init
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# Copyright 2022 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 inspect
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import tempfile
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import unittest
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from transformers import XLMRobertaXLConfig, 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 (
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DataCollatorWithFlattening,
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XLMRobertaXLForCausalLM,
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XLMRobertaXLForMaskedLM,
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XLMRobertaXLForMultipleChoice,
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XLMRobertaXLForQuestionAnswering,
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XLMRobertaXLForSequenceClassification,
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XLMRobertaXLForTokenClassification,
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XLMRobertaXLModel,
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)
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from transformers.models.xlm_roberta_xl.modeling_xlm_roberta_xl import XLMRobertaXLEmbeddings
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class XLMRobertaXLModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=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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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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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.use_token_type_ids = use_token_type_ids
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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.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.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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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.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 XLMRobertaXLConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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)
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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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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 = XLMRobertaXLModel(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 = XLMRobertaXLModel(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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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_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_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 = XLMRobertaXLForCausalLM(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.add_cross_attention = True
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model = XLMRobertaXLForCausalLM(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, 3), 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, 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_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 = XLMRobertaXLForMaskedLM(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 = XLMRobertaXLForTokenClassification(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 = XLMRobertaXLForMultipleChoice(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 = XLMRobertaXLForQuestionAnswering(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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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class XLMRobertaXLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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XLMRobertaXLForCausalLM,
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XLMRobertaXLForMaskedLM,
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XLMRobertaXLModel,
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XLMRobertaXLForSequenceClassification,
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XLMRobertaXLForTokenClassification,
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XLMRobertaXLForMultipleChoice,
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XLMRobertaXLForQuestionAnswering,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"feature-extraction": XLMRobertaXLModel,
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"fill-mask": XLMRobertaXLForMaskedLM,
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"question-answering": XLMRobertaXLForQuestionAnswering,
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"text-classification": XLMRobertaXLForSequenceClassification,
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"text-generation": XLMRobertaXLForCausalLM,
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"token-classification": XLMRobertaXLForTokenClassification,
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"zero-shot": XLMRobertaXLForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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model_split_percents = [0.5, 0.85, 0.95]
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# TODO: Fix the failed tests
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def is_pipeline_test_to_skip(
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self,
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pipeline_test_case_name,
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config_class,
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model_architecture,
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tokenizer_name,
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image_processor_name,
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feature_extractor_name,
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processor_name,
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):
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if pipeline_test_case_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
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return True
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return False
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# Overwriting to add `is_decoder` flag
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def prepare_config_and_inputs_for_generate(self, batch_size=2):
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config, inputs = super().prepare_config_and_inputs_for_generate(batch_size)
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config.is_decoder = True
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return config, inputs
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def setUp(self):
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self.model_tester = XLMRobertaXLModelTester(self)
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self.config_tester = ConfigTester(self, config_class=XLMRobertaXLConfig, 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_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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config_and_inputs[0]._attn_implementation = "eager"
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self.model_tester.create_and_check_model(*config_and_inputs)
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||||
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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)
|
||||
|
||||
def test_model_as_decoder_with_default_input_mask(self):
|
||||
(
|
||||
config,
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||||
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_past_with_large_inputs_relative_pos_emb(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
config_and_inputs[0].position_embedding_type = "relative_key"
|
||||
config_and_inputs[0]._attn_implementation = "eager"
|
||||
self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_create_position_ids_respects_padding_index(self):
|
||||
"""This is a regression test for https://github.com/huggingface/transformers/issues/1761
|
||||
|
||||
The position ids should be masked with the embedding object's padding index. Therefore, the
|
||||
first available non-padding position index is XLMRobertaXLEmbeddings.padding_idx + 1
|
||||
"""
|
||||
config = self.model_tester.prepare_config_and_inputs()[0]
|
||||
model = XLMRobertaXLEmbeddings(config=config)
|
||||
|
||||
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
|
||||
expected_positions = torch.as_tensor(
|
||||
[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
|
||||
)
|
||||
|
||||
position_ids = XLMRobertaXLEmbeddings.create_position_ids_from_input_ids(input_ids, model.padding_idx)
|
||||
self.assertEqual(position_ids.shape, expected_positions.shape)
|
||||
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
|
||||
|
||||
def test_create_position_ids_from_inputs_embeds(self):
|
||||
"""This is a regression test for https://github.com/huggingface/transformers/issues/1761
|
||||
|
||||
The position ids should be masked with the embedding object's padding index. Therefore, the
|
||||
first available non-padding position index is XLMRobertaXLEmbeddings.padding_idx + 1
|
||||
"""
|
||||
config = self.model_tester.prepare_config_and_inputs()[0]
|
||||
embeddings = XLMRobertaXLEmbeddings(config=config)
|
||||
|
||||
inputs_embeds = torch.empty(2, 4, 30)
|
||||
expected_single_positions = [
|
||||
0 + embeddings.padding_idx + 1,
|
||||
1 + embeddings.padding_idx + 1,
|
||||
2 + embeddings.padding_idx + 1,
|
||||
3 + embeddings.padding_idx + 1,
|
||||
]
|
||||
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
|
||||
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds, embeddings.padding_idx)
|
||||
self.assertEqual(position_ids.shape, expected_positions.shape)
|
||||
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
|
||||
|
||||
def attention_mask_padding_matches_padding_free_with_position_ids(
|
||||
self, attn_implementation: str, fa_kwargs: bool = False
|
||||
):
|
||||
"""
|
||||
Overwritten to account for the embeddings that rely on position ids.
