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, 1), config.vocab_size)
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# make sure that ids don't start with pad token
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mask = next_tokens.ne(config.pad_token_id).long()
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next_tokens = next_tokens * mask
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next_mask = ids_tensor((self.batch_size, 1), 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_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[:, -1:, 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_decoder_model_with_chunking(
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.use_chunking = True
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config.output_attentions = True
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config.attention_activation = "laplace"
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config.chunk_size = input_ids.size(1) * 2
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model = MegaForCausalLM(config).to(torch_device).eval()
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input_ids = input_ids.repeat(1, 8)
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# multiply the sequence length by 8 since we repeat the same ids 8 times in input_ids
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input_mask = random_attention_mask([self.batch_size, self.seq_length * 8])
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result = model(input_ids, attention_mask=input_mask)
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# test if the sequence length of attentions is same provided chunk_size
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self.parent.assertEqual(result["attentions"][0].shape[-1], config.chunk_size)
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def create_and_check_for_masked_lm(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = MegaForMaskedLM(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, token_type_ids=token_type_ids, 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 create_and_check_for_token_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = MegaForTokenClassification(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, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_for_multiple_choice(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_choices = self.num_choices
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model = MegaForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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result = model(
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multiple_choice_inputs_ids,
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attention_mask=multiple_choice_input_mask,
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token_type_ids=multiple_choice_token_type_ids,
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labels=choice_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_question_answering(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = MegaForQuestionAnswering(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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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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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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# extra checks for Mega-specific model functionality
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def create_and_check_bidirectionality(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.bidirectional = True
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model = MegaModel(config)
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model.to(torch_device)
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model.eval()
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# no mask
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result = model(input_ids)
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# with mask & token types
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
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def check_chunking_shorter_sequence(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.use_chunking = True
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config.chunk_size = input_ids.size(1) + 25
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model = MegaModel(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, token_type_ids=token_type_ids)
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
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def check_chunking_longer_sequence(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.use_chunking = True
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# we want the chunk size to be < sequence length, and the sequence length to be a multiple of chunk size
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config.chunk_size = input_ids.size(1) * 2
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model = MegaModel(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.repeat(1, 8),
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)
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length * 8, self.hidden_size))
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def check_laplace_self_attention(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.attention_activation = "laplace"
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model = MegaModel(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, token_type_ids=token_type_ids)
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
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def check_relu2_self_attention(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.attention_activation = "relu2"
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model = MegaModel(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, token_type_ids=token_type_ids)
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self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
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|
|
|
def check_sequence_length_beyond_max_positions(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
):
|
|
config.max_positions = self.seq_length - 2
|
|
model = MegaModel(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
|
|
|
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size))
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
) = config_and_inputs
|
|
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class MegaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
MegaForCausalLM,
|
|
MegaForMaskedLM,
|
|
MegaModel,
|
|
MegaForSequenceClassification,
|
|
MegaForTokenClassification,
|
|
MegaForMultipleChoice,