|
||||
"""
|
||||
if not self.has_attentions:
|
||||
self.skipTest(reason="Model architecture does not support attentions")
|
||||
|
||||
max_new_tokens = 30
|
||||
support_flag = {
|
||||
"sdpa": "_supports_sdpa",
|
||||
"flash_attention_2": "_supports_flash_attn",
|
||||
"flash_attention_3": "_supports_flash_attn",
|
||||
}
|
||||
|
||||
for model_class in self.all_generative_model_classes:
|
||||
if attn_implementation != "eager" and not getattr(model_class, support_flag[attn_implementation]):
|
||||
self.skipTest(f"{model_class.__name__} does not support {attn_implementation}")
|
||||
|
||||
# can't infer if new attn mask API is supported by assume that only model with attention backend support it
|
||||
if not model_class._supports_attention_backend:
|
||||
self.skipTest(f"{model_class.__name__} does not support new attention mask API")
|
||||
|
||||
if model_class._is_stateful: # non-transformer models most probably have no packing support
|
||||
self.skipTest(f"{model_class.__name__} doesn't support packing!")
|
||||
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
if config.is_encoder_decoder:
|
||||
self.skipTest("Model is an encoder-decoder")
|
||||
|
||||
if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict:
|
||||
self.skipTest("Model dummy inputs should contain padding in their attention mask")
|
||||
|
||||
if "input_ids" not in inputs_dict or inputs_dict["input_ids"].ndim != 2:
|
||||
self.skipTest("Model dummy inputs should contain text input ids")
|
||||
|
||||
# make sure that all models have enough positions for generation
|
||||
dummy_input_ids = inputs_dict["input_ids"]
|
||||
if hasattr(config, "max_position_embeddings"):
|
||||
config.max_position_embeddings = max_new_tokens + dummy_input_ids.shape[1] + 1
|
||||
|
||||
model = model_class(config)
|
||||
if "position_ids" not in inspect.signature(model.forward).parameters:
|
||||
self.skipTest("Model does not support position_ids")
|
||||
|
||||
if (not fa_kwargs) and "position_ids" not in inspect.signature(model.forward).parameters:
|
||||
continue # this model doesn't accept position ids as input
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
|
||||
# Drop all keys except for the minimal set. Hard to manipulate with multimodals/head_mask/etc
|
||||
inputs_dict = {k: v for k, v in inputs_dict.items() if k in ["input_ids", "attention_mask"]}
|
||||
|
||||
# Ensure left padding, to adapt for some models
|
||||
if 0 in inputs_dict["attention_mask"][:, -1]:
|
||||
inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1)
|
||||
dummy_attention_mask = inputs_dict["attention_mask"]