|
|
MegaForQuestionAnswering,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
all_generative_model_classes = (MegaForCausalLM,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": MegaModel,
|
|
"fill-mask": MegaForMaskedLM,
|
|
"question-answering": MegaForQuestionAnswering,
|
|
"text-classification": MegaForSequenceClassification,
|
|
"text-generation": MegaForCausalLM,
|
|
"token-classification": MegaForTokenClassification,
|
|
"zero-shot": MegaForSequenceClassification,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = MegaModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=MegaConfig, hidden_size=37)
|
|
|
|
# TODO: @ydshieh
|
|
@is_flaky(description="Sometimes gives `AssertionError` on expected outputs")
|
|
def test_pipeline_fill_mask(self):
|
|
super().test_pipeline_fill_mask()
|
|
|
|
# TODO: @ydshieh
|
|
@is_flaky(
|
|
description="Sometimes gives `RuntimeError: probability tensor contains either `inf`, `nan` or element < 0`"
|
|
)
|
|
def test_pipeline_text_generation(self):
|
|
super().test_pipeline_text_generation()
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
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_as_decoder(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
|
|
|
|
def test_model_as_decoder_with_default_input_mask(self):
|
|
# This regression test was failing with PyTorch < 1.3
|
|
(
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
) = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
|
|
input_mask = None
|
|
|
|
self.model_tester.create_and_check_model_as_decoder(
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
|
|
def test_for_causal_lm(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
|
|
|
|
def test_decoder_model_past_with_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
|
|
|
def test_decoder_model_with_chunking(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_decoder_model_with_chunking(*config_and_inputs)
|
|
|
|
def test_for_masked_lm(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
|
|
|
|
def test_for_token_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
|
|
|
|
def test_for_multiple_choice(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
|
|
|
|
def test_for_question_answering(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
|
|
|
|
def test_for_bidirectionality(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_bidirectionality(*config_and_inputs)
|
|
|
|
def test_for_chunking_shorter_sequence(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_chunking_shorter_sequence(*config_and_inputs)
|
|
|
|
def test_for_chunking_longer_sequence(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_chunking_longer_sequence(*config_and_inputs)
|
|
|
|
def test_for_laplace_attention(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_laplace_self_attention(*config_and_inputs)
|
|
|
|
def test_for_relu2_attention(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_relu2_self_attention(*config_and_inputs)
|
|
|
|
def test_for_sequence_length_beyond_max_positions(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.check_sequence_length_beyond_max_positions(*config_and_inputs)
|
|
|
|
@require_torch_fp16
|
|
def test_generate_fp16(self):
|
|
config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
# attention_mask = torch.LongTensor(input_ids.ne(1)).to(torch_device)
|
|
model = MegaForCausalLM(config).eval().to(torch_device)
|
|
model.half()
|
|
model.generate(input_ids, attention_mask=attention_mask)
|
|
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
|
|
|
|
def test_sequence_classification_model(self):
|
|
config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs()
|
|
config.num_labels = self.model_tester.num_labels
|
|
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
|
|
model = MegaForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
|
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
|
|
|
def test_sequence_classification_model_for_multi_label(self):
|
|
config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs()
|
|
config.num_labels = self.model_tester.num_labels
|
|
config.problem_type = "multi_label_classification"
|
|
sequence_labels = ids_tensor(
|
|
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
|
|
).to(torch.float)
|
|
model = MegaForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
|
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "mnaylor/mega-base-wikitext"
|
|
model = MegaModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
|
|
def test_cpu_offload(self):
|
|
super().test_cpu_offload()
|
|
|
|
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
|
|
def test_disk_offload(self):
|
|
super().test_disk_offload()
|
|
|
|
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
|
|
def test_model_parallelism(self):
|
|
super().test_model_parallelism()
|
|
|
|
@unittest.skip(
|
|
reason=(
|
|
"Calling `self.attention_function` in `MegaMovingAverageGatedAttention.forward` changes the submodules on "
|
|
"device 1 to device 0 (also changes `requires_grad`). No idea how this could happen for now."
|
|
)
|
|
)
|
|
def test_multi_gpu_data_parallel_forward(self):
|
|
super().test_multi_gpu_data_parallel_forward()
|
|
|
|
@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
|
|
def test_torchscript_simple(self):
|
|
super().test_torchscript_simple()
|
|
|
|
@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
|
|
def test_torchscript_output_hidden_state(self):
|
|
super().test_torchscript_output_hidden_state()
|
|
|
|
@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
|
|
def test_torchscript_output_attentions(self):
|
|
super().test_torchscript_output_attentions()
|
|
|
|
|
|
@require_torch
|
|
class MegaModelIntegrationTest(TestCasePlus):
|
|
@slow
|
|
def test_inference_masked_lm(self):
|
|
model = MegaForMaskedLM.from_pretrained("mnaylor/mega-base-wikitext")
|
|
|
|
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
|
|
with torch.no_grad():
|
|
output = model(input_ids)[0]
|
|
expected_shape = torch.Size((1, 11, 50265))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
# compare the actual values for a slice.
|
|
expected_slice = torch.tensor(
|
|
[[[67.8389, 10.1470, -32.7148], [-11.1655, 29.1152, 23.1304], [-3.8015, 66.0397, 29.6733]]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_no_head(self):
|
|
model = MegaModel.from_pretrained("mnaylor/mega-base-wikitext")
|
|
|
|
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
|
|
with torch.no_grad():
|
|
output = model(input_ids)[0]
|
|
expected_shape = torch.Size((1, 11, 128))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
# compare the actual values for a slice. taken from output[:, :3, :3]
|
|
expected_slice = torch.tensor(
|
|
[[[1.1767, -0.6349, 2.8494], [-0.5109, -0.7745, 1.9495], [-0.3287, -0.2111, 3.3367]]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|