|
||||
dummy_input_ids[~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id
|
||||
|
||||
# Main difference to other models, we need to prepare position ids according to the attention mask
|
||||
# as we use it to extract embeddings that rely on the correct position - naively increasing sequences do
|
||||
# not suffice anymore atp. The solution here calculates an increasing sequences for all 1s and puts 0s else.
|
||||
inputs_dict["position_ids"] = ((inputs_dict["attention_mask"] == 1).long().cumsum(dim=1) - 1) * (
|
||||
inputs_dict["attention_mask"] == 1
|
||||
).long()
|
||||
|
||||
model = (
|
||||
model_class.from_pretrained(
|
||||
tmpdirname,
|
||||
dtype=torch.bfloat16,
|
||||
attn_implementation=attn_implementation,
|
||||
)
|
||||
.to(torch_device)
|
||||
.eval()
|
||||
)
|
||||
|
||||
if fa_kwargs:
|
||||
# flatten
|
||||
features = [
|
||||
{"input_ids": i[a.bool()].tolist()} for i, a in zip(dummy_input_ids, dummy_attention_mask)
|
||||
]
|
||||
|
||||
# add position_ids + fa_kwargs
|
||||
data_collator = DataCollatorWithFlattening(return_tensors="pt", return_flash_attn_kwargs=True)
|
||||
batch = data_collator(features)
|
||||
padfree_inputs_dict = {
|
||||
k: t.to(torch_device) if torch.is_tensor(t) else t for k, t in batch.items()
|
||||
}
|
||||
else:
|
||||
# create packed position_ids
|
||||
position_ids = (
|
||||
torch.cat([torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()])
|
||||
.long()
|
||||
.unsqueeze(0)
|
||||
.to(torch_device)
|
||||
)
|
||||
padfree_inputs_dict = {
|
||||
"input_ids": dummy_input_ids[dummy_attention_mask.bool()].unsqueeze(0),
|
||||
"position_ids": position_ids,
|
||||
}
|
||||
|
||||
# We need to do simple forward without cache in order to trigger packed SDPA/flex/eager attention path
|
||||
res_padded = model(**inputs_dict, use_cache=False)
|
||||
res_padfree = model(**padfree_inputs_dict, use_cache=False)
|
||||
|
||||
logits_padded = res_padded.logits[dummy_attention_mask.bool()]
|
||||
logits_padfree = res_padfree.logits[0]
|
||||
|
||||
# acceptable numerical instability
|
||||
tol = torch.finfo(torch.bfloat16).eps
|
||||
torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
|
||||
|
||||
def flash_attn_inference_equivalence(
|
||||
self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2
|
||||
):
|
||||
r"""
|
||||
Overwritten to enforce decoder behavior as the model is very easily influenced
|
||||
by slight changes in the mask. One major reason for the high fluctuations is
|
||||
the extra layernom at the end of the model which shifts the logits a lot.
|
||||
"""
|
||||
if not self.has_attentions:
|
||||
self.skipTest(reason="Model architecture does not support attentions")
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.is_decoder = True
|
||||
model = model_class(config)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
model_fa = model_class.from_pretrained(
|
||||
tmpdirname, torch_dtype=torch.bfloat16, attn_implementation=attn_implementation
|
||||
)
|
||||
model_fa.to(torch_device)
|
||||
|
||||
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16)
|
||||
model.to(torch_device)
|
||||
|
||||
dummy_input = inputs_dict[model.main_input_name][:1]
|
||||
if dummy_input.dtype in [torch.float32, torch.float16]:
|
||||
dummy_input = dummy_input.to(torch.bfloat16)
|
||||
|
||||
dummy_attention_mask = inputs_dict.get("attention_mask", None)
|
||||
|
||||
if dummy_attention_mask is not None:
|
||||
dummy_attention_mask = dummy_attention_mask[:1]
|
||||
if padding_side == "left":
|
||||
dummy_attention_mask[:, 1:] = 1
|
||||
dummy_attention_mask[:, :1] = 0
|
||||
else:
|
||||
dummy_attention_mask[:, :-1] = 1
|
||||
dummy_attention_mask[:, -1:] = 0
|
||||
|
||||
# no attention mask
|
||||
processed_inputs = {
|
||||
model.main_input_name: dummy_input,
|
||||
"output_hidden_states": True,
|
||||
}
|
||||
if model.config.is_encoder_decoder:
|
||||
processed_inputs["decoder_input_ids"] = inputs_dict.get("decoder_input_ids", dummy_input)[:1]
|
||||
|
||||
prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
|
||||
prepared_inputs = {
|
||||
k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in prepared_inputs.items()
|
||||
}
|
||||
|
||||
outputs = model(**prepared_inputs)
|
||||
outputs_fa = model_fa(**prepared_inputs)
|
||||
|
||||
logits = (
|
||||
outputs.hidden_states[-1]
|
||||
if not model.config.is_encoder_decoder
|
||||
else outputs.decoder_hidden_states[-1]
|
||||
)
|
||||
logits_fa = (
|
||||
outputs_fa.hidden_states[-1]
|
||||
if not model.config.is_encoder_decoder
|
||||
else outputs_fa.decoder_hidden_states[-1]
|
||||
)
|
||||
|
||||
assert torch.allclose(logits_fa, logits, atol=atol, rtol=rtol)
|
||||
|
||||
# with attention mask
|
||||
if dummy_attention_mask is not None:
|
||||
processed_inputs["attention_mask"] = dummy_attention_mask
|
||||
if model.config.is_encoder_decoder:
|
||||
processed_inputs["decoder_attention_mask"] = dummy_attention_mask
|
||||
|
||||
prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
|
||||
prepared_inputs = {
|
||||
k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in prepared_inputs.items()
|
||||
}
|
||||
|
||||
outputs = model(**prepared_inputs)
|
||||
outputs_fa = model_fa(**prepared_inputs)
|
||||
|
||||
logits = (
|
||||
outputs.hidden_states[-1]
|
||||
if not model.config.is_encoder_decoder
|
||||
else outputs.decoder_hidden_states[-1]
|
||||
)
|
||||
logits_fa = (
|
||||
outputs_fa.hidden_states[-1]
|
||||
if not model.config.is_encoder_decoder
|
||||
else outputs_fa.decoder_hidden_states[-1]
|
||||
)
|
||||
|
||||
if padding_side == "left":
|
||||
assert torch.allclose(logits_fa[1:], logits[1:], atol=atol, rtol=rtol)
|
||||
|
||||
# check with inference + dropout
|
||||
model.train()
|
||||
_ = model_fa(**prepared_inputs)
|
||||
else:
|
||||
assert torch.allclose(logits_fa[:-1], logits[:-1], atol=atol, rtol=rtol)
|
||||
|
||||
@unittest.skip("XLM Roberta XL has some higher fluctuations, skipping for now (norm issue)")
|
||||
def test_flash_attn_2_inference_equivalence_right_padding(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("XLM Roberta XL doesn't work for some reason, FIXME")
|
||||
def test_eager_padding_matches_padding_free_with_position_ids(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("XLM Roberta XL doesn't work for some reason, FIXME")
|
||||
def test_sdpa_padding_matches_padding_free_with_position_ids(self):
|
||||
pass
|
||||
|
||||
|
||||
@require_torch
|
||||
class XLMRobertaModelXLIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_xlm_roberta_xl(self):
|
||||
model = XLMRobertaXLModel.from_pretrained("facebook/xlm-roberta-xl").to(torch_device)
|
||||
input_ids = torch.tensor(
|
||||
[[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]], device=torch_device
|
||||
)
|
||||
# The dog is cute and lives in the garden house
|
||||
|
||||
expected_output_shape = torch.Size((1, 12, 2560)) # batch_size, sequence_length, embedding_vector_dim
|
||||
expected_output_values_last_dim = torch.tensor(
|
||||
[[0.0110, 0.0605, 0.0354, 0.0689, 0.0066, 0.0691, 0.0302, 0.0412, 0.0860, 0.0036, 0.0405, 0.0170]],
|
||||
device=torch_device,
|
||||
)
|
||||
|
||||
output = model(input_ids)["last_hidden_state"].detach()
|
||||
self.assertEqual(output.shape, expected_output_shape)
|
||||
# compare the actual values for a slice of last dim
|
||||
torch.testing.assert_close(output[:, :, -1], expected_output_values_last_dim, rtol=1e-3, atol=1e-3)
|
||||
|
||||
@unittest.skip(reason="Model is too large to be tested on the CI")
|
||||
def test_xlm_roberta_xxl(self):
|
||||
model = XLMRobertaXLModel.from_pretrained("facebook/xlm-roberta-xxl").to(torch_device)
|
||||
input_ids = torch.tensor(
|
||||
[[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]], device=torch_device
|
||||
)
|
||||
# The dog is cute and lives in the garden house
|
||||
|
||||
expected_output_shape = torch.Size((1, 12, 4096)) # batch_size, sequence_length, embedding_vector_dim
|
||||
expected_output_values_last_dim = torch.tensor(
|
||||
[[0.0046, 0.0146, 0.0227, 0.0126, 0.0219, 0.0175, -0.0101, 0.0006, 0.0124, 0.0209, -0.0063, 0.0096]],
|
||||
device=torch_device,
|
||||
)
|
||||
|
||||
output = model(input_ids)["last_hidden_state"].detach()
|
||||
self.assertEqual(output.shape, expected_output_shape)
|
||||
# compare the actual values for a slice of last dim
|
||||
torch.testing.assert_close(output[:, :, -1], expected_output_values_last_dim, rtol=1e-3, atol=1e-3)
|
||||
Reference in New Issue
Block a user